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Learning in the Context of Partial Information

Ms. Nicole King
The Ohio State University ~ Psychology
Dr. Brandon Turner
The Ohio State University ~ Psychology
Dr. Emily Weichart
The Ohio State University ~ Department of Psychology
Vladimir Sloutsky
The Ohio State University ~ Psychology

In our everyday lives, there are often more aspects of the environment than we can reasonably attend. As a consequence, we selectively attend to some aspects of the environment -- usually those aspects which are most relevant to our goals -- and ignore aspects that are deemed irrelevant. It follows then, that using selective attention can limit a learner's impression of an environment, because the information that is stored in memory is only a biased sample or partially encoded version of that environment. However, many classic models of category learning make a simplifying assumption that dimensions of information are perfectly encoded. Here, we investigate the merits of this assumption by evaluating categorization and memory performance in a categorization paradigm designed to discern learning strategies and partially encoded representations. We demonstrate how particular learning strategies and corresponding representations can influence generalization to novel stimuli presented in a testing phase. We build upon existing models of categorization to illustrate how partial encoding can account for differences in learning.



cognitive development

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Cite this as:

King, N. C., Turner, B., Weichart, E. R., & Sloutsky, V. (2023, July). Learning in the Context of Partial Information. Abstract published at MathPsych/ICCM/EMPG 2023. Via