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.
| Original language | English |
|---|---|
| Pages (from-to) | 868-893 |
| Number of pages | 26 |
| Journal | Psychonomic Bulletin and Review |
| Volume | 26 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 15 Jun 2019 |
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Combining error-driven models of associative learning with evidence accumulation models of decision-making. / Sewell, David K.; Jach, Hayley K.; Boag, Russell J.; Van Heer, Christina A.
In: Psychonomic Bulletin and Review, Vol. 26, No. 3, 15.06.2019, p. 868-893.Research output: Contribution to journal › Review article
TY - JOUR
T1 - Combining error-driven models of associative learning with evidence accumulation models of decision-making
AU - Sewell, David K.
AU - Jach, Hayley K.
AU - Boag, Russell J.
AU - Van Heer, Christina A.
PY - 2019/6/15
Y1 - 2019/6/15
N2 - 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.
AB - 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.
KW - Categorization
KW - Category learning
KW - Diffusion model
KW - Error-driven learning
KW - Response time modeling
UR - http://www.scopus.com/inward/record.url?scp=85061213779&partnerID=8YFLogxK
U2 - 10.3758/s13423-019-01570-4
DO - 10.3758/s13423-019-01570-4
M3 - Review article
VL - 26
SP - 868
EP - 893
JO - Psychonomic Bulletin & Review
JF - Psychonomic Bulletin & Review
SN - 1069-9384
IS - 3
ER -