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Modeling Speed-Accuracy Tradeoffs in Recognition

Modeling Speed-Accuracy Tradeoffs in Recognition. Darryl W. Schneider John R. Anderson Carnegie Mellon University. Modeling Behavioral Data With ACT-R. Mean RT and Error Rate. Speed-Accuracy Tradeoff Functions. Correct and Error RT Distributions. Speed-Accuracy Tradeoffs.

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Modeling Speed-Accuracy Tradeoffs in Recognition

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

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

  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 Asymptote (λ) Rate (β) Shifted exponential function: Intercept (δ) Chance

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

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

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

  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 Probability that retrieval finishes in time: 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 Our Experiment • Person-location pairs • Well-learned • Fan 1 vs. Fan 2 • Associative recognition: targets vs. rearranged foils • Response signal procedure with 8 lags 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

  12. Modeling Fan Effects on SAT Functions Our Experiment Well-learned materials Wickelgren & Corbett (1977) Briefly studied 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

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