VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data

Daunizeau, Jean; Adam, Vincent; Rigoux, Lionel

doi:10.1371/journal.pcbi.1003441

Cited by 348 publications

(382 citation statements)

References 35 publications

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“…The representation of expected reward was localized principally in the vmPFC, a brain region that has been classically implicated in positive valuation of choice options Haber and Knutson, 2010;Peters and Büchel, 2010;Levy and Glimcher, 2012;Bartra et al, 2013). This finding is also consistent with the observation that damage to the vmPFC impairs reward learning in humans and monkeys (Camille et al, 2011;Noonan et al, 2012;Rudebeck et al, 2013). Effort was absent from the expectations reflected in the vmPFC, which is in keeping with previous findings in nonlearning contexts that the vmPFC discounts rewards with delay or pain, but not with effort (Kable and Glimcher, 2007;Peters and Büchel, 2009;Talmi et al, 2009;Plassmann et al, 2010;Prévost et al, 2010).…”

Section: Discussionsupporting

confidence: 63%

“…These regions have already been implicated in the representation of not only effort but also negative choice value during nonlearning tasks (Bush et al, 2002;Samanez-Larkin et al, 2008;Croxson et al, 2009;Prévost et al, 2010;Hare et al, 2011;Burke et al, 2013;Kurniawan et al, 2013). The dissociation of vmPFC from dACC is reminiscent of the rodent studies reporting that after a cingulate (but not orbitofrontal) lesion physical cost is no longer integrated into the decision value (Walton et al, 2003;Roesch et al, 2006;Rudebeck et al, 2006), although this analogy should be taken with caution due to anatomical differences between species.…”

Section: Discussionmentioning

confidence: 96%

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Learning To Minimize Efforts versus Maximizing Rewards: Computational Principles and Neural Correlates

Palminteri²,

2014

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The mechanisms of reward maximization have been extensively studied at both the computational and neural levels. By contrast, little is known about how the brain learns to choose the options that minimize action cost. In principle, the brain could have evolved a general mechanism that applies the same learning rule to the different dimensions of choice options. To test this hypothesis, we scanned healthy human volunteers while they performed a probabilistic instrumental learning task that varied in both the physical effort and the monetary outcome associated with choice options. Behavioral data showed that the same computational rule, using prediction errors to update expectations, could account for both reward maximization and effort minimization. However, these learning-related variables were encoded in partially dissociable brain areas. In line with previous findings, the ventromedial prefrontal cortex was found to positively represent expected and actual rewards, regardless of effort. A separate network, encompassing the anterior insula, the dorsal anterior cingulate, and the posterior parietal cortex, correlated positively with expected and actual efforts. These findings suggest that the same computational rule is applied by distinct brain systems, depending on the choice dimension-cost or benefit-that has to be learned.

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Section: Discussionsupporting

confidence: 63%

Section: Discussionmentioning

confidence: 96%

Learning To Minimize Efforts versus Maximizing Rewards: Computational Principles and Neural Correlates

Palminteri²,

2014

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“…Different sets of parameters were used to fit the hard and control conditions. They were inverted by minimizing free energy, using a variational Bayes approach under the Laplace approximation (40,41), as implemented in the VBA Matlab toolbox (42), available at mbb-team.github.io/VBA-toolbox/). We normalized the dependent and explanatory variables (both proportion of impulsive choice and session number varied between 0 and 1), so that we could use means corresponding to the null hypothesis (0.5 for C, 0 for β and 1 for α) and SDs equal to 1 as priors on free parameters.…”

Section: Methodsmentioning

confidence: 99%

Neural mechanisms underlying the impact of daylong cognitive work on economic decisions

Blain

Hollard

Pessiglione

2016

Proc. Natl. Acad. Sci. U.S.A.

124

133

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The ability to exert self-control is key to social insertion and professional success. An influential literature in psychology has developed the theory that self-control relies on a limited common resource, so that fatigue effects might carry over from one task to the next. However, the biological nature of the putative limited resource and the existence of carry-over effects have been matters of considerable controversy. Here, we targeted the activity of the lateral prefrontal cortex (LPFC) as a common substrate for cognitive control, and we prolonged the time scale of fatigue induction by an order of magnitude. Participants performed executive control tasks known to recruit the LPFC (working memory and task-switching) over more than 6 h (an approximate workday). Fatigue effects were probed regularly by measuring impulsivity in intertemporal choices, i.e., the propensity to favor immediate rewards, which has been found to increase under LPFC inhibition. Behavioral data showed that choice impulsivity increased in a group of participants who performed hard versions of executive tasks but not in control groups who performed easy versions or enjoyed some leisure time. Functional MRI data acquired at the start, middle, and end of the day confirmed that enhancement of choice impulsivity was related to a specific decrease in the activity of an LPFC region (in the left middle frontal gyrus) that was recruited by both executive and choice tasks. Our findings demonstrate a concept of focused neural fatigue that might be naturally induced in real-life situations and have important repercussions on economic decisions.

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“…This involves maximizing the negative log evidence for the model given the data. This procedure was run using MATLAB code provided by Daunizeau et al (2014).…”

Section: Cluster Analysismentioning

confidence: 99%

‘Subtypes’ in the Presentation of Autistic Traits in the General Adult Population

Palmer

Paton

Enticott

et al. 2014

J Autism Dev Disord

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The present study examined the presentation of autistic traits in a large adult population sample (n = 2,343). Cluster analysis indicated two subgroups with clearly distinguishable trait profiles. One group (n = 1,059) reported greater social difficulties and lower detail orientation, while the second group (n = 1,284) reported lesser social difficulties and greater detail orientation. We also report a three-factor solution for the autism-spectrum quotient, with two, related, social-themed factors (Sociability and Mentalising) and a third non-social factor that varied independently (Detail Orientation). These results indicate that different profiles of autistic characteristics tend to occur in the adult nonclinical population. Research into nonclinical variance in autistic features may benefit by considering social- and detail-related trait domains independently.

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VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data

Cited by 348 publications

References 35 publications

Learning To Minimize Efforts versus Maximizing Rewards: Computational Principles and Neural Correlates

Learning To Minimize Efforts versus Maximizing Rewards: Computational Principles and Neural Correlates

Neural mechanisms underlying the impact of daylong cognitive work on economic decisions

‘Subtypes’ in the Presentation of Autistic Traits in the General Adult Population

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