1. Modeling Speed-Accuracy Tradeoffs in Recognition Darryl W. Schneider John R. Anderson Carnegie Mellon University

2. Modeling Behavioral Data With ACT-R Speed-Accuracy Tradeoff Functions Correct and Error RT Distributions Mean RT and Error Rate

3. Speed-Accuracy Tradeoffs People can trade speed for accuracy when performing a task Speed-accuracy tradeoff functions can be measured using the response signal procedure Typically involves a choice task (e.g., recognition) A stimulus is followed at a variable lag by a signal to respond immediately (e.g., yes/no response as to whether the stimulus was studied) Examine accuracy as a function of lag

4. Speed-Accuracy Tradeoff Function Intercept (δ) Chance Rate (β) Asymptote (λ)

5. How can ACT-R produce a speed-accuracy tradeoff function?

6. ACT-R Model: Long Lag Stimulus encoding Memory retrieval (wait) Signal encoding Response execution Stimulus onset Response Response signal Lag Time available for retrieval Trial time

7. ACT-R Model: Short Lag Stimulus encoding Memory retrieval Signal encoding Response execution Stimulus onset Response Response signal Lag Trial time Guess Time available for retrieval

8. Modeling the Speed-Accuracy Tradeoff Accuracy depends on the probability that retrieval finishes in the time available If retrieval finishes, accuracy is perfect If retrieval does not finish, accuracy is lowered due to guessing Retrieval time Calculated with the standard ACT-R equations Activation noise produces a time distribution

9. Modeling the Speed-Accuracy Tradeoff Time available: External deadline (lag) Internal deadline (failure time) Shorter deadline determines the time available

10. Modeling Fan Effects on SAT Functions Fan effect: It takes longer to recognize an item as its associative fan increases Associative fan = number of associations with other items in memory ACT-R can already model the fan effect As fan increases, associative activation from the probe to items in memory decreases, resulting in memory retrieval taking longer

11. Experiments Wickelgren & Corbett (1977) Word pairs and triples Briefly studied Fan 1 vs. Fan 2 Associative recognition: targets vs. rearranged foils Response signal procedure with 8 lags Our Experiment Person-location pairs Well-learned Fan 1 vs. Fan 2 Associative recognition: targets vs. rearranged foils Response signal procedure with 8 lags

12. Modeling Fan Effects on SAT Functions Wickelgren & Corbett (1977) Briefly studied materials Our Experiment Well-learned materials Internal deadline shorter than external deadline Internal deadline longer than external deadline

13. Take-Home Message ACT-R can model speed- accuracy tradeoffs in response signal data

14. Current Directions Modeling nonmonotonic speed-accuracy tradeoff functions Different types of information are retrieved in series and inform the guessing process Modeling reaction time distributions Free-response procedure Guessing is probabilistic and occurs in parallel with retrieval

15. For More Information Schneider, D. W., & Anderson, J. R. (2012). Modeling fan effects on the time course of associative recognition. Cognitive Psychology, 64, 127-160. Available on the ACT-R website