Statistical Summary Representations in Identity Learning: Exemplar-Independent Incidental Recognition

Abstract

The literature suggests that ensemble coding (i.e., the ability to represent the gist

of sets) may be an underlying mechanism for becoming familiar with newly encountered

faces. I tested the plausibility of this suggestion using a new paradigm that involves

incidental learning of target identities interspersed among distractors. The participants

were trained on unfamiliar targets that were presented among intervening distractors

while rating the attractiveness of the faces. The participants were then given a test to

measure their familiarity with the targets. The results revealed that recognition of a

target’s face was superior when the face was the average of previously encountered

exemplars of the target compared to the average of unseen exemplars. However, this

effect diminished over time as viewers underwent more training, demonstrating an

exemplar-independent recognition that is likely achieved through ensemble coding. The

results also revealed that viewers were able to extract and encode properties relevant to

identifying the targets among several distractors. This effect was present in viewers that

learned the targets incidentally and actively. Taken together, these results suggest that

ensemble coding is a viable underlying mechanism for face learning, and faces that are

interspersed among distractors can be learned incidentally.