Biology subjects

Chambon, V.

Publications and source records attributed to Chambon, V..

3 recordsLinked to original sources

bioRxiv2018

Trading off the cost of conflict against expected rewards

Value-based decision-making involves trading off the cost associated with an action against its expected reward. Research has shown that both physical and mental effort constitute such subjective costs, biasing choices away from effortful actions, and discounting the value of obtained rewards. Facing conflicts between competing action alternatives is considered aversive, as recruiting cognitive control to overcome conflict is effortful. Yet, it remains unclear whether conflict is also perceived as a cost in value-based decisions. The present study investigated this question by embedding irrelevant distractors (flanker arrows) within a reversal-learning task, with intermixed free and instructed trials. Results showed that participants learned to adapt their choices to maximize rewards, but were nevertheless biased to follow the suggestions of irrelevant distractors. Thus, the perceived cost of being in conflict with an external suggestion could sometimes trump internal value representations. By adapting computational models of reinforcement learning, we assessed the influence of conflict at both the decision and learning stages. Modelling the decision showed that conflict was avoided when evidence for either action alternative was weak, demonstrating that the cost of conflict was traded off against expected rewards. During the learning phase, we found that learning rates were reduced in instructed, relative to free, choices. Learning rates were further reduced by conflict between an instruction and subjective action values, whereas learning was not robustly influenced by conflict between ones actions and external distractors. Our results show that the subjective cost of conflict factors into value-based decision-making, and highlights that different types of conflict may have different effects on learning about action outcomes.

neuroscience↗

bioRxiv2018

Lack of functional caveolae in Cav3 mutated human dystrophic myotubes results in deficient mechanoprotection and IL6/STAT3 mechanosignaling.

Caveolin-3 is the major structural protein of caveolae in muscle cells. Mutations in the CAV3 gene cause different type of muscle disorders mostly characterized by defects in membrane integrity and repair, deregulation in the expression of various muscle proteins and deregulation of several muscle associated signaling pathways. We show here that myotubes derived from patients bearing the CAV3 P28L and R26Q mutations present a lack of functional caveolae at the plasma membrane which results in an abnormal mechanoresponse. Mutant myotubes can no longer buffer the increase of membrane tension induced by mechanical stress and present an hyperactivation of the IL6/STAT3 signaling pathway at rest and under mechanical stress. The impaired mechanical regulation of the IL6/STAT3 signaling pathway by caveolae leads to chronic activation and a higher expression of muscle specific genes. These defects could be reversed by reassembling a pool of functional caveolae through expression of wild type Cav3. Our findings bring more mechanistic insight into human Cav3 associated muscle disorders and show a general defect in the mechanoresponse of CAV3 P28L and R26Q myotubes.

cell biology↗

bioRxiv2017

Focal Versus Distributed Temporal Cortex Activity For Speech Sound Category Assignment

Percepts and words can be decoded from largely distributed neural activity measures. The existence of widespread representations might, however, conflict with the fundamental notions of hierarchical processing and efficient coding. Using fMRI and MEG during syllable identification, we first show that sensory and decisional activity co-localize to a restricted part of the posterior superior temporal cortex. Next, using intracortical recordings we demonstrate that early and focal neural activity in this region distinguishes correct from incorrect decisions and can be machine-decoded to classify syllables. Crucially, significant machine-decoding was possible from neuronal activity sampled across widespread regions, despite weak or absent sensory or decision-related responses. These findings show that a complex behavior like speech sound categorization relies on an efficient readout of focal neural activity, while distributed activity, although decodable by machine-learning, reflects collateral processes of sensory perception and decision.

neuroscience↗