20-24 September 2004Szalma, Oron-Gilad, & Hancock – HFES Annual Meeting1 Examination of Attentional Mechanisms Underlying Stress and Performance J.L. Szalma,

20-24 September 2004Szalma, Oron-Gilad, & Hancock – HFES Annual Meeting1

Examination of Attentional Mechanisms Underlying Stress and Performance

J.L. Szalma, T. Oron-Gilad, & P.A. Hancock

MURI-OPUS Research Laboratory

University of Central Florida


Approach/MethodsHancock & Warm (1989) Model Adaptation under stress

Task dimensions impacting stress stateTime pressureNoise

Task demand (e.g., spatial uncertainty)


Approach/Methods

Individual differences in performance, workload, stress, and coping

Fuzzy Signal Detection Theory


Adaptability and Stress

Hancock & Warm (1989)

•Adaptability declines at the extremes of stress

•Tasks are proximal sources of stress

•Dimensions of task structure:

Information structure (space)

Information rate (time)


Tasks as Stressors


Significant Findings

The spatial dimension may dominate attention over the temporal

Perceptual independence?

Spatial uncertainty:Impaired performanceIncreased global workloadReduced stress symptoms

Intermittent white noise increased leniency in responding



Increased extraversion associated with lower Global Workload and lower Temporal Demand

Pessimism predicted decreased task engagement for tasks with spatial uncertainty

Pessimism predicted increased Distress regardless of task type.



Task characteristics may influence the Pessimism-coping style relation, depending on the type of coping

Pessimism predicted increased emotion-focused coping, but only in tasks with spatial uncertainty

Pessimism predicted avoidant coping regardless of task type



Traits such as Extraversion and Pessimism impact workload/stress responses, but these may be task dependent (spatial uncertainty)

Personality effects depend on the dimension of stress measured as well as task type



Fuzzy Signal Detection methodology is sensitive to a response bias manipulation in vigilance

Fuzzy ROC analysis indicates more sensitive performance by observers relative to performance evaluated using a ‘crisp’ analysis


Challenges

What are the challenges that you have faced in studying this issue or what are the lessons learned?

Identifying the relations among task dimensions is very difficult

Stress Effects depend on range of stressor (noise level, degree of time pressure, task difficulty)


Challenges

Failures to replicate (noise, personality by task interactions)

Deriving proper mapping functions for FSDT analysis

ROC analysis in ‘Fuzzy Space’

Simulation Facilities


Potential Mechanism for Extraversion Effects

Less Temporal DemandTime ‘slows down’

Event registration

External Locus of Attention

Attentional NarrowingStress/Task Demand

Extraversion


Post-Task Engagement as a Function of Pessimism

Po

st-T

ask

En

gag

emen

tP

ost

-Tas

k E

ng

agem

ent

Sta

nd

ard

ized

Sco

res

Sta

nd

ard

ized

Sco

res

PessimismPessimism

TE = -0.07P + 2.8

R2 = 0.26

-4

-3

-2

-1

0

1

2

3

4

18 24 30 36 42 48 54 60 66 72

Only for tasks with spatial uncertainty


Results: Experiment 2

M=.50 FAT M=.33

FAS M=.45 FA M=.26


Performance Operating Characteristics

Vary the allocation of attention between two tasks

Task A Task B

100% 0

90% 10%

50% 50%

10% 90%

0 100%



0

1

2

3

4

5

6

0 1 2 3 4 5 6

Spatial Task

Tem

po

ral T

ask

Perfect Tradeoff

Perfect Timesharing


Average Across All Six Participants

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.5 1 1.5 2 2.5 3 3.5 4

Spatial (d')

easy

hard

50 50

90 temp

90 spat

100 temp

100 spat

50 50

90 spat

90 temp

100 temp

100 spat


Performance Operating CharacteristicsParticipant 1

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.5 1 1.5 2 2.5 3 3.5 4

Spatial (d')

Tem

po

ral (

d')

easy

hard

for both tasks, the baselines were not significantly different

p<.01 spatial

p<.01 spatialp<.01 temporal

p<.01 spatial

p<.01, spatial

Participant 4

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.5 1 1.5 2 2.5 3 3.5 4

Spatial (d')

Tem

po

ral (

d')

easy

hard

p<.01 temporal

baselines differentp<.01

p<.01, spatial

baselines differentp<.01

p<.01 spatial

p<.01 spatial

p=.1 temporal

p<.01 temporal

Participant 5

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.5 1 1.5 2 2.5 3 3.5 4

Spatial (d')

Tem

po

ral (

d')

easy

hard

p<.01, temoral

baselines not statisticallydifferent

baselines different, p<.05

p=.07, spatial

p<.01, spatialp<.01, temporal

p<.01, temporal

p<.01, temporal

Participant 6

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.5 1 1.5 2 2.5 3 3.5 4

spatial d'

tem

po

ral d

'

easy

hard

p<.05 spatial

p<.05 spatial

no sig. differences along temporal for adjacent points

no sig diff for adjacent points for eithertask in difficult condition



Participant 2

0

1

2

3

4

5

6

0 1 2 3 4 5 6

Spatial (d')

Tem

po

ral (

d')

easy

hard

for both tasks, baselines are different, p<.01

p<.01, temporal

p=.09 temporal

Participant 3

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

-0.5 0 0.5 1 1.5 2 2.5 3 3.5 4

Spatial (d')

Tem

po

ral (

d')

easy

hard

p<.01, spatial

p<.01 temporal

for difficult task, baselines marginallydifferent, p=.07; baslines not significantly different for easy task

Attention Allocation Failures?


•How Does Stress How Does Stress

Impact d’? and Impact d’? and ββ??

•d’ is reducedd’ is reduced

•ββ increases increases

(sometimes)(sometimes)

Signal Detection under Stress

Stress Effects on d’

Stress Effects on βp(H)

p(FA)


Fuzzy Stimulus and Response: Duration Discrimination

1 2 3 4 5 6 7

20 msec 200 220 240 260 280 300 320

80 msec 200 280 360 440 520 600 680


Comparison of Fuzzy and Crisp ROC Curves

Participant 3

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

zFA

zH

Easy

Hard

Linear (Easy)

Linear (Hard)

Participant 3 fuzzy z

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

zFA

zH

Easy

Hard

Linear (Easy)

Linear (Hard)

Crisp Fuzzy


Response Bias as a function of periods of watch: Transition from Low


Application of FSDT to Tank Identification


ACKNOWLEDGEMENT

This work was supported by the Department of Defense Multidisciplinary University Research Initiative (MURI) program administered by the Army Research Office under Grant DAAD19-01-1-0621. P.A. Hancock, Principal Investigator. The views expressed in this work are those of the authors and do not necessarily reflect official Army policy. The authors wish to thank Dr. Sherry Tove, Dr. Elmar Schmeisser, and Dr. Mike Drillings for providing administrative and technical direction for the Grant.

Documents

20-24 September 2004Szalma, Oron-Gilad, & Hancock – HFES Annual Meeting1 Examination of Attentional Mechanisms Underlying Stress and Performance J.L. Szalma,