L'alimentation durable

L’alimentation est un sujet passionnant et très actuel. Elle fait partie de notre quotidien et impacte une multitude de facettes de notre vie. Les liens entre la nourriture, la santé et l’environnement sont très étroits et c’est cette complexité qui m’a particulièrement intéressé et poussé à faire un travail de recherche sur cette thématique. L’Homme n’a, durant toute l’Histoire, jamais eu une compréhension aussi précise du fonctionnement de la nature qu’aujourd’hui. Paradoxalement, il n’a jamais autant détruit l’environnement dans lequel il vivait. L’industrialisation du secteur agricole n’a pas pris en compte la question de la durabilité et les conséquences qui en découlent sont aujourd’hui un nouveau défi pour l’agriculture. Comment donc faire face à la demande de nourriture de manière durable ? Les solutions sont nombreuses mais nécessitent une réelle remise en question de nos modes actuels de production et de consommation

Inferring on the intentions of others by hierarchical Bayesian learning

Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to "player" or "adviser" roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser's incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser's intentions were presently stable. Finally, our model of the players' inference predicted the players' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers' helpfulness and the advisers' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition

Allostatic self-efficacy: a metacognitive theory of dyshomeostasis-induced fatigue and depression

This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future

First Experimental Characterization of Microwave Emission from Cosmic Ray Air Showers

We report the first direct measurement of the overall characteristics of microwave radio emission from extensive air showers. Using a trigger provided by the KASCADE-Grande air shower array, the signals of the microwave antennas of the CROME (Cosmic-Ray Observation via Microwave Emission) experiment have been read out and searched for signatures of radio emission by high-energy air showers in the GHz frequency range. Microwave signals have been detected for more than 30 showers with energies above 3*10^16 eV. The observations presented in this Letter are consistent with a mainly forward-directed and polarised emission process in the GHz frequency range. The measurements show that microwave radiation offers a new means of studying air showers at energies above 10^17 eV.Comment: Accepted for publication in PR

Hierarchical prediction errors in midbrain and septum during social learning

Social learning is fundamental to human interactions, yet its computational and physiological mechanisms are not well understood. One prominent open question concerns the role of neuromodulatory transmitters. We combined fMRI, computational modelling, and genetics to address this question in two separate samples (N=35, N=47). Participants played a game requiring inference on an advisor's intentions whose motivation to help or mislead changed over time. Our analyses suggest that hierarchically structured belief updates about current advice validity and the adviser's trustworthiness, respectively, depend on different neuromodulatory systems. Low-level prediction errors (PEs) about advice accuracy not only activated regions known to support "theory of mind", but also the dopaminergic midbrain. Furthermore, PE responses in ventral striatum were influenced by the Met/Val polymorphism of the Catechol-O-Methyltransferase (COMT) gene. By contrast, high-level PEs ("expected uncertainty") about the adviser's fidelity activated the cholinergic septum. These findings, replicated in both samples, have important implications: They suggest that social learning rests on hierarchically related PEs encoded by midbrain and septum activity, respectively, in the same manner as other forms of learning under volatility. Furthermore, these hierarchical PEs may be broadcast by dopaminergic and cholinergic projections to induce plasticity specifically in cortical areas known to represent beliefs about others. Copyright The Authors (2017). Published by Oxford University Press