The Impact of Stress on Category Learning and Consolidation

Eric Holtzman, Psych 190LResearch Assistant for Jennifer WaldschmidtComputational Cognitive Neuroscience Lab

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Introduction:

Day to day life requires categorizing objects quickly and accurately. We have evolved to have an

advanced categorization system in order to survive. Our ancestors had to be able to distinguish

quickly and accurately between animals that could be hunted for food and animals that wanted to

make us their food. Categorization also goes deeper than this sort of explicit decision-making

and takes place in more automatic processes as well. Categorization is taking place when we

automatically calculate the right distance to throw a Frisbee to reach a friend during a game of

catch.

Another universal process that impacts cognition and behavior is stress. Stress is

something that we all experience that has a huge impact on our bodies and our thinking. It is well

known that stress impacts our bodies in ways such as increasing heart rate and sweating, and

increases the chances of heart attack and other health issues. However, there has been little

research on the effects of stress on how we categorize objects. In this paper I will examine the

effects of stress on category learning. I will start with a brief overview of current research on

stress, and then introduce how we study category learning. After that, I will explain the details of

our experiment examining stress and category learning. Finally, I will present and discuss the

results.

Types of Stress: There are two types of stress, which differ according to how the individual

interprets and reacts to the stress (Lazarus & Folkman, 1984). When the individual appraises

stress as a positive challenge (a boundary that can be overcome with effort), we say the stress is

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adaptive. Physiologically, adaptive stress is associated with increased cardiac output and

decreased total peripheral resistance (Ell, Cosley, and McCoy, 2011).

The other type of stress is maladaptive stress, which occurs when an individual appraises

stress as a personal threat. Often it is the threatening of social status that is the most powerful

cause of maladaptive stress. This is why job interviews, which serve as a determinant of societal

worth, tend to be so stressful. I will provide more detail on this later when I discuss how to

induce stress. Maladaptive stress has been shown to have many negative cognitive and

physiological effects. Multiple studies have shown that maladaptive stress inhibits performance

on tasks that require working or declarative memory. In addition it has been shown that

maladaptive stress hurts performance on tasks that require hypothesis testing or executive

attention (Markman et al., 2006). Physiologically, maladaptive stress causes little change in

cardiac output, and an increase in TPR (Blascovitch, 2008).

Hypothalamic-Pituitary-Adrenal (HPA) axis: The HPA axis is activated as a result of

maladaptive stress. The HPA axis regulates the release of cortisol (a major stress hormone)

(Dickerson, 2004, pg. 355). Activation of the HPA axis is started by the hypothalamic release of

corticotropin releasing hormone (CRH). This CRH release stimulates the anterior pituitary to

secrete adrenocorticotropin hormone (ACTH). The ACTH secretion then prompts the adrenal

cortex to release cortisol into the bloodstream. This release of cortisol is the process that we are

concerned with. The HPA axis is regulated by a negative feedback system, with circulating

cortisol inhibiting activity at the level of the hippocampus, the pituitary, and the hypothalamus

(Dickerson, 2004).

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HPA activation is a necessary part of human functioning. For example, cortisol plays a

role in metabolism by turning energy resources into fuel for the body. However, prolonged HPA

activation can cause negative side effects, and has multiple implications for health and disease.

Heightened HPA activity is related to depressive symptomology as well as memory deficits

(Dickerson, 2004). For example, Kirschbaum et al. found a strong negative correlation between

stress induced cortisol levels and performance on a memory test (Kirschbaum et al. 1996).

Cortisol is a vital hormone that is responsible for much more than just stress, and is vital

for physical health (Dickerson & Kemeny, 2004). Dickerson and Kemeny hypothesize that

uncontrollable threats to the goal of maintaining the “social self” would trigger reliable and

substantial cortisol changes. I will provide more information on inducing a cortisol release in a

laboratory setting later on.

Cortisol Measurement: For our study, we measured cortisol levels as a way of assessing stress.

Cortisol is usually measured through plasma or saliva to assess stress. Both plasma and salivary

sampling have their advantages and disadvantages. Plasma samples measure free cortisol as well

as cortisol bound by protein. Salivary samples, on the other hand, only measure free cortisol.

However, salivary samples are highly effective at measuring acute responses to experimental

stressors, (Nicholson, pg. 43) which is precisely what we are attempting to measure. In addition,

salivary samples are much easier and less invasive to collect. For our study we chose to utilize

salivary measures due to the fact that they are less invasive, and highly effective at measuring

responses to stressors.

Cortisol release adheres to a circadian rhythm. For normal sleepers cortisol levels are

lowest between the hours of 10 P.M. and 4 A.M. Cortisol levels increase about two hours prior to

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waking, and increase drastically upon waking up (Nicholson, pg. 38). After waking up, cortisol

levels decrease gradually throughout the day (Dickerson & Kemeny, 2004).

Cortisol release is strongest for the first 20 minutes after a stressor is terminated

(Dickerson, 2004). Dickerson and Kemeny found that cortisol levels return to the level they were

at prior to the stressor 41-60 minutes after the termination of the stressor. According to this

finding, cortisol levels should be back to normal the day following a major stressor.

Inducing Stress: meta-analytic research by Dickerson and Kemeny (2004) indicates that a major

goal must be threatened in order for stress to be induced psychologically. According to the

Social-Self-Preservation Theory, the motive to preserve the social self is attached to specific

biological processes including HPA activation (Dickerson, 2004). The social self-preservation

system scans the environment for potential threats to one’s social standing and mobilizes systems

like the HPA axis in response to threats (Dickerson, 2004). The social self is largely based on

how others perceive us. Therefore, situations with the potential to put us in a bad light or make

us seem socially inept mobilize our social self-preservation system, which in turn activates the

HPA axis.

An effective way to mobilize the social self-preservation system and initiate the stress

response is to put subjects into a seemingly important task that reflects their social worth, then

make them feel as if they are failing the task through negative social cues. For example, a subject

could be asked to give a public speech in front of a number of research observers, while the

research observers look on with flat affect. This strategy provides an element of

uncontrollability, because the subjects receive negative social cues no matter how they perform

on the task. Uncontrollability has been shown to be an effective way to increase social evaluative

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threat. The combination of threat to a goal and an inability to overcome the threat is likely to lead

to significant HPA activation and therefore cortisol release (Dickerson, 2004). An additional

way to boost the cortisol reaction in subjects is to add a cognitive task, such as serial subtraction,

to the already stressful public speaking task. This combination of tasks has been shown to be

much more effective than uncontrollability alone, and has become a standard practice for

experimentally inducing stress (Dickerson, 2004).

One such paradigm that employs uncontrollability, social evaluation, and a cognitive

number task is the Trier Social Stress Test (TSST). The TSST is widely used for research on

stress, and is the paradigm that we chose to employ. In brief, the TSST that we administered

progresses as follows: 1) Subject given 5-10 minutes to prepare a speech stating why they are the

right candidate for their dream job: 2) Subject presents speech in front of at least two research

observers who display a flat affect throughout the speech: 3) Following the speech subjects are

told to perform a serial subtraction task for five minutes. There is also a control version of the

TSST that is designed to follow the same structure as the stress TSST yet not include the

elements that make the stress TSST stressful. For our control TSST we had subjects perform a

simple speech and numbers task in a room by themselves with no one watching or listening to

their speech.

Category Learning: Category learning requires showing subjects stimuli from unfamiliar

categories and observing the period in which their ability to assign stimuli to categories goes

from chance to a stable level (Ashby & Maddox, 2005). Ashby and Maddox (pg. 151) argue that

the most effective way to make sure subjects come into the experiment with no familiarity for the

categories is to create new arbitrary category objects. There are two well-established learning

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systems that give rise to category learning. The two major learning systems are the Rule Based

(RB) system, in which categories can be learned via an explicit reasoning process, and the

Information Integration (II) system, in which accuracy is maximized if information from two or

more stimulus dimensions is integrated at a pre-decision stage (Ashby & Ennis, 2006). RB and II

tasks can be distinguished by the difficulty of verbalizing the optimum decision strategy. The

optimum strategy for RB tasks is easy to describe verbally, whereas the optimum strategy for II

tasks often involves procedural memory and is typically difficult or impossible to describe

verbally. RB and II tasks also differ in the number of physiological steps necessary for learning.

The RB system only requires two factors for learning: strong pre- and post-synaptic activation,

while the II system requires three-factor learning: strong pre- and post-synaptic activation as well

as the release of dopamine (Ashby & Ennis, 2006).

Impact of Stress on Learning:

Animal Research: Animal research has shown that chronic stress can have negative effects on

memory and can impact the hippocampus. The hippocampus is an important factor in RB

learning, so the effect of stress on the hippocampus is relevant to our study. Sousa et al. (2000)

evaluated the effects of chronic stress on the hippocampus in rats. They found that prolonged

hypercortisolism in rats was associated with dendritic atrophy and the loss of synapses in the

hippocampus. Additionally, these structural changes were predictive of impairments in spatial

learning and memory (Sousa et al., 2000).

Copeland et al. (2005) looked at the effect of chronic stress on dopamine uptake in the

striatum in mice. The striatum consists of the caudate, putamen, and nucleus accumbens, and is

an important brain region for category learning tasks. They found that after repeated restraint

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stress there was a long-lasting increase in dopamine transporter (DAT) activity, suggesting that

dopamine uptake is regulated by stress. This finding is significant to our study, because the

three-factor learning of the II system requires dopamine release. Abercrombie et al (1989)

examined the effects of acute stress on dopamine release in various brain regions of rats. Their

data showed that stress produced increases in dopamine release in the striatum (25% increase),

nucleus accumbens (39% increase) and the medial frontal cortex (95% increase above baseline).

This increase in striatal dopamine release caused by stress could be a factor in why maladaptive

stress has been found to improve II accuracy in a recent study (Ell et al., 2011).

Impact of Stress on human learning and retention: Kirschbaum et al. (1996) performed two

studies looking at the impact of a social stressor and cortisol on learning and recall of a word list.

In the first study, subjects were exposed to the TSST and then given a declarative memory test.

Cortisol levels were also measured before and after the TSST via saliva samples. They found that

subjects with a large increase in cortisol following the stressor showed worse memory

performance than those with a smaller cortisol response.

In the second study, 40 subjects were given either 10 mg of cortisol or a placebo and

none of the subjects were exposed to the TSST. This study was designed to examine if a cortisol

increase that is independent from psychological stress would still have an effect on memory. The

results of the second study showed that a small dose of cortisol significantly impaired

performance on spatial thinking and declarative memory tasks, but did not impair performance

on a procedural memory task. These results suggest that increased cortisol levels negatively

affect performance on tasks that require executive attention, but do not affect performance on

more automatic tasks.

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Research on the Effects of Stress on Category Learning: A study performed by Ell, Cosley,

and McCoy in 2011 is the closest thing in the literature to what we did in our experiment. Ell et

al. tested thirty-three participants (31 female) on a category learning study that involved the RB

and II systems. The TSST test was administered to all subjects in order to induce stress before

the categorization task (consisting of 400 trials in an II or RB task). During the stressor, Ell et al.

measured which subjects found the stressor to be threatening by asking the subjects the extent to

which they found the task to be stressful, demanding, effortful, and distressing. These responses

were made on a 0-6 scale and averaged to form an index of threat appraisal. In addition,

cardiovascular reactivity measures (heart rate, CO, and TPR) were taken during the 5-minute

baseline period, the stressor, and the category learning task. Ell et al. predicted that the

relationship between threat appraisal and accuracy would depend on the categorization task that

subjects completed.

Ell et al. Results: Ell et al. looked at and compared accuracy for each condition and

completed a hierarchical regression. First off, they found that accuracy was higher for the RB

task than the II task, and that there was no main effect of threat appraisal on accuracy. As they

predicted, the relationship between threat appraisal and accuracy depended on the categorization

task. Increased threat appraisal was significantly associated with enhanced accuracy on II (p

< .05) and marginally significantly associated with decreased accuracy on RB (p = .09). The

more threatened subjects were more likely to use II bounds than the less threatened subjects. The

fact that Ell et al. failed to find a significant decrease in RB accuracy could be explained by

multiple factors. They hypothesized that they didn’t find a decrease in RB either because the

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categories were too easy (so easy that threat did not cause a major difference in performance) or

because the II system was sufficiently flexible to learn the RB decision bound (Ell et al. pg. 9-

10). The findings of this study are noteworthy, because they show that a maladaptive threat

response could be associated with enhanced performance on a cognitive task.

The findings of Ell et al. are significant, yet there are still some shortcomings of the

study. First of all, the study was lacking a control condition, meaning that every subject

experienced the same stressor. Also, Ell et al. failed to find a significant effect for the RB group

and do not have a definitive explanation for this lack of effect. Finally, the study by Ell et al.

lacked any form of retention measure and did not look into how learning is consolidated. For our

study, we aimed to pick up where Ell et al. left off and add the things their experiment was

lacking. We incorporated a control condition and got rid of one of the potential things that led to

the lack of effect for the RB group (made the II and RB tasks equal in difficulty). Also, we

examined the impact that stress has on retention and the consolidation of learning.

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Method

Our study followed a 2x2 experimental design with four experimental conditions, which

vary in stress received versus no stress received (control), and rule-based category learning

(Disjunctive) versus information-integration category learning. We placed 25 subjects in each

condition (assuming effect is d = 0.85 with power = .90, one-tailed alpha = .05) in an attempt to

determine if stress caused any significant differences from the control conditions for our RB or II

learning groups. Throughout the study we collected saliva samples (to measure Cortisol levels)

as well as self-report measures from the subjects to measure their stress levels.

Subjects were asked to come into the lab for two successive sessions. During the first

session (approximately 2 hours in length) the stress or control procedures were carried out.

Subjects were randomly placed into a stress condition and a category learning condition.

Subjects placed in the stress condition were asked to do a task based on the Trier Social Stress

Test (TSST). The task for the stress test involves first giving a public speech in front of two

research assistants (one male and one female) in which the research assistants were instructed to

watch silently and not provide any comfort or encouragement to the subject (display flat affect).

In the control (no-stress) condition, subjects were asked to perform a five-minute speaking task

and a five-minute mathematics task in a room by themselves. The subjects were told explicitly

that no one was watching or listening to their speech performance.

Before coming into the lab, participants received a set of guidelines that were intended to

reduce behaviors that affect cortisol levels or could contaminate the cortisol samples we

collected. Some of these guidelines included abstaining from exercise the day before the study,

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and abstaining from caffeine and nicotine. When the participants arrived on the first day of the

study they were given an informed consent form that informed them of their rights as human

subjects. Some of these rights include the option to stop participating in the study at any time

without any penalty, and their right to avoid questions they did not feel comfortable answering.

Subjects also filled out a demographic form upon arrival at the lab.

When subjects arrived they received a brief introduction stating the general goals of the

research. After this introduction they were told to relax for 30 minutes (with magazines

provided) so that we could obtain a baseline cortisol level. After the 30-minute rest, the first

saliva sample of five was obtained. The saliva samples were obtained by drooling into a spit

sample container using a straw. After the first saliva sample was collected, subjects received

instructions for the stress or control task. Subjects in the stress condition received the following

instructions:

We would like you to imagine that you have applied for a job or position that you would

really like to get. You have been invited by the selection committee to give a presentation

to introduce yourself and to describe your qualifications for the job. You will have about

ten minutes to prepare for your speech, which should last as close to five minutes as

possible. Your speech performance will be performed in front of two research observers

who will rate it for content, clarity and personal presentation.

After receiving these instructions, the stress group subjects were escorted to another room to

meet the selection committee, consisting of two research assistants in lab coats. Subjects in the

control condition received these instructions:

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We would like you to give a five-minute speech about any topic you like. You will have

ten minutes to prepare your speech, after which you will be instructed to give your speech

out loud in a room by yourself. No one will be observing you or listening to your speech.

Prior to preparing their speech, subjects in both the stress and control group filled out a pre-task

form asking how stressful they anticipate the upcoming task to be. Then, subjects were provided

with note-cards and a pen with which they could write out an outline for their upcoming speech.

After a set amount of time the second saliva sample was collected. Upon completion of the

second saliva sample the subjects were escorted to another room to complete their speech task. If

the subjects were in the control condition, they were led to an empty room. If the subjects were

in the stress condition they had their note cards taken away and they were led to another room

containing the selection committee.

Once the subject entered the room, the committee members set a stop watch for five

minutes and instructed the subject to begin their speech task. The research observers were

instructed not to smile or provide any feedback to the subjects while they were giving their

speeches. Following the speech task the clock was set for five minutes again and a surprise

numbers task was given. For this task subjects started with the number 1795 and subtracted 17

continuously. If the subjects made a mistake the research assistants told them to start over from

1795.

Subjects in the control group were left alone in an empty room for five minutes to

complete their speech task. After five minutes were up the subjects were given a non-demanding

five-minute addition task in which they started with zero and counted up continuously by 15.

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Following these tests all subjects were instructed to fill out a post-task form that asked

them to report how stressful they found the task, as well as their current mood. Upon the

completion of this post-task form (approximately five minutes after the numbers task) subjects

gave their third saliva sample. After the third saliva sample was collected subjects began the

computerized portion of the experiment.

Depending on the group subjects were randomly placed into, they began either a rule-

based category learning experiment or an information integration learning experiment. Both of

these learning experiments were run on Apple Macbook Pro computers using MatLab. The

subjects were given the following instructions from one of the research assistants:

You will now be participating in a category-learning experiment. On each trial, you will

see a disc that only varies in orientation or bar width. We want you to categorize this disc

as belonging to group “A” or group “B” by pressing the corresponding keys on the

keyboard. You will also be given feedback. If you categorized the disk correctly, you will

hear a high-pitched tone. If you classified the disc incorrectly, you will hear a low-

pitched tone. It is possible to be 100% accurate.

Once the research assistant made sure that the subject understood the task, the first portion of the

category learning experiment began.

After completing the first third of the category learning experiment (approximately 18

minutes) subjects were prompted to see a research assistant for their fourth saliva sample. Upon

completion of their fourth sample the subject began the next portion of the experiment (again

about 18 minutes). Next, the subject was prompted to provide their fifth and final saliva sample.

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Upon completion of their final saliva sample the subjects finished up the last third of the

category learning experiment. At this point subjects had completed their first day in the lab.

When subjects returned on the second day they completed the final section of the

category learning experiment (900 trials), and did not have to provide any saliva samples or

complete any speech or mathematics tasks. The subjects completed the last portion of the

category learning experiment in one session and did not need to see the experimenters at 18-

minute intervals like the first day. When the subjects completed the last portion of the category

learning experiment they received a debriefing form and verbal explanation of the goals and

details of the study. If the subjects did not have any further questions then their participation in

the experiment was finished and they were given take-home information explaining the goals of

the study and where to obtain more information on the study.

Results & Discussion

We analyzed our data by first looking at how threatening and stressful subjects found our

stressor (TSST), then analyzing how differences in actual and perceived stress level affected

category learning accuracy.

Result 1

In order to make sure that subjects in our stress condition actually experienced more stress than

those in our control condition we compared cortisol difference across time for our stress and

control groups. We obtained our cortisol difference measure by subtracting our first cortisol

measure (baseline measure) from our third cortisol measure (obtained immediately after

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stressor). According to this measure, a positive number indicates that subjects were more

stressed after the stressor (TSST) than when they began the study. Upon completion of a t-test,

we found that our stress group gained 4.65 Nnmol/l of cortisol from the first to the third sample,

while our control group dropped 2.18 Nnmol/l of cortisol from the first to the third sample. In

other words, those in the stress group experienced an increase in cortisol between the first and

third cortisol sample and those in the control group actually had an average drop in cortisol. This

difference in cortisol change between the stress and control group is significant (p < .01) and

shows that subjects in our stress group did on average experience more stress than subjects in our

control group.

Result 2

Perceived level of stress and cortisol: We found a significant correlation (r = .298; p = .001,

two tailed) between how stressful subjects reported the task to be and cortisol level. This data

shows that the subjects’ conscious perception of their stress level matched their actual

physiological stress level.

Result 3

Evaluation and stress level: We found a significant correlation (r = .348; p = .000, two tailed)

between how evaluated subjects reported feeling and cortisol difference. This indicates that the

evaluation portion of the TSST worked as it was supposed to because subjects that felt highly

evaluated had higher levels of cortisol after the TSST than subjects that felt less evaluated. These

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results are consistent with the social-self preservation theory posited by Dickerson because

feeling scrutinized was related to higher stress levels.

Result 4

We performed a two-way ANOVA test to see if there were any significant differences in

accuracy between the groups. The results are summarized in the following table:

We found a significant main effect (p = .01) between the stress and control conditions, but no

significant main effect (p = .522) between the II and RB groups. This means that there is a

significant difference in accuracy between the stress and control groups but no difference in

accuracy between the RB and II groups. Collectively RB and II control groups did significantly

better than the RB and II stress groups. Stress or Control predicted accuracy but II or RB group

membership did not. There was no significant difference in accuracy under either the RB or II

condition, implying that the tasks were equivalent in difficulty when stress is not involved.

After looking at main effects, we determined if RB and II users differed significantly on

Day1 accuracy depending on if they were in the stress or control condition. Given the past

research and our predictions, this result was surprising. We found that RB users differed

significantly depending upon whether they were in the stress or control condition (t(52) = -2.137;

p = .0373, two tailed or p = 0.0187, one tailed), while II users did not differ depending on if they

were in the stress or control condition (t(50) = -1.542; p = 0.129, two tailed or p = 0.0647, one

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Stress Control MeansRB .78 .835 .811II .805 .835 .820Means .795 .835 Grand mean = .816

tailed). Our subjects in the RB stress group were significantly less accurate compared to those in

the RB control group, while our subjects in the II stress group were only slightly (p > .05) less

accurate than those in the II control group. These results run counter to the findings by Ell et al.

who found that increased stress (by their measures) predicted a significant increase in accuracy

for the II group and a non-significant decrease in accuracy for the RB group. Our findings differ

in that we found a significant effect for the RB and not II, while they found a significant effect

for the II and not RB. Also, they found a significant increase in accuracy under stress for II while

we found a slight decrease in accuracy for II under stress.

Result 5

Next, we looked at the Day 2 accuracy scores using a two-way ANOVA test. There were no

significant main effects between stress and control (p = .262) or II and RB (p = .964). This

means that neither Stress vs. control nor II vs. RB group membership alone predicted accuracy

on Day 2. However, we did find an interaction effect. While the II control condition did worse

than the II stress condition, the RB control condition did much better than the RB stress

condition (opposite trend).

Result 6

Retention: In order to look further into the change in accuracy from Day 1 to Day 2 and see how

subjects retained the learning they did on Day 1 we created a retention measure. We calculated

our retention measure by subtracting the accuracy for the last 100 trials of the category learning

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task on day 1 from the accuracy for the first 100 trials of the category learning task on Day 2.

This way, a positive number indicated that the subjects were more accurate at the beginning trials

of Day 2 than the ending trials of day 1. Therefore, a positive number on the retention measure

would suggest that the subjects retained the learning they did on day 1 and applied this learning

on Day 2.

We did not find a significant main effect between stress and control (p = .932). This

means that the retention of our subjects was not significantly affected by whether they

experienced a stressor. However, we did find a marginally significant main effect between the II

and RB groups (p = .058), suggesting that our subjects differed on retention depending on

whether they were II or RB learners. These results are interesting, because we did not find a

significant difference between II and RB when looking only at Day 2 accuracy. Also, our

retention results are different from what we found when we looked at Day 1 accuracy, where

stress and control were significantly different but II and RB were not significantly different.

Looking more closely at these results, we found that the RB group retained less than the

II group. This means that the RB jump in accuracy from the last block of Day 1 to the first block

of Day 2 was smaller than the jump in accuracy for the II group. This finding could be the result

of a number of things. It is possible that having a night of sleep contributes to the consolidation

of II learning, but does not help consolidate RB learning. Research on sleep and memory has

shown that different types of sleep affect procedural and declarative memory differently.

Declarative memories (associated with RB learning) benefit most from slow wave sleep, while

procedural memories (associated with II learning) benefit particularly from REM sleep (Plihal

and Born 1997; Peigneux et al. 2004; Marshall and Born 2007). Unfortunately, our data on sleep

is not detailed enough to look into if differences in REM or slow wave sleep affected our RB and

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II consolidation but it would be an interesting thing to look into in future studies. It is also

possible that total time between the RB and II task on Day 1 and Day 2 is what is at work here

rather than sleep. Maybe II learning consolidation benefits more from 24 hours between the trials

than RB learning consolidation.

Result 7

Threat/positive challenge and cortisol difference: We found a significant correlation

(r = .231; p = .010, two tailed) between how threatening subjects found the task and change in

cortisol levels. This suggests that having the social-self threatened (TSST involves threatening

the social self) increases stress levels.

We found a marginal correlation (r = -.167; p=.068, two tailed) between viewing the task as a

positive challenge and cortisol difference. This data shows that there is a slight inverse

relationship between viewing the task as a positive challenge and cortisol change (as positive

challenge goes up, cortisol level goes down).

Result 8

Threat/Positive Challenge and accuracy: In order to look at whether rating the task as a threat or

positive challenge had any effect on accuracy we divided the subjects into two groups. Using a

Likert scale, we put subjects that rated the task as highly threatening into one group (threat

group) and subjects that rated the task as less threatening and more of a challenge into another

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group (challenge group). With these groups established, we tested to see if our threat group and

challenge group differed in accuracy in any significant ways. We found that those in the threat

group (rated threat higher than challenge) were significantly less accurate than the challenge

group (rated challenge higher than threat) at the RB task (Threat group mean acc. = .729,

challenge mean acc. = .818). On the other hand, the threat group was slightly more accurate (p

> .05) than the challenge group on the II task. These results reflect the findings from Ell et al.

that high threat ratings increased II performance but decreased RB performance. Interestingly,

Ell et al. found a significant relationship between threat and II but not threat and RB, whereas we

found a significant relationship between threat and RB but not threat and II. In other words, we

found the same trend as Ell et al. but the opposite significance measure. The fact that being in the

stress or control condition did not have a significant effect on II accuracy on Day 1 but feeling

threatened caused an increase in accuracy on Day 1 II accuracy is worth noting. It is possible that

threat alone is what caused an increase in II performance for Ell et al. rather than the stress that

they hypothesized it to cause.

Result 9

Day 1 learning rate and retention: We examined how the learning rate on Day 1 impacted

retention by looking at a linear fit (y = a*x+b; a is slope, b is intercept) as well as a power fit (y =

a*^n; a is intercept, higher n = steeper learning curve). Then, we correlated each of these fits

with our retention measure, which was obtained by subtracting the accuracy of the last 100 trials

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on day 1 from the accuracy of the first 100 trials on day 2. Next, we broke our accuracy data up

into blocks of 50 trials and computed our linear and power fits on the accuracy.

We found that the n in the power curve was marginally significantly correlated with our

retention measure (r = -.174, p = .064) while the a was not significantly correlated (r =-.012, p

= .897). This means that the steepness of the learning curve on Day 1 (faster learning) was

marginally significantly related to Day 2 accuracy, while the intercept was almost completely

unrelated to day 2 accuracy. For the linear trend, a was significantly correlated with our retention

measure (r = -.233, p = .012) while b was not significantly correlated (r = -.107, p = .251). This

data suggests that the slope of learning on Day 1 was significantly related to retention, but the

intercept was not significantly related. Interestingly, all four of the variables we examined (a, b

for linear fit; a, n for power fit) were negatively correlated with our retention measure.

Particularly, the variables that indicated learning across time (a for linear, n for power) suggest

that the steeper the learning curve on Day 1 the worse the retention. This inverse relationship

between learning rate and retention could be because those with a steep learning curve master the

task quickly and as a result their accuracy on the last 100 trials of Day 1 is very high. Then, even

slightly impaired performance at the beginning of Day 2 (while the subject re-acquaint

themselves with the task) would create a negative retention score. On the other hand, if subjects

are slower learners they might still be grappling with the task (faster learners would have already

mastered it) during the last 100 trials of Day 1. Then, when the slower learners come in on Day 2

even if they make a few mistakes in the beginning while re-acquainting themselves it won’t

create as large of a difference as the faster learners. Alternatively, there could be something

about more gradual learning that facilitates retention.

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Result 10

Level of reported depression and accuracy/stress: We found a marginally significant correlation

(r = -.342; p = .075, two tailed) between reported depression and RB control accuracy. There

was no significant correlation between reported depression and II accuracy. The fact that

depression affected RB control accuracy and not RB stress accuracy is interesting. The fight or

flight response that was initiated by our stressor may have potentially cancelled out the effects of

depression that we saw in our RB control condition.

Result 11

Sleep type and cortisol difference/accuracy: On our pre-task form, we asked subjects to report

their sleep type. Then, we came up with a Likert scale (from 1 – 9) in which a 1 meant subjects

were late risers who had trouble getting up early and a 9 meant subjects were early risers who

woke up refreshed. Once our data was collected, we looked to see if there were any significant

correlations between sleep type and accuracy or cortisol difference. We found a significant

correlation (r = .422, p = .029) between sleep type and accuracy for the RB stress condition.

There was no significant correlation between sleep type and RB control condition or sleep type

and II (stress and control). Interestingly, there was no correlation between sleep type and cortisol

difference or sleep type and amount of cortisol upon arrival. These results show that the

relationship between sleep type and accuracy for the RB stress condition was not caused by

differences in stress. Rather, our results suggest that individuals that wake up early and refreshed

do better at learning in a rule based task than those who go to bed late and get up late.

23

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