Performance Workload Stress Coping Strategy

Attentional Control ↑ ↑ For 4 displays ↓ For 4 displays ↓ For 4 displays

Boredom Proneness ↑ ↓ For 4 displays ↑ For 4 displays ↑ For 4 displays ↑ Emotion-focused coping,

↑Avoidant coping,↓ Task-focused coping

Cognitive Failures ↑ ↓ For 4 displays ↑ For 4 displays ↑ For 4 displays

Fluid Intelligence ↑ ↑For cognitive task, 4 displays

↓For cognitive task, 4 displays

↓For cognitive task, 4 displays

Field-dependence ↑ ↓For cognitive task, 4 displays

↑For cognitive task, 4 displays

↑For cognitive task, 4 displays

Extraversion ↑ ↑ For 4 displays

↓For 1 display↓For all conditions

↑Task-focused coping, ↓Emotion-focused coping

Conscientiousness ↑ ↑ For all conditions ↓For all conditions↑Task-focused coping,

↓Emotion-focused coping

Neuroticism ↑↑ For all

conditions↑ For all conditions

↑ Emotion-focused coping, ↑Avoidant coping,

↓Task-focused coping

Performance Research Laboratory

THE PREDICTING PERFORMANCE, SUBJECTIVE STATES, AND COPING STRATEGY IN A VIGILANCE TASK: THE ROLE OF INDIVIDUAL

DIFFERENCESGrace W.L. Teo, James L. Szalma & Tarah Schmidt

University of Central Florida, Orlando, FLThe purpose for the present study was to examine how person and task

characteristics interact to influence the performance, workload, and stress of vigilance. Task type (sensory versus cognitive) and source complexity (1, 2, or 4 displays to be monitored) were the vigilance dimensions examined, and several person characteristics selected based on an energetic-resource theory approach to vigilance. Relationships with cognitive traits were mostly influenced by task type and those of affective traits were moderated by both task and source complexity. Across outcome measures and predictors, the general pattern of results confirmed the argument that separate treatment of either task properties or task characteristics yields at best a limited understanding of the performance, workload, and stress associated with vigilance. Programmatic research should therefore examine trait-task interactions for specific combinations of taxonomic categories.

Abstract

Introduction

It has been well-established that vigilance, or sustained attention, declines with time on watch (See, Howe, Warm, & Dember, 1995). However, a consistent finding in vigilance research is the presence of large within group variability in performance (Berch & Kanter, 1984; Davies & Parasuraman, 1982). This suggests that efforts to evaluate individual differences in vigilance requires examination of the correlates of traits with performance as well as how the person characteristics interact with task properties to influence relevant outcome variables (Szalma, 2008; 2009).

A goal for the present study was to examine how person and task characteristics jointly affect the performance, workload, and stress response associated with vigilance. The traits and task properties were selected based on i) traits related to resource capacity and allocation as well as the regulation of attention, and ii) task properties that should moderate the effects of the traits. As working memory processes have been established as important factors influencing vigilance (Caggiano & Parasuraman, 2004; Helton & Russell, 2011), the independent variables for this study were task type (cognitive versus sensory discriminations) and source complexity (the number of displays to be monitored). The person characteristics examined in the present study included (i) extraversion (Matthews, 1992), (ii) neuroticism (Cox-Fuenzalida, Swickert, & Hittner, 2004), (iii) conscientiousness (Rose, Murphy, Byard, & Nikzad, 2002), (iv) attentional control (Derryberry & Reed, 2002), (v) boredom-proneness (Sawin & Scerbo, 1995), (vi) cognitive failures, (vii) field dependence (Berch & Kanter, 1984), and (viii) fluid intelligence (Davies & Parasuraman, 1982). Based on previous research, the hypotheses for the study are specified in Table 1 below:

Method & MaterialsParticipants:• 140 participants (68 Males, 72 Females),17 to 44 yrs (M=19.15 yrs, SD=12.26

yrs).

Design:Independent variables:a) Information Uncertainty or Source complexity :Participants were randomly

assigned to monitor either 1, 2, or all 4 cells (displays) for critical signals). b) Task type (Cognitive vs. Sensory): Participants were randomly assigned to

look out for either cognitive signals or sensory signals.

Person Characteristicsc) Attentional Control (Derryberry & Reid, 2002).d) Boredom Proneness (Farmer & Sundberg,1986).e) Cognitive Failures (Broadbent, Cooper, Fitzgerald, & Parkes, 1982).f) Fluid Intelligence (ETS, Educational Testing Services measure, the “Letter

Sets Test” (Ekstrom et al., 1976).g) Cognitive Style, i.e. Field Dependence/Independence (ETS, Educational

Testing Services measure, (Ekstrom, French, Harmon, & Derman, 1976).

Dependent variables:h) Vigilance performance measures: Proportions of Hits, Proportion of False

Alarms, Sensitivity and Bias.i) Perceived workload : NASA-Task Load Index (TLX; Hart & Staveland, 1988)j) Stress: Dundee Subjective State Questionnaire (DSSQ) (Helton, 2004;

Matthews et al., 2002) comprising three scales: Task Engagement, Distress, and Worry.

k) Coping Strategy: Situational Coping Inventory for Task Stress (CITS-S) (Matthews& Campbell, 1998) consisting of the Task-focused, Emotion-focused and Avoidant coping subscales.

Task:• Adaptation of the Bakan (1959) task developed by Warm et al. (1984). • Stimulus duration: 2500msec;10 signals per 3-minute block of trials• Two 2x2 grids appeared side-by-side on the screen where each of the 4 cells

was a “display” in which the numbers appeared.• The 2-digit numbers ranged from 01 to 99. Participants were instructed to

respond when a critical signal appeared on the screen.•Critical signals for the cognitive task condition were 2-digit numbers whose digits differed by 0, +1 or -1 (e.g. 01, 54, 99). Critical signals for the sensory task condition were instances where one of digits in the pair was larger than the standard font size. (see Figures 1a & 1b)

Results & Discussion

The effects of traits and trait by independent variable interactions were evaluated via hierarchical regression analyses.

The current study was designed to examine the relationships among person and task characteristics to the performance, workload, and stress of vigilance. Results indicated that individual differences in performance were moderated by task properties (e.g. Fig 2a & 2b), and that these relationships also depended on the measure of performance. Relationships with cognitive traits were mostly influenced by task type and those of affective traits were moderated by both task and source complexity. This study has established that 1) the approach can increase our understanding of the factors (person and task) that influence performance and stress of sustained attention, and 2) an experimental paradigm for a more complete program of research on tasks and person characteristics in relation to vigilance may be a fruitful avenue for future research.

ReferencesBakan, P. (1959). Extraversion-introversion and improvement in an auditory vigilance task. British J ournal of Psychology, 50, 325-332.Berch, D.B., & Kanter, D.R. (1984). Individual differences. In J.S. Warm (Ed.), Sustained attention in human performance (pp. 143-178). Chichester: Wiley.Broadbent, D.E., Cooper, P.F., Fitzgerald, P., & Parkes, K.R. (1982). The cognitive failures questionnaire (CFQ) and its correlates. British Journal of Clinical Psychology, 21, 1-16.Cox-Fuenzalida, L.E., Swickert, R., & Hittner, J.B. (2004). Effects of neuroticism and workload history on performance. Personality andIndividual Differences, 36, 447-456. Davies, D. R., & Parasuraman, R. (1982). London: Academic Press. Dember, W. N., Warm, J.S., Bowers, J.C., & Lanzetta, T. (1984). Intrinsic motivation and the vigilance decrement. In A. Mital (Ed.), Trends in Ergonomics/Human Factors I (pp. 21-26). North Holland: Elsevier.Derryberry, D., & Reed, M.A. (2002). Anxiety-related attentional biases and their regulation by attentional control Journal of AbnormalPsy-chology, 111(2), 225-236.Ekstrom, R.B., French, J.W., Harmon, H.H., & Derman, D. (1976). ETS kit of factor-referenced cognitive test, Educational Testing Service, Princeton, NJ.Farmer, R. & Sundberg, N.D. (1986). Boredom proneness: The development and correlates of a new scale. Journal of Personality Assessment, 50, 4-17.Finomore, V., Matthews, G., Shaw, T., & Warm, J. (2009). Predicting vigilance: A fresh look at an old problem. Ergonomics, 52, 791-808.Grubb, P. L., Warm, J.S., Dember, W.N., & Berch, D.B. (1995). Effects of multiple signal discrimination on vigilance performance andper-ceived workload. Proceedings of the Human Factors and Ergo-nomics Society, 39, 1360-1364.Hart, S.G. & Staveland, L.E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In. P.A. Hancock & M. Najmedin (Eds.), Human mental workload (pp. 139- 183). Oxford, England: North-Holland.Matthews, G. (1992). Extraversion. In A. P. Smith. and D. M. Jones (Ed.), Hand-book of human performance, vol. 3: State and Trait (pp. 95-126). London: Academic Press.Matthews, G., & Campbell, S.E. (1998). Task-induced stress and individual differences in coping. Proceedings of the Human Factors and Er gonomics Society, 42, 821-825.Matthews, G., Campbell, S.E., Falconer, S., Joyner, L.A., Huggins, J., Gilliland, K.,Grier, R., & Warm, J.S. (2002). Fundamental dimensions of subjective state in performance settings: Task engagement, dis-tress, and worry. Emotion, 2, 315-340.Milosevic, S. (1974). Effect of time and space uncertainty on a vigilance task. Perception & Psychophysics, 15, 331-334.Parasuraman, R., & Davies, D.R. (1977). A taxonomic analysis of vigilance performance. In: R.R. Mackie (Ed), Vigilance: Theory, operational performance, and physiological correlates (pp. 559-574). NewYork: Plenum Press.Reinerman-Jones, L. E., Matthews, G., Langheim, L.K., & Warm, J.S. (2010). Selection for vigilance assignments: A review and proposed new direction. Theoretical Issues in Ergonomics Science, 1-23.Rose, C.L., Murphy, L.B., Byard, L., & Nikzad, K. (2002). The role of the big five personality factors in vigilance performance and workload. European Journal of Personality, 16, 185-200.Sawin, D.A., & Scerbo, M.W. (1995). Effects of instruction type and boredom proneness in vigilance: Implications for boredom and workload. Human Factors, 37, 752-765.See, J.E., Howe, S.R., Warm, J.S., & Dember, W.N. (1995). Meta-analysis of the sensitivity decrement in vigilance. Psychological Bulletin, 117, 230-249.Shaw, T.H., Matthews, G., Finomore, V., & Warm, J.S. (2009). Predicting vigilance performance and stress with individual differencesmeasures. Proceedings of the Human Factors and Ergonomics Society 53rd Annual Meeting, 844-848.Teo, G. & Szalma, J.L. (in press). The effects of task type and source complexity on performance, subjective states and coping styles. Paper proposal submitted for the Human Factors and Ergonomics Society 55.Warm, J. S., Howe, S.R., Fishbein, H.D., Dember, W.N., & Sprague, R.L. (1984). Cognitive Demand and the vigilance decrement. In A. Mital (Ed.), Trends in Ergonomics/Human Factors I (pp. 15-20). North Holland: ElsevierFig 1a: Example of a Cognitive signal Fig 1b: Example of a Sensory signal

Table 1: Summary of hypotheses

Trait Dependent Measure F R2 Trait by Task Interaction?

Direction of Effect

Performance and WorkloadFD False Alarms F(1,64)=6.11* 0.09 Y; Cognitive

Task OnlyFD ↑, False Alarms ↑

C Response Bias F(1,131)=4.33* 0.06 Y; Sensory Task Only

C ↑, Bias ↓

FD Workload F(1,131)=3.15* 0.16 (ΔR2=0.04) Y; Sensory Task Only

FD ↑, WL ↑

StressAttC Distress F(1,67)=4.39* 0.06 Y; Sensory

Task OnlyAttC ↑,Distress ↓

AttC Worry F(1,133)=4.92* 0.05 N AttC ↑, Worry ↓CF Worry F(1,134)=5.38* 0.06 N CF ↑, Worry ↑

gF Worry F(1,134)=5.57* 0.05 N gF ↑, Worry ↑

Coping StrategyAttC Task-Focused Coping F(1,133)=10.47** 0.08 N AttC ↑, TC ↑AttC Emotion-Focused Coping F(1,133)=8.24** 0.10 (ΔR2=0.06) N AC ↑, Distress ↓BP Task-Focused Coping F(1,133)=6.05* 0.05 (ΔR2=0.04) N BP ↑, TC ↓BP Emotion-Focused Coping F(1,133)=14.70*** 0.14 (ΔR2=0.09) N BP ↑, EC ↑BP Avoidant Coping F(1,133)=12.55** 0.13 (ΔR2=0.08) N BP ↑, AC ↑CF Task-Focused Coping F(1,133)=6.05* 0.05 (ΔR2=0.04) N CF ↑, TC ↓CF Emotion-Focused Coping F(1,133)=15.44*** 0.14 (ΔR2=0.01) N CF ↑, EC ↑CF Avoidant Coping F(1,133)=12.71** 0.13(ΔR2=0.08) N CF ↑, AC ↑C Task-Focused Coping F(1,133)=6.66* 0.05 N C ↑, TC ↑C Emotion-Focused Coping F(1,133)=13.35*** 0.08 N C ↑, EC ↓C Avoidant Coping F(1,133)=7.58** 0.09(ΔR2=0.05) N C ↑, AC ↓N Emotion-Focused Coping F(1,133)=20.18*** 0.17(ΔR2=0.13) N N ↑, EC ↑N Avoidant Coping F(1,133)=20.18** 0.17(ΔR2=0.13) N N ↑, AC ↑

Note. * p<.05; **p<.01; *** p<.001; FD= Field Dependence; C=Conscientiousness; AttC=Attentional Control; CF=Cognitive Failure; gF=Fluid Intelligence; BP=Boredom Proneness; N=Neuroticism; TC=Task-focused coping; EC=Emotion-focused coping; AC=Avoidant coping.

20 30 40 50 60 70 80 90 1000

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f(x) = 0.00475335934449449 x + 0.380000000000001R² = 0.545381812607073

f(x) = − 0.00218606086294037 x + 0.950000000000001R² = 0.227157470962993

Extraversion

Pro

po

rtio

n C

orr

ect

20 30 40 50 60 70 80 90 100-3

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f(x) = 0.03040738220195 x − 2.8

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Fig 2a: Extraversion X Display Interaction Fig 2b: Conscientiousness X Display Interaction

KNOWLEDGE OF RESULTS AND DIAGNOSTIC POWER: IMPLICATIONS FOR VIGILANCE TRAINING TO SUPPORT IMPROVISED EXPLOSIVE DEVICE

DETECTIONJ. L. Szalma1, G. W. L. Teo1, P. A. Hancock1, J. S. Murphy2

1University of Central Florida, Orlando, FL2Army Research Institute, Orlando, FL

Improvised explosive devises (IEDs) represent the greatest threat to personnel deployed to combat zones. Improvements in the capacity to detect and neutralize these threats are therefore a crucial concern. Although technology can provide better protection against explosions and, perhaps, improve detection, IED detection will for the foreseeable future be dependent on the capacity of mounted and dismounted soldiers to sustain their attention over long periods of time. This capacity, vigilance, has been studied extensively in both laboratory and field settings over the past sixty years. In this project the approach for training for vigilance, knowledge of results, is reviewed and the implications for designing IED detection training are identified.

Abstract

Introduction

ReviewTask characteristics•Becker, Warm, Dember, & Howe, (1994) tested the specificity of transfer of KR-training using simultaneous (SIM) and successive (SUCC) versions of a line discrimination task.

•Obtained evidence for the superiority of specific transfer over general transfer (see Figure 2).•Szalma et al. (1999) tested a SIM and a SUCC version of a dot-distance discrimination task, and a vernier acuity task served as criterion task for transfer phase.

•Both SIM tasks showed stronger transfer effects compared to SUCC (Figure 4).• Training specificity occurs within task categories, not type of discrimination.

Forms of KR•The form of KR provided influences the effectiveness of feedback, but results are mixed. •Hit and False Alarm KR improve performance; Miss KR does not (Dittmar, Warm, & Dember, 1985).

Composite and Hit KR improve performance, but Miss and False Alarm KR did not differ significantly from No KR controls (Szalma et al., 2006; Figure 2).

ReferencesBecker, A.B., Warm, J.S., & Dember, W.N. (1994). Specific and nonspecific transfer effects in training for vigilance. In M. Mouloua & R. Parasuraman (Eds.), Human performance in automated systems: Current trends (pp. 294-299). Hillsdale, NJ: Erlbaum. Broadbent, D.E. (1971). Decision and stress. London: Academic Press. Davies, D.R., & Parasuraman, R. (1982). The psychology of vigilance. London: Academic Press. Dittmar, M.L., Warm, J.S., & Dember, W.N. (1985). Effects of knowledge of results on performance in successive and simultaneous vigilance tasks: A signal detectionanalysis. In: R.E. Eberts and C.G. Eberts (Eds.), Trends in ergonomics/human factors II (pp. 195-202). Amsterdam: Elsevier.Kluger, A.N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119, 254-284.Parasuraman, R., Hancock, P.A., & Olofinboba, O. (1997). Alarm effectiveness in driver-centered collision warningsystems. Ergonomics, 39, 390-399. See, J.E., Howe, S.R., Warm, J.S., & Dember, W.N. (1995). A meta-analysis of the sensitivity decrement in vigilance.Psychological Bulletin, 117, 230-249.See, J.E., Warm, J.S., Dember, W.N., & Howe, S.R. (1997). Vigilance and signal detection theory: An empirical evaluation of five measures of response bias. Human Factors, 39, 14-29.Snodgrass, J.G., & Corwin, J. (1988). Pragmatics of measuring recognition memory: Applications of dementia and amnesia. Journal of Experimental Psychology: General, 117, 34-50.Szalma, J.L., Hancock, P.A., Dember, W.N., & Warm, J.S. (2006a). Training for vigilance: The effect of KR format and dispositional optimism and pessimism onperformance and stress. British Journal of Psychology, 97, 115-135.Szalma, J.L., Hancock, P.A., Warm, J.S., Dember, W.N., & Parsons, K.S. (2006b). Training for vigilance: Using predictive power to evaluate feedback effectiveness, Human Factors, 48, 682-692.Szalma, J.L, Miller, L.C., Hitchcock, E.M., Warm, J.S., & Dember, W.N. (1999). Intraclass and interclass transfer of training for vigilance. In: M.W. Scerbo and M. Mouloua (Eds.), Automation technology and human performance: Current research and trends (pp. 183-187). Mahwah, NJ: Erlbaum.Warm, J.S., Parasuraman, R., & Matthews, G. (2008). Vigilance requires hard mental work and is stressful. Human Factors, 50, 433-441.

Alternative Performance Measures

•Problems with SDT measures: When there are zero false alarm rates the parametric SDT measures must be ‘adjusted’ (Davies & Parasuraman, 1982; Snodgrass & Corwin, 1988).

The metrics may not provide a clear picture of performance for training purposes.

•Alternative performance measure :decision diagnosticity (Table 1)Positive predictive power (PPP) and negative predictive power (NPP) provide information regarding the frequency with which an individual’s ‘yes’ (PPP) or ‘no’ (NPP) responses are accurate. PPP and NPP are post-hoc Bayesian probabilities (see Table 1).

PPP/NPP can provide direct assessment of the quality of diagnostic decision-making.

A Bayesian approach can be useful for diagnosing errors in highly sensitive detection systems (Parasuraman, Hancock, & Olofinboba, 1997).

Metric Conditional Probability Computational Formula

Correct detection

p(correct detection) = p(‘yes’|signal present) P(C)= H/S

Correct rejection

p(correct rejection)=p(‘no’|signal not present)

P(CR)=CR/N

Positive Predictive Power

PPP= p(signal present|’yes’)

PPP= H/(H+FA)

Negative Predictive Power

NPP= p(signal not present|’no’)

NPP = CR/(CR+M)

New contribution: Implications for IED detection

•Two crucial components of successful vigilance: 1. Skilled discrimination between signal and non-signal events2. Engaging in effective compensatory effort to maintain attention to

relevant information in the display over the time on watch.• Recommendations for Training Vigilance provide trial-by-trial composite KR as well as summary feedback at the end of each block of trials regarding the diagnosticity (PPP) of the observer.

Implement over multiple sessions or, if feasible, in operational settings e.g., a portable video-game format administered routinely (see Figure 4, 5, 6)

Table 1: Measures of Detection PerformanceFeedback Intervention Theory (FIT)•FIT (Kluger and DeNisi, 1996; 1998): Feedback serves to alter the locus of attention to three broad levels (See Figure 3).

•Miss KR may induce more self-related cognitions because the memory trace for the missed signal is not as strong as when they make an overt response to a stimulus after the other forms o f KR

•Hit and False alarm KR may facilitate detection by providing information regarding the stimulus responded to and the categorization of that stimulus as a signal or non-signal (for a further discussion, see Szalma et al., 2006).

Figure 2: Performance accuracy as a function of period and KR format (Szalma et al., 2006)

Feedback (i.e. Knowledge of Results, KR)

Loci of Attention affected by KR1) Meta-task processes: directs attention to self (feedback less

effective)2) Task-motivation process: directs attention to the focal task

(feedback effective)3) Task-learning process: directs attention to the details of the

focal task (feedback effective)

Figure 3. Effect of Feedback on Attention (Feedback Intervention Theory)

•IED detection depends on the capacity of mounted and dismounted soldiers to sustain their attention over long periods of time (vigilance).

•Vigilance declines over time and imposes considerable workload and stress (Warm et al. 2008) increasing vulnerability to performance impairment.

•It is essential that methodologies for training vigilance be developed and optimized for maximum impact on soldier performance.

Figure 1: KR Training Effectiveness in Specific and Non-Specific transfer (Becker et al., 1994)

Figure 4. Target circled

in red (motorcycle

battery)

Figure 5. Response in

the “No Feedback” condition

Figure 6. Summary feedback condition

Performance Research Laboratory