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.