一种基于先前规律性经验对后续类别学习影响的神经网络模型。
A neural network model of the effect of prior experience with regularities on subsequent category learning.
机构信息
Department of Psychology, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, 5000 Forbes Ave, Pittsburgh, PA 15213, USA.
Department of Psychology, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, 5000 Forbes Ave, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA.
出版信息
Cognition. 2022 May;222:104997. doi: 10.1016/j.cognition.2021.104997. Epub 2022 Jan 7.
Categories are often structured by the similarities of instances within the category defined across dimensions or features. Researchers typically assume that there is a direct, linear relationship between the physical input dimensions across which category exemplars are defined and the psychological representation of these dimensions. However, this assumption is not always warranted. Through a set of simulations, we demonstrate that the psychological representations of input dimensions developed through long-term prior experience can place very strong constraints on category learning. We compare the model's behavior to auditory, visual, and cross-modal human category learning and make conclusions regarding the nature of the psychological representations of the dimensions in those studies. These simulations support the conclusion that the nature of psychological representations of input dimensions is a critical aspect to understanding the mechanisms underlying category learning.
类别通常是通过在维度或特征上定义的类别内实例的相似性来构建的。研究人员通常假设，类别范例定义的物理输入维度之间存在直接的线性关系，以及这些维度的心理表示之间存在直接的线性关系。然而，这种假设并不总是合理的。通过一系列模拟，我们证明了通过长期的先验经验发展起来的输入维度的心理表示可以对类别学习施加很强的限制。我们将模型的行为与听觉、视觉和跨模态人类类别学习进行了比较，并就这些研究中维度的心理表示的性质得出了结论。这些模拟支持了这样的结论，即输入维度的心理表示的性质是理解类别学习背后机制的一个关键方面。
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