The face recognition literature has considered two competing accounts of how faces are represented within the visual system: Exemplar-based models assume that faces are represented via their similarity to exemplars of previously experienced faces, while norm-based models assume that faces are represented with respect to their deviation from an average face, or norm. Face identity aftereffects have been taken as compelling evidence in favor of a norm-based account over an exemplar-based account. After a relatively brief period of adaptation to an adaptor face, the perceived identity of a test face is shifted towards a face with opposite attributes to the adaptor, suggesting an explicit psychological representation of the norm. Surprisingly, despite near universal recognition that face identity aftereffects imply norm-based coding, there have been no published attempts to simulate the predictions of norm- and exemplar-based models in face adaptation paradigms. Here we implemented and tested variations of norm and exemplar models. Contrary to common claims, our simulations revealed that both an exemplar-based model and a version of a two-pool norm-based model, but not a traditional norm-based model, predict face identity aftereffects following face adaptation.

Expertise effects for nonface objects in face-selective brain areas may reflect stable aspects of neuronal selectivity that determine how observers perceive objects. However, bottom-up (e.g., clutter from irrelevant objects) and top-down manipulations (e.g., attentional selection) can influence activity, affecting the link between category selectivity and individual performance. We test the prediction that individual differences expressed as neural expertise effects for cars in face-selective areas are sufficiently stable to survive clutter and manipulations of attention. Additionally, behavioral work and work using event related potentials suggest that expertise effects may not survive competition; we investigate this using functional magnetic resonance imaging. Subjects varying in expertise with cars made 1-back decisions about cars, faces, and objects in displays containing one or 2 objects, with only one category attended. Univariate analyses suggest car expertise effects are robust to clutter, dampened by reducing attention to cars, but nonetheless more robust to manipulations of attention than competition. While univariate expertise effects are severely abolished by competition between cars and faces, multivariate analyses reveal new information related to car expertise. These results demonstrate that signals in face-selective areas predict expertise effects for nonface objects in a variety of conditions, although individual differences may be expressed in different dependent measures depending on task and instructions.

There is growing interest in the study of individual differences in face recognition, including one of its hallmarks, holistic processing, which can be defined as a failure of selective attention to parts. These efforts demand that researchers be aware of, and try to maximize, the reliability of their measurements. Here we report on the reliability of measurements using the composite task (complete design), a measure of holistic processing that has been shown to have relatively good validity. Several studies have used the composite task to investigate individual differences, yet only one study has discussed its reliability. We investigate the reliability of composite task measurements in eight datasets from five different samples of subjects. In general, we found reliability to be fairly low but there was substantial variability across experiments. Researchers should keep in mind that reliability is a property of measurements, not a task, and the ways in which measurements in this task may be improved, before embarking on individual differences research.

Facial caricatures exaggerate the distinctive features of a face and may elevate the recognition of a familiar face. We investigate whether the recognition of facial composites, or pictures of criminal faces, could be similarly enhanced. In this study, participants first estimated the degree of caricature necessary to make composites most identifiable. Contrary to expectation, an anti-caricature was found to be best, presumably as this tended to reduce the appearance of errors. In support of this explanation, more positive caricature estimates were assigned to morphed composites: representations which tend to contain less overall error. In addition, anti-caricaturing reduced identification for morphed composites but enhanced identification for individual composites. While such improvements were too small to be of value to law enforcement, a sizeable naming benefit was observed when presenting a range of caricature states, which appeared to capitalise on individual differences in the internal representation of familiar faces.

No abstract