No abstract

Debate continues as to the automaticity of the amygdala's response to threat. Accounts taking a strong automaticity line suggest that the amygdala's response to threat is both involuntary and independent of attentional resources. Building on these accounts, prominent models have suggested that anxiety modulates the output of an amygdala-based preattentive threat evaluation system. Here, we argue for a modification of these models. Functional magnetic resonance imaging data were collected while volunteers performed a letter search task of high or low perceptual load superimposed on fearful or neutral face distractors. Neither high- nor low-anxious volunteers showed an increased amygdala response to threat distractors under high perceptual load, contrary to a strong automaticity account of amygdala function. Under low perceptual load, elevated state anxiety was associated with a heightened response to threat distractors in the amygdala and superior temporal sulcus, whereas individuals high in trait anxiety showed a reduced prefrontal response to these stimuli, consistent with weakened recruitment of control mechanisms used to prevent the further processing of salient distractors. These findings suggest that anxiety modulates processing subsequent to competition for perceptual processing resources, with state and trait anxiety having distinguishable influences upon the neural mechanisms underlying threat evaluation and "top-down" control.

Photo-ID is widely used in security settings, despite research showing that viewers find it very difficult to match unfamiliar faces. Here we test participants with specialist experience and training in the task: passport-issuing officers. First, we ask officers to compare photos to live ID-card bearers, and observe high error rates, including 14% false acceptance of ‘fraudulent’ photos. Second, we compare passport officers with a set of student participants, and find equally poor levels of accuracy in both groups. Finally, we observe that passport officers show no performance advantage over the general population on a standardised face-matching task. Across all tasks, we observe very large individual differences: while average performance of passport staff was poor, some officers performed very accurately – though this was not related to length of experience or training. We propose that improvements in security could be made by emphasising personnel selection.

We are able to recognise familiar faces easily across large variations in image quality, though our ability to match unfamiliar faces is strikingly poor. Here we ask how the representation of a face changes as we become familiar with it. We use a simple imageaveraging technique to derive abstract representations of known faces. Using Principal Components Analysis, we show that computational systems based on these averages consistently outperform systems based on collections of instances. Furthermore, the quality of the average improves as more images are used to derive it. These simulations are carried out with famous faces, over which we had no control of superficial image characteristics. We then present data from three experiments demonstrating that image averaging can also improve recognition by human observers. Finally, we describe how PCA on image averages appears to preserve identity-specific face information, while eliminating non-diagnostic pictorial information. We therefore suggest that this is a good candidate for a robust face representation.2 Robust representations for face recognition: the power of averages