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INDUSTRIAL ENGINEERING
COGNITIVE ERGONOMICS LAB
○ 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 mathemat-ically’
○ Perception and attention, action or motor control, and cognition
Human Performance Modeling
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○ 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
General Issues
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○ Simplicity and understandability○ Free parameters – how to set and interpret○ Validation – correlation, mean deviation○ Gains
○ Specificity vs. qualitative and vague○ Modeler independent○ Quantitative predictions○ Explanation for observed differences
General Issues
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Signal Detention Theory (SDT)○ 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 crite-
rion parameter (β)
Perception and Attention
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COGNITIVE ERGONOMICS LAB
Perception and Attention
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Visual Search Models○ Feature integration theory (Treisman and Gelade, 1980)
Salience map (Itti and Koch, 2000)
Perception and Attention
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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)
Perception and Attention
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Workload Modeling○ Neither commonly accepted definition nor how to measure it○ Psychological refractory period (PRP) paradigm – response se-
lection bottleneck model (Pashler, 1994): perception, response selection & action
○ Multiple resource theory (Wickens, 2002 and 2008) – the stages, the codes and modalities
Perception and Attention
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Hick-Hyman Law○ Information entropy H = log2(n+1)
○ RT = a + bH
Action & Motor Performance
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Fitts’s Law○ MT = a + b*ID
○ ID = log2(2A/W) – Fitts (1954)
Action & Motor Performance
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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
Action & Motor Performance
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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)
Action & Motor Performance
INDUSTRIAL ENGINEERING
COGNITIVE ERGONOMICS LAB
Action & Motor Performance
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Historical Perspective○ GPS (Newell & Simon, 1963)
○ computational models could effectively capture key elements of human cognitive behavior
○ “modal” model of memory (Atkins & Shiffrin, 1968)
Memory & Cognition
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Routine Cognitive Skill and GOMS○ KLM-GOMS○ CPM-GOMS○ NGOMSL
Action & Motor Performance
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Models of Judgment and Decision Making○ Optimal behavior – A baseline of comparison for human perfor-
mance SEUT, Prospective theory, EBA○ Lens model (policy capturing)
Action & Motor Performance
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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 com-
pletion time and a probability of completion○ SAINT, Micro Saint Sharp, IMPRINT
Integrated Models
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Cognitive Architecture○ an embodiment of “a scientific hypothesis about those aspects of
human cognition that are relatively constant over time and rela-tively independent of task”
○ The mid-1990’s when including mechanisms for perception and action as well
○ EPIC (1995 & 1997), ACT-R (1998) & QN-MHP
Integrated Models
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Cognitive Architecture○ Several modules in ACT-R
Integrated Models
전두엽
측두엽
두정엽
후두엽
소뇌
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Cognitive Architecture○ Drawbacks
○ Knowledge in ACT-R code○ S/W integration problem with a rich simulation envi-
ronment○ Setting free parameters○ Exposition of ACT-R not always straightforward
Integrated Models
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