A neural network model of the effect of prior experience with regularities on subsequent category learning

A neural network model of the effect of prior experience with regularities on subsequent category learning
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具有规律性的先验经验对后续类别学习影响的神经网络模型

DOI:
10.1016/j.cognition.2021.104997
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发表时间:
2022
期刊:
影响因子:
3.4
通讯作者:
Holt, Lori L.
Holt, Lori L.
中科院分区:
心理学2区
文献类型:
--
作者:
Roark, Casey L.;Plaut, David C.;Holt, Lori L.

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类别通常由跨维度或特征定义的类别内的实例的相似性来构造。研究人员通常认为,在定义类别范例的物理输入维度与这些维度的心理表征之间存在直接的线性关系。然而,这种假设并不总是有道理的。通过一组模拟,我们表明,通过长期的先验经验开发的输入维度的心理表征可以放置非常强的类别学习的约束。我们比较模型的行为,听觉,视觉和跨模态的人类类别学习,并在这些研究中的维度的心理表征的性质作出结论。这些模拟支持的结论是,输入维度的心理表征的性质是理解类别学习的机制的一个重要方面。
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.
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发表时间: 2014-07
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