Similarity leads to correlated processing: A dynamic model of encoding and recognition of episodic associations.

We present a model of the encoding of episodic associations between items, extending the dynamic approach to retrieval and decision making of Cox and Shiffrin (2017) to the dynamics of encoding. This model is the first unified account of how similarity affects associative encoding and recognition, including why studied pairs consisting of similar items are easier to recognize, why it is easy to reject novel pairs that recombine items that were studied alongside similar items, and why there is an early bias to falsely recognize novel pairs consisting of similar items that is later suppressed (Dosher, 1984; Dosher & Rosedale, 1991). Items are encoded by sampling features into limited-capacity parallel channels in working memory. Associations are encoded by conjoining features across these channels. Because similar items have common features, their channels are correlated which increases the capacity available to encode associative information. The model additionally accounts for data from a new experiment illustrating the importance of similarity for associative encoding across a variety of stimulus types (objects, words, and abstract forms) and types of similarity (perceptual or conceptual), illustrating the generality of the model.