Evidence for view-invariant face recognition units in unfamiliar face learning

The inside out model of emotion recognition: how the shape of one's internal emotional landscape influences the recognition of others' emotions

Some people are exceptional at reading emotional expressions, while others struggle. Here we ask whether the way we experience emotion "on the inside" influences the way we expect emotions to be expressed in the "outside world" and subsequently our ability to read others' emotional expressions. Across multiple experiments, incorporating discovery and replication samples, we develop EmoMap (N = 20; N = 271) and ExpressionMap (N = 98; replication N = 193) to map adults' experiences of emotions and visual representations of others' emotions. Some individuals have modular maps, wherein emotional experiences and visual representations are consistent and distinct-anger looks and feels different from happiness, which looks and feels different from sadness. In contrast, others have experiences and representations that are variable and overlapping-anger, happiness, and sadness look and feel similar and are easily confused for one another. Here we illustrate an association between these maps: those with consistent and distinct experiences of emotion also have consistent and distinct visual representations of emotion. Finally (N = 193), we construct the Inside Out Model of Emotion Recognition, which explains 60.8% of the variance in emotion recognition and illuminates multiple pathways to emotion recognition difficulties. These findings have important implications for understanding the emotion recognition difficulties documented in numerous clinical populations.

The impact of stability in appearance on the development of facial representations

The way faces become familiar and what information is represented as familiarity develops has puzzled researchers in the field of human face recognition for decades. In this paper, we present three experiments serving as proof of concept for a cost-efficient mechanism of face learning describing how facial representations form over time and accounting for recognition errors. We propose that the encoding of facial information is dynamic and modulated by the intrinsic stability in individual faces' appearance. We drew on a robust and ecological method using a proxy of exposure to famous faces in the real world and manipulated test images to assess the prediction that recognition of famous faces is affected by their relative stability in appearance. We consistently show that stable facial appearances (like Tom Cruise's) facilitate recognition in early stages of familiarisation but that performance does not improve much over time. In contrast, variations in appearance (like Jared Leto's) hinder recognition at first but improve performance with further media exposure. This pattern of results is consistent with the proposed cost-efficient face learning mechanism whereby facial representations build on a foundation of large-scale diagnostic information and refine over time if needed. When coarse information loses its diagnostic value through the experience of variations in appearance across encounters, diagnostic facial details and/or their spatial relationships must receive more weights, leading to refined representations that are more discriminative and reliable than representations of stable faces.

Specialized Networks for Social Cognition in the Primate Brain

Primates have evolved diverse cognitive capabilities to navigate their complex social world. To understand how the brain implements critical social cognitive abilities, we describe functional specialization in the domains of face processing, social interaction understanding, and mental state attribution. Systems for face processing are specialized from the level of single cells to populations of neurons within brain regions to hierarchically organized networks that extract and represent abstract social information. Such functional specialization is not confined to the sensorimotor periphery but appears to be a pervasive theme of primate brain organization all the way to the apex regions of cortical hierarchies. Circuits processing social information are juxtaposed with parallel systems involved in processing nonsocial information, suggesting common computations applied to different domains. The emerging picture of the neural basis of social cognition is a set of distinct but interacting subnetworks involved in component processes such as face perception and social reasoning, traversing large parts of the primate brain.

Understanding face identification through within-person variability in appearance: Introduction to a virtual special issue

In the effort to determine the cognitive processes underlying the identification of faces, the dissimilarities between images of different people have long been studied. In contrast, the inherent variability between different images of the same face has either been treated as a nuisance variable that should be eliminated from psychological experiments or it has not been considered at all. Over the past decade, research efforts have increased substantially to demonstrate that this within-person variation is meaningful and can give insight into various processes of face identification, such as identity matching, face learning, and familiar face recognition. In this virtual special issue of the Quarterly Journal of Experimental Psychology, we explain the importance of within-person variability for face identification and bring together recent relevant articles published in the journal.

View specific generalisation effects in face recognition: Front and yaw comparison views are better than pitch

It can be difficult to recognise new instances of an unfamiliar face. Recognition errors in this particular situation appear to be viewpoint dependent with error rates increasing with the angular distance between the face views. Studies using front views for comparison have shown that recognising faces rotated in yaw can be difficult and that recognition of faces rotated in pitch is more challenging still. Here we investigate the extent to which viewpoint dependent face recognition depends on the comparison view. Participants were assigned to one of four different comparison view groups: front, ¾ yaw (right), ¾ pitch-up (above) or ¾ pitch-down (below). On each trial, participants matched their particular comparison view to a range of yaw or pitch rotated test views. Results showed that groups with a front or ¾ yaw comparison view had superior overall performance and more successful generalisation to a broader range of both pitch and yaw test views compared to groups with pitch-up or pitch-down comparison views, both of which had a very restricted generalisation range. Regression analyses revealed the importance of image similarity between views for generalisation, with a lesser role for 3D face depth. These findings are consistent with a view interpolation solution to view generalisation of face recognition, with front and ¾ yaw views being most informative.

Search templates that incorporate within-face variation improve visual search for faces

Searching for unfamiliar faces in crowds is an important task in modern society. In surveillance and security settings, it is sometimes critical to locate a target individual quickly and accurately. In this study, we examine whether we can improve search efficiency in these visual search tasks by changing the face information that is provided to participants. In Experiment 1, we compare speed and accuracy of visual search when searching for unfamiliar and familiar faces after being exposed to either a single exemplar image or a face average created from multiple images of the target face. In Experiment 2, we compare search efficiency when single exemplars and multiple exemplars are provided. Consistent with studies of unfamiliar face matching tasks, we find that, relative to a single image, having multiple images of the target improves the accuracy of visual search. In Experiment 3, we compared search performance for face averages and multiple exemplars while also varying crowd size. Multiple exemplars conferred an additional advantage over face averages, suggesting that exposure to within-face variability results in the best search performance. We discuss the implications of these findings for face-in-a-crowd search and visual search tasks more generally.