December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Individual differences in object category learning: Steep versus shallow learners
Author Affiliations & Notes
  • James Tanaka
    University of Victoria
  • Michaella Trites
  • Jose Barrios
  • Buyun Xu
  • Stuart MacDonald
  • Footnotes
    Acknowledgements  Research supported by Natural Sciences and Engineering Research Council of Canada to JWT and SM and SM
Journal of Vision December 2022, Vol.22, 3447. doi:
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      James Tanaka, Michaella Trites, Jose Barrios, Buyun Xu, Stuart MacDonald; Individual differences in object category learning: Steep versus shallow learners. Journal of Vision 2022;22(14):3447.

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      © ARVO (1962-2015); The Authors (2016-present)

  • Supplements

In category training studies, it is assumed that once participants are trained to a specified criterion, they are equivalent in their categorization abilities. However, little work has examined how individual differences in the rate of category acquisition might affect the retention of category knowledge in the post-acquisition phase. In this study, we investigate the initial trainability of participants as a predictor of their ability to retain category knowledge. In Phase 1 of the study, participants were trained to classify images of warblers into one of four species categories (Magnolia, Cape May, Prairie, Townsend). Training continued until participants achieved a 90% accuracy criterion. Multilevel modeling was used to characterize participants as having either a “steep” or “shallow” acquisition slope. The slopes were computed based on the learner’s initial baseline performance in the first block of learning trials and the number of trials required to reach the 90% criterion. Subsequently, in Phase 2 of the study, participants were asked to categorize new images of the four warbler species. Their category abilities were tested at three time points: immediately, 1 day and 2 days after category training. The key result was that relative to the original 90% training criterion, steep learners improved their categorization performance to 97%. In contrast, the category performance of the shallow learners declined to 80% - below the original training criterion of 90%. Our findings show that category learning and category retention are intimately linked, where the rate by which someone acquires a perceptual category predicts how they will retain category knowledge over time. Adopting an individual differences perspective, these findings suggest that visual category learning may be a stable, trait-like characteristic that measures a person’s ability to acquire and retain category knowledge.


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