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

2. 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

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

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

5. 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)

6. 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

7. 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

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

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

10. Summary of model predictions
DDMLab - September 7, 2007

11. 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

12. 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

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

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

15. 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

16. DDMLab - September 7, 2007

17. 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

18. 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

19. 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

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

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

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

23. 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

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

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

26. Understanding Reinforcement Learning Among Strategies
DDMLab - September 7, 2007

27. 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

28. 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