Fast and Automatic Activation of an Abstract Representation of Money in the Human Ventral Visual Pathway

Money, when used as an incentive, activates the same neural circuits as rewards associated with physiological needs. However, unlike physiological rewards, monetary stimuli are cultural artifacts: how are monetary stimuli identified in the first place? How and when does the brain identify a valid coin, i.e. a disc of metal that is, by social agreement, endowed with monetary properties? We took advantage of the changes in the Euro area in 2002 to compare neural responses to valid coins (Euros, Australian Dollars) with neural responses to invalid coins that have lost all monetary properties (French Francs, Finnish Marks). We show in magneto-encephalographic recordings, that the ventral visual pathway automatically distinguishes between valid and invalid coins, within only ∼150 ms. This automatic categorization operates as well on coins subjects were familiar with as on unfamiliar coins. No difference between neural responses to scrambled controls could be detected. These results could suggest the existence of a generic, all-purpose neural representation of money that is independent of experience. This finding is reminiscent of a central assumption in economics, money fungibility, or the fact that a unit of money is substitutable to another. From a neural point of view, our findings may indicate that the ventral visual pathway, a system previously thought to analyze visual features such as shape or color and to be influenced by daily experience, could also able to use conceptual attributes such as monetary validity to categorize familiar as well as unfamiliar visual objects. The symbolic abilities of the posterior fusiform region suggested here could constitute an efficient neural substrate to deal with culturally defined symbols, independently of experience, which probably fostered money's cultural emergence and success

Better sexual acceptability of agomelatine (25 and 50 mg) compared to escitalopram (20 mg) in healthy volunteers. A 9-week, placebo-controlled study using the PRSexDQ scale

The present double-blind, placebo-controlled study evaluates the effects of agomelatine and the selective serotonin reuptake inhibitor escitalopram on sexual dysfunction in healthy men and women

Brain dynamics for confidence-weighted learning (MEG)

This dataset accompanies the paper "Brain dynamics for confidence-weighted learning" published by Florent MEYNIEL in Plos Compational Biology (2020) https://doi.org/10.1371/journal.pcbi.1007935. Here is the abstract of the paper: Learning in a changing, uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions. However, humans also have a sense of confidence that characterizes the precision of their predictions. Bayesian models use a confidence-weighting principle to regulate learning: for a given surprise, the update is smaller when the confidence about the prediction was higher. Prior behavioral evidence indicates that human learning adheres to this confidence-weighting principle. Here, we explored the human brain dynamics sub-tending the confidence-weighting of learning using magneto-encephalography (MEG). During our volatile probability learning task, subjects’ confidence reports conformed with Bayesian inference. MEG revealed several stimulus-evoked brain responses whose amplitude reflected surprise, and some of them were further shaped by confidence: surprise amplified the stimulus-evoked response whereas confidence dampened it. Confidence about predictions also modulated several aspects of the brain state: pupil-linked arousal and beta-range (15-30 Hz) oscillations. The brain state in turn modulated specific stimulus-evoked surprise responses following the confidence-weighting principle. Our results thus indicate that there exist, in the human brain, signals reflecting surprise that are dampened by confidence in a way that is appropriate for learning according to Bayesian inference. They also suggest a mechanism for confidence-weighted learning: confidence about predictions would modulate intrinsic properties of the brain state to amplify or dampen surprise responses evoked by discrepant observations

Brain dynamics for confidence-weighted learning

International audienceLearning in a changing, uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions. However, humans also have a sense of confidence that characterizes the precision of their predictions. Bayesian models use a confidence-weighting principle to regulate learning: for a given surprise, the update is smaller when the confidence about the prediction was higher. Prior behavioral evidence indicates that human learning adheres to this confidence-weight-ing principle. Here, we explored the human brain dynamics sub-tending the confidence-weighting of learning using magneto-encephalography (MEG). During our volatile probability learning task, subjects' confidence reports conformed with Bayesian inference. MEG revealed several stimulus-evoked brain responses whose amplitude reflected surprise, and some of them were further shaped by confidence: surprise amplified the stimulus-evoked response whereas confidence dampened it. Confidence about predictions also modulated several aspects of the brain state: pupil-linked arousal and beta-range (15-30 Hz) oscillations. The brain state in turn modulated specific stimulus-evoked surprise responses following the confidence-weighting principle. Our results thus indicate that there exist, in the human brain, signals reflecting surprise that are dampened by confidence in a way that is appropriate for learning according to Bayesian inference. They also suggest a mechanism for confidence-weighted learning: confidence about predictions would modulate intrinsic properties of the brain state to amplify or dampen surprise responses evoked by discrepant observations

Au bout de l'effort : Neuropsychologie

Le succès d'un athlète dépend de sa capacité à gérer ses efforts. Cette gestion est assurée par une zone du cerveau qui surveille en permanence les coûts de l'effort et ses bénéfices

Jusqu'au bout de l'effort

Le succès d'un athlète dépend de sa capacité à gérer ses efforts. Une zone du cerveau, qui surveille en permanence les coûts et les bénéfices d'une action intense, assure cette fonction

Comment le cerveau humain alloue l'effort physique dans le temps (données comportementales, imagerie cérébrale et pharmacologie)

Faire le bon choix, c est trouver le bon compromis entre coût et bénéfice. Dans le cas de la gestion de l effort physique, ce compromis prend une dimension temporelle. Pour comprendre comment la décision d arrêter ou reprendre l effort est prise, nous avons développé un paradigme expérimental chez le sujet humain sain et un modèle computationnel dans lequel le coût estimé augmente à l effort car la fatigue affecte toute la commande motrice et diminue au repos quand nous récupérons. Le comportement reflète les variations de ce coût estimé et du compromis avec le bénéfice attendu. Grâce à la complémentarité de l imagerie fonctionnelle par résonnance magnétique et de la magnétoencéphalographie (MEG), le coût estimé a été localisé dans les régions proprioceptives du cerveau : l insula postérieure et le thalamus ventromédian. La MEG a également révélé que la désynchronisation du rythme beta moteur (13-30Hz) permet une reprise plus rapide de l effort quand les enjeux sont importants. Cette gestion stratégique du repos est liée à l utilité attendue qui peut être dissociée de l utilité réelle. Nos résultats montrent que la gestion de l effort est adaptée en ligne au coût estimé et modulée stratégiquement en fonction des coûts et bénéfices attendus. Les antalgiques (hypnose ou paracétamol) ont un effet limité sur ce processus, à l inverse de la sérotonine (Escitalopram). Notre contribution, à l interface entre médecine du sport, théorie de la décision et modèle d accumulation utilisés en neurosciences, propose un mécanisme pour optimiser la gestion de l effort physique en maximisant les gains et minimisant les dommages corporels.No pain, no gain: optimal decisions involve a tradeoff between cost and benefit. We propose that in physical effort allocation, this tradeoff is unfolded over time. We present a task to investigate this process in the laboratory with healthy humans and we suggest a computational model to account for decisions to stop and resume the effort. Costs increase during exertion, due to fatigue at all stages of the motor command and decrease during rest, due to recovery. We show that this dynamic may be captured by a cost-evidence variable and compared to the expected benefit. Functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) complementarily showed that cost-evidence may be implemented in proprioceptive regions of the brain: posterior insula and ventro-medial thalamus. In addition, MEG showed that motor beta (13-30 Hz) desynchronization mediates the effect of incentives to hasten effort resumption. This strategic invigoration of rest is supported by a behavioral dissociation: the expected utility (not the actual utility) modulates rest durations. Together, our results support that the behavior is adapted on the fly to cost-evidence levels and that this mechanism is modulated strategically according to the expected cost and benefit. This behavior was not affected by pain killers (hypnosis or paracetamol), but by serotonin (Escitalopram). This work bridges a gap between sport medicine, value-based decision-making and accumulation models in neuroscience in showing that accumulation and dissipation of cost-evidence can guide the optimization of effort allocation: this mechanism implements the maximization of benefit while the body costs are minimized.PARIS-BIUSJ-Biologie recherche (751052107) / SudocSudocFranceF

Two Determinants of Dynamic Adaptive Learning for Magnitudes and Probabilities

International audienceHumans face a dynamic world that requires them to constantly update their knowledge. Each observation should influence their knowledge to a varying degree depending on whether it arises from a stochastic fluctuation or an environmental change. Thus, humans should dynamically adapt their learning rate based on each observation. Although crucial for characterizing the learning process, these dynamic adjustments have only been investigated empirically in magnitude learning. Another important type of learning is probability learning. The latter differs from the former in that individual observations are much less informative and a single one is insufficient to distinguish environmental changes from stochasticity. Do humans dynamically adapt their learning rate for probabilities? What determinants drive their dynamic adjustments in magnitude and probability learning? To answer these questions, we measured the subjects’ learning rate dynamics directly through real-time continuous reports during magnitude and probability learning. We found that subjects dynamically adapt their learning rate in both types of learning. After a change point, they increase their learning rate suddenly for magnitudes and prolongedly for probabilities. Their dynamics are driven differentially by two determinants: change-point probability, the main determinant for magnitudes, and prior uncertainty, the main determinant for probabilities. These results are fully in line with normative theory, both qualitatively and quantitatively. Overall, our findings demonstrate a remarkable human ability for dynamic adaptive learning under uncertainty, and guide studies of the neural mechanisms of learning, highlighting different determinants for magnitudes and probabilities

Gated recurrence enables simple and accurate sequence prediction in stochastic, changing, and structured environments

International audienceFrom decision making to perception to language, predicting what is coming next is crucial. It is also challenging in stochastic, changing, and structured environments; yet the brain makes accurate predictions in many situations. What computational architecture could enable this feat? Bayesian inference makes optimal predictions but is prohibitively difficult to compute. Here, we show that a specific recurrent neural network architecture enables simple and accurate solutions in several environments. This architecture relies on three mechanisms: gating, lateral connections, and recurrent weight training. Like the optimal solution and the human brain, such networks develop internal representations of their changing environment (including estimates of the environment's latent variables and the precision of these estimates), leverage multiple levels of latent structure, and adapt their effective learning rate to changes without changing their connection weights. Being ubiquitous in the brain, gated recurrence could therefore serve as a generic building block to predict in real-life environments. Editor's evaluation There has been a longstanding interest in developing normative models of how humans handle latent information in stochastic and volatile environments. This study examines recurrent neural network models trained on sequence-prediction tasks analogous to those used in human cognitive studies. The results demonstrate that such models lead to highly accurate predictions for challenging sequences in which the statistics are non-stationary and change at random times. These novel and remarkable results open up new avenues for cognitive modelling