Probe similarity and recognition of set membership: A parallel-processing serial-feature-matching model

A model for memory scanning is proposed in which the encoded representation of a probe is compared in parallel with encoded representations of each item in the positive set. The within item matches are serial feature by feature comparisons that terminate when either a positive or negative criterion is reached. This model is shown to predict the results of a probe similarity experiment. The serial location of a similarity within an item affects negative reaction times, but the number of items in the positive set to which the probe is similar has no main effect. The model is also shown to yield predictions consonant with existing data on the relation between reaction times and set size and speed-accuracy trade offs.

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