A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to back-propagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probability of the next component’s target. Each layer then does locally Bayesian learning. The approach assumes online trial-by-trial learning. The resulting parameter updating is not globally Bayesian but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs and then maps attentionally filtered inputs to outputs. The Bayesian updating allows the associative model to exhibit retrospective revaluation effects such as backward blocking and unovershadowing, which have been challenging for associative learning models. The back-propagation of target values to attention allows the model to show trial-order effects, including highlighting and differences in magnitude of forward and backward blocking, which have been challenging for Bayesian learning models.

We show that a simple network model of associative learning can reproduce three findings that arise from particular training and testing procedures in generalization experiments: the effect of (i) ‘errorless learning’, (ii) extinction testing on peak shift, and (iii) the central tendency effect. These findings provide a true test of the network model which was developed to account for other phenomena, and highlight the potential of neural networks to study the phenomena that depend on sequences of experiences with many stimuli. Our results suggest that at least some such phenomena, e.g. stimulus range effects, may derive from basic mechanisms of associative memory rather than from more complex memory processes.