Feedback Discounting in Probabilistic Categorization: Converging Evidence from EEG and Cognitive Modeling

Sewell, David K.; Warren, Hayley A.; Rosenblatt, Daniel; Bennett, Daniel; Lyons, Maxwell; Bode, Stefan

doi:10.1007/s42113-018-0012-6

Cited by 3 publications

(6 citation statements)

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“…For each stimulus, the average choice probability is more extreme than the feedback probability associated with that stimulus. This overshooting behavior is typical of responding under probabilistic feedback (e.g., Craig et al, 2011;Nosofsky & Stanton, 2005;Sewell et al, 2018;Shanks et al, 2002). Visual inspection of the individual response profiles confirmed that all participants exhibited the same probability matching behavior, responding similarly to each stimulus over the entire course of learning.…”

Section: Resultsmentioning

confidence: 64%

“…Kruschke and Johansen (1999) argued for feedback discounting when learners are in probabilistic environments, as it is not possible to completely eliminate prediction error. Robust support for feedback discounting was found by Craig et al (2011), and individual differences in feedback discounting-and how they relate to patterns of neural activity related to feedback processingwere recently investigated by Sewell et al (2018). Whereas the feedback discounting parameter, ρ, was fixed to zero in the previous fits, we now freely estimated it from the data.…”

Section: An Integrated Model Of Learning and Response Timementioning

confidence: 90%

“…In reality, neither probability matching nor maximizing provides a completely accurate picture of performance, as people tend to Bovershoot^the feedback probabilities while not strictly maximizing either (e.g., Friedman & Massaro, 1998;Nosofsky & Stanton, 2005;Shanks, Tunney, & McCarthy, 2002). The tendency to overshoot, but not maximize, persists even in highly practiced individuals (Edwards, 1961;Sewell et al, 2018); learners are expected to assign stimuli to multiple category outcomes across all stages of learning. The variability in people's responding permits collection of RT distribution data for both frequently and infrequently reinforced category outcome responses for different stimuli.…”

Section: Probabilistic Category Learningmentioning

confidence: 99%

“…Each cue is paired with a unique feedback probability, which determines the relative frequency with which the cue is paired with a Category A or Category B outcome during learning. In tasks using a small number of nonconfusable cues, performance typically stabilizes after each cue has been presented around 30-40 times (e.g., Craig, Lewandowsky, & Little, 2011;Sewell et al, 2018). Because learning proceeds rapidly, it is possible for people to complete multiple runs through the learning task within a single experimental session.…”

Section: Probabilistic Category Learningmentioning

confidence: 99%

“…In many category learning models, the learning rate parameter is held constant across trials. However, in probabilistic learning environments, where it is impossible to completely eliminate prediction error, several authors have argued that people might progressively discount feedback as prediction error becomes less informative (Craig et al, 2011;Kruschke & Johansen, 1999; see also Sewell et al, 2018). Following Kruschke and Johansen (1999), we consider a version of the learning model that incorporates feedback discounting by multiplying the learning rate on trial n by a discounting factor,…”

Section: An Integrated Model Of Learning and Response Timementioning

confidence: 99%

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Combining error-driven models of associative learning with evidence accumulation models of decision-making

et al. 2019

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As people learn a new skill, performance changes along two fundamental dimensions: Responses become progressively faster and more accurate. In cognitive psychology, these facets of improvement have typically been addressed by separate classes of theories. Reductions in response time (RT) have usually been addressed by theories of skill acquisition, whereas increases in accuracy have been explained by associative learning theories. To date, relatively little work has examined how changes in RT relate to changes in response accuracy, and whether these changes can be accounted for quantitatively within a single theoretical framework. The current work examines joint changes in accuracy and RT in a probabilistic category learning task. We report a model-based analysis of changes in the shapes of RT distributions for different category responses at the level of individual stimuli over the course of learning. We show that changes in performance are determined solely by changes in the quality of information entering the decision process. We then develop a new model that combines an associative learning front end with a sequential sampling model of the decision process, showing that the model provides a good account of all aspects of the learning data. We conclude by discussing potential extensions of the model and future directions for theoretical development that are opened up by our findings.

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Section: Resultsmentioning

confidence: 64%