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).
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