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<p>Evidence for two qualitatively different learning strategies has emerged from the function- and category-learning literatures: a <i>rule-based</i> and an <i>exemplar-based</i> strategy. With a rule-based strategy, learners abstract some common principle from training items, which allows extrapolation to novel instances. With an exemplar-based strategy, learners encode training items without abstraction, which facilitates generalisation based on surface similarity to trained items. Previous studies offer preliminary evidence that strategies are stable; that is, convergent performance was found across pairs of disparate tasks. The current paper advances this work by examining whether performance across a battery of tasks converges, providing evidence for a latent variable underlying learning strategy. Subjects completed five learning strategy and three working memory tasks. Using data reduction and latent structure modelling methods, we found evidence for a general strategy construct that was unrelated to working memory. This is important because it shows that differences in learning strategy are not simply due to differences in working memory.</p>

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Evidence for rule versus exemplar learning strategies as stable individual differences independent from working memory

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月7日

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  • Samuel A. Herzog,
  • Micah B. Goldwater

摘要

Evidence for two qualitatively different learning strategies has emerged from the function- and category-learning literatures: a rule-based and an exemplar-based strategy. With a rule-based strategy, learners abstract some common principle from training items, which allows extrapolation to novel instances. With an exemplar-based strategy, learners encode training items without abstraction, which facilitates generalisation based on surface similarity to trained items. Previous studies offer preliminary evidence that strategies are stable; that is, convergent performance was found across pairs of disparate tasks. The current paper advances this work by examining whether performance across a battery of tasks converges, providing evidence for a latent variable underlying learning strategy. Subjects completed five learning strategy and three working memory tasks. Using data reduction and latent structure modelling methods, we found evidence for a general strategy construct that was unrelated to working memory. This is important because it shows that differences in learning strategy are not simply due to differences in working memory.