Developmental prosopagnosics and super recognizers rely on the same facial features used by individuals with normal face recognition abilities for face identification

Humans' extreme face recognition abilities challenge the well-established familiarity effect

Classification performance is better for learned than unlearned stimuli. This was also reported for faces, where identity matching of unfamiliar faces is worse than for familiar faces. This familiarity advantage led to the conclusion that variability across appearances of the same identity is partly idiosyncratic and cannot be generalized from familiar to unfamiliar identities. Recent advances in machine vision challenge this claim by showing that the performance for untrained (unfamiliar) identities reached the level of trained identities as the number of identities that the algorithm is trained with increases. We therefore asked whether humans who reportedly can identify a vast number of identities, such as super recognizers, may close the gap between familiar and unfamiliar face classification. Consistent with this prediction, super recognizers classified unfamiliar faces just as well as typical participants who are familiar with the same faces, on a task that generates a sizable familiarity effect in controls. Additionally, prosopagnosics' performance for familiar faces was as bad as that of typical participants who were unfamiliar with the same faces, indicating that they struggle to learn even identity-specific information. Overall, these findings demonstrate that by studying the extreme ends of a system's ability we can gain novel insights into its actual capabilities.

Disentangling developmental prosopagnosia: A scoping review of terms, tools and topics

The goal of this preregistered scoping review is to create an overview of the research on developmental prosopagnosia (DP). Through analysis of all empirical studies of DP in adults, we investigate 1) how DP is conceptualized and defined, 2) how individuals are classified with DP and 3) which aspects of DP are investigated in the literature. We reviewed 224 peer-reviewed studies of DP. Our analysis of the literature reveals that while DP is predominantly defined as a lifelong face recognition impairment in the absence of acquired brain injury and intellectual/cognitive problems, there is far from consensus on the specifics of the definition with some studies emphasizing e.g., deficits in face perception, discrimination and/or matching as core characteristics of DP. These differences in DP definitions is further reflected in the vast heterogeneity in classification procedures. Only about half of the included studies explicitly state how they classify individuals with DP, and these studies adopt 40 different assessment tools. The two most frequently studied aspects of DP are the role of holistic processing and the specificity of face processing, and alongside a substantial body of neuroimaging studies of DP, this paints a picture of a research field whose scientific interests and aims are rooted in cognitive neuropsychology and neuroscience. We argue that these roots - alongside the heterogeneity in DP definition and classification - may have limited the scope and interest of DP research unnecessarily, and we point to new avenues of research for the field.

Face Feature Change Detection Ability in Developmental Prosopagnosia and Super-Recognisers

In non-clinical populations, facial features (eyes, nose, mouth) may vary in their contribution to face identity perception. Changes to whole faces are easier to detect than changes to individual features, and eye changes are typically easier to detect than mouth changes, which in turn are easier to detect than nose changes. However, how this differs for people with face recognition difficulties (developmental prosopagnosia; DP) and for individuals with superior face recognition abilities (super-recognisers; SR) is not clear; although findings from previous studies have suggested differences, the nature of this difference is not understood. The aim of this study was to examine whether differences in the ability to detect feature changes in DPs and SRs were (a) quantitative, meaning that the pattern across feature changes remained the same but there was an overall upwards or downwards shift in performance, or (b) qualitative, meaning that the pattern across feature changes was different. Using a change detection task in which individual face features (eyes, nose, mouth) changed between sequentially presented faces, we found that while prosopagnosics showed a quantitative difference in performance with a downwards shift across all conditions, super-recognisers only showed qualitative differences: they were better able to detect when the face was the same and were marginally (but not non-significantly) worse at detecting when the eyes changed. Further, the only condition which distinguished between the three groups was the ability to identify when the same face was presented, with SRs being better than controls, and controls being better than DPs. Our findings suggest that, in feature-matching tasks, differences for DPs are due to them being overall worse at the task, while SRs use a qualitatively different strategy.

Using deep neural networks to disentangle visual and semantic information in human perception and memory

Mental representations of familiar categories are composed of visual and semantic information. Disentangling the contributions of visual and semantic information in humans is challenging because they are intermixed in mental representations. Deep neural networks that are trained either on images or on text or by pairing images and text enable us now to disentangle human mental representations into their visual, visual-semantic and semantic components. Here we used these deep neural networks to uncover the content of human mental representations of familiar faces and objects when they are viewed or recalled from memory. The results show a larger visual than semantic contribution when images are viewed and a reversed pattern when they are recalled. We further reveal a previously unknown unique contribution of an integrated visual-semantic representation in both perception and memory. We propose a new framework in which visual and semantic information contribute independently and interactively to mental representations in perception and memory.

Holistic and featural processing's link to face recognition varies by individual and task

While it is generally accepted that holistic processing facilitates face recognition, recent studies suggest that poor recognition might also arise from imprecise perception of local features in the face. This study aimed to examine to what extent holistic and featural processing relates to individual differences in face recognition ability (FRA), during face learning (Experiment 1) and face recognition (Experiment 2). Participants performed two tasks: (1) The "Cambridge Face Memory Test-Chinese" which measured participants' FRAs, and (2) an "old/new recognition memory test" encompassing whole faces (preserving holistic and featural processing) and faces revealed through a dynamic aperture (impairing holistic processing but preserving featural processing). Our results showed that participants recognised faces more accurately in conditions when holistic information was preserved, than when it is impaired. We also show that the better use of holistic processing during face learning and face recognition was associated with better FRAs. However, enhanced featural processing during recognition, but not during learning, was related to better FRAs. Together, our findings demonstrate that good face recognition depends on distinct roles played by holistic and featural processing at different stages of face recognition.

What is the prevalence of developmental prosopagnosia? An empirical assessment of different diagnostic cutoffs

The prevalence of developmental prosopagnosia (DP), lifelong face recognition deficits, is widely reported to be 2-2.5%. However, DP has been diagnosed in different ways across studies, resulting in differing prevalence rates. In the current investigation, we estimated the range of DP prevalence by administering well-validated objective and subjective face recognition measures to an unselected web-based sample of 3116 18-55 year-olds and applying DP diagnostic cutoffs from the last 14 years. We found estimated prevalence rates ranged from .64-5.42% when using a z-score approach and .13-2.95% when using a percentile approach, with the most commonly used cutoffs by researchers having a prevalence rate of .93% (z-score, .45% when using percentiles). We next used multiple cluster analyses to examine whether there was a natural grouping of poorer face recognizers but failed to find consistent grouping beyond those with generally above versus below average face recognition. Lastly, we investigated whether DP studies with more relaxed diagnostic cutoffs were associated with better performance on the Cambridge Face Perception Test. In a sample of 43 studies, there was a weak nonsignificant association between greater diagnostic strictness and better DP face perception accuracy (Kendall's tau-b correlation, τb =.18 z-score; τb = .11 percentiles). Together, these results suggest that researchers have used more conservative DP diagnostic cutoffs than the widely reported 2-2.5% prevalence. We discuss the strengths and weaknesses of using more inclusive cutoffs, such as identifying mild and major forms of DP based on DSM-5.

Unfamiliar face matching ability predicts the slope of face learning

We provide the first examination of individual differences in the efficiency of face learning. Investigating individual differences in face learning can illuminate potential mechanisms and provide greater understanding of why certain individuals might be more efficient face learners. Participants completed two unfamiliar face matching tasks and a learning task in which learning was assessed after viewing 1, 3, 6, and 9 images of to-be-learned identities. Individual differences in the slope of face learning (i.e., increases in sensitivity to identity) were predicted by the ability to discriminate between matched (same-identity) vs. mismatched (different-identity) pairs of wholly unfamiliar faces. A Dual Process Signal Detection model showed that three parameters increased with learning: Familiarity (an unconscious type of memory that varies in strength), recollection-old (conscious recognition of a learned identity), and recollection-new (conscious/confident rejection of novel identities). Good (vs. poor) matchers had higher Recollection-Old scores throughout learning and showed a steeper increase in Recollection-New. We conclude that good matchers are better able to capitalize on exposure to within-person variability in appearance, an effect that is attributable to their conscious memory for both learned and novel faces. These results have applied implications and will inform contemporary and traditional models of face identification.

EXPRESS: Distinct abilities associated with matching same identity faces vs. discriminating different faces: Evidence from individual differences in prosopagnosics and controls

Previous face matching studies provide evidence that matching same identity faces (match trials) and discriminating different face identities (non-match trials) rely on distinct processes. For example, instructional studies geared towards improving face matching in applied settings have often found selective improvements in match or non-match trials only. Additionally, a small study found that developmental prosopagnosics (DPs) have specific deficits in making match but not non-match judgments. In the current study, we sought to replicate this finding in DPs and examine how individual differences across DPs and controls in match vs. non-match performance relate to featural vs. holistic processing abilities. 43 DPs and 27 controls matched face images shown from similar front views or with varied lighting or viewpoint. Participants also performed tasks measuring featural (eyes/mouth) and holistic processing (part-whole task). We found that DPs showed worse overall matching performance than controls and that their relative match vs. non-match deficit depended on image variation condition, indicating that DPs do not consistently show match- or non-match-specific deficits. When examining the association between holistic and featural processing abilities and match vs. non-match trials in the entire group of DPs and controls, we found a very clear dissociation: Match trials significantly correlated with eye processing ability (r=.48) but not holistic processing (r=.11), whereas non-match trials significantly correlated with holistic processing (r=.32) but not eye processing (r=.03). This suggests that matching same identity faces relies more on eye processing while discriminating different faces relies more on holistic processing.