Event-related brain potentials (ERPs) are important research tools because they provide insights into mental processing at high temporal resolution. Their usefulness, however, is limited by the need to average over a large number of trials, sacrificing information about the trial-by-trial variability of latencies or amplitudes of specific ERP components. Here we propose a novel method based on an iteration strategy of the residues of averaged ERPs (RIDE) to separate latency-variable component clusters. The separated component clusters can then serve as templates to estimate latencies in single trials with high precision. By applying RIDE to data from a face-priming experiment, we separate priming effects and show that they are robust against latency shifts and within-condition variability. RIDE is useful for a variety of data sets that show different degrees of variability and temporal overlap between ERP components.

Recognizing faces swiftly and accurately is of paramount importance to humans as a social species. Individual differences in the ability to perform these tasks may therefore reflect important aspects of social or emotional intelligence. Although functional models of face cognition based on group and single case studies postulate multiple component processes, little is known about the ability structure underlying individual differences in face cognition. In 2 large individual differences experiments (N = 151 and N = 209), a broad variety of face-cognition tasks were tested and the component abilities of face cognition-face perception, face memory, and the speed of face cognition-were identified and then replicated. Experiment 2 also showed that the 3 face-cognition abilities are clearly distinct from immediate and delayed memory, mental speed, general cognitive ability, and object cognition. These results converge with functional and neuroanatomical models of face cognition by demonstrating the difference between face perception and face memory. The results also underline the importance of distinguishing between speed and accuracy of face cognition. Together our results provide a first step toward establishing face-processing abilities as an independent ability reflecting elements of social intelligence.

Individual differences in face recognition are often contrasted with differences in object recognition using a single object category. Likewise, individual differences in perceptual expertise for a given object domain have typically been measured relative to only a single category baseline. In Experiment 1, we present a new test of object recognition, the Vanderbilt Expertise Test (VET), which is comparable in methods to the Cambridge Face Memory Task (CFMT) but uses eight different object categories. Principal component analysis reveals that the underlying structure of the VET can be largely explained by two independent factors, which demonstrate good reliability and capture interesting sex differences inherent in the VET structure. In Experiment 2, we show how the VET can be used to separate domain-specific from domain-general contributions to a standard measure of perceptual expertise. While domain-specific contributions are found for car matching for both men and women and for plane matching in men, women in this sample appear to use more domain-general strategies to match planes. In Experiment 3, we use the VET to demonstrate that holistic processing of faces predicts face recognition independently of general object recognition ability, which has a sex-specific contribution to face recognition. Overall, the results suggest that the VET is a reliable and valid measure of object recognition abilities and can measure both domain-general skills and domain-specific expertise, which were both found to depend on the sex of observers.

Dual-route models of face recognition suggest separate cognitive and affective routes. The predictions of these models were assessed in recognition tasks with unfamiliar, famous, and personally familiar faces. Whereas larger autonomic responses were only triggered for personally familiar faces, priming effects in reaction times to these faces, presumably reflecting cognitive recognition processes, were equal to those of famous faces. Activation of stored structural representations of familiar faces (face recognition units) was assessed by recording the N250r component in event-related brain potentials. Face recognition unit activation increased from unfamiliar over famous to personally familiar faces, suggesting that there are stronger representations for personally familiar than for famous faces. Because the topographies of the N250r for personally and famous faces were indistinguishable, a similar network of face recognition units can be assumed for both types of faces.

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