Master of Science
Minda, John Paul
The present study examined whether the temporal distribution of procedural category learning experiences would impact learning outcomes. Participants completed the remote category learning study on a smartphone in one of two learning conditions: Massed (control) or distributed. Consistent with expectations, distributed learners reached higher accuracy levels. This effect disappeared after accounting for reaction time differences, suggesting that it was driven by attentional mechanisms. Distribution may have made participants more likely discover the optimal categorization strategy and more robust to sensory habituation. Counter to previous findings, participants favored distributed learning. These results suggest that adult category learning is facilitated by temporal spacing. Future work may further explore the effects of temporal and contextual distinctiveness of learning experiences on category learning outcomes.
Summary for Lay Audience
Throughout life, people learn to sort items into categories to help them make sense of the world. People rarely spend long periods of time studying new categories; instead, categories are usually learned in short experiences spaced out over time. For example, children don’t study the differences between cats and dogs, they slowly learn to distinguish between them through experience. The goal of this study was to see if spacing out learning experiences over time would improve a person’s ability to sort imaginary items into abstract categories. Participants learned to sort items on a smartphone either all at once (massed) or in short sessions spaced out over several days (distributed). Distributed learners were better at sorting the items. Massed learners became less sensitive to the differences between items and paid less attention over time. Distributed learners were more satisfied and keener to learn again. Both types of learners indicated a preference for distributed learning if trained again in the future. Future research should see if this learning method is effective for real-world categories such as skin lesions, mushrooms, or animal groups.
Cruz, Anthony, "The Spacing Effect in Remote Information-Integration Category Learning" (2022). Electronic Thesis and Dissertation Repository. 8718.
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