Face recognition abilities vary tremendously in the general population. People at the lower end of the spectrum, those with developmental prosopagnosia, report stress, anxiety or social interaction issues due to their poor face recognition abilities. It is thus important to develop adequate diagnostic tools convenient to use for clinicians and to examine relationships between face recognition skills and negative affects. In the present study, we provide a validated French translation of the 20-item prosopagnosia index (PI20), a self-report measure used to detect people with developmental facial identity recognition deficits (Shah et al., 2015; Tsantani et al., 2021). We also examined links between face recognition skills measured with the PI20 and a standard face recognition test (Cambridge face memory test-CFMT; Duchaine & Nakayama, 2006) and measures of social anxiety (social interaction anxiety scale, social phobia scale) and negative affects (state trait anxiety scale, Beck depression inventory). We did not find any significant correlation between the CFMT and measures of psychosocial well-being and only found a weak positive association between the PI20 and social interaction anxiety. Although this association is weak and warrants further research, raising awareness about developmental face recognition issues may help improve the well-being of people with facial identity recognition deficits and provide new investigation or intervention avenues for clinicians who treat patients with social interaction anxiety.

Time of day can alter memory performance in general. Its influence on memory recognition performance for faces, which is important for daily encounters with new persons or testimonies, has not been investigated yet. Importantly, high levels of the stress hormone cortisol impair memory recognition, in particular for emotional material. However, some studies also reported high cortisol levels to enhance memory recognition. Since cortisol levels in the morning are usually higher than in the evening, time of day might also influence recognition performance. In this pre-registered study with a two-day design, 51 healthy men encoded pictures of male and female faces with distinct emotional expressions on day one around noon. Memory for the faces was retrieved two days later at two consecutive testing times either in the morning (high and moderately increased endogenous cortisol levels) or in the evening (low endogenous cortisol levels). Additionally, alertness as well as salivary cortisol levels at the different timepoints was assessed. Cortisol levels were significantly higher in the morning compared to the evening group as expected, while both groups did not differ in alertness. Familiarity ratings for female stimuli were significantly better when participants were tested during moderately increased endogenous cortisol levels in the morning than during low endogenous cortisol levels in the evening, a pattern which was previously also observed for stressed versus non-stressed participants. In addition, cortisol levels during that time in the morning were positively correlated with the recollection of face stimuli in general. Thus, recognition memory performance may depend on the time of day and as well as on stimulus type, such as the difference of male and female faces. Most importantly, the results suggest that cortisol may be meaningful and worth investigating when studying the effects of time of day on memory performance. This research offers both, insights into daily encounters as well as legally relevant domains as for instance testimonies.

Children with autism spectrum disorder (ASD) show impairments in response inhibition, especially in socio-emotional contexts. A single aerobic exercise session has the potential to temporarily reduce such impairments as findings from neurotypical children support acute benefits of this exercise type for inhibitory control and emotion recognition. In children with ASD, we therefore aimed to investigate the effects of an aerobic exercise bout on response inhibition in an emotional Go/NoGo task and gaze fixation as possible mechanism underlying changes in performance. Using a cross-over design, 29 patients completed a 20-min aerobic exercise bout at moderate intensity on a cycling ergometer and a control condition in a randomized order. An emotional Go/NoGo task was administered before and after both experimental conditions. Eye-tracking was performed during the cognitive task to assess the duration of gaze fixation of eyes and mouth parts of faces expressing happy or sad emotions. The results support no beneficial effect of exercise on performance on the emotional Go/NoGo task. Instead, patients showed a greater decrease in accuracy on Go trials displaying happy faces in the exercise compared to the control condition. This change was associated with a more pronounced decrease in the fixation duration of the eyes for faces expressing either happy or sad emotions. In conclusion, while a single session of moderately intense aerobic exercise does not affect response inhibition, it temporarily aggravates ASD-specific deficits in the processing of and response to facial emotions.

While it is widely known that humans are typically highly accurate at recognizing familiar faces, it is less clear how efficiently recognition is achieved. In a series of three experiments, we used event-related brain potentials (ERP) in a repetition priming paradigm to examine the efficiency of familiar face recognition. Specifically, we varied the presentation time of the prime stimulus between 500 ms and 33 ms (Experiments 1 and 2), and additionally used backward masks (Experiment 3) to prevent the potential occurrence of visual aftereffects. Crucially, to test for the recognition of facial identity rather than a specific picture, we used different images of the same facial identities in repetition conditions. We observed clear ERP repetition priming effects between 300 and 500 ms after target onset at all prime durations, which suggests that the prime stimulus was sufficiently well processed to allow for facilitated recognition of the target in all conditions. This finding held true even in severely restricted viewing conditions including very brief prime durations and backward masks. We conclude that the facial recognition system is both highly effective and efficient, thus allowing for our impressive ability to recognise the faces that we know.

Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk-benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.

UNASSIGNED: Research has shown that some autistic people have severe difficulties in recognising other people\'s faces. However, little is understood about how these difficulties impact the daily life and the mental well-being of autistic people. In this study, we asked 60 autistic adults with varying degrees of face recognition ability to complete two tests of face recognition, a questionnaire about social anxiety and a bespoke survey which asked participants about their experiences of face recognition and social interaction. We found that participants who had poor face recognition reported experiencing higher levels of social anxiety compared to those with average or better face recognition skills. More than half felt that their face recognition difficulties affected their social interactions, and over a third believed it hindered their ability to make friends. These findings suggest that face recognition difficulties may contribute to social anxiety among autistic individuals.

Deep convolutional neural networks (DCNNs) have achieved human-level accuracy in face identification (Phillips et al., 2018), though it is unclear how accurately they discriminate highly-similar faces. Here, humans and a DCNN performed a challenging face-identity matching task that included identical twins. Participants (N = 87) viewed pairs of face images of three types: same-identity, general imposters (different identities from similar demographic groups), and twin imposters (identical twin siblings). The task was to determine whether the pairs showed the same person or different people. Identity comparisons were tested in three viewpoint-disparity conditions: frontal to frontal, frontal to 45° profile, and frontal to 90°profile. Accuracy for discriminating matched-identity pairs from twin-imposter pairs and general-imposter pairs was assessed in each viewpoint-disparity condition. Humans were more accurate for general-imposter pairs than twin-imposter pairs, and accuracy declined with increased viewpoint disparity between the images in a pair. A DCNN trained for face identification (Ranjan et al., 2018) was tested on the same image pairs presented to humans. Machine performance mirrored the pattern of human accuracy, but with performance at or above all humans in all but one condition. Human and machine similarity scores were compared across all image-pair types. This item-level analysis showed that human and machine similarity ratings correlated significantly in six of nine image-pair types [range r = 0.38 to r = 0.63], suggesting general accord between the perception of face similarity by humans and the DCNN. These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN.

The functional importance of the anterior temporal lobes (ATLs) has come to prominence in two active, albeit unconnected literatures-(i) face recognition and (ii) semantic memory. To generate a unified account of the ATLs, we tested the predictions from each literature and examined the effects of bilateral versus unilateral ATL damage on face recognition, person knowledge, and semantic memory. Sixteen people with bilateral ATL atrophy from semantic dementia (SD), 17 people with unilateral ATL resection for temporal lobe epilepsy (TLE; left = 10, right = 7), and 14 controls completed tasks assessing perceptual face matching, person knowledge and general semantic memory. People with SD were impaired across all semantic tasks, including person knowledge. Despite commensurate total ATL damage, unilateral resection generated mild impairments, with minimal differences between left- and right-ATL resection. Face matching performance was largely preserved but slightly reduced in SD and right TLE. All groups displayed the familiarity effect in face matching; however, it was reduced in SD and right TLE and was aligned with the level of item-specific semantic knowledge in all participants. We propose a neurocognitive framework whereby the ATLs underpin a resilient bilateral representation system that supports semantic memory, person knowledge and face recognition.

Carragher and Hancock (2023) investigated how individuals performed in a one-to-one face matching task when assisted by an Automated Facial Recognition System (AFRS). Across five pre-registered experiments they found evidence of suboptimal aided performance, with AFRS-assisted individuals consistently failing to reach the level of performance the AFRS achieved alone. The current study reanalyses these data (Carragher and Hancock, 2023), to benchmark automation-aided performance against a series of statistical models of collaborative decision making, spanning a range of efficiency levels. Analyses using a Bayesian hierarchical signal detection model revealed that collaborative performance was highly inefficient, falling closest to the most suboptimal models of automation dependence tested. This pattern of results generalises previous reports of suboptimal human-automation interaction across a range of visual search, target detection, sensory discrimination, and numeric estimation decision-making tasks. The current study is the first to provide benchmarks of automation-aided performance in the one-to-one face matching task.

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.