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Gepubliceerd in: Psychological Research 3/2019

26-02-2019 | Original Article

Novel representations that support rule-based categorization are acquired on-the-fly during category learning

Auteurs: Fabian A. Soto, F. Gregory Ashby

Gepubliceerd in: Psychological Research | Uitgave 3/2019

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Abstract

Humans learn categorization rules that are aligned with separable dimensions through a rule-based learning system, which makes learning faster and easier to generalize than categorization rules that require integration of information from different dimensions. Recent research suggests that learning to categorize objects along a completely novel dimension changes its perceptual representation, making it more separable and discriminable. Here, we asked whether such newly learned dimensions could support rule-based category learning. One group received extensive categorization training and a second group did not receive such training. Later, both groups were trained in a task that made use of the category-relevant dimension, and then tested in an analogical transfer task (Experiment 1) and a button–switch interference task (Experiment 2). We expected that only the group with extensive pre-training (with well-learned dimensional representations) would show evidence of rule-based behavior in these tasks. Surprisingly, both groups performed as expected from rule-based learning. A third experiment tested whether a single session (less than 1 h) of training in a categorization task would facilitate learning in a task requiring executive function. There was a substantial learning advantage for a group with brief pre-training with the relevant dimension. We hypothesize that extensive experience with separable dimensions is not required for rule-based category learning; rather, the rule-based system may learn representations “on the fly” that allow rule application. We discuss what kind of neurocomputational model might explain these data best.
Voetnoten
1
Technically, both the ID-new group and the GENxEMO group were exposed to information-integration tasks. However, the GENxEMO task involves integration of information from a pair of clearly differentiated dimensions (i.e., that can be selectively attended). On the other hand, the ID-new task involves integration of information from a variety of unknown dimensions, which may or may not be separable. Thus, the demands from the two tasks are different, but neither can be solved by an explicit rule based on selective attention to a known dimension. That is, both require a procedural strategy to be learned.
 
2
Note, however, that correlational studies that have measured individual differences in working memory capacity in general do not support an association between such differences and performance in a specific task (Kalish et al., 2017; Lewandowsky et al., 2012).
 
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Metagegevens
Titel
Novel representations that support rule-based categorization are acquired on-the-fly during category learning
Auteurs
Fabian A. Soto
F. Gregory Ashby
Publicatiedatum
26-02-2019
Uitgeverij
Springer Berlin Heidelberg
Gepubliceerd in
Psychological Research / Uitgave 3/2019
Print ISSN: 0340-0727
Elektronisch ISSN: 1430-2772
DOI
https://doi.org/10.1007/s00426-019-01157-7