A Nonparametric Bayesian Model of Visual Short-Term Memory

Abstract

We present a nonparametric Bayesian model of the organization of visual short-term memory based on the Dirichlet process mixture model. Our model implements the idea that items in visual short-term memory can be encoded at multiple levels of abstraction, where the appropriate levels of abstraction and how much weight should be given to each level can be automatically determined. A capacity limit is implemented in this model by favoring small numbers of clusters of items. We show that various biases and distortions reported in visual short-term recall and recognition memory literatures can be quite naturally and elegantly explained by the model.


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