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International audience; Humans can readily assess their degree of confidence in their decisions. Two models of confidence computation have been proposed: post hoc computation using post-decision variables and heuristics, versus online computation using continuous assessment of evidence throughout the decision-making process. Here, we arbitrate between these theories by continuously monitoring finger movements during a manual sequential decision-making task. Analysis of finger kinematics indicated that subjects kept separate online records of evidence and confidence: finger deviation continuously reflected the ongoing accumulation of evidence, whereas finger speed continuously reflected the momentary degree of confidence. Furthermore, end-of-trial finger speed predicted the post-decisional subjective confidence rating. These data indicate that confidence is computed on-line, throughout the decision process. Speed-confidence correlations were previously interpreted as a post-decision heuristics, whereby slow decisions decrease subjective confidence, but our results suggest an adaptive mechanism that involves the opposite causality: by slowing down when unconfident, participants gain time to improve their decisions.

Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This "confidence weighting" implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an ideal-observer analysis, we demonstrate that the brain's learning algorithm relies on confidence weighting. While in the fMRI scanner, human adults attempted to learn the transition probabilities underlying an auditory or visual sequence, and reported their confidence in those estimates. They knew that these transition probabilities could change simultaneously at unpredicted moments, and therefore that the learning problem was inherently hierarchical. Subjective confidence reports tightly followed the predictions derived from the ideal observer. In particular, subjects managed to attach distinct levels of confidence to each learned transition probability, as required by Bayes-optimal inference. Distinct brain areas tracked the likelihood of new observations given current predictions, and the confidence in those predictions. Both signals were combined in the right inferior frontal gyrus, where they operated in agreement with the confidence-weighting model. This brain region also presented signatures of a hierarchical process that disentangles distinct sources of uncertainty. Together, our results provide evidence that the sense of confidence is an essential ingredient of probabilistic learning in the human brain, and that the right inferior frontal gyrus hosts a confidence-based statistical learning algorithm for auditory and visual sequences.

Confidence in a decision is defined statistically as the probability of that decision being correct. Humans, however, display systematic confidence biases, as has been exposed in various experiments. Here, we show that these biases vanish when taking into account participants' prior expectations, which we measure independently of the confidence report. We use a wagering experiment to show that modeling subjects' choices allows for classifying individuals according to their prior biases, which fully explain from first principles the differences in their later confidence reports. Our parameter-free confidence model predicts two counterintuitive patterns for individuals with different prior beliefs: pessimists should report higher confidence than optimists, and, for the same task difficulty, the confidence of pessimists should increase with the generosity of the task. These findings show how systematic confidence biases can be simply understood as differences in prior expectations.

International audience; Auditory novelty detection has been associated with different cognitive processes. Bekinschtein et al. (2009) developed an experimental paradigm to dissociate these processes, using local and global novelty, which were associated, respectively, with automatic versus strategic perceptual processing. They have mostly been studied using event-related potentials (ERPs), but local spiking activity as indexed by gamma (60-120 Hz) power and interactions between brain regions as indexed by modulations in beta-band (13-25 Hz) power and functional connectivity have not been explored. We thus recorded 9 epileptic patients with intracranial electrodes to compare the precise dynamics of the responses to local and global novelty. Local novelty triggered an early response observed as an intracranial mismatch negativity (MMN) contemporary with a strong power increase in the gamma band and an increase in connectivity in the beta band. Importantly, all these responses were strictly confined to the temporal auditory cortex. In contrast, global novelty gave rise to a late ERP response distributed across brain areas, contemporary with a sustained power decrease in the beta band (13-25 Hz) and an increase in connectivity in the alpha band (8-13 Hz) within the frontal lobe. We discuss these multi-facet signatures in terms of conscious access to perceptual information.

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.

When forming and updating beliefs about future life outcomes, people tend to consider good news and to disregard bad news. This tendency is assumed to support the optimism bias. Whether this learning bias is specific to ‘high-level’ abstract belief update or a particular expression of a more general ‘low-level’ reinforcement learning process is unknown. Here we report evidence in favour of the second hypothesis. In a simple instrumental learning task, participants incorporated better-than-expected outcomes at a higher rate than worse-than-expected ones. In addition, functional imaging indicated that inter-individual difference in the expression of optimistic update corresponds to enhanced prediction error signalling in the reward circuitry. Our results constitute a step towards the understanding of the genesis of optimism bias at the neurocomputational level. Lefebvre et al. present behavioural and neural evidence showing that the ‘optimism bias’ is a manifestation of a general cognitive tendency for preferential learning from positive, compared with negative, outcomes.

The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge., Author Summary We explore the possibility that the computation of time-varying transition probabilities may be a core building block of sequence knowledge in humans. Humans may then use these estimates to predict future observations. Expectations derived from such a model should conform to several properties. We list six such properties and we test them successfully against various experimental findings reported in distinct fields of the literature over the past century. We focus on five representative studies by other groups. Such findings include the “sequential effects” evidenced in many behavioral tasks, i.e. the pervasive fluctuations in performance induced by the recent history of observations. We also consider the “surprise-like” signals recorded in electrophysiology and even functional MRI, that are elicited by a random stream of observations. These signals are reportedly modulated in a quantitative manner by both the local and global statistics of observations. Last, we consider the notoriously biased subjective perception of randomness, i.e. whether humans think that a given sequence of observations has been generated randomly or not. Our model therefore unifies many previous findings and suggests that a neural machinery for inferring transition probabilities must lie at the core of human sequence knowledge.

While forming and updating beliefs about future life outcomes, people tend to consider good news and to disregard bad news. This tendency is supposed to support the optimism bias. Whether learning bias is specific to high-level abstract belief update or a particular expression of a more general low-level reinforcement learning process is unknown. Here we report evidence in favor of the second hypothesis. In a simple instrumental learning task, participants incorporated worse-than-expected outcomes at a lower rate compared to better-than-expected ones. This asymmetry was correlated across subjects with standard measure of dispositional optimism. Multimodal imaging indicated that inter-individual variability in the expression of asymmetric update relies on the dorsomedial prefrontal cortex at both morphological and functional levels. Our results constitute a new step in the understanding of the genesis of optimism bias at the neurocomputational level.

Research on confidence spreads across several sub-fields of psychology and neuroscience. Here, we explore how a definition of confidence as Bayesian probability can unify these viewpoints. This computational view entails that there are distinct forms in which confidence is represented and used in the brain, including distributional confidence, pertaining to neural representations of probability distributions, and summary confidence, pertaining to scalar summaries of those distributions. Summary confidence is, normatively, derived or “read out” from distributional confidence. Neural implementations of readout will trade off optimality versus flexibility of routing across brain systems, allowing confidence to serve diverse cognitive functions. Fil: Meyniel, Florent. Inserm; Francia Fil: Sigman, Mariano. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina Fil: Mainen, Zachary F.. Champalimaud Centre for the Unknown; Portugal

A pervasive case of cost-benefit problem is how to allocate effort over time, i.e. deciding when to work and when to rest. An economic decision perspective would suggest that duration of effort is determined beforehand, depending on expected costs and benefits. However, the literature on exercise performance emphasizes that decisions are made on the fly, depending on physiological variables. Here, we propose and validate a general model of effort allocation that integrates these two views. In this model, a single variable, termed cost evidence, accumulates during effort and dissipates during rest, triggering effort cessation and resumption when reaching bounds. We assumed that such a basic mechanism could explain implicit adaptation, whereas the latent parameters (slopes and bounds) could be amenable to explicit anticipation. A series of behavioral experiments manipulating effort duration and difficulty was conducted in a total of 121 healthy humans to dissociate implicit-reactive from explicit-predictive computations. Results show 1) that effort and rest durations are adapted on the fly to variations in cost-evidence level, 2) that the cost-evidence fluctuations driving the behavior do not match explicit ratings of exhaustion, and 3) that actual difficulty impacts effort duration whereas expected difficulty impacts rest duration. Taken together, our findings suggest that cost evidence is implicitly monitored online, with an accumulation rate proportional to actual task difficulty. In contrast, cost-evidence bounds and dissipation rate might be adjusted in anticipation, depending on explicit task difficulty., Author Summary Imagine that ahead of you is a long time of work: when will you take a break? This sort of issue – how to allocate effort over time – has been addressed by distinct theoretical fields, with different emphasis on reactive and predictive processes. An intuitive view is that you start working, stop when you are tired, and start again when fatigue goes away. Biologically, this means that decisions are taken when some physiological variable reaches a given bound on the risk of homeostatic failure. In a more economic perspective, fatigue translates into effort cost, which must be anticipated and compared to expected benefit before engaging an action. We proposed a computational model that bridges these perspectives from sport physiology and decision theory. Decisions are made in reaction to bounds being reached by an implicit cost variable that accumulates during effort, at a rate proportional to task difficulty, and dissipates during rest. However, some latent parameters (bounds and dissipation rate) are adjusted in anticipation, depending on explicit costs and benefits. This model was supported by behavioral data obtained using a paradigm where participants squeeze a handgrip to win a monetary payoff proportional to effort duration.

Dysfunctions of the emotion processing circuitry are associated with Major Depressive Disorder (MDD) and can be modulated by antidepressants like selective serotonin reuptake inhibitors (SSRIs). Moreover, motivation, pleasure or interest are important neuropsychological states of emotional life, and are known to be impaired in MDD patients. Under SSRIs, some patients report decreases in motivation and/or in emotional responsiveness commonly described as emotional detachment (Corruble E et al , 2013) and having significant impact on drug compliance and quality of life. The antidepressant agomelatine displays a unique neurochemical profile, different from SSRIs, being a MT1/MT2 receptors agonist and a 5HT2C receptors antagonist (De Bodinat C et al , 2010). Data from healthy volunteers and patients suggest that agomelatine is associated with less emotional detachment as compared to SSRIs notably with a more specific action in the facial expression recognition task (Harmer CJ et al , 2011), lower scores on scales for blunting (Corruble E et al , 2013), and absence of sexual dysfunction (Montejo AL et al , 2010). Here, the effects of two antidepressants, agomelatine (25 and 50mg) and escitalopram (10–20mg) on emotional detachment, emotional processing, motivation and sexual function were assessed during a 9-week randomised, double-blind, placebo-controlled, parallel-designed study in healthy male and female volunteers aged between 18-45 years. A battery of neuropsychological and motivation tasks/questionnaires available in the literature were used. Investigating antidepressant effects on key psychological processes in healthy volunteers allows elucidation of the direct actions of antidepressants unconfounded by changes in mood symptoms. The study is on progress.