ACT-R models of training Cleotilde Gonzalez Brad Best, Varun Dutt, Octavio Juarez Dynamic Decision Making Laboratory (DDMLab) www.cmu.edu/ddmlab Carnegie Mellon University

Agenda A cognitive account of fatigue in a data entry task Summary of theoretical insights (paper in preparation) The Training difficulty principle in the RADAR task The RADAR task: A visual, auditory, memory and decision-making components ACT-R models of training involving unrelated difficulty ACT-R models of training under conditions of related difficulty Summary of this year’s accomplishments and next steps DDMLab - September 7, 2007

The 2x2 levels of ACT-R http://act. psy. cmu The 2x2 levels of ACT-R http://act.psy.cmu.edu (Anderson & Lebiere, 1998) Likelihood of retrieval Activation Base level learning Latency Conflict Resolution Utility Probability learning

Speed Accuracy trade off with extended practice in data entry From Healy et al., 2004 DDMLab - September 7, 2007

ACT-R 5.0 model of fatigue in Data Entry Task Speedup: Production compilation From Visual  Retrieval (key loc)  Motor To Visual  Motor faster access to key location Decrease in Accuracy: A gradual decrease in source activation (W) Gradual decrease in goal value (G)

An account of fatigue in ACT-R Cognition* Affect Reduce accuracy and response time in extended, repetitive tasks Cognitive factors: Maintenance of goals, Prioritization of goals, Attention to novel versus familiar information Emotional factors: Willingness to engage in an effortful task (Motivation) If, our theory is correct, best fits to human data would occur only when BOTH, W and G decay with practice. DDMLab - September 7, 2007

Data collected under this project include: One Model of the Data Entry task fits data from 3 experiments: Experiment 1: The speed-accuracy tradeoff (Healy, Kole, Buck-Gengler and Bourne, 2004) Experiment 2: Another proof of speed-accuracy tradeoff (Healy, Kole, Buck-Gengler and Bourne, 2004) Experiment 3: Cognitive and motoric stressors (Kole, Healy and Bourne, in press) The same model results in predictions that support the Cognitive-Affective interaction account of fatigue DDMLab - September 7, 2007

The W*G Predictions Experiment 1 DDMLab - September 7, 2007

The W*G Predictions Experiment 2 DDMLab - September 7, 2007

Summary of model predictions DDMLab - September 7, 2007

In Conclusion: Extended practice in repetitive tasks results in a speed-accuracy tradeoff According to our ACT-R models reduced reaction time is due to production compilation while increased errors are due to a fatigue effect: W*G Best fit to human data is found when both, W and G decay over time Neither of the W or G parameters alone produce the fatigue effect, supporting the Cognition*Affect hypothesis DDMLab - September 7, 2007

Agenda A cognitive account of fatigue in a data entry task Summary of theoretical insights (paper in preparation) The Training difficulty principle in the RADAR task The RADAR task: A visual, auditory, memory and decision-making tool ACT-R models of training involving unrelated difficulty ACT-R models of training under conditions of related difficulty Summary of this year’s accomplishments and next steps DDMLab - September 7, 2007

The RADAR task (from Gonzalez & Thomas, 2007) DDMLab - September 7, 2007

A video of the RADAR task DDMLab - September 7, 2007

ACT-R 6.0 models of the training difficulty principle Strategy-based (production-based) models of RADAR involve: Cognitive Modeling of Visual Search Cognitive Modeling of “parallel” auditory processing Cognitive modeling of Decision Making The ACT-R models reproduce data from 2 experiments DDMLab - September 7, 2007

DDMLab - September 7, 2007

ACT-R 6 update Reinforcement among strategies: Utility and reward If Ui(n-1) is the utility of a production i, after its n-1st application and Ri(n) is the reward the production receives for its nth application, then its utility Ui(n) after its nth application will be DDMLab - September 7, 2007

Visual Search Strategies: the good, the bad, and the ugly Main Learning in the model occurs through the selection among visual search strategies, determined by the utility of the productions Four strategies (4 productions) were identified: The good: Exhaustive comparison (high hits, low fa) The bad: Random comparison (low hit, high fa) The ugly: Partial found (high hits, high fa) The ugly: Partial comparison (low hits, low fa) DDMLab - September 7, 2007

Training difficulty principle: Experiment 1 Main results from Healy’s experiment: CM/VM effects and load effects Training difficulty did not help in this task: tone counting during training hurts test performance DDMLab - September 7, 2007

Experiment 1: Effects of mapping & load at test DDMLab - September 7, 2007

Experiment 1: Effects of tone when testing in CM/VM Detection time Detection Accuracy (d’) DDMLab - September 7, 2007

Experiment 1: Effect of tone when testing with Tone/Notone DDMLab - September 7, 2007

Training difficulty principle: Experiment 2 Decision making task, related to visual detection (VM, high load only, and detection of planes among planes) Main results from Healy’s experiments: Damaging effect of Decision Making task High false alarms with DM Low Hits with DM Low d’ with DM DDMLab - September 7, 2007

Experiment 2: high FA rates with decision making task DDMLab - September 7, 2007

Experiment 2: Effects of decision making Main effect of DM Interaction of DM task and session DDMLab - September 7, 2007

Understanding Reinforcement Learning Among Strategies DDMLab - September 7, 2007

Summary of this year’s accomplishments Concluded a theoretical view of fatigue through modeling in ACT-R (paper in preparation): Reduced accuracy in extended, repeated tasks is explained by the interaction of Cognitive and Affective states Created ACT-R 6.0 models for RADAR: Strategy-based implementation Use of new utility mechanism Reproduced the effects of the Training difficulty experiments 1 & 2 (experiment 2 work is in progress) DDMLab - September 7, 2007

Plans for next year Conclude the cognitive view of Fatigue paper Conclude a paper on the RADAR task including the behavioral and model data fits Perform an investigation of the Instance-Strategy modeling approaches in the RADAR task Start modeling on Proctor's findings on the S-R compatibility DDMLab - September 7, 2007