Familiarity breeds differentiation: a subjective-likelihood approach to the effects of experience in recognition memory.

With repeated exposure, people become better at identifying presented items and better at rejecting items that have not been presented. This differentiation effect is captured in a model consisting of item detectors that learn estimates of conditional probabilities of item features. The model is used to account for a number of findings in the recognition memory literature, including (a) the basic differentiation effect (strength-mirror effect), (b) the fact that adding items to a list reduces recognition accuracy (list-length effect) but extra study of some items does not reduce recognition accuracy for other items (null list-strength effect), (c) nonlinear effects of strengthening items on false recognition of similar distractors, (d) a number of different kinds of mirror effects, (e) appropriate z-ROC curves, and (f) one type of deviation from optimality exhibited in recognition experiments.