McDaniel Mark A, Busemeyer Jerome R
Department of Psychology, Washington University, One Brookings Drive, St. Louis, MO 63130-4899, USA.
Psychon Bull Rev. 2005 Feb;12(1):24-42. doi: 10.3758/bf03196347.
The purpose of this article is to provide a foundation for a more formal, systematic, and integrative approach to function learning that parallels the existing progress in category learning. First, we note limitations of existing formal theories. Next, we develop several potential formal models of function learning, which include expansion of classic rule-based approaches and associative-based models. We specify for the first time psychologically based learning mechanisms for the rule models. We then present new, rigorous tests of these competing models that take into account order of difficulty for learning different function forms and extrapolation performance. Critically, detailed learning performance was also used to conduct the model evaluations. The results favor a hybrid model that combines associative learning of trained input-prediction pairs with a rule-based output response for extrapolation (EXAM).
本文的目的是为功能学习提供一个更正式、系统和综合的方法奠定基础，该方法与类别学习中的现有进展并行。首先，我们指出现有形式理论的局限性。接下来，我们开发了几种功能学习的潜在形式模型，其中包括对经典基于规则的方法和基于联想的模型的扩展。我们首次为规则模型指定了基于心理的学习机制。然后，我们对这些相互竞争的模型进行了新的、严格的测试，这些测试考虑了学习不同功能形式的难度顺序和外推性能。至关重要的是，详细的学习性能也被用于进行模型评估。结果支持一种混合模型，该模型将训练输入-预测对的联想学习与基于规则的输出响应进行外推（EXAM）相结合。
