The participation of attentional and associative mechanisms in extinction, spontaneous recovery, external disinhibition, renewal, reinstatement, and reacquisition was evaluated through computer simulations with an extant computational model of classical conditioning (N. A. Schmajuk, Y. Lam, & J. A. Gray, 1996; N. A. Schmajuk & J. A. Larrauri,…

We present a neurocomputational model with self-organizing maps that accounts for the emergence of taxonomic responding and fast mapping in early word learning, as well as a rapid increase in the rate of acquisition of words observed in late infancy. The quality and efficiency of generalization of word-object associations is directly related to…

Cue competition is one of the most studied phenomena in associative learning. However, a theoretical disagreement has long stood over whether it reflects a learning or performance deficit. The comparator hypothesis, a model of expression of Pavlovian associations, posits that learning is not subject to competition but that performance reflects a…

This article presents an extended version of the convolution-correlation memory model TODAM (theory of distributed associative memory) that eliminates some inadequacies of previous versions and provides a unified treatment of item, associative, and serial-order information. TODAM2 extends the chunking model to provide a general model for episodic…

A detailed description of a layered network model is provided, with computer simulations of key associative learning phenomena and predictions generated from the model. The model is compared to more conventional theories of learning to learn and configural learning. (SLD)

An associative model of serial learning is described based on the assumption that the effective stimulus for a serial-list item is generated by adaptation-level coding of the item's ordinal position. How the model can generate predictions of aspects of serial-learning data is illustrated. (SLD)