A signal-detection-based confidence-similarity model of face-matching
Description
Face-matching consists of the ability to decide whether two face-images (or more) belong to the same person or to different identities. Face-matching is crucial for efficient face recognition, and plays an important role in applied setting such as passport control and eyewitness memory. However, despite extensive research, the mechanisms that govern face-matching performance are still not well understood. Moreover, to-date, many researchers hold on to the belief that match and mismatch responses are governed by two separate systems, an assumption that likely thwarted the development of a unified model of face-matching. The present study proposes a unified unequal variance confidence similarity signal-detection-based model of face-matching performance, one that facilitates the use of receiver operating characteristics (ROC) and confidence-accuracy plots analyses to better understand the relations between match and mismatch responses, and their relations to factors of confidence and similarity. The model can account for the presence of both within-identity and between-identity sources of variation in face recognition, and explains a myriad of face-matching phenomena, including the match-mismatch dissociation. The model is also capable of generating new predictions concerning the role of confidence and similarity and their intricate relations with accuracy. The new model was tested against six alternative competing models (some postulate discrete rather than continuous representations) in three experiments. Data analyses consisted of hierarchically-nested model fitting, ROC curve analyses, and confidence-accuracy plots analyses. All of these provided substantial support in the signal-detection-based confidence-similarity model. The model suggests that the accuracy of face-matching performance can be predicted by the degree of similarity/dissimilarity of the depicted faces and the level of confidence in the decision. Moreover, according to the model confidence and similarity ratings are strongly correlated.