Human Performance Modeling
○ Model – ‘a simplified representation of a system
or phenomenon, as in the sciences or
economics, with any hypotheses required to
describe the system or explain the phenomenon,
often mathematically’
○ Perception and attention, action or motor control,
and cognition
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General Issues
○ Misconception -- construction of “intelligent
system”  AI
○ predictions of human performance on human
factors problems not necessarily from basic
psychological processes
○ All models are abstractions and by necessity
omit certain details
○ Accuracy and generality
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General Issues
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Simplicity and understandability
Free parameters – how to set and interpret
Validation – correlation, mean deviation
Gains
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Specificity vs. qualitative and vague
Modeler independent
Quantitative predictions
Explanation for observed differences
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Perception and Attention
Signal Detention Theory (SDT)
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Make a binary judgment btn signal and noise
Hit, False Alarm, Correct Rejection, Miss
p(H) + p(FA) =1; p(CR) + p(M) =1
Type I error (FA) & Type II error (Miss)
Decompose performance into detection
efficiency (d’) and criterion parameter (β)
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Perception and Attention
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Perception and Attention
Visual Search Models
○ Feature integration theory (Treisman and
Gelade, 1980)  Salience map (Itti and Koch,
2000)
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Perception and Attention
Visual Sampling Models
○ Senders (1964, 1983) – a signal at W Hz can be
reconstructed by sampling every 1/W s
○ Wickens (2008) – Salience, Effort, Expectancy,
Value (SEEV) Model
○ p(A) = sS – efEF + (exEX)(vV)
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Perception and Attention
Workload Modeling
○ Neither commonly accepted definition nor how
to measure it
○ Psychological refractory period (PRP) paradigm
– response selection bottleneck model (Pashler,
1994): perception, response selection & action
○ Multiple resource theory (Wickens, 2002 and
2008) – the stages, the codes and modalities
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Action & Motor Performance
Hick-Hyman Law
○ Information entropy H = log2(n+1)
○ RT = a + bH
○ 𝐻=
1
𝑛
𝑖=1 𝑝𝑖 log(𝑝
𝑖
+ 1)
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Action & Motor Performance
Fitts’s Law
○ MT = a + b*ID
○ ID = log2(2A/W) – Fitts (1954)
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Action & Motor Performance
Manual Control Theory
○ Continuous tracking task
○ Between the desired and their actual behavior
○ Transfer function
○ As system frequency increases, the gain
decreases and the lag increases
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Action & Motor Performance
Manual Control Theory
○ Crossover model (McRuer & Jex, 1967)
○ Two crossover points: the frequency at which the
gain is zero and the frequency at which the lag
reaches 180°
○ Optimal control model (Pew & Baron, 1978)
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Action & Motor Performance
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Memory & Cognition
Historical Perspective
○ GPS (Newell & Simon, 1963)
○ computational models could effectively capture key
elements of human cognitive behavior
○ “modal” model of memory (Atkins & Shiffrin,
1968)
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Action & Motor Performance
Routine Cognitive Skill and GOMS
○ KLM-GOMS
○ CPM-GOMS
○ NGOMSL
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Action & Motor Performance
Models of Judgment and Decision Making
○ Optimal behavior – A baseline of comparison for
human performance  SEUT, Prospective
theory, EBA
○ Lens model (policy capturing)
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Integrated Models
Task Network Modeling
○ Network model – a modeling procedure
involving Monte Carlo simulation
○ Decomposition of the Task into discrete subtasks;
PERT chart
○ Nodes represented by a statistically specified
completion time and a probability of completion
○ SAINT, Micro Saint Sharp, IMPRINT
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Integrated Models
Cognitive Architecture
○ an embodiment of “a scientific hypothesis about
those aspects of human cognition that are
relatively constant over time and relatively
independent of task”
○ The mid-1990’s when including mechanisms for
perception and action as well
○ EPIC (1995 & 1997), ACT-R (1998) & QN-MHP
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Integrated Models
Cognitive Architecture
○ Several modules in ACT-R
두정엽
전두엽
후두엽
측두엽
소뇌
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Integrated Models
Cognitive Architecture
○ Drawbacks
○ Knowledge in ACT-R code
○ S/W integration problem with a rich simulation
environment
○ Setting free parameters
○ Exposition of ACT-R not always straightforward
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