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Hierarchical Bayesian models of subtask learning

Anglim, Jeromy and Wynton, Sarah K. A. 2015, Hierarchical Bayesian models of subtask learning, Journal of experimental psychology: learning, memory, and cognition, vol. 41, no. 4, pp. 957-974, doi: 10.1037/xlm0000103.

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Title Hierarchical Bayesian models of subtask learning
Author(s) Anglim, JeromyORCID iD for Anglim, Jeromy orcid.org/0000-0002-1809-9315
Wynton, Sarah K. A.
Journal name Journal of experimental psychology: learning, memory, and cognition
Volume number 41
Issue number 4
Start page 957
End page 974
Total pages 18
Publisher American Psychological Association
Place of publication Washington, DC
Publication date 2015-07
ISSN 0278-7393
1939-1285
Summary  The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking task, which logged participant actions, enabling measurement of strategy use and subtask performance. Model comparison was performed using deviance information criterion (DIC), posterior predictive checks, plots of model fits, and model recovery simulations. Results showed that although learning tended to be monotonically decreasing and decelerating, and approaching an asymptote for all subtasks, there was substantial inconsistency in learning curves both at the group- and individual-levels. This inconsistency was most apparent when constraining both the rate and the ratio of learning to asymptote to be equal across subtasks, thereby giving learning curves only 1 parameter for scaling. The inclusion of 6 strategy covariates provided improved prediction of subtask performance capturing different subtask learning processes and subtask trade-offs. In addition, strategy use partially explained the inconsistency in subtask learning. Overall, the model provided a more nuanced representation of how complex tasks can be decomposed in terms of simpler learning mechanisms.
Language eng
DOI 10.1037/xlm0000103
Field of Research 170112 Sensory Processes, Perception and Performance
Socio Economic Objective 970117 Expanding Knowledge in Psychology and Cognitive Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, American Psychological Association
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071939

Document type: Journal Article
Collection: School of Psychology
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Created: Tue, 31 Mar 2015, 17:16:19 EST

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