Modelling the effect of stress on Human Behaviour
May 12 1999
LTSS51 Orlando
Andy BelyavinCHS DERA
Aims of the presentation
Outline the scope of the problem from a modelling perspective
Sketch a structure in which the problem might be solved
Outline implementation in IPME
Generalise the approach to broader class of architectures
Man as system of systems - possible solutions
Aims of the presentation
Outline the scope of the problem from a modelling perspective
Sketch a structure in which the problem might be solved
Outline implementation in IPME
Generalise the approach to broader class of architectures
Man as system of systems - possible solutions
Role of constructive simulation
Entities involved in man-in-the-loop virtual simulation for training In future analysis of military systems it can be anticipated that
there will be more use of man-in-the-loop virtual simulation This will be effective for managing the burden of the analysis of
tactics and outline questions on crewing and systems definition It will not support the analysis of system performance in all
contexts There will be a large role for the constructive simulation of
human behaviour under stress
Constructive modelling of human performance
Based on a structure of what the crew has to do
Task analysis leading to task networks
– IMPRINT
– MicroSAINT
Task frames in SAFs
– ModSAF
Rule bases in command agents coupled to SAFs
Classical approach to stress representation
Define task taxonomy
– Cognitive task
– Perceptual task
– Physical task etc.
Map environmental stress to task types
– ‘Arousal’ affects cognitive performance etc.
Model effect as a crude degradation
– Adjust task time and precision
Long term strategy for stress description
Three things have to be achieved: Define the phenomenon we are trying to represent
– Define the stressors we need to consider
– Define nature of best scientific knowledge
Review current approaches
– How is it done in current tools?
– SAFs, IMPRINT, IPME
Project how these methods should develop
Aims of the presentation
Outline the scope of the problem from a modelling perspective
Sketch a structure in which the problem might be solved
Outline implementation in IPME
Generalise the approach to broader class of architectures
Man as system of systems - possible solutions
Environmental stressors
A 1994 review at DERA identified more than 40 stressors
These include both regular environmental factors and social effects
Suggest that even a concise list of the most important is 10 long
Environmental stressors (2)
Sleep loss fatigue / circadian effects and time on task Physical fatigue Thermal effects (Thermal strain / dehydration / discomfort) Visual environment Fear / Anxiety / Morale Task demand - workload Noise (continuous and impulse) Vibration Hypoxia (Loss of oxygen in high flying fast jets) High G (Fast jets only)
Metrics of “behaviour”
What is the crew / operator going to do? Generally domain of cognitive analysis – possibly open
ended
– How good is Situation Awareness?
– What course of action is selected?
Given what the crew /operator does, how well do they do it? Generally domain of task analysis and task performance
– How fast is the task completed?
– Is the task performed accurately?
Relationship between Environment and Performance
Environment State Change
Operator/ Crew State Change
Operator / Crew Performance Change
Effect of sleep loss / Time of dayon performance
Sleep loss and time of day affect operator state
State variable is “Mental Alertness”
Mental Alertness affects performance
Different effects for different tasks
Current analysis covers “Vigilance” and “Cognitive” tasks
Alertness Model
-15
-10
-5
0
5
10
15
7 9 11 13 15 17 19 21 23 25
0
10
20
30
40
50
60
70
7 9 11 13 15 17 19 21 23 25
Circadian Effects (time of day)
‘S’ Effects (time since sleep)
t timeof day
current time
y t
tod
tod tod
13 4 2 24. cos( ( ) / )
t time ce sleep
y t t
tss
tss tss tss
sin
. exp . / . 865 0 317 0 0612
Resultant Alertness
0
10
20
30
40
50
60
70
80
7 9 11 13 15 17 19 21 23 25
A y ytss tod 13 4.
Alertness effectVigilance Misses
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50 60 70 80 90 100
Alertness
Su
stai
ned
Att
enti
on
Pro
po
rtio
n C
orr
ect
Proportion correct adjusted for constant
Predicted proportion correct
TG5 WP 1997
CHS Whole body thermal model
Solves diffusion equation for linked cylinders Represents blood flow inside the body in moderate detail Handles radiation / evaporation / conduction at surface Handles active controllers:
– Sweating
– Shivering
– Blood flow modification
Handles sweat evaporation through dry clothing Coupled to IPME through socket interface
Thermal strain and performance
Preliminary indications
Dehydration affects error rate on cognitive tasks Dehydration affects physical performance
High temperature speeds performance Discomfort slows performance Dehydration slows performance
Nature of states
Candidate examples:
Anxiety
– Possibly influences whether the Operator / Crew may or may not participate
– Possibly influences nature of Situational Awareness etc.
Motivation
– Possibly influences participation / course of action
Alertness / Arousal
– Influences performance and errors
– Influences decision to act
– In extreme case leads to falling asleep
Task demand
Military operations frequently involve high task demand reflected by the need to do more than one thing at once
Classically represented by a “state” – workload Workload then determines allocation of priorities and
performance of the task and / or choice of action
Two models do not involve state DERA Prediction of Operator Performance (POP) model Canadian Information Processing / Perceptual Control
Theory (IP / PCT) model Both based on interference effects
Relation between Environment and Performance / behaviour
Original proposed simple model: Environment to State to Performance / Behaviour Incomplete
More complex model needed Add interference between tasks and its effects Multiple states have to be considered Initial evidence is that interaction effects can be ignored
Aims of the presentation
Outline the scope of the problem from a modelling perspective
Sketch a structure in which the problem might be solved
Outline implementation in IPME
Generalise the approach to broader class of architectures
Man as system of systems - possible solutions
System information
Background environment information
– Scenario details (Threats)
– Conditions (Temperature, Duty pattern etc.)
Team characteristics
– Fatigue state
– Training etc.
Performance modifiers
– Fatigue degaradations etc.
– Determined by task taxonomy etc.
Task data required
Time distribution
Probability of failure
Consequences of failure
Who is doing the task
Nature of the task according to the taxonomy
Associated task demand (optional)
OperatorTrait
EnvironmentState
OperatorState
TaskExecution
OperatorPerformance
Feedback (Workload)
Performance shaping model
Areas covered and under studyunder IPME project
Effects of circadian / sleep loss cycle (CHS alertness model)
Effects of heat / dehydration / discomfort on task performance (Cognitive and physical)
Effects of visual environment on performance Effects of terrain on movement speed Effect of task demand (workload) on task performance
(POP model) Alternative model of stressor degradation (interference
hypothesis - applied to anxiety)
Aims of the presentation
Outline the scope of the problem from a modelling perspective
Sketch a structure in which the problem might be solved
Outline implementation in IPME
Generalise the approach to broader class of architectures
Man as system of systems - possible solutions
Possible structure
Environment / state Model 1
Environment / state Model 2
Crew / OperatorStates
Task demandInterference model
Cognition / PerceptionModel
Performance / ActionModel
Five classes of model identified Model of cognition / perception
– Situational Awareness
– Perception of environmental information (Sensory models)
Model of course of action / performance
– Decision making (NDM / Rule base / task network)
Model of task interference effects (“Workload”) Model of influence of state on first two models
– Performance degradation
– Choice of action modification
Model of influence of environment on state
Environment to state
Models of relationship between environment and state can be complex
Full CHS Alertness model taking account of shift work / time zone shift involves solution of differential equations
Wide range of thermal models with varying degrees of complexity
Interpolation formulae to full systems of differential equations
Different applications demand different levels of detail and complexity
Argues for a modular solution to this component
Task demand
Range of solutions of varying degrees of complexity Simple compounding models based on task characteristics
(VACP) More complex models handling interference effects (DERA
POP) Yet more complex models handle prioritisation and
modifications to courses of action (IP / PCT) Again the level of complexity dictated by the application
arguing for a modular approach
Effects of state
Less well developed topic Some simple interpolation formulae available for task
performance Some more complex models of impact of state on
perception Few well developed models of effects of state on course of
action Last point important to overall effectiveness Non-participation / suppression a very important effect
Crew as system of systems
Many highly developed models of aspects of human behaviour
Varying levels of complexity and applicability Re-use and long term development argues strongly for a
modular design with a standard interface between the models
HLA architecture can be applied below the level of the system to the crew
Aims of the presentation
Outline the scope of the problem from a modelling perspective
Sketch a structure in which the problem might be solved
Outline implementation in IPME
Generalise the approach to broader class of architectures
Man as system of systems - possible solutions
Strategic solution
Modular replaceable blocks: Perceptual engine – take account of state Cognitive engine – take account of state Possibly use NDM pattern recogniser and ignore state State predictors from environment can be simple or
complex Task demand managers can be simple or complex Re-use of existing models implied
Major issues for future
Definition of modular architecture
Defining set of states which we need to recognise
Defining how state interacts with cognition and perception
Defining relationship between environment and state
Defining relationship between traits and state
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