A signal-detection-based confidence-similarity model of face-matching
The ability to match faces correctly is crucial for efficient face recognition. Face-matching also plays an important role in applied setting such as passport control and eyewitness memory. However, despite extensive research on face-matching the mechanisms that govern this task 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 thwarted the development of a unified model. The present study outlines a signal-detection-based model of face-matching performance. The model can explain 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, all within the confines of a single system. The new model was tested against six alternative competitors models (some postulate discrete rather than continuous representations) in three experiments. Data analyses consisted of hierarchically-nested model fitting, ROC curve analyses, and calibration curves analyses. All of the analyses provided substantial support in the signal-detection-based confidence-similarity model.