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Task difficulty and the specificity of perceptual learning

Author

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  • Merav Ahissar

    (Weizmann Institute of Science
    Keck Center for Integrative Neuroscience, UCSF)

  • Shaul Hochstein

    (Institute of Life Sciences, Hebrew University of Jerusalem)

Abstract

Practising simple visual tasks leads to a dramatic improvement in performing them. This learning is specific to the stimuli used for training. We show here that the degree of specificity depends on the difficulty of the training conditions. We find that the pattern of specificities maps onto the pattern of receptive field selectivities along the visual pathway. With easy conditions, learning generalizes across orientation and retinal position, matching the spatial generalization of higher visual areas. As task difficulty increases, learning becomes more specific with respect to both orientation and position, matching the fine spatial retinotopy exhibited by lower areas. Consequently, we enjoy the benefits of learning generalization when possible, and of fine grain but specific training when necessary. The dynamics of learning show a corresponding feature. Improvement begins with easy cases (when the subject is allowed long processing times) and only subsequently proceeds to harder cases. This learning cascade implies that easy conditions guide the learning of hard ones. Taken together, the specificity and dynamics suggest that learning proceeds as a countercurrent along the cortical hierarchy. Improvement begins at higher generalizing levels, which, in turn, direct harder-condition learning to the subdomain of their lower-level inputs. As predicted by this reverse hierarchy model, learning can be effective using only difficult trials, but on condition that learning onset has previously been enabled. A single prolonged presentation suffices to initiate learning. We call this single-encounter enabling effect 'eureka'.

Suggested Citation

  • Merav Ahissar & Shaul Hochstein, 1997. "Task difficulty and the specificity of perceptual learning," Nature, Nature, vol. 387(6631), pages 401-406, May.
  • Handle: RePEc:nat:nature:v:387:y:1997:i:6631:d:10.1038_387401a0
    DOI: 10.1038/387401a0
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    Citations

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    Cited by:

    1. Katharine Molloy & David R Moore & Ediz Sohoglu & Sygal Amitay, 2012. "Less Is More: Latent Learning Is Maximized by Shorter Training Sessions in Auditory Perceptual Learning," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-13, May.
    2. Sygal Amitay & Lorna Halliday & Jenny Taylor & Ediz Sohoglu & David R Moore, 2010. "Motivation and Intelligence Drive Auditory Perceptual Learning," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-8, March.
    3. Gesa Lange & Eric Lowet & Mark J Roberts & Peter De Weerd, 2018. "Within-quadrant position and orientation specificity after extensive orientation discrimination learning is related to performance gains during late learning," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-37, September.
    4. Susana T L Chung & Roger W Li & Dennis M Levi, 2012. "Learning to Identify Near-Acuity Letters, either with or without Flankers, Results in Improved Letter Size and Spacing Limits in Adults with Amblyopia," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-11, April.
    5. Stefanie Duyck & Hans Op de Beeck, 2019. "An investigation of far and near transfer in a gamified visual learning paradigm," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-23, December.
    6. Jacqueline M Fulvio & C Shawn Green & Paul R Schrater, 2014. "Task-Specific Response Strategy Selection on the Basis of Recent Training Experience," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-16, January.

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