Metacognitive evaluation of postdecisional perceptual representations.

Perceptual confidence is thought to arise from metacognitive processes that evaluate the underlying perceptual decision evidence. We investigated whether metacognitive access to perceptual evidence is constrained by the hierarchical organization of visual cortex, where high-level representations tend to be more readily available for explicit scrutiny. We found that the ability of human observers to evaluate their confidence did depend on whether they performed a high-level or low-level task on the same stimuli, but was also affected by manipulations that occurred long after the perceptual decision. Confidence in low-level perceptual decisions degraded with more time between the decision and the response cue, especially when backward masking was present. Confidence in high-level tasks was immune to backward masking and benefitted from additional time. These results can be explained by a model assuming confidence heavily relies on postdecisional internal representations of visual stimuli that degrade over time, where high-level representations are more persistent.

Specifying the timescale of early life unpredictability helps explain the development of internalising and externalising behaviours.

Early life unpredictability is associated with both physical and mental health outcomes throughout the life course. Here, we classified adverse experiences based on the timescale on which they are likely to introduce variability in children's environments: variations unfolding over short time scales (e.g., hours, days, weeks) and labelled Stochasticity vs variations unfolding over longer time scales (e.g., months, years) and labelled Volatility and explored how they contribute to the development of problem behaviours. Results indicate that externalising behaviours at age 9 and 15 and internalising behaviours at age 15 were better accounted for by models that separated Stochasticity and Volatility measured at ages 3 to 5. Both externalising and internalising behaviours were specifically associated with Volatility, with larger effects for externalising behaviours. These findings are interpreted in light of evolutionary-developmental models of psychopathology and reinforcement learning models of learning under uncertainty.

A zero-cost attention-based approach to promote cleaner streets: A Signal Detection Theory approach in Parisian streets.

In an effort to inform interventions targeting littering behaviour, we estimate how much a change in trash-bag colour increases trash can visibility in Paris. To that end, we applied standard Signal Detection techniques to test how much changing trash-bag colour affects subjects' trash can detection rates. In three pre-registered studies, we found that changing trash bag colour from grey to either red, green or blue considerably increases the perception of bins in British (tourist) and Parisian (resident) samples. We found that changing the bag colour from grey to blue increased visibility the most.

Adaptive tuning of human learning and choice variability to unexpected uncertainty.

Human value-based decisions are notably variable under uncertainty. This variability is known to arise from two distinct sources: variable choices aimed at exploring available options and imprecise learning of option values due to limited cognitive resources. However, whether these two sources of decision variability are tuned to their specific costs and benefits remains unclear. To address this question, we compared the effects of expected and unexpected uncertainty on decision-making in the same reinforcement learning task. Across two large behavioral datasets, we found that humans choose more variably between options but simultaneously learn less imprecisely their values in response to unexpected uncertainty. Using simulations of learning agents, we demonstrate that these opposite adjustments reflect adaptive tuning of exploration and learning precision to the structure of uncertainty. Together, these findings indicate that humans regulate not only how much they explore uncertain options but also how precisely they learn the values of these options.

Controllability boosts neural and cognitive signatures of changes-of-mind in uncertain environments.

In uncertain environments, seeking information about alternative choice options is essential for adaptive learning and decision-making. However, information seeking is usually confounded with changes-of-mind about the reliability of the preferred option. Here, we exploited the fact that information seeking requires control over which option to sample to isolate its behavioral and neurophysiological signatures. We found that changes-of-mind occurring with control require more evidence against the current option, are associated with reduced confidence, but are nevertheless more likely to be confirmed on the next decision. Multimodal neurophysiological recordings showed that these changes-of-mind are preceded by stronger activation of the dorsal attention network in magnetoencephalography, and followed by increased pupil-linked arousal during the presentation of decision outcomes. Together, these findings indicate that information seeking increases the saliency of evidence perceived as the direct consequence of one's own actions.

Efficient stabilization of imprecise statistical inference through conditional belief updating.

Statistical inference is the optimal process for forming and maintaining accurate beliefs about uncertain environments. However, human inference comes with costs due to its associated biases and limited precision. Indeed, biased or imprecise inference can trigger variable beliefs and unwarranted changes in behaviour. Here, by studying decisions in a sequential categorization task based on noisy visual stimuli, we obtained converging evidence that humans reduce the variability of their beliefs by updating them only when the reliability of incoming sensory information is judged as sufficiently strong. Instead of integrating the evidence provided by all stimuli, participants actively discarded as much as a third of stimuli. This conditional belief updating strategy shows good test-retest reliability, correlates with perceptual confidence and explains human behaviour better than previously described strategies. This seemingly suboptimal strategy not only reduces the costs of imprecise computations but also, counterintuitively, increases the accuracy of resulting decisions.

Motor Coordination and Strategic Cooperation in Joint Action.

Naturalistic joint action between two agents typically requires both motor coordination and strategic cooperation. However, these two fundamental processes have systematically been studied independently. We presented 50 dyads of adult participants with a novel collaborative task that combined different levels of motor noise with different levels of strategic noise, to determine whether the sense of agency (the experience of control over an action) reflects the interplay between these low-level (motor) and high-level (strategic) dimensions. We also examined how dominance in motor control could influence prosocial behaviors. We found that self-agency was particularly dependent on motor cues, whereas joint agency was particularly dependent on strategic cues. We suggest that the prime importance of strategic cues to joint agency reflects the co-representation of coagents' interests during the task. Furthermore, we observed a reduction in prosocial strategies in agents who exerted dominant motor control over joint action, showing that the strategic dimension of human interactions is also susceptible to the influence of low-level motor characteristics.

Premature commitment to uncertain decisions during human NMDA receptor hypofunction.

Making accurate decisions based on unreliable sensory evidence requires cognitive inference. Dysfunction of n-methyl-d-aspartate (NMDA) receptors impairs the integration of noisy input in theoretical models of neural circuits, but whether and how this synaptic alteration impairs human inference and confidence during uncertain decisions remains unknown. Here we use placebo-controlled infusions of ketamine to characterize the causal effect of human NMDA receptor hypofunction on cognitive inference and its neural correlates. At the behavioral level, ketamine triggers inference errors and elevated decision uncertainty. At the neural level, ketamine is associated with imbalanced coding of evidence and premature response preparation in electroencephalographic (EEG) activity. Through computational modeling of inference and confidence, we propose that this specific pattern of behavioral and neural impairments reflects an early commitment to inaccurate decisions, which aims at resolving the abnormal uncertainty generated by NMDA receptor hypofunction.

Separable neural signatures of confidence during perceptual decisions.

Perceptual confidence is an evaluation of the validity of perceptual decisions. While there is behavioural evidence that confidence evaluation differs from perceptual decision-making, disentangling these two processes remains a challenge at the neural level. Here, we examined the electrical brain activity of human participants in a protracted perceptual decision-making task where observers tend to commit to perceptual decisions early whilst continuing to monitor sensory evidence for evaluating confidence. Premature decision commitments were revealed by patterns of spectral power overlying motor cortex, followed by an attenuation of the neural representation of perceptual decision evidence. A distinct neural representation was associated with the computation of confidence, with sources localised in the superior parietal and orbitofrontal cortices. In agreement with a dissociation between perception and confidence, these neural resources were recruited even after observers committed to their perceptual decisions, and thus delineate an integral neural circuit for evaluating perceptual decision confidence.

Interacting with volatile environments stabilizes hidden-state inference and its brain signatures.

Making accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Multivariate patterns of magnetoencephalographic (MEG) activity reflected this behavioral difference in the neural interaction between inferred beliefs and incoming evidence, an effect originating from associative regions in the temporal lobe. Together, these findings indicate that the degree of control over the sampling of volatile environments shapes human learning and decision-making under uncertainty.

The Human Brain Encodes a Chronicle of Visual Events at Each Instant of Time Through the Multiplexing of Traveling Waves.

The human brain continuously processes streams of visual input. Yet, a single image typically triggers neural responses that extend beyond 1s. To understand how the brain encodes and maintains successive images, we analyzed with electroencephalography the brain activity of human subjects while they watched ∼5000 visual stimuli presented in fast sequences. First, we confirm that each stimulus can be decoded from brain activity for ∼1s, and we demonstrate that the brain simultaneously represents multiple images at each time instant. Second, we source localize the corresponding brain responses in the expected visual hierarchy and show that distinct brain regions represent, at each time instant, different snapshots of past stimulations. Third, we propose a simple framework to further characterize the dynamical system of these traveling waves. Our results show that a chain of neural circuits, which each consist of (1) a hidden maintenance mechanism and (2) an observable update mechanism, accounts for the dynamics of macroscopic brain representations elicited by visual sequences. Together, these results detail a simple architecture explaining how successive visual events and their respective timings can be simultaneously represented in the brain.SIGNIFICANCE STATEMENT Our retinas are continuously bombarded with a rich flux of visual input. Yet, how our brain continuously processes such visual streams is a major challenge to neuroscience. Here, we developed techniques to decode and track, from human brain activity, multiple images flashed in rapid succession. Our results show that the brain simultaneously represents multiple successive images at each time instant by multiplexing them along a neural cascade. Dynamical modeling shows that these results can be explained by a hierarchy of neural assemblies that continuously propagate multiple visual contents. Overall, this study sheds new light on the biological basis of our visual experience.

Social motivation is associated with increased weight granted to cooperation-related impressions in face evaluation tasks.

It is a trope in psychological science to define the human species as inherently social. Yet, despite its key role in human behaviour, the mechanisms by which social bonding actually shapes social behaviour have not been fully characterized. Across six studies, we show that the motivation for social bonding does not indiscriminately increase individuals' willingness to approach others but that it is instead associated with specific variations in social evaluations. Studies 1-4 demonstrate that social motivation is associated with a larger importance granted to cooperation-related impressions, i.e. perceived trustworthiness, during social evaluations. Studies 5 and 6 further reveal that this weighting difference leads strongly socially motivated participants to approach more partners that are perceived as both dominant and trustworthy. Taken together, our results provide support for the idea that humans' social motivation is associated with specific social preferences that could favour successful cooperative interactions and a widening of people's cooperative circle.

Confidence controls perceptual evidence accumulation.

Perceptual decisions are accompanied by feelings of confidence that reflect the likelihood that the decision was correct. Here we aim to clarify the relationship between perception and confidence by studying the same perceptual task across three different confidence contexts. Human observers were asked to categorize the source of sequentially presented visual stimuli. Each additional stimulus provided evidence for making more accurate perceptual decisions, and better confidence judgements. We show that observers' ability to set appropriate evidence accumulation bounds for perceptual decisions is strongly predictive of their ability to make accurate confidence judgements. When observers were not permitted to control their exposure to evidence, they imposed covert bounds on their perceptual decisions but not on their confidence decisions. This partial dissociation between decision processes is reflected in behaviour and pupil dilation. Together, these findings suggest a confidence-regulated accumulation-to-bound process that controls perceptual decision-making even in the absence of explicit speed-accuracy trade-offs.

Computational noise in reward-guided learning drives behavioral variability in volatile environments.

When learning the value of actions in volatile environments, humans often make seemingly irrational decisions that fail to maximize expected value. We reasoned that these 'non-greedy' decisions, instead of reflecting information seeking during choice, may be caused by computational noise in the learning of action values. Here using reinforcement learning models of behavior and multimodal neurophysiological data, we show that the majority of non-greedy decisions stem from this learning noise. The trial-to-trial variability of sequential learning steps and their impact on behavior could be predicted both by blood oxygen level-dependent responses to obtained rewards in the dorsal anterior cingulate cortex and by phasic pupillary dilation, suggestive of neuromodulatory fluctuations driven by the locus coeruleus-norepinephrine system. Together, these findings indicate that most behavioral variability, rather than reflecting human exploration, is due to the limited computational precision of reward-guided learning.

The ecological roots of human susceptibility to social influence: a pre-registered study investigating the impact of early-life adversity.

There is considerable variability in the degree to which individuals rely on their peers to make decisions. Although theoretical models predict that environmental risks shift the cost-benefit trade-off associated with social information use, this idea has received little empirical support. Here we aim to test the effect of childhood environmental adversity on humans' susceptibility to follow others' opinion in the context of a standard face evaluation task. Results collected in a pilot study involving 121 adult participants tested online showed that susceptibility to social influence and childhood environmental adversity are positively associated. Computational analyses further confirmed that this effect is not explained by the fact that participants exposed to early adversity produce noisier decisions overall but that they are indeed more likely to follow the group's opinion. To test the robustness of these findings, a pre-registered direct replication using an optimal sample size was run. The results obtained from 262 participants in the pre-registered study did not reveal a significant association between childhood adversity and task performance but the meta-analysis ran on both the pilot and the pre-registered study replicated the initial finding. This work provides experimental evidence for an association between individuals' past ecology and their susceptibility to social influence.

Human susceptibility to social influence and its neural correlates are related to perceived vulnerability to extrinsic morbidity risks.

Humans considerably vary in the degree to which they rely on their peers to make decisions. Why? Theoretical models predict that environmental risks shift the cost-benefit trade-off associated with the exploitation of others' behaviours (public information), yet this idea has received little empirical support. Using computational analyses of behaviour and multivariate decoding of electroencephalographic activity, we test the hypothesis that perceived vulnerability to extrinsic morbidity risks impacts susceptibility to social influence, and investigate whether and how this covariation is reflected in the brain. Data collected from 261 participants tested online revealed that perceived vulnerability to extrinsic morbidity risks is positively associated with susceptibility to follow peers' opinion in the context of a standard face evaluation task. We found similar results on 17 participants tested in the laboratory, and showed that the sensitivity of EEG signals to public information correlates with the participants' degree of vulnerability. We further demonstrated that the combination of perceived vulnerability to extrinsic morbidity with decoding sensitivities better predicted social influence scores than each variable taken in isolation. These findings suggest that susceptibility to social influence is partly calibrated by perceived environmental risks, possibly via a tuning of neural mechanisms involved in the processing of public information.

Pervasive influence of idiosyncratic associative biases during facial emotion recognition.

Facial morphology has been shown to influence perceptual judgments of emotion in a way that is shared across human observers. Here we demonstrate that these shared associations between facial morphology and emotion coexist with strong variations unique to each human observer. Interestingly, a large part of these idiosyncratic associations does not vary on short time scales, emerging from stable inter-individual differences in the way facial morphological features influence emotion recognition. Computational modelling of decision-making and neural recordings of electrical brain activity revealed that both shared and idiosyncratic face-emotion associations operate through a common biasing mechanism rather than an increased sensitivity to face-associated emotions. Together, these findings emphasize the underestimated influence of idiosyncrasies on core social judgments and identify their neuro-computational signatures.

Leveraging decision consistency to decompose suboptimality in terms of its ultimate predictability.

Although the suboptimality of perceptual decision making is indisputable in its strictest sense, characterizing the nature of suboptimalities constitutes a valuable drive for future research. I argue that decision consistency offers a rarely measured, yet important behavioral metric for decomposing suboptimality (or, more generally, deviations from any candidate model of decision making) into ultimately predictable and inherently unpredictable components.

The Importance of Falsification in Computational Cognitive Modeling.

In the past decade the field of cognitive sciences has seen an exponential growth in the number of computational modeling studies. Previous work has indicated why and how candidate models of cognition should be compared by trading off their ability to predict the observed data as a function of their complexity. However, the importance of falsifying candidate models in light of the observed data has been largely underestimated, leading to important drawbacks and unjustified conclusions. We argue here that the simulation of candidate models is necessary to falsify models and therefore support the specific claims about cognitive function made by the vast majority of model-based studies. We propose practical guidelines for future research that combine model comparison and falsification.

Shared mechanism for emotion processing in adolescents with and without autism.

Although, the quest to understand emotional processing in individuals with Autism Spectrum Disorders (ASD) has led to an impressive number of studies, the picture that emerges from this research remains inconsistent. Some studies find that Typically Developing (TD) individuals outperform those with ASD in emotion recognition tasks, others find no such difference. In this paper, we move beyond focusing on potential group differences in behaviour to answer what we believe is a more pressing question: do individuals with ASD use the same mechanisms to process emotional cues? To this end, we rely on model-based analyses of participants' accuracy during an emotion categorisation task in which displays of anger and fear are paired with direct vs. averted gaze. Behavioural data of 20 ASD and 20 TD adolescents revealed that the ASD group displayed lower overall performance. Yet, gaze direction had a similar impact on emotion categorisation in both groups, i.e. improved accuracy for salient combinations (anger-direct, fear-averted). Critically, computational modelling of participants' behaviour reveals that the same mechanism, i.e. increased perceptual sensitivity, underlies the contextual impact of gaze in both groups. We discuss the specific experimental conditions that may favour emotion processing and the automatic integration of contextual information in ASD.