Neurocomputational account of how the human brain decides when to have a break
Motivation, Brain and Behavior Laboratory, Physiological Investigations of Clinical Normal and Impaired Cognition Laboratory, Centre de NeuroImagerie de Recherche, Brain and Spine Institute, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France. Proceedings of the National Academy of Sciences
(Impact Factor: 9.67).
01/2013; 110(7). DOI: 10.1073/pnas.1211925110
No pain, no gain: cost-benefit trade-off has been formalized in classical decision theory to account for how we choose whether to engage effort. However, how the brain decides when to have breaks in the course of effort production remains poorly understood. We propose that decisions to cease and resume work are triggered by a cost evidence accumulation signal reaching upper and lower bounds, respectively. We developed a task in which participants are free to exert a physical effort knowing that their payoff would be proportional to their effort duration. Functional MRI and magnetoencephalography recordings conjointly revealed that the theoretical cost evidence accumulation signal was expressed in proprioceptive regions (bilateral posterior insula). Furthermore, the slopes and bounds of the accumulation process were adapted to the difficulty of the task and the money at stake. Cost evidence accumulation might therefore provide a dynamical mechanistic account of how the human brain maximizes benefits while preventing exhaustion.
Available from: Marc Guitart-Masip
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ABSTRACT: Neural representations of the effort deployed in performing actions, and the valence of the outcomes they yield, form the foundation of action choice. To discover whether brain areas represent effort and outcome valence together or if they represent one but not the other, we examined these variables in an explicitly orthogonal way. We did this by asking human subjects to exert one of two levels of effort to improve their chances of either winning or avoiding the loss of money. Subjects responded faster both when exerting greater effort and when exerting effort in anticipation of winning money. Using fMRI, we inspected BOLD responses during anticipation (before any action was executed) and when the outcome was delivered. In this way, we indexed BOLD signals associated with an anticipated need to exert effort and its affective consequences, as well as the effect of executed effort on the representation of outcomes. Anterior cingulate cortex and dorsal striatum (dorsal putamen) signaled the anticipation of effort independently of the prospect of winning or losing. Activity in ventral striatum (ventral putamen) was greater for better-than-expected outcomes compared with worse-than-expected outcomes, an effect attenuated in the context of having exerted greater effort. Our findings provide evidence that neural representations of anticipated actions are sensitive to the expected demands, but not to the expected value of their consequence, whereas representations of outcome value are discounted by exertion, commensurate with an integration of cost and benefit so as to approximate net value.
The Journal of Neuroscience : The Official Journal of the Society for Neuroscience 04/2013; 33(14):6160-9. DOI:10.1523/JNEUROSCI.4777-12.2013 · 6.34 Impact Factor
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ABSTRACT: Much research has been devoted to characterizing brain representations of reward and movement. However, the mechanisms allowing expected rewards to influence motor commands remain poorly understood. Unraveling such mechanisms is crucial to providing explanations of how behavior can be driven by goals, hence accounting for apathy cases in clinics. Here, we propose that the reduction of motor beta synchrony (MBS) before movement onset could participate in this incentive motivation process. To test this hypothesis, we recorded brain activity using magnetoencenphalography (MEG) while human participants were exerting physical effort to win monetary incentives. Knowing that the payoff was proportional to the time spent above a target force, subjects spontaneously took breaks when exhausted and resumed effort production when repleted. Behavioral data indicated that the rest periods were shorter when higher incentives were at stake. MEG data showed that the amplitude of MBS reduction correlated to both incentive level and rest duration. Moreover, the time of effort initiation could be predicted by MBS reduction measured at the beginning of rest periods. Incentive effects on MBS reduction and rest duration were also correlated across subjects. Finally, Bayesian comparison between possible causal models suggested that MBS reduction mediates the impact of incentive level on rest duration. We conclude that MBS reduction could represent a neural mechanism that speeds the initiation of effort production when the effort is more rewarded.
The Journal of Neuroscience : The Official Journal of the Society for Neuroscience 01/2014; 34(1):1-9. DOI:10.1523/JNEUROSCI.1711-13.2014 · 6.34 Impact Factor
Available from: Vincent Adam
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ABSTRACT: This work is in line with an on-going effort tending toward a computational (quantitative and refutable) understanding of human neuro-cognitive processes. Many sophisticated models for behavioural and neurobiological data have flourished during the past decade. Most of these models are partly unspecified (i.e. they have unknown parameters) and nonlinear. This makes them difficult to peer with a formal statistical data analysis framework. In turn, this compromises the reproducibility of model-based empirical studies. This work exposes a software toolbox that provides generic, efficient and robust probabilistic solutions to the three problems of model-based analysis of empirical data: (i) data simulation, (ii) parameter estimation/model selection, and (iii) experimental design optimization.
PLoS Computational Biology 01/2014; 10(1):e1003441. DOI:10.1371/journal.pcbi.1003441 · 4.62 Impact Factor
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