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

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

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

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)

Perception and Attention

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

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)

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

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)

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

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

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)

Action & Motor Performance

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)

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

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)

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

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

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

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