1. 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

2. 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

3. General Issues 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

4. Perception and Attention
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 criterion parameter (β)

5. Perception and Attention

6. Perception and Attention
Visual Search Models
Feature integration theory (Treisman and Gelade, 1980)  Salience map (Itti and Koch, 2000)

7. 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)

8. 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

9. Action & Motor Performance
Hick-Hyman Law
Information entropy H = log2(n+1)
RT = a + bH
𝐻= 𝑖=1 𝑛 𝑝 𝑖 log⁡( 1 𝑝 𝑖 +1)

10. Action & Motor Performance
Fitts’s Law
MT = a + b*ID
ID = log2(2A/W) – Fitts (1954)

11. 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

12. 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)

13. Action & Motor Performance

14. 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)

15. Action & Motor Performance
Routine Cognitive Skill and GOMS
KLM-GOMS
CPM-GOMS
NGOMSL

16. 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)

17. 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

18. 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

19. Integrated Models Cognitive Architecture Several modules in ACT-R 두정엽
전두엽
후두엽
측두엽 소뇌

20. 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