September 2019
Volume 19, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2019
Individual differences in learning: Relations between cognition, personality, and responsiveness to perceptual training
Author Affiliations & Notes
  • Aaron K Cochrane
    University of Wisconsin -- Madison
  • C. Shawn Green
    University of Wisconsin -- Madison
Journal of Vision September 2019, Vol.19, 186a. doi:https://doi.org/10.1167/19.10.186a
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      Aaron K Cochrane, C. Shawn Green; Individual differences in learning: Relations between cognition, personality, and responsiveness to perceptual training. Journal of Vision 2019;19(10):186a. https://doi.org/10.1167/19.10.186a.

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

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Abstract

There is considerable interest in understanding whether individual-level factors exist that predict either the ability to acquire new skills or the ability to generalize skills acquired in one context to new contexts. Understanding such factors would not only contribute to mechanistic theories of learning, but could also identify potential foci of interventions designed to enhance the utility of behavioral training. Here we report an experiment designed to address three core questions: (1) To what extent is learning on two distinct perceptual tasks correlated across individuals? (2) to what extent is the ability to transfer perceptual learning gains correlated across individuals and (3) do other measures of cognition and personality relate to learning and/or generalization? Participants were trained for four days on two different visual perception tasks (motion orientation discrimination and oddball texture detection). The training was followed by tests of generalization on each task (same task, but new directions/orientations). Participants also completed a battery of individual-difference level measures including tasks designed to assess reasoning, attention, working memory, various life experiences (e.g., media use), and personality factors. We estimated participant-level learning trajectories in a nonlinear hierarchical regression framework. We observed robust correlations between participants’ asymptotic performance levels on each learning task, with weaker evidence for correlations between parameters directly linked to changes in performance. Measures of generalization also correlated across learning tasks. Finally, while we identified several individual difference level predictors of learning (e.g., measures that load on speed of processing and/or executive function were related to both learning rate and initial performance), we found minimal evidence for variations in transfer that were explained by measures apart from performance on the trained tasks themselves. While clearly preliminary, these results suggest that the ability to transfer learning is inherently linked to the overall ability to learn.

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