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首页> 外文会议>Eighth Neural Computation and Psychology Workshop; 20030828-30; University of Kent(GB) >LIMITED CAPACITY DIMENSIONAL ATTENTION AND THE CONFIGURAL-CUE MODEL OF STIMULUS REPRESENTATION
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LIMITED CAPACITY DIMENSIONAL ATTENTION AND THE CONFIGURAL-CUE MODEL OF STIMULUS REPRESENTATION

机译：有限能力维度注意和刺激表示的构象提示模型

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摘要

Gluck and Bower's configural cue model is a network that represents stimuli using independent nodes for each feature and feature combination within the stimulus. One of its main limitations is the lack of any clear method for incorporating secondary learning processes such as selective attention. A new configural cue model is proposed in which node activation is dependent on the average characteristics of a dimensional sampling process. This process may be described in terms of a Markov process. Learning algorithms are used to alter the matrix of transition probabilities governing the behavior of the sampling process on each trial. This allows the model to qualitatively simulate learning effects that seem to be based on limited-capacity dimensional attention. The approach used also allows the model to be used to simulate attention learning and associative learning with feature-based stimuli. This represents a potential advance over many models used in category learning research where dominant models are either only applicable to stimuli that do not vary in terms of their dimensionality (such as ALCOVE), or make use of stimulus representations that are incapable of learning nonlinear discriminations (such as EXIT).
机译：Gluck和Bower的配置提示模型是一个网络，该网络使用独立节点为刺激中的每个特征和特征组合表示刺激。它的主要局限性之一是缺乏任何清晰的方法来结合中学学习过程，例如选择性注意。提出了一种新的配置提示模型，其中节点的激活取决于维采样过程的平均特征。可以根据马尔可夫过程来描述该过程。学习算法用于更改过渡概率矩阵，该矩阵控制每次试验中采样过程的行为。这使模型可以定性地模拟似乎是基于容量有限的维度注意的学习效果。所使用的方法还允许将模型用于模拟基于特征的刺激的注意力学习和联想学习。这代表了在类别学习研究中使用的许多模型的潜在进步，在这些模型中，主导模型要么仅适用于维数不变的刺激（例如ALCOVE），要么使用无法学习非线性判别的刺激表示（例如EXIT）。

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