Le sérieux du rire : la comédie polémique et les convulsionnaires de Saint-Médard

Il peut paraître curieux que la querelle entourant l'épisode convulsionnaire du jansénisme des Lumières se soit trouvée à s'exprimer en comédies, mais cela se comprend lorsque l'on examine l'itinéraire général du jansénisme, et le parcours particulier des comédies en tant que telles. Les deux comédies, une écrite par un père jésuite et l'autre par un janséniste inconnu, ouvrent une fenêtre inusitée sur le phénomène convulsionnaire, la réception sociale qu'il connut et les malaises politico-religieux qu'il souleva. Les échos littéraires de la querelle janséniste révèlent un déplacement de ses lieux et modalités habituels d'affrontements. La querelle janséniste du XVIIIe siècle laisse place à une polémique furieuse qui investit l'espace public. La discussion théologique de la première querelle reste présente, mais elle sert davantage de paysage dans lequel se dessinent de nouveaux motifs de disputes tels que le rôle des miracles, la paix de l'Église et la question de l'autorité. La guerre entre jansénistes tourne autour de lieux communs et de caricatures qui couraient à l'époque. L'application comique à travers une intrigue exagérée et des personnages stéréotypés sert davatage la polémique. Les comédies furent reçues comme de la littérature polémique autour d'un sujet d'actualité. Elles sont une tentative réussie d'ouvrir la querelle précise de Saint-Médard à un certain public. Plus encore, elles sont une réflexion sur la reconnaissance du public comme instance nouvelle d'autorité et sur les moyens légitimes de lui parler

A cable-suspended intelligent crane assist device for the intuitive manipulation of large payloads

This paper presents a cable-suspended crane system to assist operators in moving and lifting large payloads. The main objective of this work is to develop a simple and reliable system to help operators in industry to be more productive while preventing injuries. The system is based on the development of a precise and reliable cable angle sensor and a complete dynamic model of the system. Adaptive horizontal and vertical controllers designed for direct physical human-robot interaction are then proposed. Different techniques are then proposed to estimate the payload acceleration in order to increase the controller performances. Finally, experiments performed on a full-scale industrial system are presented

Neural computations in prosopagnosia

We report an investigation of the neural processes involved in the processing of faces and objects of brain-lesioned patient PS, a well-documented case of pure acquired prosopagnosia. We gathered a substantial dataset of high-density electrophysiological recordings from both PS and neurotypicals. Using representational similarity analysis, we produced time-resolved brain representations in a format that facilitates direct comparisons across time points, different individuals, and computational models. To understand how the lesions in PS’s ventral stream affect the temporal evolution of her brain representations, we computed the temporal generalization of her brain representations. We uncovered that PS’s early brain representations exhibit an unusual similarity to later representations, implying an excessive generalization of early visual patterns. To reveal the underlying computational deficits, we correlated PS’ brain representations with those of deep neural networks (DNN). We found that the computations underlying PS’ brain activity bore a closer resemblance to early layers of a visual DNN than those of controls. However, the brain representations in neurotypicals became more akin to those of the later layers of the model compared to PS. We confirmed PS’s deficits in high-level brain representations by demonstrating that her brain representations exhibited less similarity with those of a DNN of semantics

Neural computations in prosopagnosia

We report an investigation of the neural processes involved in the processing of faces and objects of brain-lesioned patient PS, a well-documented case of pure acquired prosopagnosia. We gathered a substantial dataset of high-density electrophysiological recordings from both PS and neurotypicals. Using representational similarity analysis, we produced time-resolved brain representations in a format that facilitates direct comparisons across time points, different individuals, and computational models. To understand how the lesions in PS’s ventral stream affect the temporal evolution of her brain representations, we computed the temporal generalization of her brain representations. We uncovered that PS’s early brain representations exhibit an unusual similarity to later representations, implying an excessive generalization of early visual patterns. To reveal the underlying computational deficits, we correlated PS’ brain representations with those of deep neural networks (DNN). We found that the computations underlying PS’ brain activity bore a closer resemblance to early layers of a visual DNN than those of controls. However, the brain representations in neurotypicals became more akin to those of the later layers of the model compared to PS. We confirmed PS’s deficits in high-level brain representations by demonstrating that her brain representations exhibited less similarity with those of a DNN of semantics

An articulated assistive robot for intuitive hands-on-payload manipulation

This paper presents an intelligent assistive robot designed to help operators in lifting and moving large payloads through direct physical contact (hands-on-payload mode). The mechanical design of the robot is first presented. Although its kinematics are similar to that of a cable-suspended system, the proposed mechanism is based on articulated linkages, thereby allowing the payload to be offset from the rail support on which it is suspended. A dynamic model of the robot is then developed. It is shown that a simplified dynamic model can be obtained using geometric assumptions. Based on the simplified dynamic model, a controller is then presented that handles the physical human-robot interaction and that provides the operator with an intuitive direct control of the payload. Experimental validation on a full-scale prototype is presented in order to demonstrate the effectiveness of the proposed robot and controller

Decoding face recognition abilities in the human brain

Why are some individuals better at recognizing faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusive. To tackle this challenge, we used a multimodal data-driven approach combining neuroimaging, computational modeling, and behavioral tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities—super-recognizers—and typical recognizers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 s of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared representations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognizers, we found stronger associations between early brain representations of super-recognizers and midlevel representations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognizers and representations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multimodal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain

Decoding face recognition abilities in the human brain

Why are some individuals better at recognising faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusive. To tackle this challenge, we used a multi-modal data-driven approach combining neuroimaging, computational modelling, and behavioural tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities—super-recognisers—and typical recognisers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 second of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared representations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognisers, we found stronger associations between early brain representations of super-recognisers and mid-level representations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognisers and representations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multi-modal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain

Training physicians in behavioural change counseling: A systematic review

Background: Poor health behaviours (e.g., smoking, physical inactivity) represent major underlying causes of non-communicable chronic diseases (NCDs). Prescriptive behaviour change interventions employed by physicians show limited effectiveness. Physician training in evidence-based behaviour change counselling (BCC) may improve behavioural risk factor management, but the efficacy and feasibility of current programs remains unclear. Objective: (1) To systematically review the efficacy of BCC training programs for physicians, and (2) to describe program content, dose and structure, informing better design and dissemination. Methods: Using PRISMA guidelines, a database search up to January 2018, yielded 1889 unique articles, screened by 2 authors; 9 studies met inclusion criteria and were retained for analysis. Results: 100% of studies reported significant improvements in BCC skills among physicians, most programs targeting provider-patient collaboration, supporting patient autonomy, and use of open questions to elicit “change-talk”. Limitation included: poor reporting quality, high program heterogeneity, small sample sizes, 78% of studies having no comparison group, and less than 30% of skills taught being formally assessed. Conclusion: Training programs were efficacious, but methodological weaknesses limit the ability to determine content and delivery. Caution is necessary when interpreting the results