i

Training Attention through Intensive Meditation:

Improvements in Sustained Performance and Response Inhibition

By

KATHERINE ANNE MACLEAN

A.B. (Dartmouth College) 2003

M.A. (University of California, Davis) 2006

DISSERTATION

Submitted in partial satisfaction of the requirements for the degree of

DOCTOR OF PHILOSOPHY

in

Psychology

in the

OFFICE OF GRADUATE STUDIES

of the

UNIVERSITY OF CALIFORNIA

DAVIS

Approved:

Dr. George R. Mangun

Dr. Clifford D. Saron

Dr. Steven J. Luck

Dr. Phillip R. Shaver

Committee in Charge

2009

UMI Number: 3379639

All rights reserved

INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted.

In the unlikely event that the author did not send a complete manuscript

and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion.

UMI 3379639

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ii

Training Attention through Intensive Meditation:

Improvements in Sustained Performance and Response Inhibition

DISSERTATION

KATHERINE ANNE MACLEAN

2009

iii

Acknowledgments

Many people have guided me along my journey through graduate school. The

completion of this dissertation would not have been possible without their thoughtful

contributions, inspiring ideas, and friendship. I would like to start by thanking my

advisors, Ron Mangun and Clifford Saron, for allowing me the freedom to pursue my

ideas and challenging me to strive for excellence. Ron – You have supported me

wholeheartedly from the very beginning, and I will always look up to you as an example

of how to be a successful scientist while also enjoying life. Cliff – You are truly a fearless

leader and have shown me that the wildest dreams are attainable, through hard work and

a little mystical charm. I am lucky to have had both of you as mentors. I would also like

to thank the members of my dissertation committee: Phillip Shaver, for bravely

confronting errors in writing style and grammar and diligently passing on your wisdom to

me at every opportunity; and Steven Luck, for always being available at a moment’s

notice to provide thoughtful feedback and suggestions. I would also like to thank other

faculty members in the Department of Psychology who have contributed to my research

and provided valuable guidance: Ewa Wojciulik, David Whitney, Petr Janata, Robert

Post, and Debra Long. Finally, I would like to thank my undergraduate advisor, Yale

Cohen, for exercising the right balance of skepticism and encouragement that enabled me

to pursue my craziest career goals.

I arrived at UC Davis just as a courageous, once-in-a-lifetime project was about to

begin. The Shamatha Project was a large-scale, collaborative research endeavor aimed at

understanding the longitudinal effects of intensive meditation training on behavioral,

psychological, and neural functioning. This is just the sort of project that is usually

iv

impossible to undertake as a graduate student. And yet, with the support of my committee

and other faculty members, I successfully developed a dissertation that would be

embedded within the Shamatha Project. The findings presented here reflect the tireless

work of many collaborators, and I extend my deepest gratitude to all of them for making

this dissertation possible. First, I would like to thank the team of researchers who endured

the stress of living in close (quiet) quarters while collecting data for 12 hours a day for

many months: Stephen Aichele, Anthony Zanesco, David Bridwell, Tonya Jacobs, and

Brandon King. I would also like to thank Baljinder Sahdra, for a truly pleasant and

rewarding collaborative writing experience; Erika Rosenberg, for thoughtful

contributions and compassionate support; and Emilio Ferrer, for invaluable statistical

advice and insights. Finally, I would like to thank Alan Wallace, meditation teacher

extraordinaire, for introducing me to meditation and inspiring me to push the envelope in

my science career. Working on the Shamatha Project has taught me that great science is

produced when creative, intelligent, hard-working people come together to bravely

attempt to achieve what others deem impossible.

I would like to thank my laboratory mates, past and present: Mita Puri, Santani Teng,

Evelijne Bekker, Sean Fannon, Bong Walsh, Joy Geng, Risa Sawaki, Sharon Corina,

Jesse Bengson, Andre Bastos, and Ali Mazaheri. Thank you also to all of the faculty

members and researchers at the Center for Mind and Brain for helping create a collegial,

productive, and fun atmosphere to work in. Also, a huge thank you to the administrative

and support staff at the Center for Mind and Brain: Noelle Blalock, Carmina Caselli,

Patricia Schuler, Jeremy Phillips, and Chris Brick. This dissertation literally would not

have been completed without your help.

v

I would not have made it through graduate school without the love and support of my

friends and family. Thank you to Erin Sullivan and Jason Haberman, who have been with

me every step of the way since we arrived in Davis as first-year psychology graduate

students. Thank you to my parents, Julie and Richard, for raising me to always

confidently and passionately pursue my dreams; to my sister, Rebecca, for inspiring me

to be competitive and strong; and to my brother, Edward, for always making me smile.

Finally, thank you to my partner, John, for showing unconditional love and amazing

patience, for following me to the ends of the earth (or, at least to a remote meditation

retreat center in the mountains of Colorado), for listening to me rant about data and

theories and new ideas, and above all, for being a great human being. John – You are the

love of my life, and I am lucky to have found someone to share my dreams.

vi

Abstract

The ability to focus attention underlies success on many everyday tasks, but voluntary

attention cannot be sustained indefinitely. In the laboratory, perceptual sensitivity

declines with increasing time on task, a phenomenon called the vigilance decrement. The

aim of this dissertation was to demonstrate that vigilance – that is, performance over

extended periods of time - can be improved in healthy adults.

The experiments in Chapter 1 describe the effects of attentional cues on performance in

two related vigilance tasks: a sustained attention task that required responses to rare

targets occurring in a sequence of non-targets, and a sustained response inhibition task

that required responses to the frequent non-targets and withholding of responses to the

rare targets. In the sustained attention task, sudden-onset cues presented immediately

before each stimulus improved overall perceptual sensitivity while predictable timing

cues attenuated the decline in perceptual sensitivity over time. Attentional cues did not

improve response inhibition performance. Finally, performance on the most challenging

versions of the sustained attention and response inhibition tasks did not improve with

repeated task practice, indicating the suitability of these tasks as outcome measures of the

effects of attention training.

The experiments in Chapters 3 and 4 describe the effects of intensive meditation training

on vigilance. Training (~5 hours/day for 3 months) consisted of a meditation practice that

entailed learning to regulate and control voluntary attention by sustaining attention

selectively on a chosen stimulus (e.g., the breath). Participants were randomly assigned

vii

either to receive training first (N = 30) or to serve as wait-list controls and receive

training second (N = 30). Training produced improvements in visual discrimination

which led to increases in perceptual sensitivity and reductions in the vigilance decrement

during the sustained attention task. Training also produced increases in response

inhibition accuracy without concomitant slowing of reaction time. Finally, improvements

in discrimination and response inhibition were maintained several months after the

completion of formal training, indicating enduring changes in behavior.

viii

Table of Contents

Chapter 1 Introduction 1

Chapter 2 Benefits of Attentional Cues during Vigilance:

A Comparison of Sustained Attention and

Response Inhibition 13

Chapter 3 Improvements in Perception and Sustained Attention

with Intensive Meditation Training 60

Chapter 4 Training-related Changes in Sustained Response

Inhibition: The Influence of Age and Response Speed

on Improvements in Accuracy 85

Chapter 5 Conclusion 110

1

CHAPTER 1

INTRODUCTION

2

The ability to control the focus of attention while resisting distraction is a central

feature of adaptive human behavior. Attention can be voluntarily guided in line with

personal goals, prior knowledge, or explicit instructions to improve the accuracy and

efficiency of behavior (referred to as endogenous or voluntary attention). Attention can

also be automatically drawn to stimuli and actions because of momentary salience or

well-established habits (referred to as exogenous attention). Attentional control involves

exerting voluntary, goal-directed control over which stimuli, thoughts, emotions, and

actions are selected despite automatic or habitual tendencies (see reviews in Fan &

Posner, 2004; Posner & Petersen, 1990).

Although attention can be successfully maintained over relatively short time intervals

(e.g., the 1-second interval between a cue and a subsequent stimulus; Posner, 1980;

Posner & Cohen, 1984), voluntary attention cannot be sustained indefinitely. Decades of

research on sustained attention has consistently demonstrated declines in accuracy with

increasing time on task, a phenomenon called the vigilance decrement (see Parasuraman,

1986 for review). Although traditional sustained attention tasks required observers to

respond to rare targets, recent research has shown that performance also declines on tasks

that require response inhibition (i.e., responding to frequent non-targets and withholding

responses to rare targets; Grier et al., 2003; Helton et al., 2005). Both sustained target

discrimination and sustained response inhibition place high demands on the attentional

control system to maintain performance over extended periods. The overarching goal of

this dissertation is to investigate how vigilance - that is, performance over extended

periods of time - can be improved in healthy adults.

3

In this chapter, I will first provide an overview of the major theories of vigilance and

outline the task factors that have been shown to modulate the vigilance decrement. Next,

I will discuss results from previous training studies and introduce meditation training as a

technique for improving sustained voluntary attention and attentional control. At the end

of the chapter, I will introduce the series of experiments that were carried out to

investigate (1) whether attentional cues could improve vigilance performance in

untrained adults and (2) whether improvements in vigilance could be achieved with

intensive meditation training.

Theories of Vigilance

Most vigilance tasks are variations of the original Mackworth Clock Test

(Mackworth, 1948) used to test vigilance performance in radar operators. In the Clock

Test, observers monitored a pointer moving in single-step increments across a blank

clock face for periods up to 2 hours and were instructed to indicate when they detected a

rare “double jump” in the movement of the pointer. Current vigilance tasks share several

core design elements of the Clock Test in that they require subjects to monitor the

sequential display of neutral, non-target stimulus events over a long period of time and

initiate responses to rare target events occurring unpredictably. Typically, performance

declines over time, with the greatest decrement in correct detections occurring within the

first 15-20 minutes (Davies & Tune, 1969). Of particular interest are those tasks that

produce a decline in perceptual sensitivity (d' or A'), indicating that subjects are actually

detecting fewer targets over time, not simply adopting a more conservative response

criterion (D. M. Green & Swets, 1966).

4

Since Mackworth’s (1948) initial investigation, a multitude of experiments have

contributed to a better understanding of why humans are not very good at maintaining

accurate performance for long periods of time. Several theoretical models have been

proposed to explain the classic vigilance decrement, including the arousal model

(Frankmann & Adams, 1962), which views the decrement as a consequence of reductions

in general alertness over time, and the mindlessness model (Manly, Robertson, Galloway,

& Hawkins, 1999; Robertson, Manly, Andrade, Baddeley, & Yiend, 1997), which views

the decrement as the result of failures of a supervisory attention system to appropriately

direct awareness toward a relatively boring, undemanding task. Experimental findings

seem to challenge both of these theories and instead support an attentional resource

model (Davies & Parasuraman, 1982) as the best explanation of performance decrements.

Recent studies have used self-report measures in combination with vigilance tasks to

test key predictions of these competing theories and to more fully characterize sustained

attention as effortful and resource-demanding (Warm, Parasuraman, & Matthews, 2008).

For example, adding irrelevant stimuli to reduce task monotony (i.e., increase overall

arousal) does not improve vigilance performance (Smit, Eling, & Coenen, 2004b), in

contradiction to the arousal theory of vigilance. With respect to the mindlessness theory,

studies have shown that observers find vigilance tasks to be effortful rather than

undemanding (Grier et al., 2003; Szalma et al., 2004), that performance is influenced by

implicit stimulus patterns during the task (i.e., observers do not withdraw awareness from

the task; Helton et al., 2005), and that self-reported mind wandering (e.g., task-unrelated

thought) is not related to the vigilance decrement (Helton & Warm, 2008).

5

In addition, brain imaging studies offer direct evidence for the model’s assumption

that vigilance relies on attentional resources. Early studies suggested reductions in

cerebral blood flow in right frontal cortex with increasing time on task (Paus et al., 1997).

More recently, Hitchcock and colleagues (2003) showed that these reductions were

directly related to the extent of the vigilance decrement, and that the change in cerebral

blood flow was specific to task factors that had previously been interpreted as increasing

resource demands (e.g., difficult target discrimination). Furthermore, recent

neuroimaging and electrophysiological investigations of sustained attention suggest a link

between pre-stimulus patterns of brain activity and subsequent attention failures. For

example, reductions in brain activity in attentional control regions (prefrontal and parietal

cortex) predict errors during sustained attention (Weissman, Roberts, Visscher, &

Woldorff, 2006). Changes in brain activity are also observed in regions that receive

projections from attentional control areas. Specifically, increases in the amplitude of the

ongoing electroencephalograph in specific frequency bands related to visual and motor

processing predict lapses in attention during sustained response inhibition (Mazaheri,

Nieuwenhuis, van Dijk, & Jensen, in press). Together these findings indicate that ongoing

brain activity in attentional control regions and in regions that control stimulus and

response processing underlies successful sustained performance.

Factors Affecting Sustained Performance

A combination of experimental (e.g., Parasuraman, 1979) and meta-analytic

approaches (e.g., See, Howe, Warm, & Dember, 1995) have been used to identify the key

factors that increase resource demands and thus promote declines in perceptual sensitivity

during vigilance. Event rate (i.e., the rate of stimulus events that must be inspected to

6

detect the presence of the rare target event) has been judged to be one of the prepotent

parameters that affects vigilance performance, with high event rate (> 24 events per

minute, Parasuraman & Davies, 1977) increasing the resource demands of the task and

thereby increasing the perceptual sensitivity decrement (Davies & Parasuraman, 1982;

Parasuraman & Davies, 1977; See et al., 1995; Warm & Jerison, 1984). Perceptual

sensitivity also declines more reliably in tasks in which the target discrimination process

loads memory (Parasuraman, 1979). For example, when the currently visible stimulus in

a series of successive presentations is compared to a standard, non-target held in memory

(e.g., detecting a short line target occurring in a sequence of long line non-targets),

observers perform more poorly over time compared to performance on tasks that require

a comparative judgment based only on the currently visible stimuli (e.g., detecting a line

length difference between two lines presented at the same time). Finally, vigilance

decrements are large and reliable when target discrimination is difficult. Parasuraman and

Mouloua (1987) demonstrated that targets that are initially perceptually difficult to

discriminate from non-targets become more difficult to detect over time than targets that

are easy to discriminate, regardless of working memory load. Even tasks with numeric

targets, which do not consistently produce decrements in healthy subjects because they

are easy to discriminate, promote declines in perceptual sensitivity if the targets are

highly degraded and thus rendered more difficult to discriminate from non-target

numbers (Nuechterlein, Parasuraman, & Jiang, 1983).

In addition to event rate, target discriminability, and working memory load, other

factors contribute to the overall resource demand. In a meta-analysis of vigilance studies,

See and colleagues (1995) found that several task factors, such as sensory modality,

7

signal regularity, and spatial uncertainty did not independently predict the extent of the

vigilance decrement but were correlated with the overall level of sensitivity during

vigilance. Overall sensitivity in turn significantly predicted the extent of the vigilance

decrement: When overall sensitivity is low, vigilance decrements are large. Thus, these

authors proposed that “the average level of sensitivity… might well be viewed as an

index of total task demand brought about by the combination of task parameters…” (p.

243). Together, these findings demonstrate that the vigilance decrement is modulated by

the resource demands associated with task performance.

Finally, studies that have directly compared performance on perceptually demanding

tasks with both response initiation and inhibition requirements have found similar

decrements in accurate performance over time, as well as similar estimates of stress and

effort between the two types of responses (Grier et al., 2003; Helton et al., 2005). These

findings indicate that specific response requirements are not major determinants of the

vigilance decrement. Rather, both sustained target discrimination and sustained response

inhibition place high demands on resources needed for attentional and behavioral control.

Training Sustained Attention

In many perceptual, cognitive, and motor domains, skill learning is often limited to

the training task. That is, individuals show improved task performance with repetitive

practice but show no improvements on related tasks that have not been practiced but

presumably recruit similar skills. There has been growing research interest in identifying

training procedures that lead to generalizable improvements in cognition (see review in

C. S. Green & Bavelier, 2008). For example, computer-based training programs have

been shown to improve working memory capacity and non-verbal intelligence (Jaeggi,

8

Buschkuehl, Jonides, & Perrig, 2008; Olesen, Westerberg, & Klingberg, 2004). Likewise,

more naturalistic “training” regimens, such as playing action video games (C. S. Green &

Bavelier, 2003, 2006) or practicing a musical instrument (Graziano, Peterson, & Shaw,

1999) can improve performance on untrained tasks, suggesting the transfer of learned

skills.

Most studies that have attempted to improve vigilance in healthy individuals have

examined the effects of repetitive task practice or performance feedback (see review in

Davies & Parasuraman, 1982). Although moderate practice (1- 4 hours) of perceptually

demanding vigilance tasks does not improve performance (e.g., Nuechterlein et al., 1983;

Williams, 1986), extensive task practice (e.g., 20 sessions of a 30-minute vigilance task)

has been shown to increase overall accuracy and reduce the vigilance decrement in young

and elderly adults (Parasuraman & Giambra, 1991). But even this extensive practice did

not completely eliminate the vigilance decrement in all individuals who underwent

training. Moreover, task-specific improvements following repetitive practice do not

inform us about the possibility for more general improvements in vigilance. A recent

study suggests that learning to self-regulate arousal or alertness (as measured by electrical

skin conductance) can improve overall accuracy during a sustained response inhibition

task (Self Alert Training, O'Connell et al., 2008). However, participants in this study

made few errors and did not show performance decrements over time before training.

Thus, it is unclear whether training could improve performance over extended periods of

time on demanding vigilance tasks.

In contrast to the relative paucity of psychological research on attention training,

experts in the Buddhist contemplative tradition have long recognized the possibility of

9

improving attention through mental training. Historical accounts (see translations in

(Buddhaghosa, 1979; Tsong-kha-pa, 2002) describe Shamatha as a class of meditation

practices specifically designed to improve sustained attention. Shamatha involves the

repeated practice of maintaining voluntary attention on a chosen stimulus or event, such

as the tactile sensations of the breath (Wallace, 1999). During training, practitioners aim

to develop attentional stability (maintaining attentional focus on the chosen stimulus) and

perceptual vividness (enhancing the detail with which the attended stimulus is perceived).

While attention is sustained in this manner, introspection is used to recognize when

attention has wandered and guide attention back to the chosen stimulus. In this way,

practitioners develop general (i.e., non-stimulus-specific) skill in regulating and

controlling voluntary attention. In particular, attentional control related to response

inhibition is cultivated as practitioners learn to resist the automatic tendency to attend to

and engage with passing thoughts or momentary emotions. Historical texts note that

response inhibition is not used to suppress normal thoughts and emotions from arising,

but rather to refrain from (mentally) pushing away undesirable thoughts and emotions or

clinging to desirable ones (Lingpa, referenced in Wallace, 2006); (Padmasambhava,

1997). Thus, the practice entails using self-monitoring to control attention and behavior.

This metacognitive or meta-attentive aspect of training may underlie the transfer of these

skills to other domains and daily life activities (Wallace & Shapiro, 2006).

Overview of Experiments

In the following chapters, I present results from a series of experiments that

investigated how attentional cues (Chapter 2) and meditation training (Chapters 3 and 4)

modulate performance over extended periods of time. In Chapter 2, I describe three

10

behavioral experiments that were designed to examine the benefits of exogenous and

endogenous attentional cues during vigilance. In the first experiment, participants were

presented with sudden-onset luminance changes that were designed to capture attention

exogenously and thus improve perception. We tested the effect of exogenous cues on

performance on two related vigilance tasks that differed in response requirements: The

sustained attention task required responses to rare (10%) short lines (targets) while the

response inhibition task required responses to frequent (90%) long lines (non-targets) and

withholding of responses to the short target lines. In both tasks, line stimuli were

presented at a constant rate (interstimulus interval [ISI] = 1.85 seconds). The results

demonstrated that exogenous cues reduced the vigilance decrement in the sustained

attention task but not in the response inhibition task. In a second experiment, I explored

the influence of predictable timing on performance in the sustained attention task by

varying the ISI. I reasoned that the constant ISI in Experiment 1 provided information

about when to pay attention, thus acting as an endogenous attention cue. The results

showed that exogenous cues improved overall accuracy when the ISI was variable, but

did not reduce the vigilance decrement. This result suggested a strong interaction

between exogenous and endogenous cues during sustained attention. In addition, a direct

comparison of the results from Experiment 1 (constant ISI) and Experiment 2 (variable

ISI) showed that predictable timing independently reduced the vigilance decrement.

Finally, in a third experiment, I explored the stability of task performance across repeated

testing sessions using the versions of the sustained attention and response inhibition tasks

that produced the largest vigilance decrements in Experiment 1. The results from this

experiment showed that performance did not improve in either task with simple practice

11

and thus indicated that these tasks were appropriate to use as outcome measures in the

subsequent training studies.

Next, I investigated the influence of intensive meditation training on performance of

the sustained attention task (Chapter 3) and the response inhibition task (Chapter 4). Each

chapter presents the results from two studies: The first study was a between-groups

comparison of participants who underwent three months of intensive meditation training

(retreat participants) and participants who underwent testing at the same time points

(wait-list control participants). The second study was a within-subjects assessment of the

wait-list control participants when they subsequently underwent the same meditation

training approximately three months after the end of the first training period.

Performance was measured at three testing points during each training period (pre, mid,

and post) using the versions of the sustained attention and response inhibition tasks that

reliably produced significant performance decrements in untrained participants (Chapter

1, Experiment 3).

In Chapter 3, I present data demonstrating training-related improvements in target

perception that led to improved performance on the sustained attention task. In the first

study, target parameters were set according to individual threshold at each assessment.

Because retreat participants showed improvements in threshold, more difficult targets

were presented as training progressed. Thus, there were no observed improvements in

vigilance. In the second study, target parameters were held constant for each individual,

and increases in average sensitivity and reductions in the vigilance decrement were

observed. The results suggest that meditation training leads to improved perception which

12

reduces the resource demand associated with difficult target discrimination and thus

improves vigilance.

In Chapter 4, I present data showing training-related improvements in accuracy on the

response inhibition task. Participants in both groups showed increases in overall response

inhibition accuracy during their respective training period. I also investigated the

influence of response speed, age, and previous meditation experience on response

inhibition accuracy. Although older participants and participants with more previous

meditation experience had slower reaction times than younger participants and

participants with less previous meditation experience, respectively, reaction times did not

change with training and did not influence the observed changes in accuracy. Controlling

for demographic factors and reaction time also did not change the results. Thus, these

findings suggest replicable and robust training-related improvements in response

inhibition accuracy.

Finally, performance on a subset of the tasks was assessed at a follow-up that was

conducted approximately five months after the completion of training. Improvements in

discrimination (Chapter 3) and response inhibition accuracy (Chapter 4) were maintained

at the follow-up assessment, indicating long-term stability in training-related behavioral

changes. Moreover, discrimination performance was correlated with the amount of

continued daily meditation, suggesting a link between the maintenance of training-related

improvements and regular but less intensive meditation practice.

13

CHAPTER 2

BENEFITS OF ATTENTIONAL CUES DURING VIGILANCE:

A COMPARISON OF SUSTAINED ATTENTION AND RESPONSE INHIBITION

14

Goal-driven attention is referred to as top-down or endogenous attention, whereas

stimulus-driven attention is referred to as bottom-up or exogenous attention, being driven

by external events (e.g., Posner & Cohen, 1984). Allocating attention over short time

periods can be referred to as phasic orienting (Posner, 1980), while maintaining attention

over longer time periods is referred to as sustained attention, or vigilance (Parasuraman,

1986). The interactions among these varieties of attention come into to play in our

momentary experience as our goals and intentions compete with the attractions of the

environment over milliseconds, seconds and minutes (for review see Ruz & Lupianez,

2002). The primary focus of this initial set of experiments was to investigate the effects

of endogenous and exogenous attention during vigilance.

Endogenous attention can be engaged explicitly to select one thing over another (e.g.,

attend to one location and ignore others, Posner, 1980), as well as to follow a particular

instructional set or general rule (e.g., attend to and report the color of the presented word,

Stroop, 1935). Endogenous attention can be also guided by spatiotemporal regularities

that are learned implicitly over time (Large & Jones, 1999). In contrast to both explicit

and implicit forms of endogenous attention, exogenous attention refers to effects that are

generated externally by the physical properties of stimuli (e.g., brightness, color, shape).

Exogenous attention allows novel or salient information (e.g., a sudden change in

luminance, Jonides & Yantis, 1988) to transiently interrupt goal-directed behavior. The

dynamic and flexible interplay between endogenous and exogenous attention has been

well-studied in relation to visuospatial selective attention over short time periods (Folk,

Leber, & Egeth, 2002; Folk, Remington, & Johnston, 1992), and this interaction

continues to be characterized in electrophysiological and brain imaging studies (e.g.,

15

Hopfinger & West, 2006; Serences et al., 2005) that seek to delineate functionally the

brain networks supporting these attention systems. Current theories posit that endogenous

and exogenous attention systems interact and compete over short time periods to guide

behavior (Corbetta, Patel, & Shulman, 2008; Corbetta & Shulman, 2002), with the

outcome of that interaction resulting in real-time prioritization of attentional focus and

stimulus processing based on both endogenous and exogenous factors (Fecteau & Munoz,

2006; Gottlieb, 2007).

However, less is known about how exogenous and endogenous attention systems

interact over longer periods of time. Among the major theories of vigilance, the resource

model (Davies & Parasuraman, 1982) proposes that the drop-off in performance accuracy

over time – the vigilance decrement - is a result of the exhaustion of information

processing resources that are not replenished over time. In support of this model, task

factors that increase processing demands also increase the vigilance decrement. For

example, vigilance decrements are large and reliable when stimulus events occur at a

high rate (~ 30 events/minute), when targets are difficult to discriminate from non-

targets, and when the task loads working memory (Parasuraman, 1979; Parasuraman &

Mouloua, 1987; See et al., 1995). Moreover, vigilance is deemed to be effortful and

stressful (Grier et al., 2003; Szalma et al., 2004; Warm et al., 2008). More recently,

researchers have demonstrated that the vigilance decrement is not specific to tasks that

require rare responses in that accuracy also declines over time in tasks that require

response inhibition (Grier et al., 2003; Helton et al., 2005). Sustained response inhibition

tasks combine the effort associated with normal vigilance, with the challenge of

inhibiting impulsive behavioral responses (Helton, 2009; Helton, Kern, & Walker, 2009).

16

In general, most vigilance research has focused on describing the task factors, such as

stimulus and response features, that contribute to worse performance over time (see

Chapter 1).

Assuming that attentional resources are limited and vigilance requires attentional

effort, it is important to determine whether endogenous and/or exogenous attention

manipulations might improve vigilance performance, and how these effects are related to

the response requirements of the task. Although benefits from endogenous and exogenous

attention cues during vigilance have been demonstrated, the interaction between

exogenous attention and endogenous attention during vigilance remains unclear,

primarily owing to the fact that no investigators have examined effects of exogenous and

endogenous cuing in the same study. Thus, this was the purpose of the current study. I

directly investigated the interaction between endogenous and exogenous attention during

vigilance in order to better understand how manipulations of both forms of attention

would affect the performance decrement. Specifically, I investigated (1) whether simple,

sudden-onset visual events could improve sustained performance (exogenous attention

manipulation), (2) whether benefits due to sudden onsets would interact with fluctuations

in attention as a function of the predictability of stimulus timing (endogenous attention

manipulation), and (3) whether performance on the most resource-demanding versions of

the vigilance tasks would change with simple task practice.

Perceptual Benefits of Endogenous Spatial Attention

Many studies outside of the vigilance domain have investigated the extent to which

endogenous attention improves performance under conditions of low sensitivity (e.g., low

contrast, short stimulus duration). In the current study, I interpret internally represented

17

forms of attention, either explicit (voluntary) or implicit, as aspects of endogenous

attention, although I will discuss the behavioral and neural effects of each in turn. In

designs that manipulate attention explicitly on a trial-by-trial basis, voluntary spatial

attention improves the accuracy and speed with which stimuli are detected (e.g., Posner,

1980). Voluntary attention can also be deployed to varying degrees, with associated

perceptual sensitivity benefits commensurate with the amount of attention (e.g., 100%

versus 75%) to a given spatial location (Mangun & Hillyard, 1990). Event-related

potential (ERP) studies in humans have shown that voluntary attention improves

sensitivity by enhancing the gain of attended as compared to unattended stimulus

representations at early levels of visual processing. For example, many studies have

found amplitude enhancements of early latency visual evoked responses to stimuli that

occur in the location of voluntary attention (Mangun & Hillyard, 1991; Martinez, Di

Russo, Anllo-Vento, & Hillyard, 2001; Van Voorhis & Hillyard, 1977). Moreover,

behavioral and neurophysiological enhancements occur at the fovea with focused versus

distributed attention (Miniussi, Rao, & Nobre, 2002), demonstrating that directed

voluntary attention can enhance processing even when spatial resolution is already high.

In light of the benefits of endogenous attention, it is perhaps not surprising that

performance on sustained attention tasks suffers as voluntary attention wanes over time.

Endogenous attention can also be guided by regularities or patterns that are learned

implicitly over time, such as the probability of a target item appearing in a particular

spatial location (Chun & Jiang, 1998; Geng & Behrmann, 2005; Hoffmann & Kunde,

1999) or temporal patterns that predict when task-relevant stimuli will occur (Martin et

al., 2005; Olson & Chun, 2001). Like explicit manipulations of attention, implicit

18

manipulations of endogenous attention have been shown to be strong predictors of

behavioral improvements. For example, Geng and Behrmann (2005) showed that spatial

probability robustly influenced attention, with behavioral improvements (faster reaction

times, better accuracy) occurring at likely versus unlikely locations. Moreover, they

showed that implicit cueing through spatial probability had just as strong an influence on

attention as explicit endogenous cues and was effective in reducing distraction during

task performance.

Perceptual Benefits of Exogenous Spatial Attention

Exogenous selective attention also improves performance under conditions of low

sensitivity. Behaviorally, exogenous attention improves detection of, and facilitates

responses to, salient sudden-onset stimuli (Jonides & Yantis, 1988; Posner & Cohen,

1984). In addition, sudden-onset cues that occur within ~200 milliseconds of an

upcoming stimulus have been shown to enhance apparent contrast (Carrasco, Ling, &

Read, 2004; Carrasco, Penpeci-Talgar, & Eckstein, 2000; Ling & Carrasco, 2006) and

increase spatial resolution (Carrasco, Williams, & Yeshurun, 2002a; Yeshurun &

Carrasco, 1999), thus improving perceptual sensitivity to stimuli that occur in the location

of the cue. Although most studies of exogenous attention have examined peripheral

cueing, sudden onsets have been shown to capture attention at the fovea as well. For

example, Coull, Frith, Buchel, and Nobre (2000) demonstrated that targets that occurred

unexpectedly at fixation while subjects prepared to attend to a later point in time (but still

at fixation) captured attention in a stimulus-driven manner that was similar to exogenous

capture in the periphery. In supporting of the exogenous influence of these stimuli, visual

cortex activity to the unexpected targets (as measured using functional magnetic

19

resonance imaging, fMRI) was preferentially enhanced, supporting the idea that

exogenous attention results in sensitivity and gain enhancements in visual processing,

even at the fovea.

Interactions between Endogenous and Exogenous Attention

In cases of highly focused voluntary attention, observers are usually able to resist

distraction from salient, abrupt onsets outside the focus of attention (Theeuwes, 1991;

Yantis & Jonides, 1990). However, several lines of research indicate that endogenous and

exogenous visuospatial attention interact, in that the consequences of sudden-onset

salient events (exogenous attention) can be modulated by the current goal state

(endogenous attention). Although these combined influences may sometimes lead to

performance impairments, such as when you are distracted from paying attention to a

certain location in space by a salient interruption in the periphery (van der Lubbe &

Postma, 2005), exogenous orienting is not truly automatic (for review see Santangelo &

Spence, 2008). Instead, the total impact of a sudden-onset event on endogenous attention

has been shown to depend on how much the event shares in common with the target of

voluntary attention (e.g., the target and the sudden-onset distracter are the same color), a

mechanism labeled “contingent attentional capture” (Folk et al., 2002). Relevant to the

present investigation, such sudden onsets can lead to performance enhancements, such as

when endogenous and exogenous attention cues converge on the same location. In a

study of directed spatial attention, Hopfinger and West (2006) showed that brief,

exogenous cues that occurred in the location of voluntary attention (e.g., left or right

visual field) amplified the behavioral benefits of endogenous attention: Subjects were

faster to respond to an upcoming target when an exogenous cue occurred in the location

20

where they were already paying voluntary attention. Overall, these findings demonstrate

that endogenous and exogenous attention systems together optimize both behavior and

the distribution of neural resources.

Sustained Interactions between Endogenous and Exogenous Attention

Robertson and colleagues (Robertson, Mattingley, Rorden, & Driver, 1998) examined

the relationship between vigilance and spatial attention, and demonstrated that

improvements in sustained attention can lead to better spatial attention (e.g., less severe

symptoms in patients with hemispatial neglect after sustained attention training). But the

reverse - that spatial attention benefits may lead to better vigilance performance - has

received less consideration. Exogenous and endogenous cues may improve vigilance in

different ways. For example, endogenous cues may help the observer conserve attentional

resources over time. Previous studies using voluntary attention cues, such as the

presentation of explicit warning cues (Hitchcock, Dember, Warm, Moroney, & See,

1999; Hitchcock et al., 2003) have shown better vigilance when cues reliably (80%)

predict the occurrence of an upcoming target. That is, observers can use the cues to direct

their attention over short intervals of time (i.e., the cue indicates that a target will occur in

the next few seconds) rather than maintain attention continuously over long time

intervals. Manipulations of implicit endogenous attention also appear to be strong

predictors of better sustained attention. Observers benefit from temporal regularities in

the structure of stimulus presentation, showing better performance over time when

stimuli occur at a constant, rhythmic rate (Scerbo, Warm, Doettling, Parasuraman, &

Fisk, 1987) or when targets occur at constant intervals (e.g., 1 target every 30 seconds,

Helton et al., 2005).

21

The deleterious effects of insufficient resources over time during a vigilance task

might also be offset by manipulating exogenous attention, such as by inserting attention-

grabbing, salient stimuli to transiently prime resources. In this vein, previous research on

the effects of exogenous stimulation during vigilance has shown promising effects for

procedures that capitalize on the effects alerting cues (O'Connell et al., 2008; Robertson,

Tegner, Tham, Lo, & Nimmo-Smith, 1995). However, in these studies, the benefits to

sustained performance were the result of intensive training with loud auditory cues that

primarily affected overall physiological arousal (i.e., increases in skin conductance;

O'Connell et al., 2008). It is therefore still an open question as to whether the kind of

simple, sudden-onset visual events, such as those used in classic cueing studies of

exogenous attention over short time periods (e.g., Posner & Cohen, 1984), can change the

trajectory of sustained attention performance.

The benefits of exogenous attention due to sudden-onsets during vigilance tasks that

require response inhibition are not as straightforward. Abundant evidence suggests that

exogenous attention boosts stimulus perception regardless of the particular response

mapping (e.g., Hopfinger & Mangun, 1998). However, response inhibition tasks place

the additional demand of controlling impulsive motor responses (Helton et al., 2009).

Thus, improved target perception due to exogenous attention may not be enough to

counteract the efficient but occasionally error-prone response routine of responding to

most stimuli, especially without strong, endogenous attentional control to inhibit

responses to rare targets (Robertson et al., 1997).

22

Overview

The overarching goal of this set of experiments was to examine how vigilance is

affected by exogenous and endogenous attention cues that I hypothesized would reduce

resource demands. Although the primary aim was to determine whether such cues

benefited performance, the eventual aim was to use this information to design maximally

challenging vigilance tasks that could be used in training studies. In the first experiment, I

investigated how exogenous attention affects performance on vigilance tasks requiring a

demanding perceptual discrimination. I compared performance on two display versions

that differed in features designed to capture exogenous attention. I examined the

influence of display version during a vigilance task that required responses to rare targets

(hereafter referred to as the sustained attention task) and a vigilance task that required

withholding of responses to rare targets (hereafter referred to as the response inhibition

task). I hypothesized that sudden onsets would improve sensitivity by drawing attention

exogenously to the task, and thus attenuate the usual vigilance decrement that occurs

when observers fail to maintain high levels of voluntary attention. However, I anticipated

that this benefit due to exogenous cueing might not improve performance in the response

inhibition task due to the competing and detrimental influence of a habitual motor

routine. In a second experiment, I varied interstimulus timing to determine whether the

benefit due to sudden onsets observed in Experiment 1 depended on precise and

predictable temporal information about when to allocate endogenous attention. In the

third experiment, I assessed performance across a two-week interval on the most difficult

versions of the sustained attention and response inhibition tasks to confirm that vigilance

decrements would not be reduced with simple repeated testing. To anticipate the results, I

23

will mention that exogenous attention cues (Experiment 1) improved performance on the

sustained attention task but not on the response inhibition task. Predictable timing also

improved performance during sustained attention (Experiment 2). Relevant to choosing

outcome measures to examine training effects, vigilance did not improve with simple

practice on either the sustained attention or the response inhibition tasks (Experiment 3).

Experiment 1

The purpose of this experiment was to examine how sudden-onset exogenous

attention cues affected performance on perceptually demanding vigilance tasks. One of

the necessary objectives was to create target stimuli that were equally demanding across

individuals. Vigilance researchers have often noted striking individual differences in

baseline performance (Davies & Tune, 1969; Parasuraman, 1986), which poses serious

problems for comparing performance over time across individuals. Simply put, a target

that is initially difficult for one person to discriminate might be easy for another person to

discriminate, and hence these two people will experience very different resource demands

in maintaining endogenous attention for a long period of time. While some studies have

set target parameters based on average group discrimination performance (Parasuraman

& Mouloua, 1987), vigilance tasks are not commonly based on individually determined

target parameters. The few exceptions have included adaptive threshold procedures to

change a target stimulus parameter (e.g., brightness) throughout the task, in effect making

the targets easier to detect in order to compensate for performance decline (Bakan, 1955;

Berger & Mahneke, 1954; Frome, MacLeod, Buck, & Williams, 1981; Wiener, 1973). In

contrast, my approach was to determine target parameters for each participant using an

individual discriminability threshold procedure, and then present that threshold-level

24

target throughout the duration of the subsequent sustained attention task. This novel

design ensures equivalent attentional and perceptual demands across individuals, which

makes the task highly sensitive to fluctuations of attention over time. In this first

experiment, I implemented threshold-based targets and compared perceptual sensitivity

over time on two versions of the vigilance tasks that differed in display features that were

designed to capture exogenous attention. I predicted that perceptual sensitivity would

decline less in the sustained attention task that displayed brief, sudden-onset cues

immediately before a potential target stimulus due to the influence of exogenous

attention. However, I predicted that the influence of exogenous attention might not

benefit performance when response inhibition was required.

Methods

Participants. Forty volunteers (mean age = 20, range = 18 - 34; 29 females) from the

University of California, Davis gave informed consent and participated in exchange for

course credit. All participants had normal or corrected-to-normal vision and were free

from neurological insult. Two participants could not complete the practice and were

excluded from further participation. Additionally, two participants were unable to

complete the response inhibition task and were excluded. Analyses of performance over

time did not include those participants who could not perform the task to criterion

initially. Four participants performed below the initial accuracy cut-off (at least 50%

correct detections) during the first 4 min of at least one version of the assigned task (3

performed the response inhibition task) and were excluded from all analyses. Data from

the remaining 32 participants are presented here.

25

Stimuli. For all tasks, participants were seated comfortably in a dark, sound-

attenuating booth, with a chin rest ensuring a viewing distance of 57 cm. Presentation

software (Neurobehavioral Systems, http://www.neurobs.com) was used to control

display of all stimuli on a 53-cm CRT monitor (Eizo Flexscan F980) at a refresh rate of

60 Hz. Responses were made using the top right key on a ProPad game pad response

device. Participants were assigned to one of two successive-presentation (Parasuraman,

1979) vigilance tasks: a sustained attention task (N = 16) that required button presses to

rare targets or a response inhibition task (N = 16) that required withholding button

presses to rare targets. Uninterrupted task duration was 32 minutes. During both tasks,

single lines (light grey; 1.31 cd/m2) were presented at the center of the screen against a

black background (0.02 cd/m2) while participants fixated a small red square (see Figure

2-1). The fixation square was continuously visible at the center of the screen and

participants were instructed to maintain their gaze on the square throughout the task.

Stimuli were presented at a high event rate (30 events/minute; stimulus duration = 150

ms, interstimulus interval [ISI] = 1850 ms) that is considered to be resource demanding

and has been shown previously to promote declines in perceptual sensitivity over time

(Parasuraman, 1979). Long non-target lines (4.5°) were presented 90% of the time and

short target lines (M = 3.4°, range = 3.2°- 4.1°) were presented 10% of the time. Line

width (0.035°) did not differ between short and long lines. Two target lines occurred

pseudo-randomly every 20 trials, with the restriction that only 2 short lines could occur

consecutively at any point in the task. Instructions emphasized accuracy in making

simple button responses with the right index finger to the target short lines (sustained

attention task) or the non-target long lines (response inhibition task). Each participant

26

performed two versions (order counterbalanced) of their assigned task that differed

according to display features: The stable version displayed a mask pattern (same

brightness as line stimuli) during the entire interstimulus interval, and the transient

version displayed the same mask pattern for 100 ms before and after each line stimulus,

with a blank intervening screen (see Figure 2-1). The sudden onset of the mask before the

line in the transient display was designed as an exogenous attention cue, in line with

previous findings showing a sudden change in luminance contrast to be one of the

strongest forms of exogenous attentional cueing (Steinman, Steinman, & Lehmkuhle,

1997). The presentation of the mask following the line was included to equate visibility

between the transient and stable displays (i.e., to prevent a persisting after-image in the

transient display). The mask was composed of many short lines positioned throughout a

5.0° x 1.0° space; each short line was 0.07° wide and ranged in height from 0.28° to

0.45°. The mask pattern was never present during line stimulus presentation. The

Michelson contrast ratio between the line/mask stimuli and the black background was

97% (Coren, Ward, & Enns, 1999).

Threshold procedure. The length of the short target line was set to each participant’s

discrimination threshold using a self-paced “3 down/1 up” staircase procedure that

determined an 80% accuracy threshold (see Leek, 2001). Display characteristics and

event durations were matched to the stable and transient versions of the subsequent

vigilance task. During each trial of the threshold procedure, a warning signal

(enlargement of the red fixation square) preceded two sequential lines at fixation and

participants judged the length difference between the two lines, indicating same or

different length with the top left (left index finger) and top right (right index finger) game

27

Figure 2-1. Stimuli and timing for the transient display version of the vigilance task. Sequence of events is shown for one non-target trial (long line stimulus; 90% of stimuli) and one target trial (short line stimulus; 10% of stimuli). Participants made responses to the short lines during the sustained attention task and to the long lines during the response inhibition task. The transient mask appeared before and after every line (both targets and non-targets) as depicted. In the stable display version, the mask was presented continuously throughout the entire 1850 ms inter-event interval (e.g., in place of the sole fixation square shown in the figure). Stimuli are shown in white for illustrative clarity, but were presented in light grey during the tasks; the fixation square was presented in red in Experiment 1 and 3, and yellow in Experiment 2. In Experiment 2, the inter-event interval varied randomly across trials (M = 1850, range = 1350 - 2350 ms). The fixation square was continuously visible at the center of the screen during all tasks.

28

pad buttons, respectively. Each trial was composed of (a) a long and short line (order

randomized), (b) two short lines, or (c) two long lines. Participants made an untimed

response and then received sound feedback (a ding sound for correct, a whoosh sound for

incorrect) indicating response accuracy. The length of the short line varied throughout the

task in ~0.07° steps (the first short line subtended 3.6°; possible range = 3.2°- 4.4°), with

increments in line length occurring after 3 successive correct responses and decrements in

line length occurring after each incorrect response. The task terminated after eight length

reversals (i.e., a switch from an increment in line length to a decrement in line length).

The length of the long line was the same as in the main task and never varied.

Overview of testing procedures. Participants were introduced to the task through brief

verbal instructions and practice of the threshold procedure (20 trials with the short line

length held constant at a length that would be easily detectable by all participants).

Participants then completed the threshold procedure (~10 minutes) followed by practice

on the assigned vigilance task with the target line length set to their threshold (5 minutes).

An extra 5 minutes of practice was allowed if participants correctly detected fewer than

75% of targets. (Note: One participant received extra practice and subsequently

performed at 87% accuracy during the first block of the task, well within the range of

normal [< 1 SD] group performance.) Participants then performed the first version (e.g.,

stable display) of the assigned vigilance task (sustained attention or response inhibition)

for 32 minutes (960 trials) without sound feedback or breaks. This sequence was repeated

for the second version (e.g., transient display) after a 15-minute break. Order of display

version (stable or transient first) was counterbalanced across participants.

29

Overview of data analysis. Hit and false alarm rates were calculated over 4

contiguous blocks (240 trials each) for each version of the vigilance task. To allow a

comparison between tasks with opposite response requirements, hits were defined as

correct detections of targets (short lines), whether that corresponded to a button press

(sustained attention task) or a correct inhibition (response inhibition task). Likewise, false

alarms were defined as failures to detect non-targets (i.e., responding to a long line as if it

were a short line), corresponding to incorrect button presses to non-targets in the

sustained attention task and accidental inhibitions to non-targets in the response

inhibition task. The non-parametric index of perceptual sensitivity, referred to as A', was

calculated from the hit and false alarm rates (Stanislaw & Todorov, 1999) and served as

the main dependent measure of performance over time. The non-parametric index of

response bias, ß''D (See, Warm, Dember, & Howe, 1997) was also calculated and used to

assess changes in response tendencies over time. Finally, correct RTs were examined;

RTs were trimmed to remove the top and bottom 5% outliers on a cell-by-cell basis for

each individual.

Separate analyses were conducted for the sustained attention task and for the response

inhibition task. Multivariate analysis of variance (Maxwell & Delaney, 2004, pp. 671-

675, 682-751), which makes no assumption about compound sphericity, was used to

assess all within- and between-subjects effects, and Bonferroni correction was used to

control the family-wise error rate for all post-

two simple main effects to follow-up a significant interaction).

30

Results

Threshold. The threshold procedure successfully produced a ~.80 hit rate during the

first block (240 trials) of the sustained attention task (M = .77, SD = .14) and the response

inhibition task (M = .78, SD = .17). Difference thresholds – the visual angle difference

between the standard long line and the participant’s threshold-level short line – were

analyzed in separate mixed-model ANOVA with the within-subjects factor of display

(stable or transient) and the between-subjects factor of order (stable first or transient

first). The only significant source of variance in each analysis was an interaction between

display and order (sustained attention: F (1, 14) = 18.29, p < .001; response inhibition: F

(1, 14) = 27.98, p < .001), which revealed that thresholds were lower (i.e., smaller

difference in visual angle between the two lines) on the threshold task that was performed

second regardless of display version (see Table 2-1). The results suggest that participants’

perceptual thresholds improved with exposure to the tasks in general, but did not differ

according to display version.

Order effects on performance. Measures of performance over time were subjected to

separate mixed-model ANOVAs with within-subjects factors of time (4 blocks) and

display version, and between-subjects factor of order of display version. It was first

important to determine if task order affected performance. In terms of accuracy (A')

during the sustained attention task, the omnibus ANOVA showed no main effects of

order, but a significant interaction between order and display version (F (1, 14) = 6.57, p

= .022). Overall accuracy was higher in the task that was performed first, regardless of

display version (see Table 2-1), although order did not modulate performance over time.

31

The same interaction was just significant in the ANOVA of response inhibition (F (1, 14)

= 4.54, p = .05). Due to this effect, order was retained as a between-subject factor in

all subsequent analyses of accuracy. Order did not influence reaction times (all p values >

.08) and was excluded from RT analyses (Note: Including order in the analyses of RT did

not change the reported results).

Table 2-1 Order Effects on Threshold and Performance ________________________________________________________________________ Difference Thresholda Overall Perceptual Sensitivity (A')

______________________________________________________ Task Order First Task Second Task First Task Second Task ________________________________________________________________________ Stable First 1.08 0.84 .946 .916 Transient First 0.99 0.71 .919 .880 ________________________________________________________________________ a Visual angle difference between the long line and the 80%-threshold-level short line obtained through a 3 down/1-up staircase procedure. Values represent the average across sustained attention and response inhibition tasks.

Perceptual sensitivity. I examined changes in perceptual sensitivity (A') over time for

each task. In the sustained attention task, mean A' values during the initial block did not

differ between display versions (M = .940 for transient and .937 for stable, t < 1),

indicating that the threshold procedure successfully matched initial performance.

Perceptual sensitivity declined over time in both versions, although the main effect of

time in the omnibus ANOVA was a non-significant trend (Ms blocks 1 - 4 = .939, .934,

.917, .907, F (3, 12) = 3.13, p = .065). Importantly, the decline in A' over time was

32

qualified by a significant interaction between time and display version (F (3, 12) = 5.35,

p = .015). Examination of the performance trajectories over time revealed that there was

comparatively less performance decline in the transient version as compared to the stable

version, in line with our prediction (see Figure 2-2). A test of the simple main effect of

time within each display separately indicated that the performance decline was significant

in the stable version (F (3, 12) = 4.87, p = .019), but not in the transient version (F (3, 12)

= 1.27, p = .32). No other sources of variance were significant in the analysis of A'. A

similar model of response bias revealed non-significant effects of time (F (3, 12) = .386,

p = .76), display (F (1, 14) = 1.02, p = .32) and the interaction between display and time

(F (3, 12) = 1.09, p = .38). Thus, changes in response bias did not accompany the

observed changes in perceptual sensitivity.

In the response inhibition task, mean A' values during the initial block did not differ

between display versions (M = .946 for transient and .931 for stable, t (15) = 1.22, p =

.23), indicating that the threshold task successfully matched initial performance.

Perceptual sensitivity declined over time in both display versions, as indicated by a main

effect of time in the omnibus ANOVA (Ms blocks 1 - 4 = .939, .917, .905, .887, F (3, 12)

= 5.74, p = .011). There were no other significant sources of variance (p’s > .24).

However, an examination of the performance trajectories revealed that the performance

decrement was numerically worse on the transient display, the exact opposite pattern as

in the sustained attention task (see Figure 2-2). Finally, the model of response bias

revealed no significant sources of variance (all p’s > .26).

33

a.

b.

Figure 2-2. Changes in perceptual sensitivity (A') as a function of time on task in Experiment 1. A' plotted over four contiguous 8-minute blocks (240 trials each) for each display version (stable and transient) during (a) the sustained attention task (N = 16) and (b) the response inhibition task (N = 16).

0.88

0.9

0.92

0.94

0.96

1 2 3 4

A'

Block (8 min)

TransientStable

0.86

0.88

0.90

0.92

0.94

0.96

1 2 3 4

A'

Block (8 min)

TransientStable

34

Reaction time. Although participants were encouraged to respond accurately rather

than quickly, I wanted to determine whether changes in perceptual sensitivity were

accompanied by changes in RT (e.g., a speed-accuracy trade-off). In the sustained

attention task, mean correct RTs (correct detections of targets) were submitted to a

repeated-measures ANOVA with within-subjects factors of time (blocks 1-4) and display

version. A main effect of time revealed that RTs significantly increased over time (Ms =

691 msec, 745 msec, 748 msec, 745 msec, F (3, 13) = 9.23, p = .002), but RTs did not

differ according to display (F (1, 15) = .009, p = .92). In addition, the interaction between

time and display version was not significant (F (3, 13) = 2.37, p = .11). These results

confirm that RTs did not become differentially faster in the stable display, which is the

display version that showed the most decline in A' (i.e., there was no speed-accuracy

trade-off). In the response inhibition task, mean correct RTs (correct responses to non-

targets) were submitted to a repeated-measures ANOVA with within-subjects factors of

time (blocks 1-4) and display version. A significant main effect of time revealed that RTs

significantly decreased over time (Ms = 556 msec, 537 msec, 523 msec, 506 msec, F (3,

13) = 5.68, p = .01). In addition, an effect of display revealed that RTs were significantly

slower in the transient display (M = 559 msec for transient and 502 msec for stable, F (1,

15) = 6.00, p = .027). The interaction between display and time was not significant (p >

.45). These results suggest that the decrease in accuracy over the course of the task was

accompanied by a speeding of reaction time, regardless of display version. Thus, there

appeared to be a speed-accuracy trade-off in the response inhibition task that did not exist

in the sustained attention task. This finding is consistent with a recent report that

35

characterizes sustained response inhibition tasks as measures of impulsive responding

(Helton et al., 2009).

Discussion

The effects of exogenous attention and response requirements on vigilance were

examined. Using threshold-based targets, I found that perceptual sensitivity declined

predictably over time, which was mitigated by the influence of exogenous attention

during performance of the sustained attention task. The perceptual benefit due to

exogenous attention did not improve performance over time when response inhibition

was required, indicating that this type of responding demands a high level of voluntary

attentional control. Taken together, the results extend previous findings suggesting that

errors in sustained attention tasks are a consequence of the limits of effortful, voluntary

attention when resource demands are high (Grier et al., 2003; Helton et al., 2005).

In the sustained attention task, the typical decline in performance over time was

attenuated when display features of the transient task presumably facilitated exogenous

capture of attention and thereby enhanced perception. When the positive effects of

exogenous attention were removed in the stable display, the decline in perceptual

sensitivity significantly increased. I interpret the improvement in perceptual sensitivity to

be a beneficial consequence of sudden-onset cues exogenously attracting attentional

resources to the display. In addition, there were concomitant changes in target response

speed over time. Because accuracy was strongly emphasized over speed in both

experiments, I interpret the modulation of target responses as further support that sudden

onsets made the perceptual decision easier (Parasuraman, 1986, p. 14). In line with this

interpretation, Parasuraman and Davies (1976) examined changes in response latencies in

36

a vigilance task that emphasized accuracy over speed in making different button

responses to both targets and non-targets. They found that while correct detection and

false alarm response times (i.e., “positive response latency”) increased over time,

response times associated with correct rejections and errors to non-targets (i.e., “negative

response latency”) decreased or remained stable over time. The increase in RT associated

with positive responses was taken as a marker of the relative strength of evidence

supporting a positive (target) v. negative (non-target) decision. That is, as observers

attempt to be as accurate as possible, correct discrimination of targets is faster in the case

where better perception supports the target v. non-target decision. In the present study,

sudden-onsets led to both better perception and faster responses to targets over time on

task.

The present study demonstrated that resource demands can be reduced by enhancing

perception through simple sudden-onset cues. Importantly, sudden onsets occurred on

every trial, and they presumably increased sensitivity on both target and non-target trials.

In a sustained attention task with rare targets, maintaining a reliable percept of the non-

target stimulus is important for accurate identification of the target when it does occur.

Indeed, when the percept of the non-target does not have to be held in working memory,

such as in simultaneous-presentation tasks (e.g., deciding whether two lines presented

simultaneously are different in length), sensitivity does not reliably decline over time

(Parasuraman, 1979). Furthermore, Caggiano and Parasuraman (2004) demonstrated that

simultaneous performance of a spatial working memory task interfered with the

discrimination of a rare target defined by its spatial location (i.e., eccentricity). They

interpreted the increased perceptual sensitivity decrement under the condition of a

37

competing working memory task to reflect the consequence of worse working memory

representations of both the standard, non-target position and the infrequent target

position. In line with the critical role of memory load in vigilance (Parasuraman, 1979;

Parasuraman & Davies, 1977), the improvement in perception with exogenous attention

in the present study could help maintain an accurate working memory representation of

the non-target line, thus making target discrimination easier. This finding is important

since it demonstrates for the first time that sustained discrimination depends not only on

the availability of voluntary attention resources, but is also a result of an interaction

between voluntary attentional control and the exogenous features of the task.

In the response inhibition task, the presumed perceptual benefit following exogenous

cues did not attenuate the decline in performance: There was not a significant difference

in the vigilance decrement between transient and stable display versions. Although

exogenous attention to the sudden-onset stimuli in the transient display may have

improved target detection during the response inhibition task as it did during the

sustained attention task, this perceptual enhancement was not enough to offset waning

voluntary attention and inhibitory control. To the contrary, features of the transient

display may have actually hurt performance during response inhibition. In the transient

display, the sudden on/offsets are recurrent and rhythmic visual events that may have

served to synchronize the simple motor response with each stimulus (for a review, see

Repp, 2005). Indeed, non-target responses became faster over time while accuracy

decline, indicating a speed-accuracy trade-off. This mode of responding is consistent with

synchronous, habitual motor responding. Furthermore, overall RTs were significantly

slower in the transient display, which may reflect a strategic attempt to resist accidental

38

inhibition errors by making slower, more careful responses to non-targets. Together,

these data suggest that sustained response inhibition places high resource demands on the

attentional control system to maintain voluntary attention while also counteracting

habitual motor responses to non-targets when presented with a target (Robertson et al.,

1997).

Experiment 2

In Experiment 1, I found that sudden-onset cues improved performance over time

during sustained attention. The primary purpose of Experiment 2 was to examine the

impact of predictable timing cues on the perceptual benefits due to sudden-onset cues. I

introduced a variable ISI in Experiment 2 to prevent subjects from taking precisely timed

and efficient breaks in attention, thus increasing the overall resource demand. This

manipulation allowed the assessment of the independent effects of sudden onsets and

predictable timing on sustained performance.

The secondary purpose of Experiment 2 was to clarify the nature of changes in

threshold and overall accuracy observed in the second task in Experiment 1.

Improvements in perceptual threshold on the second task suggested either short-term

perceptual changes (i.e., true changes in threshold due to stimulus exposure) or practice

effects (i.e., improved performance on the threshold procedure itself rather than true

changes in threshold). In Experiment 2, I designed a more ecologically valid threshold

procedure that would be less susceptible to procedure-specific practice effects. To test for

any changes in threshold due to stimulus exposure, threshold was re-measured

immediately following completion of the sustained attention task. In Experiment 1, I also

observed that overall accuracy was lower in the second task performed. This pattern

39

suggested a possible interaction between threshold changes and changes in accuracy. If

improvements in threshold were mainly driven by procedure-specific practice effects,

then thresholds would be artificially lower on average, leading to a more difficult target

during the sustained attention task and lower overall accuracy. At the group level, this

could be expressed as accuracy going down as thresholds get better, as we observed. The

decrease in accuracy could alternatively reflect fatigue; however, the 15-minute break

between task versions should have restored performance to normal levels (e.g.,

Mackworth, 1948). The between-subjects manipulation of display in Experiment 2

directly addressed practice effects and fatigue issues related to threshold and accuracy

during sustained attention.

Methods

Participants. Forty volunteers (mean age = 20, range = 18 – 34, 26 females) from the

University of California, Davis gave informed consent and participated in exchange for

course credit. All participants had normal or corrected-to-normal vision and were free

from neurological insult.

Stimuli. Task and stimulus parameters (including visual angle, contrast ratio and

average event rate) for the sustained attention tasks were the same as in Experiment 1,

except for a few important modifications. First, a variable ISI was included representing a

range of 500 ms above and below the interval used in Experiment 1 (range = 1350 – 2350

ms). Second, debriefing in Experiment 1 revealed that some participants attempted to

assess line length by comparing the currently presented line with the background mask

parts. To prevent this strategy, each of the lines forming the mask was vertically

repositioned on every presentation. The change in position of each mask part (0.07° x

40

0.28°) was randomly chosen within a range of -0.14° to +0.14°, with negative values

indicating a downward shift and positive values indicating an upward shift. The subtle

shift was not immediately noticeable, but prevented the comparison strategy if a

participant attempted it. Finally, the type of display (stable or transient) was manipulated

between subjects (n = 20 in each task version) to avoid possible fatigue or order effects

on performance. As in Experiment 1, instructions emphasized accuracy in making simple

responses with the left mouse button (right index finger) to the target short lines. Stimuli

were presented on a 21-inch CRT monitor (Viewsonic AccuSync 120) at a refresh rate of

60 Hz. Line and mask stimuli were presented in light grey (3.25 cd/m2) on a black

background (0.05 cd/m2; Michelson contrast ratio = 97%).

Threshold procedure. The length of the short target line was set to each participant’s

80% discrimination threshold at each assessment, which was determined using a variant

of Parameter Estimation through Sequential Testing (PEST; Taylor & Creelman, 1967).

PEST is an adaptive thresholding procedure that dynamically adjusts the amount of

change (step size) between testing levels based on current task performance. This design

ensures that testing levels quickly converge on a threshold value. PEST is more efficient,

accurate, and flexible than fixed thresholding methods such as the staircase procedure

used in Experiment 1.

In PEST, one specifies a testing level for the first target stimulus, and testing levels of

subsequent target stimuli are determined using a Wald sequential likelihood-ratio test

(Wald, 1947). The Wald test indicates whether the percent of correct responses at a given

testing level is within a range determined by the threshold probability (Pt) and the

deviation limit of the sequential test (W) chosen by the experimenter at the beginning of

41

the procedure. If the current testing level is above or below the range determined by the

Wald test, it is decreased or increased appropriately by a specified step size. The initial

testing level (e.g., the length of the short line), as well as Pt, W and the starting step size,

are all pre-determined by the experimenter at the beginning of the procedure. Subsequent

step sizes (e.g., halving the step size with a reversal in direction) are determined as

described by Taylor and Creelman (1967, p. 783). The PEST procedure terminates when

a minimum step size value is obtained. The resulting threshold estimate is equal to the

testing level that would have next been presented.

According to Taylor and Creelman (1967), the starting values of W, Pt and minimum

step size influence the outcome of the procedure. Specifically, the starting value of W

primarily affects the power of the test, such that “small values of W yield quick but not

very powerful decisions, while large values of W give, after many trials, decisions of

great power” (p. 783). The starting values of Pt and the minimum step size affect the

efficiency and the precision of the test estimate, respectively (pp. 783-784). In the current

study, I chose starting values of .75 for the threshold probability (Pt), .85 for W, and a

minimum step size of 1 pixel (.035°).

Overview of testing procedures. Participants followed the same sequence of tasks as

in Experiment 1. The display characteristics, event durations, and response requirements

of the PEST threshold procedure exactly matched those of the corresponding sustained

attention task, except that the target short line occurred 33% of the time (cf. 10% target

probability during the sustained attention task). Participants received sound feedback

indicating (a) correct detections (ding sound), (b) target misses (whoosh sound), and (c)

incorrect responses to non-targets (whoosh sound). Next, participants perform the

42

sustained attention task without breaks or sound feedback. After completing the sustained

attention task, participants performed the threshold task again to assess short-term

changes in threshold.

Results

Threshold. The PEST threshold procedure successfully produced an ~.80 hit rate

during the first block of the corresponding sustained attention task (M = .81, SD = .13).

Difference thresholds were subjected to a mixed-model ANOVA with a between-subjects

factor of display (stable or transient) and a within-subjects factor of exposure (pre or

post). No significant differences were found between pre and post thresholds (M = 0.98°

for pre and 0.96° for post; F (1, 38) = .196, p = .66). No other sources of variance were

significant (range of p values = .18 - .34). These results demonstrate that low-level

perception, as measured in a threshold task with similar structure to the actual sustained

attention task, did not change after continuous performance.

Performance over time. Perceptual sensitivity (A') over time was assessed with a

mixed-model ANOVA with a between-subjects factor of display and a within-subjects

factor of time (blocks 1- 4). A main effect of time demonstrated that perceptual

sensitivity declined steadily over time for both display versions (Ms = .944, .910, .887,

.872, F (3, 36) = 37.7, p < .0001). In addition, a main effect of display (F (1, 38) = 4.74, p

= .036) revealed that overall performance was better in the transient display. However,

the interaction between display and time was not significant (F (3, 36) = .547, p = .65)

(see Figure 2-3). Thus, the data show that sudden-onset cues improved overall sensitivity,

but did not prevent a decline in perceptual sensitivity with unpredictable timing.

43

Figure 2-3. Changes in perceptual sensitivity (A') as a function of time on task in Experiment 2. A' plotted over four contiguous 8-minute blocks (240 trials each) for each display version (stable and transient) of the sustained attention task.

A similar model of response bias (ß''D) revealed a main effect of time (Ms = .69, .78,

.90, .83, F (3, 36) = 5.78, p = .002), indicating that subjects were less likely to respond as

time passed. The non-significant interaction between display and time (F (3, 36) = 1.09, p

= .36) indicated that this increase in response bias over time was not different between

the two displays. Although there was no difference in response bias over time between

the two display versions, a significant main effect of display (F (1, 38) = 4.11, p = .049)

indicated that subjects exhibited a more liberal overall response bias (i.e., were more

likely to respond) in the transient (M = .70) than in the stable display version (M = .90).

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Finally, trimmed mean correct RTs were submitted to a mixed-model ANOVA with

between-subject factor of display and within-subjects factor of time (blocks 1-4). In line

with Experiment 1, reaction times significantly increased over time (Ms = 683, 739, 737,

780 ms, F (3, 36) = 16.87, p < .0001). In addition, a significant interaction between time

and display revealed that the pattern of RT over time differed according to display (F (3,

36) = 3.06, p = .04). Although subjects slowed down over time with both displays,

subjects were able to maintain faster responses to targets for a longer portion of the task

in the transient display condition (transient display: Ms = 665 msec, 699 msec, 701 msec,

778 msec; stable display: Ms = 701 msec, 799 msec, 773 msec, 783 msec). No other

sources of variance were statistically significant (main effect of display: F (1, 38) = 2.12,

p = .15). These results replicate the pattern of increasing target RT over time revealed in

Experiment 1, but also suggest that reaction time performance suffered more in the stable

version with unpredictable timing.

Effects of event timing on performance over time. To assess the independent effect of

event timing on perceptual sensitivity over time, we compared performance between

participants who completed the transient display version of the sustained attention task

with a constant ISI (Experiment 1, N =16) and those who completed the transient display

version with a variable ISI (Experiment 2, N = 20). A mixed-model ANOVA with a

between-subjects factor of ISI (constant or variable) and a within-subjects factor of time

(blocks 1- 4) revealed a significant main effect of time (F (3, 32) = 13.3, p < .0001) that

was moderated by a significant interaction between time and ISI (F (3, 32) = 5.87, p =

.003; see Figure 2-4). The main effect of ISI was not significant (F < 1). The data show

45

that performance over time was better with predictable interstimulus timing, controlling

for the effect of sudden-onset attention cues.

Figure 2-4. Changes in perceptual sensitivity (A') as a function of time on task in Experiments 1 and 2 for the transient display version of the sustained attention task. A' plotted over four contiguous 8-minute blocks (240 trials each) for constant (Experiment 1; N = 16) and variable (Experiment 2; N = 20) ISI conditions.

Discussion

The results from Experiment 2 revealed that the perceptual benefit of exogenous cues

depended on the endogenous modulation of attention by predictable timing. When event

timing was variable, exogenous cues did not prevent a decline in perceptual sensitivity

over time. However, exogenous cues did lead to an increase in overall perceptual

sensitivity and a decrease in response times to targets in the transient display. These

effects support the conclusion that exogenous cues captured attention and improved

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Experiment 1 - Constant ISI

Experiment 2 - Variable ISI

46

performance, even in the face of variable timing, but not to the same extent as when

exogenous cues coincided with maximal endogenous attention guided by the implicit

endogenous cue of predictable timing (Experiment 1). In addition, predictable timing was

a strong independent predictor of successful performance over time. The results from the

comparison of performance over time on the transient tasks in Experiment 1 and 2

demonstrate that in tasks equated for individual difficulty and initial accuracy,

performance declined significantly more over time with variable stimulus timing

(Experiment 2). The independent benefit due to predictable timing confirms previous

reports of better sustained attention with rhythmic stimulus presentation (Scerbo et al.,

1987).

Predictable timing likely reduced resource demands by providing information about

when to pay maximal attention. Studies of attention in both the visual (Martin et al.,

2005; Olson & Chun, 2001) and the auditory domain (Jones, Moynihan, MacKenzie, &

Puente, 2002) have shown that endogenous attention is modulated according to the

underlying temporal structure of stimulus events, leading to more accurate performance

with predictable timing. This kind of anticipatory attending to specific moments in time

has been described as an important mechanism for synchronizing attentional resources

with critical external events in a way than can flexibly improve behavior (Large & Jones,

1999). Moreover, the modulation of endogenous attention in this manner is an efficient

way to conserve attentional resources so that maximal attention coincides primarily with

task-relevant events. The current replication of the effects of implicit timing cues on

sustained performance points to a critical role for this kind of attentional cueing in

preserving limited resources over long periods of time.

47

Temporal predictability can modulate endogenous attention without an explicit

intention on the part of the observer to voluntarily direct attention differently across time

(Martin et al., 2005). However, it is unclear whether the regular underlying temporal

structure in the current study also manipulated voluntary attention explicitly. Previous

studies of the effects of both spatial and temporal probability on attention have shown

large variability in how much observers notice and volitionally use the underlying

expectancies to direct attention during the task (Geng & Behrmann, 2005). The constant

ISI in Experiment 1 (1.85 seconds) provided observers with a relatively long and

predictable period during which attention was not required. Since voluntary attention can

be generated toward a particular location in space in ~300 ms after the onset of an

instructional cue (Muller & Rabbitt, 1989; Posner & Cohen, 1984), observers could

afford to take a “break” in attention after presentation of the previous line and then

explicitly redirect attention toward the end of the ISI to prepare for the next line. Indeed,

studies of temporal attention within the range of the ISI used in the present study have

shown that observers can accurately direct voluntary selective attention to specific

moments in time based on instructional cues (Coull, 2004; Coull et al., 2000; Coull &

Nobre, 1998). In terms of temporal complexity, the present task was relatively simple and

straightforward. Thus, some participants likely became aware of the regular temporal

pattern and used it to take voluntary “time outs” during the interstimulus interval and then

direct maximal attention to the point in time of the next stimulus. Although we can

confidently conclude that temporal regularity modulated endogenous attention implicitly,

it is possible that temporal regularity also modulated endogenous attention explicitly.

48

Given that observers could afford to take an attention “time-out,” why would they?

Although voluntary attention can be maintained in a given location for many seconds

(e.g., Ling & Carrasco, 2006; Silver, Ress, & Heeger, 2006), there is evidence that

maintaining attention continuously even for relatively short periods of time (~5 minutes)

is not always successful, resulting in attentional lapses along with behavioral and neural

processing consequences (Weissman et al., 2006). In this vein, there is mounting

evidence for a phenomenological basis of mind-wandering, or breaks in attention, that

occur spontaneously during performance of a central task. Behavioral studies using self-

reports, thought probes, and RT measures (for a review see Smallwood & Schooler,

2006) and neuroimaging studies of non-task related brain activity (Mason et al., 2007;

Smallwood, Beach, Schooler, & Handy, 2007) have demonstrated that observers often

experience disruptions in focused attention while performing sustained attention tasks,

such as visual monitoring or reading. Thus, voluntary attention is not usually maintained

at a maximum level but rather appears to wax and wane over long periods of time.

Predictable timing information about when to pay attention can be used to efficiently

deploy maximal attention only during presentation of a potential target, and this helps a

person avoid the consequences of unintentional mind wandering.

Experiment 3

The primary purpose of Experiment 3 was to use a within-subjects design to assess

the effect of repeated practice on vigilance performance. Previous studies have found that

vigilance does not improve with moderate practice (e.g., Nuechterlein et al., 1983),

although vigilance decrements can be reduced with extensive practice (e.g., > 20 practice

sessions of a 30-minute vigilance task; Parasuraman & Giambra, 1991). However, such

49

repeated exposure to visual stimuli can produce a limited (i.e., stimulus-specific) form of

perceptual expertise referred to as perceptual learning (Seitz & Watanabe, 2005). In this

way, repetitive stimulus exposure during training might lead to incidental improvements

in baseline perception, which could influence vigilance performance. In order to use these

tasks as outcome measures of training-related improvement, it was critical to characterize

how repeated task practice influenced both baseline thresholds and vigilance.

I reasoned that performance on the most resource-demanding versions of the

vigilance tasks would be robust to simple practice effects and would also be most

sensitive to real training-related change. Because exogenous attention aided performance

in the transient display version of the sustained attention task, I chose the stable version

as the most demanding sustained attention task. While sudden-onset display features in

the transient display version did not significantly affect accuracy during the response

inhibition task, the transient version certainly produced a larger numeric decrement

compared to the stable version; thus, I chose the transient version as the most demanding

response inhibition task. I predicted that performance would decline significantly in both

the sustained attention and response inhibition tasks, and that it would not improve with

repeated practice.

Methods

Participants. Seventeen volunteers (mean age = 25, range = 20 – 32; 8 females) gave

informed consent and were paid $10/hour for participation. Three participants performed

below the initial accuracy cut-off (at least 50% correct detections) during the first 4 min

of at least one of the tasks and were excluded from all analyses. Additionally, three

50

participants did not complete all testing sessions and were excluded. Data from the

remaining 11 participants are presented here.

Stimuli. At each of two assessments, each participant performed the stable version of

the sustained attention task and the transient version of the response inhibition task. Task

and stimulus parameters (including visual angle) for these tasks were the same as in

Experiment 1, except for a few modifications. First, each participant was pseudo-

randomly assigned a non-variable ISI for each task (M = 1880 ms), with assigned ISIs

representing a range of 300 ms above and below the ISI used in Experiment 1 (range =

1550 – 2150 ms). Second, instructions emphasized both speed and accuracy. Stimuli

were presented on an LCD monitor with 2ms black/white transition times at a refresh rate

of 60 Hz (Viewsonic VX-922). Line and mask stimuli were presented in light grey (40.29

cd/m2) on a black background (.35 cd/m2). (Note: Neither task order nor ISI influenced

performance and were excluded from all analyses.)

Overview of testing procedures. Participants visited the lab for four testing sessions:

one session on each of two consecutive days for the first assessment and the same format

repeated two weeks later for the second assessment. The order of tasks (e.g., sustained

attention task on day 1 followed by response inhibition task on day 2) and time of day

were counterbalanced across participants and then held constant for each participant at

the second assessment. On the first day of testing, participants followed the same

sequence of tasks as in Experiments 1 and 2 (e.g., brief practice followed by the threshold

procedure and then the sustained attention task). We used the PEST procedure from

Experiment 2 to determine 75% threshold-level target line lengths at each assessment.

(Note: We increased the difficulty to 75% to make the target discrimination challenging

51

across repeated testing sessions.) Additionally, so that I could assess short-term changes

in perceptual threshold, participants performed the threshold procedure again after

completing the vigilance task. This sequence was repeated for the second task (e.g.,

response inhibition task) on the second day.

Analysis of vigilance performance. Initial data processing (e.g., calculating perceptual

sensitivity for each block of trials) followed the same approach as in Experiment 1.

However, instead of analyzing vigilance performance using repeated measures ANOVA,

I analyzed performance using hierarchical linear regression. Hierarchical linear models

produce estimates of group performance (as in standard linear regression) as well as

individual deviations from group estimates. This modeling approach is well-suited for

analyzing nested data (i.e., individuals testing repeatedly over time) (see details in

Raudenbush & Bryk, 2002).

Analyses were conducted in SAS 9.1 using the proc mixed function (Littell, Miliken,

Stoup, & Wolfinger, 1996; Singer, 1998). Full maximum likelihood estimation was

implemented and the error covariance structure was modeled as unstructured. Separate

analyses were conducted for the sustained attention task and for the response inhibition

task. Each model of perceptual sensitivity included the fixed (i.e., group-level) effects of

time (the slope of A' across blocks) and assessment (test vs. retest), and the interaction

between time and assessment. Random effects on the intercept (i.e., A' during the first

block) were included to allow for individual differences in initial performance. The

following model was implemented for each task:

A' = 0 1 ( 2 3 (time*assessment) + e

0

52

0 1

2 estimate

3 estimate represents the

change in the vigilance decrement due to repeated testing.

Results

Threshold. The PEST threshold procedure successfully produced an ~.75 hit rate

during the first block (120 trials) of the corresponding vigilance task (M = .76, SD = .15).

Difference thresholds were subjected to two separate repeated measures ANOVAs with

within-subjects factors of assessment (test vs. retest) and exposure (pre-task vs. post-

task). For the sustained attention task, I found no significant effects of assessment (M =

.69° at test and .70° at retest, F (1, 10) = .37, p = .55) or exposure (M = .69° for pre-task

and .60° for post-task, F (1, 10) = 3.31, p = .099). The interaction was also not significant

(p > .36). Likewise, in the response inhibition task, I found no significant effects of

assessment (M = .86° at test and .88° at retest, F (1, 10) = .32, p = .58) or exposure (M =

.88° for pre-task and .97° for post-task, F (1, 10) = 3.33, p = .098). The interaction was

also not significant (p > .39). These findings confirm that thresholds did not improve

significantly after either short-term exposure (pre-task to post-task) or repeated practice

of the vigilance tasks.

Perceptual sensitivity. Perceptual sensitivity (A') was assessed in two separate

hierarchical linear models as a function of time (blocks 1 - 8) and assessment (test vs.

retest) and their interaction. In the sustained attention task, the effect of time was

-.006, p = .028) demonstrating that perceptual sensitivity declined over

time. Examination of the performance trajectories showed that performance was very

53

similar at the two testing points (see Figure 2-5). Neither the effect of assessment nor the

interaction between assessment and time was significant (p values > .20), confirming that

performance did not change with repeated testing.

-.009, p = .005)

demonstrating that perceptual sensitivity declined over time as in the sustained attention

task. Again, the performance trajectories at test and retest were very similar (see Figure

2-5). Neither the effect of assessment nor the interaction between assessment and time

was significant (p values > .89), confirming that performance did not change with

repeated testing.

Reaction time. In contrast to the instructions in Experiment 1, instructions in

Experiment 3 emphasized speed and accuracy equally. To assess the effect of this

instruction variation, trimmed mean correct RTs were submitted to two separate

hierarchical linear regression analyses (similar to the analyses of perceptual sensitivity).

In the sustained attention task, reaction times to correctly detected targets significantly

p = .004), replicating the findings from Experiments 1

and 2. Neither the effect of assessment nor the interaction between assessment and time

was significant (p values > .60), indicating that response speed did not change with

repeated testing.

In the response inhibition task, reaction times to non-targets significantly decreased

-5.14, p = .03) as in Experiment 1. In addition, a significant effect of

assessment revealed that reaction times were significantly faster at the second assessment

(M -30.51, p = .034). The interaction

between time and assessment was not significant (p = .74). These results suggest that

54

a.

b.

Figure 2-5. Changes in perceptual sensitivity (A') as a function of time on task in Experiment 3. A' plotted over four contiguous 4-minute blocks (120 trials each) for (a) the sustained attention task (N =11) and (b) the response inhibition task (N = 11). The two testing points (test and retest) were separated by a 2-week interval.

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overall response speed decreased at retest, possibly as a result of participants becoming

more familiar with the response requirements of the task. Importantly, controlling for

changes in reaction time did not change the reported results for perceptual sensitivity.

In addition, overall reaction times were ~ 50 msec faster in Experiment 3 than in

Experiment 1. These results suggest that instructions that emphasize both speed and

accuracy (Experiment 3) motivate faster responding overall compared to instructions that

emphasize only accuracy (Experiment 1). To further investigate this result, I examined

whether reaction time correlated with accuracy. Although there was not a significant

correlation at the first testing point (r = .29, p = .38), worse accuracy was significantly

correlated with faster reaction times at the second testing point (r = .61, p = .048). This

finding is consistent with previous reports of speed-accuracy trade-offs during sustained

response inhibition (Helton, 2009), and it indicates that one should carefully examine the

correlations between speed and accuracy across repeated testing sessions.

Discussion

The results demonstrate that perceptual sensitivity declined significantly over time in

the most resource-demanding versions of the sustained attention and response inhibition

tasks. Importantly, performance within the same individuals did not improve with

repeated testing. However, reaction times sped up on the response inhibition task at

retest, and thus it became important to track changes in both accuracy and reaction time

in subsequent training studies. Finally, perceptual thresholds did not change over either a

short period of time (30 minutes on task) or a longer period of time (at the end of the

second testing session), suggesting that baseline perception did not improve with simple

stimulus exposure. Together, the results from Experiment 3 demonstrate that threshold

56

and vigilance performance was stable across repeated testing sessions, making these tasks

appropriate indices of training-related improvements.

Summary

I examined changes in perceptual sensitivity over time – the vigilance decrement – as

a function of various experimental manipulations that we hypothesized would modulate

resource demands. In all tasks, I employed parameters previously shown to increase

resource demands and promote declines in perceptual sensitivity, including a high event

rate and successive presentation. In addition, I experimentally manipulated resource

demands in four different ways: (1) by individualizing target discrimination difficulty

using threshold-based targets; (2) by manipulating response requirements (response

initiation vs. response inhibition); (3) by manipulating display features to vary the degree

of exogenous attention drawn to the task; and (4) by manipulating the predictability of

stimulus timing, which provided information about when to allocate endogenous

attention. Using threshold-based targets, I found that sensitivity declined reliably over

time in tasks that required response initiation to rare targets (sustained attention task) and

in tasks that required withholding of responses to rare targets (response inhibition task).

In the sustained attention task, I found that the vigilance decrement was mitigated by the

influence of sudden-onset exogenous attention cues and by predictable stimulus-timing

information. In the response inhibition task, exogenous cues did not help performance.

Indeed, response inhibition appeared to increase demands on attentional resources

independently such that cues were not beneficial in preventing performance decline. In

sum, the best sustained performance was achieved (1) when response inhibition was not

required; (2) when predictable timing information guided the efficient deployment of

57

endogenous attention over time; and (3) when exogenous sudden-onset cues attracted

attention to the task to increase overall sensitivity. These data extend previous findings

that have shown that errors in vigilance tasks are a consequence of the limits of effortful,

voluntary attention given high resource demands (Grier et al., 2003; Helton et al., 2005;

Helton & Warm, 2008; Smit et al., 2004b; Szalma et al., 2004). Here, I have shown that

attentional cues can improve performance by reducing resource demands.

Together, these findings support the idea that successful vigilance performance is a

result of a dynamic interaction between endogenous attentional control and exogenous

events that capture attention, in line with theories concerning the neurobiological basis of

attention (Corbetta et al., 2008; Corbetta & Shulman, 2002). I interpret the effects of

exogenous and endogenous attention in light of evidence supporting a resource model of

vigilance. Specifically, exogenous cues independently improved overall sensitivity by

transiently increasing attentional resources, but they were arguably not effective in

preventing a decline in vigilance (Experiment 2). On the other hand, implicit endogenous

cues independently improved sustained performance (Experiment 1 vs. 2), in line with

previous reports of better vigilance with rhythmic, regular presentation of stimuli (Scerbo

et al., 1987). If the major limiting factor during vigilance is the overall resource demand

imposed on the endogenous attention system, then it appears that manipulations of

endogenous attention that result in the conservation of resources over time are more

effective at preventing the vigilance decrement than exogenous cues that only transiently

prime resources. The combined benefit of exogenous and endogenous cues was clearly

evident in that the best performance over time was achieved when exogenous cueing

coincided with maximal endogenous attention (Experiment 1, transient display).

58

Controlling for individual differences in low-level perception is critical in any study

of vigilance. My novel approach of setting target parameters according to individual

threshold using the PEST procedure (Experiments 2 and 3) proved to be an effective way

to create vigilance tasks that were equally challenging, both attentionally and

perceptually, for all individuals. Because the PEST method can flexibly converge on any

chosen threshold level (within an appropriate range), this approach allows an assessment

of sustained attention at varying levels of baseline performance. In effect, standardizing

baseline sensitivity helps control the total demand of the task (the amount of effort

required for task completion). In a meta-analysis of the sensitivity decrement in 42

vigilance studies, See and colleagues (1995) suggested that total task demand helps

explain the variation in the vigilance decrement not directly attributable to other critical

design factors. The present threshold-calibration approach is a promising method for

equating overall task difficulty according to individual perceptual ability and thus

equating the extent of the vigilance decrement across individuals. Indeed, although

thresholds did not change with moderate exposure to the line stimuli in Experiment 3 (~2

hours of total stimulus exposure between the two tasks and two assessments), it is

possible that more extensive stimulus exposure could lead to perceptual learning.

Alternatively, training interventions might influence baseline perception. Thus, setting

target parameters according to threshold at each testing point is an effective way to

control the difficulty of target discrimination with repeated testing.

Finally, repeated testing did not improve vigilance performance on the most resource-

demanding versions of the sustained attention and response inhibition tasks. This finding

is consistent with previous reports showing no improvements in vigilance with moderate

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practice (Nuechterlein et al., 1983; Williams, 1986). These results indicate that simple

practice is not sufficient to reduce the information-processing demands associated with

perceptually challenging vigilance tasks. In other words, the vigilance decrement

observed in naïve participants is not due to lack of familiarity with overall task demands

or with specific task features such as target probability and task duration. The stability of

performance in the response inhibition task further indicates that normal performance

decrements are not due to lack of practice with the particular motor requirements of this

kind of vigilance task. The level of performance stability I observed across repeated

testing suggests that these tests are robust to simple practice effects and will likely be

sensitive to real training-related changes in voluntary attention.

60

CHAPTER 3

IMPROVEMENTS IN PERCEPTION AND SUSTAINED

ATTENTION WITH INTENSIVE MEDITATION TRAINING

61

The ability to focus attention while resisting distraction is critical for adaptive, goal-

directed behavior. Voluntary attention – guided by goals, previous knowledge, or explicit

instructions – can be directed to spatial locations (e.g., Posner, 1980) and moments in

time (e.g., Correa, Lupianez, Madrid, & Tudela, 2006) to improve behavioral accuracy

and efficiency. However, voluntary attention is limited and cannot be sustained

indefinitely. Research on sustained attention consistently demonstrates declines in target-

discrimination accuracy (perceptual sensitivity) with increasing time on task, a

phenomenon called the vigilance decrement (see Parasuraman, 1986, for review). Despite

this evidence, few attempts have been made to improve healthy adults’ sustained

attention with training. Studies have demonstrated improvements on vigilance tasks

following repetitive practice (e.g., Parasuraman & Giambra, 1991), but no study has yet

identified a general training regimen for reducing the vigilance decrement. Similarly,

training-related improvements in other domains are often limited to the training task.

However, some forms of training, such as practice on action video games, can improve

performance on untrained attention tasks, indicating the transfer of learned skills to new

situations (see review in C. S. Green & Bavelier, 2008). The present study investigated

improvements in voluntary sustained attention with meditation training.

The attentional resource model attributes the vigilance decrement to exhaustion of

limited information-processing resources (Davies & Parasuraman, 1982; Warm et al.,

2008). Specifically, factors known to increase information-processing demands, such as

perceptually difficult targets (Nuechterlein et al., 1983) or working memory load

(Parasuraman, 1979) cause rapid and reliable decrements in perceptual sensitivity. When

the total task demand (the combined resource demands of multiple aspects of a task) is

62

high, vigilance decrements are large (See et al., 1995). Attentional cues can mitigate the

impact of resource demands during vigilance. Hitchcock and colleagues (2003) found

that both accuracy and cerebral blood flow (a measure of resource consumption) declined

less over time when a warning cue reliably (80%) predicted the occurrence of an

upcoming target. Similarly, other kinds of attentional cues have been shown to improve

vigilance (see MacLean, Aichele, Bridwell, Mangun et al., 2009 for review). Less is

known about how training might affect vigilance.

Historical accounts from the Buddhist contemplative tradition describe meditation

practices (Shamatha) that are designed to improve sustained attention (see translations in

Buddhaghosa, 1979; Tsong-kha-pa, 2002). Shamatha practitioners learn to stabilize

attention on a chosen stimulus, such as the tactile sensation of breathing, and enhance the

perceived detail of the stimulus in order to cultivate non-task-specific skill in regulating

and controlling voluntary attention (Wallace, 1999). Introspection is used to monitor the

quality of attention, recognize when attention has wandered, and guide attention back to

the chosen stimulus. This metacognitive or meta-attentive aspect of training may support

the transfer of meditation skills to other domains (Wallace & Shapiro, 2006) and

improvements in perception and attention that are reported in daily life (Wallace, 1999, p.

185).

The present study tests this claim empirically. Findings from longitudinal research on

meditation training in non-experts (1-3 months of full-time daily practice) suggest

improvements on laboratory tests of temporal attention (Slagter et al., 2007; Slagter,

Lutz, Greischar, Nieuwenhuis, & Davidson, 2009) and attentional alerting (Jha,

Krompinger, & Baime, 2007, one-month group). Cross-sectional studies have shown that

63

long-term meditation practitioners show superior sustained attention compared to

meditation-naïve controls (Brefczynski-Lewis, Lutz, Schaefer, Levinson, & Davidson,

2007; Valentine & Sweet, 1999). However, longitudinal improvements in sustained

attention with meditation training have not been demonstrated.

I examined the effects of Shamatha training on 60 participants who were divided into

two groups by random assignment: a group of individuals who would enter training first

(N = 30; Retreat 1) and a group of individuals who would serve as wait-list controls for

the first retreat, then attend a second retreat (N = 30; Retreat 2). Retreat participants lived

in a remote mountain setting (Shambhala Mountain Center, Red Feather Lakes,

Colorado) for three months and received instruction from B. A. Wallace in Shamatha, as

well as complementary meditation practices that use imagery and concentration to

develop compassion and kindness toward others (see Wallace, 2006). Participants

attended two daily group meditation and discussion sessions, practiced in solitude (M = 5

hours/day of Shamatha, 45 min/day of complementary practices), and had weekly

individual meetings with the instructor.

Outcomes were assessed at three points during each retreat - before the start of the

retreat (pre), halfway through the retreat (mid), and at the end of the retreat (post) - using

a test of sustained visual attention that produced significant decrements in perceptual

sensitivity before training. I predicted that meditation training would reduce this

decrement. I also predicted that improvements in vigilance would be related to the

resource demands of the task. Specifically, training-related improvements in baseline

perception could improve vigilance by reducing the resource demands associated with

discriminating difficult targets (Nuechterlein et al., 1983; Parasuraman & Mouloua,

64

1987). To characterize the relationship between perception, resource demand, and

vigilance, I analyzed: (1) discrimination (visual threshold on a task that did not require

sustained attention), (2) average sensitivity during sustained attention (an index of total

resource demand; See et al., 1995), and (3) vigilance (decrement in perceptual sensitivity

over time).

Method

Participants

Volunteers were recruited using magazine and online advertisements. Participants

were selected who were (i) between 21 and 70 years old; (ii) willing to be randomly

assigned to either retreat; (iii) familiar with intensive meditation practice (at least three 5-

day retreats); and (iv) willing to abstain from recreational drugs, tobacco and alcohol one

month prior to, and during the study. Selected participants had normal or corrected-to-

normal vision and hearing, and no known neurological or Axis I psychiatric impairments

(M.I.N.I. screen, Sheehan et al., 1998). After selection (~50% inclusion rate), participants

were assigned to either the first retreat group or the wait-list control group through

stratified (age, sex, handedness, ethnicity, meditation experience) random assignment.

Retreat and wait-list control participants were matched on demographic factors and

psychological characteristics (N = 60, see Table 3-1).1 Three months after the end of

Retreat 1 wait-list control participants began training and underwent assessment in

Retreat 2 (N = 29).

Due to technical problems, performance data were missing for one assessment for

two participants. Analyses of average sensitivity and vigilance included only participants

with complete data (Retreat 1: N = 59; Retreat 2: N = 27).

65

Table 3-1 Group Matching on Demographic Information and Self-report Questionnaires Retreat Wait-list Control All t value ______________________________________________________________________________________ Agea 49 (23 – 69) 46 (22 – 65) 48 0.79 Sex 14 M, 16 F 14 M, 16 F 28 M, 32 F Handednessb 29 R, 1 L 28 R, 2 L 57 R, 3 L Educationc 4.9 (3 - 6) 5.2 (1 - 6) 5.07 1.09 Non-verbal IQd 10.8 (4.0 – 17.0) 11.2 (6.0 – 17.0) 11.0 0.65 Meditation Experience

Retreatse 13 (2 – 50) 16 (2 – 100) 14.3 0.67 Daily meditationf 56 (8.5 – 180) 54 (12.8 – 155) 55 0.18 Lifetime hoursg 2800 (250 – 9500) 2644 (200 – 15,000) 2716 0.16

BFIh

Openness 5.5 (3.8 – 7.0) 5.6 (4.0 – 7.0) 5.57 0.45 Conscientiousness 5.2 (2.4 – 6.8) 5.0 (2.3 – 6.9) 5.1 0.73 Neuroticism 3.2 (1.2 – 5.2) 3.2 (1.7 – 5.1) 3.25 0.04 Extroversion 4.2 (2.7 – 6.7) 4.3 (2.6 – 6.5) 4.27 0.67 Agreeableness 5.2 (3.3 – 7.0) 5.3 (3.5 – 6.8) 5.25 0.25

STAIi 1.7 (1.0 – 2.3) 1.8 (1.0 – 2.7) 1.77 1.06 CES-Dj 1.6 (1.0 – 4.9) 1.8 (1.0 – 2.9) 1.71 0.96 Wellbeingk 5.6 (4.1 – 6.6) 5.4 (4.5 – 6.6) 5.54 0.37 _____________________________________________________________________________________ Note. Descriptive statistics (mean value with range in parentheses) of demographic factors reported for participants who completed all assessments during the first retreat (n = 30 in each group) except as noted. Scores on all questionnaire measures are based on a 7-point Likert-type scale. Scores at pre assessment were available for 30 retreat and 28 wait-list control participants for the BFI and STAI, and for 28 participants in each group for CES-D. There were no significant differences between groups on any demographic or self-report measure at pre assessment (p > .05) aAge in years at the beginning of participation in the study. bHand dominance as assessed by the Edinburgh Inventory (Oldfield, 1971). cScale of educational achievement (1 = less than high school; 2 = high school degree; 3 = some college; 4 = college degree; 5 = some graduate; 6 = graduate degree). dTotal correct on a timed version of half of the full Raven’s Progressive Matrices (Raven, Court, & Raven, 1988); second half administered at post assessment. eTotal number of meditation retreats lasting at least 5 consecutive days. Reports available for 28 wait-list control and 30 retreat participants. fAverage daily minutes of formal meditation practice. Reports available for 28 wait-list control and 25 retreat participants. gTotal lifetime hours of formal meditation practice. Reports available for 30 wait-list control and 29 retreat participants. hBig Five Inventory, 44 items (John, Donahue, & Kentle, 1991). iState and Trait Anxiety Inventory (Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983). jCenter for Epidemiologic Studies Depression Scale (Radloff, 1977). kPsychological Wellbeing Scale (Ryff, 1989).

66

All study and task details were approved by the University of California, Davis

Institutional Review Board. Participants gave informed consent and were debriefed after

training. Participants paid for room and board during the retreat (~$5300) but were paid

$20/hr during data collection. Travel expenses were covered for wait-list controls, who

were flown to the retreat center to be tested during Retreat 1.

Measures

On-site testing. Testing sessions were conducted in two laboratories in the building

where participants lived and meditated. Each included a sound-attenuated, darkened

testing room and an adjacent control room. Presentation software

(http://www.neurobs.com) was used to deliver stimuli on an LCD monitor (Viewsonic

VX-922). At each of the three assessments in Retreat 1, wait-list controls arrived three

days (range = 65-75 hours) before testing for acclimatization.2

At each assessment, participants completed the threshold procedure (~10 min)

followed by the sustained attention task (32 min). In both tasks, participants saw a single

vertical line appear at the center of the screen; the line could be either long (frequent non-

target) or short (rare target). Instructions emphasized speed and accuracy in making

responses with the left mouse button to the short lines (see Figure 3-1).

In the threshold procedure the length of the short target line varied according to a

PEST algorithm (Taylor & Creelman, 1967), which determined the short line length that

participants could correctly detect at a given level of accuracy (e.g., 75%). Participants

received auditory feedback indicating correct detections (ding), target misses (whoosh)

and incorrect responses to non-targets (whoosh). We defined discrimination as the visual

angle difference between the threshold-level short line and the long line, with smaller

67

Figure 3-1. Stimuli and timing for the threshold procedure and sustained attention task. Single lines (light grey; 40.29 cd/m2) were presented at the center of the screen against a black background (.35 cd/m2) while participants fixated a small yellow square (shown in white) at a viewing distance of 57 cm. In the threshold procedure, long non-target lines (4.82°) were presented 70% of the time and short target lines (possible range = 2.76° - 4.78°) were presented 30% of the time. In the sustained attention task, target frequency was reduced to 10% of stimuli. A mask was presented whenever a line was not on the screen, during the variable inter-stimulus interval (ISI = 1550 - 2150 ms). Instructions emphasized speed and accuracy in responding to the short target lines.

68

threshold values representing better discrimination. In Retreat 1, all participants

performed the threshold procedure again after completing the sustained attention task to

assess short-term changes.

In Retreat 1, the length of the short target line was set to each participant’s threshold

at each assessment, an approach I have used previously (MacLean, Aichele, Bridwell,

Mangun et al., 2009). Threshold was set to 85% at pre-assessment of Retreat 1 and to

75% at all other assessments. Thresholds accordingly decreased for all participants from

pre to mid assessment. Because testing level was not the same across all assessments, I

conducted separate analyses of group differences in outcome measures at each

assessment in Retreat 1.

Follow-up testing. Participants completed two follow-up assessments of

discrimination after the completion of each retreat (approximately 5 and 15 months after

training), which were conducted via laptop computers sent to each participant’s home.

Participants received instructions to set viewing distance and ambient lighting. The

threshold procedure at follow-up exactly matched the laboratory procedure.

Analysis

The non-parametric index of perceptual sensitivity, A' (Stanislaw & Todorov, 1999),

was calculated from hit and false alarm rates for each of 8 contiguous 120-trial blocks

during the sustained attention task. The decline in A' was largest during the first half of

the task, and thus we defined improvements in vigilance as positive changes in the slope

of A' during the first 4 blocks. I analyzed changes in slope using hierarchical linear

models, which produced estimates of fixed effects at the group level (similar to standard

regression models) as well as estimates of random effects at the individual level (i.e.,

69

variability around fixed-effects estimates). Analyses of vigilance were conducted in SAS

9.1 (see details in Littell et al., 1996; Singer, 1998).

Results

Retreat 1

Discrimination. Group differences in threshold at each assessment were tested using

analysis of variance (ANOVA; SPSS 17.0). Each ANOVA included group (retreat vs.

control) as the between-subjects factor and task exposure (threshold tested before vs.

after the sustained attention task) as the within-subjects factor. At the pre assessment I

found no differences between groups (F (1, 58) = .32, p = .57) indicating that the groups

were matched on discrimination ability at the beginning of training. I found significantly

lower thresholds in the retreat group at mid (F (1, 58) = 4.80, p = .032) and post (F (1,

58) = 5.80, p = .019) indicating training-specific improvements in discrimination (see

Figure 3-2).

I found no significant effects of task exposure or the interaction of task exposure and

group at any assessment (p > .41 at pre; p >.08 at mid; p >.31 at post), indicating no

short-term change in threshold tested before versus after the sustained attention task (M =

0.79° vs. 0.78°). Therefore, participants did not repeat the threshold procedure after the

sustained attention task in Retreat 2.

Average sensitivity. I found no significant group differences in average A' (across all

8 blocks) at pre (M = .95 for both groups, p = .61), mid (M = .90 for both groups, p = .83)

or post (M = .89 for retreat and .90 for control, p = .22), indicating that the groups were

matched on total task demand.

70

a.

b.

*p < .05 **p < .01

Figure 3-2. Improvements in discrimination in Retreat 1 and Retreat 2. (a) Thresholds plotted for each assessment in Retreat 1 (N = 30 in each group) and the first follow-up (N = 29 in each group). Retreat participants exhibited significantly better discrimination (lower thresholds) than wait-list control participants at mid, post, and follow-up. (b) Thresholds plotted for each assessment in Retreat 2 (N = 29) and the first follow-up (N = 27). Participants showed significant improvements in discrimination from pre to mid assessment, and these improvements were maintained at the post and first follow-up assessments. There were no significant changes in discrimination during non-training periods.

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Vigilance. At each assessment, perceptual sensitivity declined linearly over time, and

the mean vigilance decrement did not appear to differ between retreat and control groups

at any assessment (see Figure 3-3). Using hierarchical linear regression, changes in

vigilance were modeled as a function of the fixed effects of block and group, and their

interaction. Random effects on the intercept were included in the models to allow for

individual differences in initial performance (A' during the first block). At all

assessments, the best-fitting model of performance over time confirmed significant

declines in A' across blocks for all participants, regardless of group membership (p <

.0001; see Table 3-2). Thus, contrary to my prediction, vigilance did not improve with

training.

Influence of discrimination on vigilance. There were no differences in vigilance

between retreat and control groups when task demand (average A') was equated by

experimentally adjusting target length according to individual threshold at each

assessment. However, I also explored how threshold, task demand, and vigilance were

related at the individual level. Before training, lower average sensitivity correlated with

poorer vigilance (r = .45, p < .0001) underscoring the relationship between task demand

and vigilance. As anticipated, there was no correlation between threshold and average

sensitivity or vigilance before training (p’s > .27). Next, I correlated change scores in

each pair of the three performance indices. I calculated change on each behavioral

measure by regressing post score on pre score and retaining the standardized residual for

each individual as the index of change from pre to post. I then calculated partial

correlation coefficients between pairs of change scores, controlling for the third change

score. Consistent with the results at pre assessment, decreases in average sensitivity

72

a.

b.

c.

Figure 3-3. Vigilance performance in Retreat 1. Data plotted for participants with complete data at all assessments (N = 59). Average sensitivity (A') declined significantly during the first 4 blocks (16 minutes) of the sustained attention task at (a) pre, (b) mid, and (c) post assessments. Retreat and control participants did not differ in the extent of the vigilance decrement at any assessment.

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Table 3-2 Parameter Estimates from Models of Vigilance in Retreat 1 Model Parameter Estimate Test Statistica BICb

_________________________________________________________________________________ Pre Fixed effects 0 – intercept .972 212 *** 1 – slope -.006 3.94 *** -902 Random effects 2

0 - intercept .001 4.06 ***

2e – residual variance .001 9.41 ***

Mid Fixed effects 0 – intercept .939 142***

1 – slope -.012 5.37*** -759 Random effects 2

0 – intercept .001 4.39***

2e – residual variance .001 9.41 ***

Post Fixed effects 0 – intercept .936 149 ***

1 – slope -.013 6.12 *** -765 Random effects 2

0 – intercept .001 4.20 ***

2e – residual variance .001 9.41 ***

__________________________________________________________________________________ * p < .05 ** p < .01 *** p < .001 Note. Full maximum likelihood estimates reported for the best-fitting models of change in perceptual sensitivity during sustained performance (N = 59 at all assessments). Slope estimates refer to the amount of decrease in sensitivity per block (4 blocks). At each assessment, slope was centered to the first block of the sustained attention task (block 1 = 0). In all cases, the simpler models (shown) were better fits than models that included group and interaction effects (not shown; BIC = -898 for pre, -751 for mid and -759 for post). a Test statistics are reported as t values for fixed effects estimates and as z values for random effects estimates. bBayesian information criterion. Smaller (more negative) values indicate a better model fit.

correlated with decreases in vigilance (pr = .27, p = .036). In addition, decreases in

average sensitivity correlated with improvements in discrimination (pr = .53, p < .0001)

indicating increases in task demand for participants with improvement in threshold. Thus,

adjusting line length during training created systematic differences in task demand

between individuals with the most improvement in threshold (the retreat group) and

74

individuals with the least improvement in threshold (the control group). These results

suggest that although retreat participants may have actually improved in their ability to

sustain attention, the setting of line length based on threshold at each assessment

precluded the observation of such improvements as reflected in measured vigilance.

Retreat 2

The aim in Retreat 2 was to examine how vigilance changed with training when target

parameters were held constant. I fixed the length of the target line for each individual to

the threshold value achieved at the beginning of training, so that task demand would not

increase systematically as training progressed and discrimination improved. Participants

completed the threshold procedure before the sustained attention task at each assessment

and were unaware of the design modification.

Discrimination. Repeated-measures ANOVA was used to test within-subjects change

in threshold across the five assessments that had been tested at the 75% threshold level.

The effect of assessment was significant (F (4, 25) = 4.05, p = .011) indicating

improvement in discrimination over time. Comparisons between pairs of assessments

confirmed that discrimination improved significantly from pre to mid assessment during

Retreat 2 (F (1, 28) = 8.27, p = .008) with no additional improvement from mid to post (F

(1, 28) = .45, p = .50) (see Figure 3-2). No significant changes in discrimination were

observed during the non-training period (including Retreat 1 mid and post, and Retreat 2

pre; all p’s > .21). These findings replicate the training-specific changes in discrimination

observed in Retreat 1.3

Average sensitivity. A repeated-measures ANOVA of within-subjects change in

average sensitivity revealed a significant effect of assessment (F (4, 23) = 3.26, p = .03).

75

Sensitivity improved significantly from pre to mid during Retreat 2 (F (1, 26) = 8.38, p =

.008) with no additional improvement from mid to post (F (1, 26) = .004, p = .95; see

Figure 3-3). No significant changes in sensitivity were observed between non-training

assessments (all p’s > .83). The observed increases in average sensitivity during training

suggest reductions in total task demand due to improved discrimination.

Vigilance. As can be seen in Figure 3-4, perceptual sensitivity declined over time at

each assessment during the non-training period (mid and post assessments of Retreat 1,

and pre assessment of Retreat 2). However, the performance decrement was reduced at

mid and post assessments of the training period (i.e., the slope of the decrement was

flatter, or less negative, at mid and post assessments of Retreat 2). Using hierarchical

linear models, I modeled changes in vigilance as a function of the fixed effects of block

and assessment, and their interaction. As in Retreat 1, random effects on the intercept

were included in the models. The model of vigilance during the non-training period

revealed a significant effect of block ( -.009, p = .008) but no significant effects of

assessment or the interaction between assessment and block (p > .32; see Table 3-3),

confirming that the vigilance decrement did not change with simple task practice before

training. I then modeled within-subjects change in vigilance during the training period,

with the prediction that training would reduce the vigilance decrement. The model

revealed a significant effect of block ( -.013, p < .0001) and a non-significant effect of

assessment (p = .94). A significant interaction between block and assessment ( p

= .012) indicated that vigilance improved with training, consistent with my prediction.

76

a.

b.

*p < .05 **p < .01

Figure 3-4. Improvements in average sensitivity and vigilance in Retreat 2. Data plotted for participants with complete data at all assessments (N = 27). The non-training period of Retreat 1 is designated by “R1” and the training period of Retreat 2 is designated by “R2”. (a) Average sensitivity (A') across 8 blocks (32 minutes) of the sustained attention task at each assessment. Participants showed significant increases in average sensitivity during the training period. (b) A' trajectories plotted across four contiguous 4-min blocks at each assessment (120 trials/block; 16 min total duration). Participants showed significant reductions in the vigilance decrement (i.e., less negative slope) across the first half of the sustained attention task during the training period.

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Table 3-3 Parameter Estimates from Models of Vigilance in Retreat 2 Model Parameter Estimate Test Statistica BICb

_______________________________________________________________________________ Non-training Fixed effects 0 – intercept .935 111 *** 1 – slope -.009 2.67 ** 2 – assessment .005 0.91 3 – slope x assessment -.003 1.00 -1001 Random effects 2

0 – intercept .001 2.80 **

2e – residual variance .002 12.2 ***

_______________________________________________________________________________ Training Baseline Model Fixed effects 0 – intercept .944 119 ***

1 – slope -.013 4.81*** 2 – assessment .001 0.07 3 – slope x assessment .005 2.52 * -1165 Random effects 2

0 – intercept .001 3.32 **

2e – residual variance .001 12.2 ***

Threshold Model Fixed effects 0 – intercept .944 122 *** 1 – slope -.013 4.82 *** 2 – assessment .001 0.10 3 – threshold change .001 0.04 4 – slope x .005 2.51*

assessment 5 – threshold change x -.008 2.06 *

assessment 5 – slope x -.003 1.34

threshold change 6 – slope x

threshold change x .004 1.74 assessment

-1158 Random effects 2

0 – intercept .001 3.30 ***

2e – residual variance .001 12.2 ***

________________________________________________________________________________ * p < .05 ** p < .01 *** p < .001 Note. Full maximum likelihood estimates reported for models of change in perceptual sensitivity across blocks (slope) and assessments (N = 27 participants in all models). Slope was centered to the first block of the sustained attention task (block 1 = 0) and assessment was centered to the first assessment in the model. Change in threshold was calculated as the standardized residual of the regression of post threshold on pre threshold, and was centered to zero residual change. a Test statistics are reported as t values for fixed effects estimates and as z values for random effects estimates. bBayesian information criterion. Smaller (more negative) values indicate a better model fit.

78

Follow-up

Follow-up 1. The stability of discrimination improvements was examined at two

follow-up assessments, the first of which was conducted approximately five months after

each retreat. In Retreat 1, I compared performance of Retreat 1 participants at the first

follow-up (N = 29) with wait-list control performance at the beginning of Retreat 2 (N =

29). Retreat 1 participants’ thresholds were significantly lower than control participants’

thresholds (F (1, 56) = 4.58, p = .037) confirming the maintenance of discrimination

improvements after completion of training. In Retreat 2, participants exhibited

significantly lower thresholds at their first follow-up (N = 27) compared to the beginning

of their training (F (1, 26) = 16.9, p < .0001). These results demonstrate enduring

changes in discrimination after training (Figure 3-2).

Next, I explored whether the maintenance of discrimination improvements might be

related to the amount of post-retreat daily meditation by combining participants from

both retreats (N = 54 with complete meditation reports and threshold data). T tests

confirmed that there were no significant differences between retreat groups in daily

meditation after retreat (129 min/day vs. 112 min/day, t (52) = 0.41, p = .68) or follow-up

thresholds (0.56º vs. 0.55º, t (52) = 0.20, p = .84). Amount of daily meditation

significantly predicted thresholds at the first follow-up (r = -.36, p = .007) indicating a

correlation between the stability of training-induced discrimination improvement and the

maintenance of regular, but less intensive, meditation (see Figure 3-5).4

79

Figure 3-5. Daily meditation after retreat predicts discrimination 5 months after completion of training. Data shown for participants with complete meditation report (N = 54). Thresholds (visual angle of short line) at the first follow-up plotted as a function of average minutes of daily practice. Amount of daily meditation after retreat significantly predicted follow-up threshold.

Follow-up 2. The second follow-up assessment was conducted approximately 15

months after each retreat. I defined performance stability as no significant difference in

discrimination between the first and second follow-up assessments. Paired t tests

confirmed that thresholds were not different between the first and second follow-ups for

either Retreat 1 (N = 23; 0.55° vs. 0.51°, t (22) = .93, p = .36) or Retreat 2 (N = 22; 0.55°

vs. 0.56°, t (22) = .34, p = .73). I also explored whether discrimination performance at the

second follow-up was related to the amount of daily meditation since the first follow-up

(N = 40 with complete meditation reports and threshold data). Amount of daily

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meditation (M = 113 min/day) did not significantly predict thresholds at the second

follow-up (r = -.15, p = .36). Thresholds were also not significantly related to amount of

change in daily meditation from the first to second follow-up (M = -7 min/day, range = -

210 to +150 min/day; r = -.26, p = .10). Together, the results from both follow-up

assessments suggest that regular meditation may be more critical for maintaining and

consolidating behavioral improvements during the first few months following training.

Although thresholds at the second follow-up were not directly related to meditation, the

stability of discrimination performance more than one year after training nevertheless

suggests long-lasting changes in perception.

Discussion

In a longitudinal wait-list controlled study of long-term daily meditation training, I

found improved vigilance performance in healthy adults. I also found improvements in

visual discrimination, which is consistent with a previous report of increases in visual

sensitivity with intensive meditation training (Brown, Forte, & Dysart, 1984). The results

suggest that mental training of attention on non-visual perceptions (e.g., sensations of

breathing, mental events) generalized to improved visual perception of task-relevant

stimuli.

Previous vigilance research has described task factors that increase information-

processing resource demands, leading to poorer performance over time (Warm et al.,

2008). The present findings suggest that training-related improvements in perception can

decrease resource demands and thus lead to better vigilance. The variation in Retreat 1

versus Retreat 2 illustrates the important relationship between task features, resource

demand, and vigilance. When target length was adjusted according to individual

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threshold in Retreat 1, I observed systematic increases in task demands (reductions in A')

as threshold improved with training and no changes in observed vigilance. However,

when target length was held constant in Retreat 2, improvements in target discrimination

ability led to reductions in task demands (increases in A') and therefore to improved

vigilance. I interpret these results as follows: Training-related improvements in

perception reduced the resources required to discriminate an unchanging target, which in

turn increased the resources available for sustaining voluntary attention. This implies that

a limited central resource is tapped by both increased perceptual and attentional demands.

Line discrimination performance relies on several stages of information processing

that could be affected by training, from early-stage perceptual processing to later-stage

decision and response execution. Because the dependent measures used in the present

study took into account both hit and false alarm rates, improvements in threshold and

perceptual sensitivity likely reflect improvements in perceptual encoding (Macmillan &

Creelman, 2005). This interpretation is supported by electrophysiological studies that

have linked improvements in perceptual sensitivity to early-stage perceptual processing

(e.g., Luck et al., 1994). Critically, I found no significant group differences or changes in

target reaction time (median correct RT) or response bias ´ D) (See et al., 1997) in

either retreat (all p’s > .15), suggesting a decision/response-stage account of the results

can be largely ruled out.

However, improvements in vigilance remained significant after controlling for

changes in threshold (see Table 3-3), suggesting that improvements in perception could

not fully explain the results. In the line discrimination task, an accurate working memory

representation of the non-target supports the perceptual identification of the rare target

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when it occurs. Using stimuli similar to those in the present study, Parasuraman and

Mouloua (1987) demonstrated that both perceptual manipulations (changing the length

difference between the target and non-target lines) and working memory manipulations

(requiring a comparison between the currently visible target and a non-target line held in

memory) modulated decrements in perceptual sensitivity. During meditation,

practitioners receive extensive experience in attending to the representations of sensory

experience and observing how these representations change moment-to-moment. Thus, a

core aspect of meditation training involves maintaining and accessing information in

working memory from decaying sensory traces. It is therefore possible that training-

related improvements in the precision of visual working memory representations (see

Zhang & Luck, 2008) contributed to observed changes in vigilance.

Interpretations of previous meditation findings have been complicated by possible

alternate explanations, such as pre-existing group differences (in cross-sectional designs)

and self-selection bias (when assignment to training is not random). Although the use of a

longitudinal, wait-list controlled design in the present study eliminated these particular

confounds, factors other than meditation may still have contributed to the present results.

First, features of the training environment, such as the secluded retreat setting and regular

interactions with a committed teacher could have influenced performance, but the

persistence of discrimination improvements after completion of formal training indicates

that retreat-specific factors were not necessary for superior discrimination. Moreover, the

significant correlation between daily meditation and thresholds at the first follow-up

suggests that discrimination ability was directly related to meditation per se and not to

incidental factors. Second, changes in personal motivation during training could have

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played a role in improved vigilance (e.g., Tomporowski & Tinsley, 1996). Before data

collection, all participants had committed $5300 and were interested in meditation, the

project’s scientific goals, and the instructor’s teachings. Given strongly motivated retreat

and control participants, and the stability of control group data during non-training

assessments, it is unlikely that motivation differentially contributed to results between

groups. In future training studies it will be important to empirically assess the

contributions of motivation and environment, for example by comparing meditation and

active control treatments that are dispensed by the same teacher in the same environment.

The present results add to the growing body of evidence that meditation training can

improve aspects of attention (Lutz, Slagter, Dunne, & Davidson, 2008) while specifically

suggesting that the enhanced sustained attention ability that has been linked to long-term

meditation practice (Brefczynski-Lewis et al., 2007; Valentine & Sweet, 1999) likely

reflects plasticity in the adult brain. The present findings also add to reports of training-

induced improvements in other core cognitive processes, such as working memory

capacity and non-verbal intelligence (Jaeggi et al., 2008; Olesen et al., 2004). Together,

these findings raise the possibility of general improvements in mental function that can

benefit daily activities.

Footnotes

Footnote 1: Age, sex, non-verbal IQ, and previous meditation were not significant

predictors of training-related changes in discrimination, average sensitivity or vigilance

in either retreat (all p values > .16).

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Footnote 2: Deficits in perception and vigilance were unlikely to occur at an altitude of

2500 m (Virues-Ortega, Buela-Casal, Garrido, & Alcazar, 2004). However, the 3-day

period was sufficient for any such deficits to resolve (Tripathi, Apte, & Mukandan,

2005).

Footnote 3: Improvements in discrimination remained significant after controlling for

visual acuity (M = 20/20, Titmus T2a vision screener; Retreat 1: group difference at post,

p = .009; Retreat 2: effect of assessment, p = .018).

Footnote 4: A similar analysis showed that daily meditation during retreat (M = 368

min/day, SD = 88 min/day) did not significantly predict threshold, average sensitivity or

vigilance at post (p > .51), suggesting that average meditation throughout the reported

range was effective for producing training-related changes in performance.

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CHAPTER 4

TRAINING-RELATED CHANGES IN SUSTAINED RESPONSE INHIBITION:

THE INFLUENCE OF AGE AND RESPONSE SPEED ON

IMPROVEMENTS IN ACCURACY

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In their tripartite model of attention, Posner and Petersen (1990) proposed a

functionally distinct executive component of attention that prioritizes and selects among

competing stimuli and actions (see also Fan & Posner, 2004). Executive attention is

involved in exerting voluntary, goal-directed control over which stimuli, thoughts,

emotions, and actions are selected despite automatic or habitual tendencies. The ability to

inhibit habitual or impulsive responses can be tested in the laboratory using tasks that

require quickly responding to frequently occurring stimuli (e.g., 90% probability) and

withholding responses to infrequently occurring stimuli (e.g., 10% probability). Despite

the seemingly simple nature of these response inhibition tasks, errors in responding to the

infrequent stimulus are common. Response inhibition tasks place high processing

demands on executive attention, requiring a person to monitor his or her performance,

counteract the habitual motor response when the low-frequency stimulus appears, and

make behavioral adjustments when errors occur (Carter, Botvinick, & Cohen, 1999;

Miller & Cohen, 2001; Ridderinkhof, van den Wildenberg, Segalowitz, & Carter, 2004).

Moreover, response inhibition errors in the laboratory predict attentional lapses and

action slips in everyday life (Robertson et al., 1997), suggesting that response inhibition

tasks tap general attentional control skills.

Although healthy individuals are often unable to prevent errors during response

inhibition, strategic behavioral adjustments can improve performance (see review in

Ridderinkhof et al., 2004). Specifically, speed-accuracy trade-offs have been observed

during response inhibition tasks, such that responding more slowly to the frequent

stimulus improves overall response inhibition accuracy (Helton et al., 2009). Similarly,

studies have found that speeding of responses prior to the infrequent stimulus increases

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the likelihood of accidental response inhibition errors (Manly et al., 1999; Robertson et

al., 1997). Emphasizing speed over accuracy can also improve performance in tasks that

require more indirect forms of response inhibition, such as tasks that present conflicting

stimulus-response mappings (e.g., trial-to-trial adjustments in speed during performance

of the Stroop task; Kerns et al., 2004). These findings indicate that individuals can

improve response inhibition performance by adopting a more careful or cautious response

strategy to avoid accidental errors. However, it is unknown whether individuals can

improve response inhibition accuracy through training, possibly without sacrificing

response efficiency.

In the present study, I investigated whether training in Shamatha meditation could

improve response inhibition. During Shamatha meditation individuals cultivate

attentional and behavioral control skills that may transfer to other domains and tasks (see

Chapter 1). In Chapter 3, I observed training-related improvements in perception and

sustained voluntary attention that map onto the primary goals of the practice, including

learning to stabilize attention on the chosen stimulus for long periods of time and

enhancing the perceived detail of the attended stimulus. However, successfully

maintaining voluntary attention on the chosen stimulus requires learning to regulate and

control attention. During meditation, distractions inevitably arise that automatically draw

attention away from the chosen stimulus. In other domains, such as vision-related

attention, executive control plays a key role in guiding attention back to the chosen

stimulus following a salient distraction, thus bringing the focus of attention in line with

current goals (Corbetta & Shulman, 2002). In this way, meditation training likely

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produces improvements in attentional control that may transfer to other domains and

tasks that also require attentional control, such as response inhibition.

Response inhibition is also cultivated more directly during meditation training.

Response inhibition is involved at all levels of meditation practice, from resisting

habitual behavioral movements while meditating (e.g., not scratching an itch), to resisting

the desire to follow interesting trains of spontaneous thought, to refraining from engaging

with momentary emotions. Learning to recognize and regulate (but not suppress) one’s

automatic tendencies to attend to and engage with thoughts and emotions that arise is

deemed a significant step in achieving attentional stability and progressing in the practice

(Wallace & Shapiro, 2006). In addition, classical meditation manuals emphasize

refraining from usual activities and habitual tendencies during formal training (e.g.,

Asanga, 1950; Bodhi, 1995, 2000; Shantideva, 2008). For example, practitioners may

limit normal behaviors such as speaking and reading when not engaged directly in

meditation. Thus, many aspects of formal meditative training cultivate response

inhibition and behavioral control.

There is empirical evidence that meditation can improve executive attention. Jha,

Krompinger, and Baime (2007) recently reported that experienced meditators, compared

to meditation-naïve controls, had superior executive control as indexed by less

interference from irrelevant distracting stimuli (on the executive component of the

Attention Network Task; Fan, McCandliss, Sommer, Raz, & Posner, 2002). However, it

is unclear whether this result reflects meditation training per se or other pre-existing

differences between regular meditators and non-meditators.

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The aim of the present study was to assess longitudinal improvements in response

inhibition as a consequence of Shamatha training. Previous studies (Grier et al., 2003;

Helton et al., 2005) have shown that sustained response inhibition tasks combine the

effort associated with normal vigilance (see review in Warm et al., 2008) and the

challenge of inhibiting impulsive behavioral responses (Helton, 2009; Helton et al.,

2009). Thus, I developed a 32-minute response inhibition task to measure sustained

behavioral control (see Chapter 1).

Method

Study design, participant details, and stimuli were the same as in Chapter 3, except as

noted.

Measures

At each assessment, participants first completed a threshold procedure (duration = 10

min) used to calibrate task difficulty for each individual (see Chapter 2). Stimulus timing

and task details were the same as in the threshold procedure for the sustained attention

task (see Figure 3-1) except that a blank screen was presented during the interstimulus

interval (see Figure 2-1). Instructions required making responses with the left mouse

button to frequent long lines (70% of stimuli) while inhibiting responses to rare short

lines (30% of stimuli). Participants received sound feedback indicating (a) correct

inhibitions of responses to short lines, (b) accidental responses to short lines, and (c)

missed responses to long lines. The procedure determined the length of the short line

required for each participant to perform at 85% overall accuracy. Immediately after the

threshold procedure, participants performed the sustained response inhibition task

(duration = 32 minutes) with the short line set to his or her individual threshold. The

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response inhibition task exactly matched the threshold procedure except that it did not

include sound feedback and the short line occurred less frequently (10% of stimuli).

Analysis

Response inhibition performance can be quantified as overall error rate, or the rate of

accidental responses to the rare target. However, error rate does not take into account the

tendency to respond (or not respond) during the task (i.e., response bias; see Macmillan

& Creelman, 2005). For example, a participant could achieve many correct inhibitions

using a strategy of responding to fewer trials overall. Thus, I chose a dependent measure

of accuracy that captured how well a participant could correctly inhibit responses to short

lines while also correctly responding to long lines. Specifically, I calculated the non-

parametric index of perceptual sensitivity, A', using correct and incorrect inhibition rates

(see formula in Stanislaw & Todorov, 1999, in applying this formula, I defined correct

inhibitions as hits, and incorrect inhibitions as false alarms). Because I was interested in

how accuracy changed over time during task performance, I calculated A' for each of

eight contiguous blocks of trials (120 trials per block, 4 minutes each) within the

response inhibition task at each assessment. Thus, the dependent measures of response

inhibition task accuracy were (1) average A' and (2) the slope of A' across blocks. I

analyzed changes in response inhibition accuracy using hierarchical linear regression;

analyses were conducted in SAS 9.1 (Singer, 1998).

Some participants unexpectedly did not show large declines in accuracy at pre

assessment. To keep task demands high for all individuals across multiple sessions, I

increased the overall difficulty level of the task by setting target threshold at 75%

accuracy at all subsequent assessments in both retreats. To compare Retreat 1 pre-

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assessment performance (set at 85% threshold level) to all other assessments (set at 75%

threshold level), I mathematically adjusted A' for each individual at each block at pre-

assessment to compute performance at 75% (adjusted A' = (original A' x .75) / .85).

Assessment Schedule

Before assignment to groups (~4 months prior to the beginning of Retreat 1), all

participants completed a pre-assignment assessment to confirm that the eventual groups

would be matched on performance before training. This testing was conducted via laptop

computers (Dell 600D) sent to each participant’s home. Participants received detailed

instructions for setting viewing distance and controlling ambient light and sound. The

task procedures completed at pre-assignment exactly matched the procedures completed

at all other assessments.

Participants completed three on-site testing sessions during each retreat (pre, mid, and

post) that were conducted in two laboratories located in the same building where

participants lived and practiced meditation. At each of the three assessments in Retreat 1,

wait-list control participants arrived three days before testing for acclimatization. The

response inhibition task was performed on the second day of lab testing at each

assessment. (Note: The sustained attention task in Chapter 3 was performed on the first

day of lab testing at each assessment.)

Finally, participants completed a follow-up assessment approximately five months

after the completion of each retreat. Testing was similar to the pre-assignment assessment

and was again conducted via laptop computers (Dell 600D for Retreat 1, IBM T-40 for

Retreat 2) sent to each participant’s home.

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Results

Pre-assignment

Threshold. A between-subjects analysis of variance (ANOVA) revealed no significant

differences in threshold between retreat and wait-list control groups (M = 1.24° vs. 1.27°,

F (1, 58) = .02, p = .89) confirming that the groups did not differ on RIT threshold before

training.

Response inhibition task. An examination of the observed performance trajectories

revealed that response inhibition accuracy (A') declined over time for retreat and control

groups (see Figure 4-1). Using hierarchical linear regression, response inhibition

performance was modeled as a function of the fixed effects of block (1 - 8) and group

(retreat and control), and the interaction of block and group. Random effects on the

intercept were included to allow for individual differences in initial performance (A

during the first block). This model revealed a significant effect of block (ß = -.009, p <

.001), confirming that performance declined over time before training. The effect of

group was not significant (ß = -.013, p = .59), nor was the interaction between block and

group (ß = .002, p = .50), confirming that the groups were matched on performance

before training.

Retreat 1

Participants were removed from analyses who were outliers (3 SD below the grand

mean) in either accuracy at pre assessment (M = .80, SD = .06) or change in accuracy (M

= .07, SD = .07) on the RIT. One Retreat 1 participant was an outlier on change in

accuracy and one control participant was an outlier on accuracy at pre assessment. Thus,

the analyses in Retreat 1 included 58 individuals.

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Figure 4-1. Response inhibition performance before assignment to groups. Response inhibition accuracy (A') plotted as a function of time on task (eight 4-minute blocks) for retreat and control groups (N = 60) at the pre-assignment assessment. Performance declined significantly across blocks, and the groups did not differ in response inhibition accuracy.

Threshold. Analysis of variance (ANOVA) was used to test the between-subjects

effect of group (retreat v. control) and the within-subjects effect of assessment (pre-, mid-

, and post-retreat) on threshold. A main effect of assessment (F (2, 55) = 48.5, p < .0001)

revealed that thresholds decreased (i.e., improved) across assessments in both groups

(Retreat: M = 1.0° at pre-retreat, .71° at mid-retreat, and .65° at post-retreat; Control: M =

1.2° at pre-retreat, .84° at mid-retreat, and .74° at post-retreat). Although thresholds were

generally lower in the retreat group at all assessments, the main effect of group was only

a trend (F (1, 56) = 3.31, p = .07). Moreover, the interaction between group and

assessment was not significant (p = .81). These findings are consistent with practice

effects and not with training-related change (cf., training-specific improvements in target

discrimination threshold, Chapter 3). However, because I set target line length according

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Control Retreat

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to threshold at each assessment, individual differences in threshold change may have

influenced response inhibition performance. Thus, I statistically controlled for individual

differences in threshold and change in threshold in all models of response inhibition

performance reported next.

Response inhibition task. Similar to performance at the pre-assignment assessment,

response inhibition accuracy declined over time at pre assessment of Retreat 1 (see

Figure 4-2). An examination of the performance trajectories revealed increases in

accuracy from pre to mid assessment for both retreat and control groups. Although the

groups showed similar increases in accuracy during the first half of the task (i.e., blocks 1

– 4), the retreat group showed larger increases in accuracy toward the end of the task (i.e.,

blocks 6 – 8) compared to the control group (see Figure 4-2). These observations

suggested training-specific improvements in response inhibition performance.

To analyze changes in response inhibition accuracy, I first tested a model that

included the fixed effects of block, assessment, group and threshold. Random effects on

the intercept were included to allow for individual differences in initial performance. This

model revealed a significant effect of block (ß = -.009, p < .0001), a significant effect of

threshold (ß = .034, p = .001), and a non-significant effect of group (p = .79).

Importantly, the effect of assessment was significant (ß = .043, p < .0001), indicating

increases in overall accuracy. Next, I added the interaction between group and assessment

to the model to test the prediction that increases in accuracy would be greater for retreat

participants. This model also included the interaction between threshold and assessment

to control for individual differences in threshold change. Consistent with my prediction,

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a.

b.

Figure 4-2. Response inhibition performance during Retreat 1. Response inhibition accuracy (A') plotted as a function of time on task (eight 4-minute blocks) for (a) retreat and (b) control groups (n = 29 participants in each group). Both groups showed increases in overall response inhibition accuracy across testing points (pre, mid, and post). Increases in accuracy were significantly greater for the retreat group.

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the interaction between group and assessment was significant (ß = .012, p = .013).

Moreover, this model fit the data better than the baseline model (see Table 4-1). Finally,

the addition of 2-way and 3-way interaction effects (e.g., the interaction between block

and assessment) did not improve the model fit. Thus, the findings demonstrate training-

related increases in average response inhibition accuracy.

Table 4-1 Parameter Estimates from Models of Response Inhibition in Retreat 1 Model Parameter Estimate Test Statistica BICb

_______________________________________________________________________________ Baseline Fixed effects 0 – intercept .842 76.2 *** 1 – slope -.009 10.9 *** 2 – group .003 0.26

3 – threshold -.034 3.23 ** 4 – assessment .043 12.8 ***

-3055 Random effects 2

0 – intercept .002 4.90 ***

2e – residual variance .006 25.8 ***

_______________________________________________________________________________ Group Differences Fixed effects 0 – intercept .851 74.7*** 1 – slope -.009 11.0 *** 2– group -.006 0.39 3 – threshold -.010 0.90

4 – assessment .041 9.76 *** 5 – assessment x -.056 6.53 ***

threshold 6 – assessment x .012 2.48 *

group -3090 Random effects 2

0 – intercept .002 4.88 ***

2e – residual variance .005 25.8 ***

________________________________________________________________________________ * p < .05 ** p < .01 *** p < .001 Note. Full maximum likelihood estimates reported for models of response inhibition accuracy (A') as a function of block (slope), assessment (pre, mid and post), group (retreat and control) and threshold (N = 58 participants in all models). Slope was centered to the first block; assessment was centered to pre assessment of Retreat 1; and threshold was centered to the grand mean. Retreat group membership was coded as 1 and control group membership as 0. a Test statistics are reported as t values for fixed effects estimates and as z values for random effects estimates. bBayesian information criterion. Smaller (more negative) values indicate a better model fit.

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Retreat 2

Threshold. In the model of response inhibition performance in Retreat 1, individual

differences in threshold improvement influenced response inhibition accuracy (p < .0001;

see Table 4-1). In Retreat 2, I addressed this issue by fixing the length of the target line

for each participant to the threshold achieved at the beginning of training. A repeated-

measures ANOVA across four assessments (Retreat 1 post, Retreat 2 pre, Retreat 2 mid,

and Retreat 2 post) confirmed that thresholds did not change significantly after the end of

Retreat 1 (F (3, 26) = 1.06, p = .38). Thus, changes in response inhibition accuracy in

Retreat 2 could be attributed to the direct effect of training and not to the indirect

influence of threshold.

Response inhibition task. As in Retreat 1, I modeled response inhibition performance

using hierarchical linear regression. I tested separate models of (1) the non-training

period (including mid and post assessments of Retreat 1, and pre assessment of Retreat 2)

and (2) the training period (including pre, mid, and post assessments of Retreat 2). Each

model included the fixed effects of block and assessment, and their interaction, as well as

random effects on the intercept. As can be seen in Figure 4-3, response inhibition

accuracy declined similarly over time at each assessment during the non-training period.

The model of performance revealed a significant effect of block (ß = -.013, p < .0001)

and non-significant effects of assessment and the interaction of block and assessment (p >

.44), confirming that performance did not change with simple task practice before

training. During the training period, retreat participants showed increases in overall

accuracy from pre to mid assessments, as well as a flatter slope over time (see Figure 4-

3). The model of the training period revealed a significant effect of block (ß = -.015, p <

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Figure 4-3. Improvements in response inhibition during Retreat 2. Data plotted for participants with complete data at all assessments (N = 29). The non-training period of Retreat 1 is designated by “R1” and the training period of Retreat 2 is designated by “R2”. Response inhibition accuracy (A') plotted across 8 blocks (32 minutes) of the response inhibition task at each assessment. Participants showed significant increases in average response inhibition accuracy and better sustained performance (i.e., less negative slope) during the training period.

.0001), a significant effect of assessment (ß = .011, p = .02), and a significant interaction

between assessment and block (ß = .003, p = .02). The significant effect of assessment

confirmed the result from Retreat 1 of improvements in average response inhibition

accuracy with training. In addition, the significant interaction between block and

assessment demonstrated that training led to reductions of the vigilance decrement when

task difficulty and target parameters were held constant.

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Follow-up

I observed significant training-related improvements in overall response inhibition

accuracy in both retreats, and an examination of the observed performance trajectories

confirmed that increases in accuracy were evident throughout the 32-min task (see Figure

4-4 for combined data from both retreats). I explored whether these improvements in

accuracy were maintained after completion of training by combining participants from

Figure 4-4. Improvements in response inhibition are maintained after the completion of training. Response inhibition accuracy (A') plotted as a function of time on task (eight 4-minute blocks) for each of four testing points. Data shown for participants of both retreats during their respective training periods (N = 59), and at a follow-up assessment (N = 54) conducted approximately five months after the end of each retreat. Both retreat groups showed significant improvements in overall accuracy with training that endured after the completion of formal training.

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both retreats with complete data at follow-up (n = 27 for each retreat). A repeated-

measures ANOVA revealed significantly higher accuracy at follow-up compared to pre

assessment (F (1, 53) = 42.38, p < .001). Follow-up t tests confirmed that this result was

independently significant for Retreat 1 participants (M = .87 at follow-up and .80 at pre;

t(26) = 5.80, p < .001) and Retreat 2 participants (M = .91 at follow-up and .87 at pre;

t(26) = 3.52, p = .002). For both groups, accuracy at follow-up was nearly identical to

accuracy at post (M = .89 at both assessments), indicating long-term stability of training-

related improvements (see Figure 4-4).

Influence of Reaction Time on Accuracy

Reaction time and accuracy. I combined participants from both retreats (N = 59) to

examine the relationship between reaction time (median correct reaction time to non-

targets) and response inhibition accuracy. Reaction time and accuracy were not

significantly correlated before training (r = .11, p = .39), and there were no significant

changes in reaction time from pre to post (482 msec vs. 478 msec; t(58) = .50, p = .61).

However, slower reaction times were correlated with higher accuracy at post (r = .38, p =

.003). This result suggests a possible speed-accuracy trade-off, which would be consistent

with the relationship between response inhibition errors and the speed of non-target

responses reported in other studies (e.g., Helton et al., 2009).

I further explored this finding by examining the influence of demographic variables

on reaction time and accuracy. To anticipate the following results, both age and previous

meditation were significantly related to reaction time. Importantly, controlling for

reaction time, age, and previous meditation experience in the hierarchical regression

models of response inhibition accuracy did not change the reported results in either

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retreat (Retreat 1: interaction between group and assessment, p = .004; Retreat 2: main

effect of assessment, p = .016, interaction between assessment and block, p = .018). Thus,

training-related improvements in accuracy (Retreats 1 and 2) and slope (Retreat 2) were

robust to the influence of these variables.

Reaction time and age. Due to the large age range tested and the possibility for an

interaction between age and response speed (see Caserta et al., 2009), I investigated

whether age was related to changes in either reaction time or accuracy with training. At

pre assessment, age was not correlated with accuracy (r = .12, p = .36) but was

significantly correlated with reaction time (r = .52, p < .001). The findings at post

assessment were similar: Age was not correlated with accuracy (r = .12, p = .34) but was

significantly correlated with reaction time (r = .46, p < .001). These results indicate that

older participants responded more slowly than younger participants.

Improvements in accuracy in younger and older participants. To explore the relations

between age, reaction time, and accuracy, I compared performance in younger (n = 30, M

= 36 years, range = 22 - 50 years) and older (n = 29, M = 59 years, range = 51 - 69 years)

participants. Although older participants had numerically higher accuracy than younger

participants at all assessments, accuracy did not differ significantly between the two

groups (p > .21). However, older participants were significantly slower than younger

participants both before (t (57) = 4.05, p < .001) and after training (t (57) = 3.92, p <

.001; see Figure 4-5). Next, I examined training-related changes separately in the two age

groups using repeated-measures ANOVA across three assessments (pre, mid and post).

The younger group showed significant improvements in overall accuracy (F (2, 28) =

23.70, p < .001) and no change in reaction time with training (F (2, 28) = .23, p = .79).

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Similarly, the older group showed significant improvements in accuracy (F (2, 27) =

17.63, p < .001) and no change in reaction time (F (2, 27) = 1.11, p = .34). These results

confirm that both younger and older participants improved in response inhibition

accuracy with training (see Figure 4-5). Importantly, training-related improvements in

accuracy were not accompanied by changes in reaction time in either group. However,

the data suggest that older participants may have relied more on slowing of non-target

responses to achieve accurate response inhibition at all assessments.

Influence of Previous Meditation Experience on Performance

I found that older participants were significantly slower than younger participants at

all time points. Although this result could reflect changes in response speed that occur

during the normal ageing process, I also wanted to explore the influence of other

demographic factors on this result. Specifically, older participants entered training with

more previous meditation experience (on average) compared to younger participants.

Thus, presumed age-related differences in response speed could instead reflect the

influence of long-term meditation practice.

Previous meditation and performance before training. Previous meditation

experience (N = 58 with complete data, M = 2572 total hours) was correlated with both

reaction time (r = .44, p < .001) and age (r = .52, p < .001) at pre assessment.

Furthermore, the correlation between previous meditation and reaction time held up after

controlling for the effect of age (pr = .30, p = .02). This result indicates that participants

with more meditation experience performed more slowly on the response inhibition task.

Previous meditation was not related to accuracy (r = .11, p = .39).

103

a.

b.

*p < .05 **p < .01

Figure 4-5. Age effects on accuracy and reaction time. (a) Response inhibition accuracy (A') and (b) non-target reaction time (RT) plotted as a function of assessment for younger (n = 30, M = 36 years) and older (n = 29, M = 59 years) participants. Both age groups showed significant increases in A', but no changes in RT with training. Older participants were significantly slower than younger participants at all assessments, but the age groups did not differ in response inhibition accuracy.

0.76

0.8

0.84

0.88

0.92

0.96

1

PRE MID POST

Accu

racy

(A')

Younger Older

300

400

500

600

700

PRE MID POST

Non

-targ

et re

actio

n tim

e (m

s)

Younger Older

** ** **

****

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Previous meditation and training-related changes in accuracy. I explored the

influence of previous meditation experience on training outcomes by examining training-

related changes in response inhibition accuracy separately in participants with less

experience (N = 29, M = 933 hours, range = 200 – 1700 hours) and participants with

more experience (N = 29, M = 4211 hours, range = 1800 – 15,000 hours). Repeated-

measures ANOVA revealed that accuracy increased across assessments (pre, mid, and

post) both for participants with less experience (F (2, 27) = 29.42, p < .001) and for

participants with more experience (F (2, 27) = 19.36, p < .001). This result suggests that

training-related improvements in response inhibition accuracy were not influenced by

previous meditation experience.

Discussion

In a longitudinal wait-list controlled study of meditation training, I found replicable

improvements in accuracy during sustained response inhibition. Improvements in

accuracy were greatest toward the end of the 32-minute response inhibition task (see

Figures 4-2a and 4-3), indicating that participants were better able to maintain accurate

performance over extended periods of time after training.

These results suggest training-related improvements in executive control, specifically

the voluntary control of impulsive responses. However, improvements in response

inhibition accuracy could also reflect training-related changes in perceptual

discrimination. Previous vigilance studies have reported increases in accuracy and

reductions in the vigilance decrement when targets are easy to discriminate from non-

targets (e.g., Parasuraman & Mouloua, 1987); however, the ease of target discrimination

does not improve performance when response inhibition is required. For example,

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response inhibition errors are common in vigilance tasks that present perceptually

unambiguous (e.g., alphanumeric) stimuli (e.g., Conners, 1995). In addition, participants

commit response inhibition errors despite being perceptually aware of the rare target

(e.g., the 'Oops' phenomenon; see Robertson et al., 1997). These findings suggest that

improvements in response inhibition accuracy were not likely due to improvements in

perception. Indeed, controlling for changes in baseline target discrimination (i.e., the

measure of threshold reported in Chapter 3) in the analyses of response inhibition

performance did not change the reported results in either retreat, suggesting that

improvements in response inhibition accuracy were not primarily due to changes in

perceptual discrimination. However, it will be important in ongoing analyses to

characterize changes in both perceptual and response-related brain activity that are

correlated with behavioral improvements in response inhibition accuracy (see Chapter 5).

There is growing interest in both research and clinical fields in the possibility for

flexible changes in cognitive ability throughout the life span. In particular, researchers

have attempted to develop cognitive training programs (e.g., Mahncke, Connor et al.,

2006) for ameliorating age-related declines in perception, attention, and cognition (see

reviews in C. S. Green & Bavelier, 2008; Mahncke, Bronstone, & Merzenich, 2006). The

present finding that meditation training improves response inhibition accuracy in older as

well as younger participants is promising (see Figure 4-2). Healthy ageing is

characterized by a natural loss of volume and white matter structural integrity,

particularly in prefrontal cortex, which may be associated with cognitive decline (see

Caserta et al., 2009 for review). Evidence from structural brain studies of long-term

meditators suggests that meditation may reverse this pattern. Lazar and colleagues (2005)

106

demonstrated increased cortical thickness in long-term meditators compared to

meditation-naïve controls. In particular, the group differences in prefrontal cortical

thickness were most pronounced in older participants, suggesting that meditation may

attenuate normal age-related deterioration in frontal brain structures. In addition, a recent

study demonstrated that changes in gray matter volume with long-term meditation are

also evident in other areas of frontal cortex implicated in emotion regulation and response

inhibition (Luders, Toga, Lepore, & Gaser, 2009). Although such cross-sectional studies

cannot definitively link meditation training with changes in brain structure, the results

suggest an intriguing connection between the improvements in behavioral response

inhibition observed in older adults in the present study and possible structural and/or

functional changes prefrontal cortex. To test this hypothesis, further research is needed

that tracks longitudinal changes in both behavioral measures of executive control and

changes in brain structure and function in the same participants.

In addition to response inhibition accuracy, I also explored changes in reaction time

with training. Researchers have observed speed-accuracy trade-offs during response

inhibition tasks such that faster responding is linked to a greater likelihood of accidental

errors and slower responding enables better inhibition (Garavan, Hester, Murphy,

Fassbender, & Kelly, 2006; Helton, 2009; Helton et al., 2009; Manly et al., 1999).

Consistent with these findings, I observed a significant correlation between increases in

accuracy and increases in reaction time at post assessment. However, reaction times did

not change with training, suggesting that improvements in accuracy could not be

explained by strategic changes in response speed. I did observe that reaction time was

related to age: Older participants were slower than younger participants at all testing

107

points. This result may reflect normal age-related declines in speed of information

processing (Caserta et al., 2009) or strategic changes in response speed to enable better

inhibitory control. Importantly, older participants did not differ from younger participants

in accuracy before training, nor in amount of improvement in accuracy with training. It is

unclear why meditation training improved accuracy in older participants but did not

affect response speed. Considering the possible relationship between meditation and age-

related changes in prefrontal brain structure discussed above, it will be necessary in

future research to explore how training-related changes in response inhibition accuracy

and response speed map differently onto daily life functioning in older adults.

Slower reaction times were also independently correlated with more previous

meditation experience after controlling for the influence of age. Participants entered the

study with experience in various kinds of meditation practice, and thus it is not possible

to attribute slowing of reaction time to any particular type of training. In addition, slower

reaction times may not be related to meditation per se but rather to life experiences

and/or characteristics specific to individuals who have chosen to dedicate much of their

life to regular meditation practice (see C. S. Green & Bavelier, 2008, for a general

discussion of this topic). In contrast, previous meditation experience was not related to

response inhibition accuracy before training. Thus, the observed changes in accuracy

after Shamatha training are likely mediated by a different mechanism than possible

changes in response speed with long-term meditation.

Executive attention and response inhibition are core dimensions of adaptive, goal-

directed behavior (Miller & Cohen, 2001), and training-related improvements on the

response inhibition task may translate into beneficial changes in daily life. This

108

possibility is supported by previous research showing a relation between response

inhibition performance in the laboratory and attentional and behavioral mistakes in daily

life (Robertson et al., 1997). Motivated by the prediction that response inhibition

performance might be related to positive changes outside the laboratory, my colleagues

and I explored the influence of training-related improvements in response inhibition on

socio-emotional functioning (Sahdra et al., under review). We operationalized self-

reported adaptive functioning (AF) as a single latent factor underlying self-report

measures of anxious and avoidant attachment, mindfulness, ego resilience, empathy, the

five major personality traits, difficulties in emotion regulation, depression, anxiety, and

psychological well-being (all assessed by a battery of questionnaires that participants

completed before and after training). Participants in both retreats showed significant

improvements in AF with training. Longitudinal dynamic models with data combined

across retreats showed that improvement in response inhibition accuracy positively

influenced the change in AF over time. This finding is consistent with a key claim in the

Buddhist literature that enhanced inhibitory control is an important precursor of positive

subjective changes experienced following meditation training. Importantly, these results

suggest that the observed improvements in behavioral response inhibition contribute to

improvements in mental, emotional and psychological functioning.

To conclude, improvements in response inhibition accuracy were observed in two

training studies, suggesting a strong link between Shamatha meditation training and

improvements in executive attention and behavioral control. Moreover, improvements in

response inhibition accuracy endured several months after the completion of formal

training. Although the demographic factors of age and previous meditation experience

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influenced aspects of task performance (reaction time), improvements in accuracy were

not limited to a particular subset of participants (i.e., all results remained significant after

controlling for individual differences in reaction time, age and previous meditation

experience). Future studies with larger sample sizes will be required to fully characterize

these improvements in individuals of different ages and with varying amounts of previous

experience with meditation. Nevertheless, these findings suggest that flexible changes in

executive attention and behavioral control are possible throughout the life span.

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CHAPTER 5

CONCLUSION

111

The overarching aim of the experiments contained in this dissertation was to

demonstrate that vigilance can be improved in healthy adults. In this chapter, I will

provide an overview of the main results and discuss the neural mechanisms that may

underlie the observed training-related improvements in sustained performance.

The series of experiments in Chapter 1 explored the effects of attentional cues on

sustained attention and sustained response inhibition. In the sustained attention task,

exogenous attention cues (sudden-onset luminance changes) increased overall perceptual

sensitivity, while endogenous attention cues (predictable stimulus timing) reduced the

perceptual sensitivity decrement. These results suggest that (1) overall resource demands

can be reduced by manipulations that transiently improve target perception (exogenous

cues), and (2) limited resources can be conserved by using implicit timing information

(endogenous cues) to pay maximum attention at certain moments in time. The best

performance was achieved in the version of the sustained attention task that combined

both types of cues, demonstrating a strong interaction between exogenous and

endogenous attention during vigilance. On the other hand, improvements in perception

due to exogenous cues did not facilitate better sustained response inhibition. This result

suggests that inhibiting impulsive behavioral responses for extended periods of time is

more resource-demanding than sustained target discrimination. Finally, performance on

the most resource-demanding versions of the sustained attention and response inhibition

tasks did not change with repeated testing, indicating that the detrimental effects of high

resource demands are not reduced by increased familiarity with task stimuli or more

practice with executing correct responses.

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The series of experiments in Chapters 3 and 4 showed that intensive mental training

of voluntary attention through meditation improved performance on the most resource-

demanding vigilance tasks. Training led to increases in overall perceptual sensitivity and

reductions of the vigilance decrement during sustained attention (Chapter 3). These

improvements were specifically linked to training-induced changes in visual perception,

indicating that sustained attention can be improved by reducing the resource demand

associated with difficult target discrimination. Training also improved accuracy on a

vigilance task that required response inhibition (Chapter 4). Improvements in response

inhibition accuracy were not accompanied by adjustments in reaction time (i.e., there was

no speed-accuracy trade-off), indicating that participants were able to inhibit habitual

responses without sacrificing speed.

The observed behavioral improvements are consistent with aspects of meditation

training that entail learning to (1) stabilize attention on a chosen stimulus (sustained

attention), (2) enhance the perceived detail of that stimulus (perceptual discrimination),

and (3) regulate and control attention and behavior (response inhibition). Now it will be

important to elucidate the underlying neural mechanisms of meditation-related

improvements in each of these behavioral domains. Such investigations will also

contribute to a better understanding of the brain resources required for successful

vigilance. During the training studies presented in Chapters 3 and 4,

electroencephalograph (EEG) activity was recorded continuously while participants

performed the behavioral tasks. In the following I will present preliminary EEG results

and discuss how changes in brain activity may support training-related changes in

behavior.

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Perceptual Processing and Stimulus-Evoked Neural Activity

Behavioral studies of visual attention have shown that endogenous and exogenous

attention cues improve perceptual sensitivity by enhancing the quality of stimulus

representations. For example, attention enhances apparent contrast (Carrasco et al., 2004;

Carrasco et al., 2000; Ling & Carrasco, 2006) and increases spatial resolution (Carrasco,

Williams, & Yeshurun, 2002b; Yeshurun & Carrasco, 1999), particularly when a target is

difficult to discriminate. Changes in stimulus-evoked brain electrical activity (event-

related potentials [ERPs]) have been used to describe time-related modulations of brain

activity that underlie such attention-related improvements in perception. In general, ERP

studies of visual-spatial selective attention have shown that representations of attended

visual stimuli are enhanced relative to ignored stimuli at early stages of visual processing

(i.e., within the first 200 milliseconds after stimulus presentation). Specifically, voluntary

attention increases the amplitude of early-latency visual ERPs (P1 and N1) to the

attended stimulus (Mangun & Hillyard, 1991; Martinez et al., 2001; Van Voorhis &

Hillyard, 1977). Moreover, attention-related changes in perceptual sensitivity have been

linked to early-stage perceptual processing rather than later-stage decision processing

(Luck et al., 1994). Enhancements in the amplitude of early-latency components also

occur with focused (versus distributed) attention at the fovea (Miniussi et al., 2002),

demonstrating that directed attention can enhance perceptual processing when spatial

resolution is high.

In addition to early perceptual processing, attention also modulates post-perceptual

target processing. For example, the P300 (also referred to as the P3) is a positive-going

ERP that occurs between 300 and 600 ms after the correct detection of a task-relevant

114

target. The amplitude of the P3 varies according to the amount of attentional resources

devoted to the task, with greater attentional resources resulting in larger amplitude. The

P3 is also modulated by various task manipulations such as target frequency (larger for

infrequent targets), stimulus intensity (smaller for degraded targets), and uncertainty

about target/non-target categories (smaller amplitude with greater uncertainty) (see

Soltani & Knight, 2000 for a review).

Findings from ERP studies of vigilance support the general conclusion that changes

in behavioral performance during vigilance are related to changes in post-perceptual

target processing (see review in Parasuraman, Warm, & See, 1998). For example, Davies

and Parasuraman (1977) found reductions in both early- and late-latency visual ERPs

with increasing time on task; however, only changes in the relatively late components,

including the P3 were related to the behavioral performance decrement. Decreases in P3

amplitude during vigilance could reflect reductions in attentional resources or increasing

uncertainty associated with target identification. However, there is unfortunately little

evidence that demonstrates that changes in stimulus-evoked activity are necessary to

produce the vigilance decrement.

Linking changes in stimulus-evoked brain activity with training-related changes in

sustained attention will advance our understanding of the processing resources that are

required for successful vigilance. Although the studies mentioned above suggest that the

normal vigilance decrement is related to changes in post-perceptual target processing,

training-related improvements in target perception (i.e., perceptual sensitivity during

vigilance) may instead reflect changes in early-stage perceptual processing. Preliminary

analyses I have conducted indicate training-related increases in the amplitude of the N1

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component following correctly detected targets (vs. non-targets) (MacLean, Aichele,

Bridwell, Jacobs et al., 2009). The N1 component reflects a discrimination process that is

applied within the focus of attention (Vogel & Luck, 2000). Thus, increases in N1

amplitude with training indicate improvements in perceptually discriminating targets

from non-targets. The focus of my ongoing analyses is to describe how changes in N1

amplitude relate to the observed behavioral improvements in baseline threshold, overall

perceptual sensitivity, and sustained performance.

Error-related Processing during Response Inhibition

In the preceding section I discussed how changes in stimulus-locked neural activity

may underlie improvements in target perception during sustained attention. However,

improvements in response inhibition accuracy are not likely mediated by the same

mechanism. Rather, because successful response inhibition requires controlling impulsive

motor responses and because accidental errors occur even after training, improvements in

response inhibition accuracy may be related to changes in error- and response-related

processing. Functional neuroimaging studies have shown that errors in response

inhibition result in recruitment of the anterior cingulate cortex (ACC), which is involved

in monitoring for conflict (e.g., the conflict between a habitual response and a task-

relevant response) and signaling the need for increased attentional control (Carter et al.,

1998; MacDonald, Cohen, Stenger, & Carter, 2000). Several lines of evidence (Dehaene,

1994; van Veen & Carter, 2002a, 2002b) point to the ACC as the source of the error-

related negativity (ERN), a negative deflection in the ERP that peaks between 50 and 150

milliseconds after a subject makes an erroneous response (Falkenstein, Hohnsbein,

Hoormann, & Blanke, 1991). Training-related improvements in response inhibition

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accuracy may depend on being able to effectively recover from accidental errors by

making attentional and behavioral adjustments. Specifically, enhancements in the

amplitude or changes in the latency of the ERN could play a role in signaling increases in

attentional control after an error has been committed, thus preventing subsequent errors

and improving overall accuracy.

Individuals are usually aware of response inhibition errors, as evidenced by the

common utterance of “Oops!” (or other expressions of frustration) following an

accidental response to the infrequent stimulus (Robertson et al., 1997). While the ERN

does not appear to reflect conscious error processing, the error positivity (Pe) that occurs

at a later latency (between 200 and 400 milliseconds after an erroneous response) is

modulated by error awareness (Endrass, Franke, & Kathman, 2005; Endrass, Reuter, &

Kathmann, 2007; Nieuwenhuis, Ridderinkhof, Blom, Band, & Kok, 2001; O'Connell et

al., 2007). Changes in the amplitude or latency of the Pe component may thus provide a

window into changes in the subjective awareness of errors following training. In ongoing

analyses I am examining how training-related changes in subconscious (ERN) and

conscious (Pe) error processing contribute to improvements in response inhibition

accuracy.

Modulations of Ongoing EEG

As discussed above, the evoked neural response to discrete events reveals much about

how attention enhances perceptual processing and how errors are processed in the brain.

At the same time, the ongoing EEG also reflects changes in attention during task

performance. The EEG is composed of several frequency bands, some of which show

consistent modulations in oscillatory power (i.e., amplitude) in correspondence with

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space- and feature-based attention, attentional load/effort, and conscious perception (see

review in Womelsdorf & Fries, 2007). A rapidly growing body of evidence suggests that

modulations in the amplitude of the alpha band (8-14 Hz) over occipital cortex relate to

visual-spatial attention. First, reductions in pre-stimulus alpha are spatially specific and

reflect the location of attention (i.e., reductions in alpha are observed contralateral to the

attended location in space; Kelly, Lalor, Reilly, & Foxe, 2006; Thut, Nietzel, Brandt, &

Pascual-Leone, 2006; Worden, Foxe, Wang, & Simpson, 2000; Yamagishi, Goda, Callan,

Anderson, & Kawato, 2005). Second, decreases in alpha amplitude predict increases in

target detection accuracy (Ergenoglu et al., 2004; Hanslmayr et al., 2007; Hanslmayr et

al., 2005; Linkenkaer-Hansen, Nikulin, Palva, Ilmoniemi, & Palva, 2004; Thut et al.,

2006; van Dijk, Schoffelen, Oostenveld, & Jensen, 2008). Together, these findings

suggest that lower levels of occipital alpha reflect the positive top-down influence of

voluntary attention on visual information processing.

Changes in ongoing EEG during vigilance are generally characterized by a shift

toward slower-frequency bands with increasing time on task (see reviews in Oken,

Salinsky, & Elsas, 2006; Parasuraman et al., 1998). For example, increases in the theta

band (4-8 Hz) have been reported during auditory (Paus et al., 1997) and visual vigilance

tasks (Smit, Eling, & Coenen, 2004a). However, despite the strong link between alpha

activity and visual attention/perception, there is little evidence that the vigilance

decrement is related to changes in alpha activity. One study of the effects of sleep

deprivation on vigilance showed that increases in alpha reliably predicted subsequent

target misses during a 40-minute vigilance task (Kaida, Akerstedt, Kecklund, Nilsson, &

Axelsson, 2007). However, because sleepiness was explicitly manipulated in this study, it

118

is unclear whether these findings reflect changes in overall arousal or visual attention.

More promising are results from a recent study showing that increases in pre-stimulus

alpha predicted subsequent response inhibition errors (Mazaheri et al., in press).

Specifically, errors were related to increases in alpha oscillations (~10 Hz) over occipital

and sensorimotor scalp regions. This finding suggests that the state of the brain before the

occurrence of a stimulus is critical for successful target discrimination (i.e., visual

processing) and subsequent behavior (i.e., sensorimotor processing).

Changes in ongoing brain activity have been hypothesized to underlie meditation-

related changes in perception and attention. However, a recent longitudinal study of

meditation training failed to find changes in pre-stimulus EEG patterns related to

successful attentional performance (Slagter et al., 2009). In contrast, my preliminary

analyses indicate reliable training-related reductions in pre-stimulus alpha (8 – 14 Hz)

during sustained attention and response inhibition (MacLean, Aichele, Bridwell, Jacobs

et al., 2009). Moreover, training-related reductions in pre-stimulus alpha over occipital

cortex predicted the amount of improvement in vigilance (i.e., reduction in the slope of

performance over time) in both tasks. These results indicate a strong link between pre-

stimulus alpha activity and successful performance over extended periods of time. The

aim of my ongoing analyses is to relate changes in pre-stimulus EEG with behavioral

indices of target discrimination and response speed, as well as to explore how changes in

pre-stimulus EEG relate to changes in stimulus- and response-locked ERPs.

Conclusions

The results from this dissertation demonstrate improvements in perception, sustained

attention, and response inhibition after three months of intensive meditation training. In

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addition, the preliminary analyses discussed above indicate neurophysiological changes

that are associated with improved behavioral performance. These findings add to the

growing body of evidence that meditation training can improve behavioral performance

(see review in Lutz, Slagter et al., 2008) and lead to enduring changes in brain function

(Brefczynski-Lewis et al., 2007; Lutz, Brefczynski-Lewis, Johnstone, & Davidson, 2008;

Lutz, Greischar, Rawlings, Ricard, & Davidson, 2004; Slagter et al., 2009). Indeed, the

persistence of improvements in discrimination and response inhibition after the

completion of formal training points to the potential for long-lasting behavioral changes

with regular, but less intensive meditation practice. Finally, my ongoing collaborative

work shows that improvements in response inhibition during the formal training period

predicted positive changes in adaptive socio-emotional function (Sahdra et al., under

review), suggesting that the ability to control impulsive behavioral responses in the

laboratory contributes to self-reported psychological wellbeing. It will be important in

future research to continue to explore how training-related changes in behavior and brain

function relate to the more lofty goals of meditation practice, such as learning to regulate

one’s emotions and achieving enhanced psychological wellbeing (Wallace & Shapiro,

2006).

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