statistical summary representations

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

The literature suggests that ensemble coding, the ability to represent the gist of sets, may be an underlying mechanism for becoming familiar with newly encountered faces. This phenomenon was investigated by introducing a new training paradigm that involves incidental learning of target identities interspersed among distractors. The effectiveness of this training paradigm was explored in Study 1, which revealed that unfamiliar observers who learned the faces incidentally performed just as well as the observers who were instructed to learn the faces, and the intervening distractors did not disrupt familiarization. Using the same training paradigm, ensemble coding was investigated as an underlying mechanism for face familiarization in Study 2 by measuring familiarity with the targets at different time points using average images created either by seen or unseen encounters of the target. The results revealed that observers whose familiarity was tested using seen averages outperformed the observers who were tested using unseen averages, however, this discrepancy diminished over time. In other words, successful recognition of the target faces became less reliant on the previously encountered exemplars over time, suggesting an exemplar-independent representation that is likely achieved through ensemble coding. Taken together, the results from the current experiment provide direct evidence for ensemble coding as a viable underlying mechanism for face familiarization, that faces that are interspersed among distractors can be learned incidentally.