La dynamique du traitement des visages : du percept à la familiarité

Reconnaître rapidement les visages familiers est une fonction fondamentale qui joue un rôle important dans nos interactions sociales. D'un point de vue évolutionniste, il semble en effet crucial de pouvoir rapidement déterminer si la personne qui nous fait face est amie ou ennemie pour adapter son comportement en conséquence. C'est sans doute pour cela que, dans l'opinion publique, mais également dans la littérature scientifique, la reconnaissance des visages est supposée être un processus très rapide, efficace et exécuté sans effort. Cependant, la reconnaissance des visages familiers est-elle réellement si rapide ? Ne serait-ce pas simplement une idée reçue ? Rapide, admettons, mais à quelle vitesse ? En adaptant des protocoles de catégorisation visuelle rapide (tâche de go/no-go) développés initialement pour étudier la rapidité du système visuel et en mettant en place de nouveaux protocoles de catégorisation ultra-rapide (" Speed and Accuracy Boosting procedure " ; SAB), nous avons pu déterminer les latences comportementales et électrophysiologiques les plus précoces pour reconnaître explicitement un visage célèbre. Nous nous sommes intéressés d'une part à la reconnaissance de type " bottom-up " (reconnaître plusieurs visages célèbres sans savoir au préalable de qui il s'agit) et d'autre part à la reconnaissance " top-down " (reconnaître une personne en particulier parmi des inconnus). Le temps de réaction minimum pour reconnaître des visages célèbres parmi des inconnus (reconnaissance " bottom-up ") est d'environ 360-390 ms, ce temps de réponse ne pouvant être amélioré ni par un apprentissage intensif des stimuli (Article 1), ni par un protocole de catégorisation ultra-rapide (Article 2). Ce temps de réaction est environ 100 ms plus tardif que lors d'une tâche de détection de visage (Article 1) ou de genre (Article 1). Ces latences sont très différentes lorsque la procédure SAB est appliquée à une reconnaissance de type " top-down ", descendant à environ 300 ms contre 270 ms dans une tâche de détection de visages (Article 3). De plus, en appliquant une méthode de MVPA (Multi-Variate Pattern Analysis) à des données d'EEG de surface, nous avons montré que l'activité neuronale liée à la reconnaissance des visages célèbres était disponible dès 230 ms après la présentation du stimulus (voire 200 ms pour le sujet les plus rapides) alors que l'activité neuronale liée à a détection d'un visage humain parmi des visages d'animaux était disponible dés 80 ms (Articles 4 et 5). L'activité neuronale était de plus fortement corrélée aux temps de réponses minimales en reconnaissance, confirmant ainsi son rôle dans la prise de décision. Nous discutons ces latences au regard des modèles de la voie visuelle ventrale et des modèles de la reconnaissance des visages. Nous distinguons trois modèles différents pouvant théoriquement être à l'origine de la familiarité et en favorisons un en particulier.Recognizing familiar faces rapidly seems crucial in everyday life. The actual speed at which a familiar face can be recognized remains however unknown. The current thesis aimed at tracking down the minimal behavioral and neural processing time necessary to recognize known faces. To address this issue, we used different go/no-go paradigms and a new task relying on highly time-constraining task (the Speed and Accuracy Boosting procedure, "SAB"). Relying on minimum reaction times analyses, we report that 360-390 ms are needed to recognize famous faces among unknown ones when bottom-up recognition task is required (subjects did not know the identity of the celebrities that they had to recognize before the test; this situation can be compare to the ecological situation of unexpectedly bumping into someone in the street) (Article 1). This latency could not be decreased even after extensive training (Article 1), or using the SAB (Article 2). Overall, this is 100 ms more than when subjects have to detect human faces in natural scene or process gender (Article 1). Bottom-up recognition is much slower than top-down recognition (recognizing somebody whom you know you are going to meet, corresponding to the ecological situation of looking for someone in particular in a crowd), which takes about 300 ms (Article 3). Additionally, MVPA (Multivariate pattern analysis) was applied on EEG data recorded from the scalp surface to determine at which latency familiarity could be read-out. We report that famous faces could be robustly distinguished from unknown faces as soon as 230 ms after stimulus onset. This familiarity-selective signal was directly linked to the subject's recognition speed (Article 5). Such latency was agin much longer than the latencies observed in face categorisation task, in which case category could be read out starting around 80 ms post-stimulus (Article 4). These latencies are with respect to the different models of visual ventral stream and models of face recognition. Three main models are identified and one is favored in particular

The repeatability of cognitive performance: a meta-analysis

International audienceOne contribution of 15 to a theme issue 'Causes and consequences of individual differences in cognitive abilities'. Behavioural and cognitive processes play important roles in mediating an individual's interactions with its environment. Yet, while there is a vast literature on repeatable individual differences in behaviour, relatively little is known about the repeatability of cognitive performance. To further our understanding of the evolution of cogni-tion, we gathered 44 studies on individual performance of 25 species across six animal classes and used meta-analysis to assess whether cognitive performance is repea-table. We compared repeatability (R) in performance (1) on the same task presented at different times (temporal repeat-ability), and (2) on different tasks that measured the same putative cognitive ability (contextual repeatability). We also addressed whether R estimates were influenced by seven extrinsic factors (moderators): type of cognitive performance measurement, type of cognitive task, delay between tests, origin of the subjects, experimental context, taxonomic class and publication status. We found support for both temporal and contextual repeatability of cognitive performance, with mean R estimates ranging between 0.15 and 0.28. Repeatability estimates were mostly influenced by the type of cognitive performance measures and publication status. Our findings highlight the widespread occurrence of consistent inter-individual variation in cog-nition across a range of taxa which, like behaviour, may be associated with fitness outcomes. This article is part of the theme issue 'Causes and consequences of individual differences in cognitive abilities'

The chemokine receptor CXCR2 and coronavirus-induced neurologic disease.

Inoculation with the neurotropic JHM strain of mouse hepatitis virus (MHV) into the central nervous system (CNS) of susceptible strains of mice results in an acute encephalomyelitis in which virus preferentially replicates within glial cells while excluding neurons. Control of viral replication during acute disease is mediated by infiltrating virus-specific T cells via cytokine secretion and cytolytic activity, however sterile immunity is not achieved and virus persists resulting in chronic neuroinflammation associated with demyelination. CXCR2 is a chemokine receptor that upon binding to specific ligands promotes host defense through recruitment of myeloid cells to the CNS as well as protecting oligodendroglia from cytokine-mediated death in response to MHV infection. These findings highlight growing evidence of the diverse and important role of CXCR2 in regulating neuroinflammatory diseases

The repeatability of cognitive performance:A meta-analysis

This is the author accepted manuscript. The final version is available from The Royal Society via the DOI in this record.Behavioural and cognitive processes play important roles in mediating an individual's interactions with its environment. Yet, while there is a vast literature on repeatable individual differences in behaviour, relatively little is known about the repeatability of cognitive performance. To further our understanding of the evolution of cognition, we gathered 44 studies on individual performance of 25 species across six animal classes and used meta-analysis to assess whether cognitive performance is repeatable. We compared repeatability (R) in performance (1) on the same task presented at different times (temporal repeatability), and (2) on different tasks that measured the same putative cognitive ability (contextual repeatability). We also addressed whether R estimates were influenced by seven extrinsic factors (moderators): type of cognitive performance measurement, type of cognitive task, delay between tests, origin of the subjects, experimental context, taxonomic class and publication status. We found support for both temporal and contextual repeatability of cognitive performance, with mean R estimates ranging between 0.15 and 0.28. Repeatability estimates were mostly influenced by the type of cognitive performance measures and publication status. Our findings highlight the widespread occurrence of consistent inter-individual variation in cognition across a range of taxa which, like behaviour, may be associated with fitness outcomes.PKYC is supported by Japan Society for the Promotion of Science (PE1801); JOvH was funded by an ERC consolidator grant (616474). MC and this research was supported by a grant from the Human Frontier Science Program to ASC and JM-F (RGP0006/2015)

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

La dynamique du traitement des visages : du percept à la familiarité

Recognizing familiar faces rapidly seems crucial in everyday life. The actual speed at which a familiar face can be recognized remains however unknown. The current thesis aimed at tracking down the minimal behavioral and neural processing time necessary to recognize known faces. To address this issue, we used different go/no-go paradigms and a new task relying on highly time-constraining task (the Speed and Accuracy Boosting procedure, "SAB"). Relying on minimum reaction times analyses, we report that 360-390 ms are needed to recognize famous faces among unknown ones when bottom-up recognition task is required (subjects did not know the identity of the celebrities that they had to recognize before the test; this situation can be compare to the ecological situation of unexpectedly bumping into someone in the street) (Article 1). This latency could not be decreased even after extensive training (Article 1), or using the SAB (Article 2). Overall, this is 100 ms more than when subjects have to detect human faces in natural scene or process gender (Article 1). Bottom-up recognition is much slower than top-down recognition (recognizing somebody whom you know you are going to meet, corresponding to the ecological situation of looking for someone in particular in a crowd), which takes about 300 ms (Article 3). Additionally, MVPA (Multivariate pattern analysis) was applied on EEG data recorded from the scalp surface to determine at which latency familiarity could be read-out. We report that famous faces could be robustly distinguished from unknown faces as soon as 230 ms after stimulus onset. This familiarity-selective signal was directly linked to the subject's recognition speed (Article 5). Such latency was agin much longer than the latencies observed in face categorisation task, in which case category could be read out starting around 80 ms post-stimulus (Article 4). These latencies are with respect to the different models of visual ventral stream and models of face recognition. Three main models are identified and one is favored in particular.Reconnaître rapidement les visages familiers est une fonction fondamentale qui joue un rôle important dans nos interactions sociales. D'un point de vue évolutionniste, il semble en effet crucial de pouvoir rapidement déterminer si la personne qui nous fait face est amie ou ennemie pour adapter son comportement en conséquence. C'est sans doute pour cela que, dans l'opinion publique, mais également dans la littérature scientifique, la reconnaissance des visages est supposée être un processus très rapide, efficace et exécuté sans effort. Cependant, la reconnaissance des visages familiers est-elle réellement si rapide ? Ne serait-ce pas simplement une idée reçue ? Rapide, admettons, mais à quelle vitesse ? En adaptant des protocoles de catégorisation visuelle rapide (tâche de go/no-go) développés initialement pour étudier la rapidité du système visuel et en mettant en place de nouveaux protocoles de catégorisation ultra-rapide (" Speed and Accuracy Boosting procedure " ; SAB), nous avons pu déterminer les latences comportementales et électrophysiologiques les plus précoces pour reconnaître explicitement un visage célèbre. Nous nous sommes intéressés d'une part à la reconnaissance de type " bottom-up " (reconnaître plusieurs visages célèbres sans savoir au préalable de qui il s'agit) et d'autre part à la reconnaissance " top-down " (reconnaître une personne en particulier parmi des inconnus). Le temps de réaction minimum pour reconnaître des visages célèbres parmi des inconnus (reconnaissance " bottom-up ") est d'environ 360-390 ms, ce temps de réponse ne pouvant être amélioré ni par un apprentissage intensif des stimuli (Article 1), ni par un protocole de catégorisation ultra-rapide (Article 2). Ce temps de réaction est environ 100 ms plus tardif que lors d'une tâche de détection de visage (Article 1) ou de genre (Article 1). Ces latences sont très différentes lorsque la procédure SAB est appliquée à une reconnaissance de type " top-down ", descendant à environ 300 ms contre 270 ms dans une tâche de détection de visages (Article 3). De plus, en appliquant une méthode de MVPA (Multi-Variate Pattern Analysis) à des données d'EEG de surface, nous avons montré que l'activité neuronale liée à la reconnaissance des visages célèbres était disponible dès 230 ms après la présentation du stimulus (voire 200 ms pour le sujet les plus rapides) alors que l'activité neuronale liée à a détection d'un visage humain parmi des visages d'animaux était disponible dés 80 ms (Articles 4 et 5). L'activité neuronale était de plus fortement corrélée aux temps de réponses minimales en reconnaissance, confirmant ainsi son rôle dans la prise de décision. Nous discutons ces latences au regard des modèles de la voie visuelle ventrale et des modèles de la reconnaissance des visages. Nous distinguons trois modèles différents pouvant théoriquement être à l'origine de la familiarité et en favorisons un en particulier

Children with higher screen time exposure were less likely to show patience and to make school friends at 4–6 years of age

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The neural speed of familiar face recognition

International audienceRapidly recognizing familiar people from their faces appears critical for social interactions (e.g., to differentiate friend from foe). However, the actual speed at which the human brain can distinguish familiar from unknown faces still remains debated. In particular, it is not clear whether familiarity can be extracted from rapid face individualization or if it requires additional time consuming processing. We recorded scalp EEG activity in 28 subjects performing a go/no-go, famous/non-famous, unrepeated, face recognition task. Speed constraints were used to encourage subjects to use the earliest familiarity information available. Event related potential (ERP) analyses show that both the N170 and the N250 components were modulated by familiarity. The N170 modulation was related to behaviour: subjects presenting the strongest N170 modulation were also faster but less accurate than those who only showed weak N170 modulation. A complementary Multi-Variate Pattern Analysis (MVPA) confirmed ERP results and provided some more insights into the dynamics of face recognition as the N170 differential effect appeared to be related to a first transitory phase (transitory bump of decoding power) starting at around 140 ms, which returned to baseline afterwards. This bump of activity was henceforth followed by an increase of decoding power starting around 200 ms after stimulus onset. Overall, our results suggest that rather than a simple single-process, familiarity for faces may rely on a cascade of neural processes, including a coarse and fast stage starting at 140 ms and a more refined but slower stage occurring after 200 ms

Cognition in Context: Plasticity in Cognitive Performance in Response to Ongoing Environmental Variables

International audienceCognition has evolved to allow organisms to process, use and store information in their natural environment. Yet, cognitive abilities are traditionally measured in controlled laboratory conditions to obtain consistent and accurate measurements. Consequently, little is known about the actual effect of natural environmental variation on cognitive performances. Being able to modify cognitive performance according to environmental conditions (e.g., plasticity of attentional performances according to current predator densities) could provide evolutionary advantages. In this mini-review, we compile evidence for what we call “cognitive performance plasticity” (i.e., flexible adjustment of cognitive performance in response to the current environment). We then discuss methodological approaches associated with measurement of cognitive performance plasticity and cognition in general. Finally, we discuss the implications of acknowledging plasticity in cognitive performance, including a better understanding of the reproducibility crisis observed in cognitive sciences (Open Science Collaboration, 2015) and new lines of inquiry into the evolution of cognition and the adaptive value of cognitive performance plasticity