Relational Discovery in Category Learning

doi:10.1037/xge0000387

Relational Discovery in Category Learning

2018-01-01

39

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AI

DOI：10.1037/xge0000387

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Learning categories defined by the relations among objects supports the transfer of knowledge from initial learning contexts to novel contexts that share few surface similarities. Often relational categories have correlated (but nonessential) surface features, which can be a distraction from discovering the category-defining relations, preventing knowledge transfer. This is one explanation for the inert knowledge problem in education wherein many students fail to spontaneously apply their learning outside the classroom. Here we present a series of experiments using artificial categories that correlate surface features and relational patterns during learning. Our goal was to determine what task parameters and individual differences in learners shift focus to the relational aspect of the category and foster transfer to novel disparate exemplars. We consistently showed that the effectiveness of task structure manipulations (e.g., the sequence of learning exemplars) depended on the learners' strategies (e.g., whether learners are oriented toward discovering rules or focusing on exemplars). Further, we found support that inference-learning, wherein learners are presented with incomplete exemplars and learn how to infer the missing pieces, is an effective way to promote relational discovery and transfer, even for learners who are not predisposed to make such discoveries.

Keyword:

categories and concepts

individual differences

inert knowledge

knowledge transfer

relational categories

Relational Discovery in Category Learning

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