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• W2913261796 endingPage "893" @default.
• W2913261796 startingPage "868" @default.
• W2913261796 abstract "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." @default.
• W2913261796 created "2019-02-21" @default.
• W2913261796 creator A5010633017 @default.
• W2913261796 creator A5013254092 @default.
• W2913261796 creator A5015242703 @default.
• W2913261796 creator A5083923302 @default.
• W2913261796 date "2019-02-04" @default.
• W2913261796 modified "2023-10-13" @default.
• W2913261796 title "Combining error-driven models of associative learning with evidence accumulation models of decision-making" @default.
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• W2913261796 doi "https://doi.org/10.3758/s13423-019-01570-4" @default.
• W2913261796 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30719625" @default.
• W2913261796 hasPublicationYear "2019" @default.
• W2913261796 type Work @default.

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