From Features to Identity: Evidence for a Hierarchical Model of Unfamiliar Face Matching

How does nominal identity emerge from featural information in a face-matching task with unfamiliar faces? The present study extends the classic same–different paradigm to the domain of unfamiliar face matching and proposes a hierarchical serial self-terminating model. The model assumes that similarity between images of faces can be assessed at three representational levels—featural, physical, and nominal—and that observers compare faces sequentially from lower to higher levels, guided by instructional set (physical vs. nominal) and terminating once a discriminating feature is found. Two experiments tested these predictions with upright (Experiment 1) and inverted (Experiment 2) faces. Upright faces showed key diagnostic effects consistent with the model: the absence of a fast-same effect, the presence of the name–physical disparity effect, and costs of nominal relative to physical instructions. Signal-detection analyses confirmed that these reflected changes in sensitivity rather than bias. In contrast, inversion disrupted this pattern, attenuating or reversing effects. The results provide strong but qualified support for a hierarchical account of face matching and highlight the boundary conditions of this architecture.