VBA: a probabilistic treatment of nonlinear models for neurobiological and behavioural data

The timescale and direction of influence of a third inferior alternative in human value-learning

The way humans and other animals represent the values of alternatives is context-dependent, as it can be distorted by inferior alternatives that are immediately available for choice (immediate context); or that were encountered in previous episodes (temporal context). Yet, the extent to which the immediate and temporal context (co-) shape context-dependent valuation remains unclear. Here, we asked human participants (onsite: N = 30, online: N = 68) to learn the values associated with three alternatives and explicitly report these values before making binary and ternary choices among the alternatives. We show that context-dependent valuation is evident in the pre-choice value estimates and manifests equally in binary and ternary choices. Accordingly, we conclude that value representations are modulated by the temporal (and not the immediate) context. The direction and across-participants variability of this modulation cannot be captured by extant normalization theories but by a mechanism constructing values through sequential binary comparisons.

How mood-related physiological states bias economic decisions

When making decisions, humans are susceptible to all sorts of biases, relative to rational norms. An important factor is incidental changes in affective states, such as variations in mood between happiness and sadness. We previously developed a computational model, in which mood affects choice by forming a predisposition to face costs and seek more rewards. Here, we generalized this theory to account for how specific inductions of happiness and sadness affect different types of economic decisions involving a tradeoff between costs (risk, delay, effort) and benefits (financial rewards). Across exploratory and confirmatory studies (N = 94), we observed a consistent bias exerted by transitory mood states, whether they were assessed through self-reports (rated happiness minus sadness) or inferred from physiological measures (valence of facial expression times intensity of autonomous arousal). This choice bias was best explained by our computational model, with a mood-scaled bonus added to the value of the more rewarded but more costly option, irrespective of the cost type (risk, delay or effort). Additionally, gaze tracking during decision making confirmed that the choice bias was driven by an early preference for the mood-congruent option. Together, these results demonstrate the feasibility of predicting irrational choices from objective measures of affective states.

Relative social status alters the synchrony of attribute integration in altruistic decisions

Social status, which represents the relative dominance structure in societies, forms the backdrop against which most social decisions are made. Effective social decision-making demands flexible integration of decision attribute weight (importance of an attribute) and attribute latency (when attributes start to affect decisions). However, current understanding of how attribute weight and latency are influenced by relative social status is limited. In three experiments, we dynamically manipulated subjects' relative social status before they engaged in an altruistic decision task and found that their altruistic behavior was better explained by a time-varying drift diffusion model, in which relative social status selectively modulated attribute latency but not attribute weights. Furthermore, prosocial subjects exhibited higher sensitivity to attribute latency in response to changes in relative social status compared with individualistic subjects. Our results introduce a new dimension to the computational mechanisms underlying the intricate interplay between relative social status and attribute integration.

Dissociation of value and confidence signals in the orbitofrontal cortex during decision-making: an intracerebral electrophysiology study in humans

Some decisions, such as selecting a food item in a novel menu, are not based on rational norms, or on trained habits, but on subjective preferences. How the human brain makes these preference-based decisions is still debated in cognitive neuroscience. Classical models focus on the comparison mechanism that achieves the selection of the option with best expected value. Recent models suggest that estimates of option values are refined until reaching sufficient confidence in the considered choice. Neuroimaging studies in humans and electrophysiology studies in animals have gathered evidence that value and confidence estimates are both represented in medial and lateral regions of the orbitofrontal cortex (OFC). Here, we took advantage of electrodes implanted within the OFC of human patients with pharmaco-resistant epilepsy (14 women, 12 men) to investigate whether value and confidence estimates can be dissociated in electrophysiology activity recorded during preference-based binary decisions. The overall value (liking ratings summed over options) and choice confidence (selection probability of the chosen option) were identified in low-frequency (4-8 Hz) OFC activity. These value and confidence signals were time-locked to the decision, showed opposite signs of correlation and were recorded in separate sites. This pattern of results is not consistent with the simulations of an attractor neural network model implementing a comparison of option values. However, it is compatible with the notion of a neural network generating sparse representations of option values and choice confidence estimates, based on which decisions can be made.Significance statement The orbitofrontal cortex (OFC) is known to play a critical role in decisions based on subjective preferences, such as choosing between food items in a menu. However, the information provided by the human OFC has remained elusive, due to limitations of neuroimaging techniques. Here, taking advantage of electrodes implanted in patients for clinical purposes, we present a rare dataset of electrophysiological activity recorded during preference-based decisions. Our analyses suggest that the OFC signals two distinct constructs on which decisions could be based: the subjective values of available options and the confidence in the intended choice.

Thermosensory predictive coding underpins an illusion of pain

The human brain has a remarkable ability to learn and update its beliefs about the world. Here, we investigate how thermosensory learning shapes our subjective experience of temperature and the misperception of pain in response to harmless thermal stimuli. Through computational modeling, we demonstrate that the brain uses a probabilistic predictive coding scheme to update beliefs about temperature changes based on their uncertainty. We find that these expectations directly modulate the perception of pain in the thermal grill illusion. Quantitative microstructural brain imaging further revealed that individual variability in computational parameters related to uncertainty-driven learning and decision-making is reflected in the microstructure of brain regions such as the precuneus, posterior cingulate gyrus, cerebellum, as well as basal ganglia and brainstem. These findings provide a framework to understand how the brain infers pain from innocuous thermal inputs, with important implications for the etiology of thermosensory symptoms under chronic pain conditions.

Prefrontal default-mode network interactions with posterior hippocampus during exploration

Hippocampal maps and ventral prefrontal cortex (vPFC) value and goal representations support foraging in continuous spaces. How might hippocampal-vPFC interactions control the balance between behavioral exploration and exploitation? Using fMRI and reinforcement learning modeling, we investigated vPFC and hippocampal responses as humans explored and exploited a continuous one-dimensional space, with out-of-session and out-of-sample replication. The spatial distribution of rewards, or value landscape, modulated activity in the hippocampus and default network vPFC subregions, but not in ventrolateral prefrontal control subregions or medial orbitofrontal limbic subregions. While prefrontal default network and hippocampus displayed higher activity in less complex, easy-to-exploit value landscapes, vPFC-hippocampal connectivity increased in uncertain landscapes requiring exploration. Further, synchronization between prefrontal default network and posterior hippocampus scaled with behavioral exploration. Considered alongside electrophysiological studies, our findings suggest that locations to be explored are identified through coordinated activity binding prefrontal default network value representations to posterior hippocampal maps.

Automatic multisensory integration follows subjective confidence rather than objective performance

It is well known that sensory information from one modality can automatically affect judgments from a different sensory modality. However, it remains unclear what determines the strength of the influence of an irrelevant sensory cue from one modality on a perceptual judgment for a different modality. Here we test whether the strength of multisensory impact by an irrelevant sensory cue depends on participants' objective accuracy or subjective confidence for that cue. We created visual motion stimuli with low vs. high overall motion energy, where high-energy stimuli yielded higher confidence but lower accuracy in a visual-only task. We then tested the impact of the low- and high-energy visual stimuli on auditory motion perception in 99 participants. We found that the high-energy visual stimuli influenced the auditory motion judgments more strongly than the low-energy visual stimuli, consistent with their higher confidence but contrary to their lower accuracy. A computational model assuming common principles underlying confidence reports and multisensory integration captured these effects. Our findings show that automatic multisensory integration follows subjective confidence rather than objective performance and suggest the existence of common computations across vastly different stages of perceptual decision making.

A neurometabolic mechanism involving dmPFC/dACC lactate in physical effort-based decision-making

Motivation levels vary across individuals, yet the underlying mechanisms driving these differences remain elusive. The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) and the anterior insula (aIns) play crucial roles in effort-based decision-making. Here, we investigate the influence of lactate, a key metabolite involved in energy metabolism and signaling, on decisions involving both physical and mental effort, as well as its effects on neural activation. Using proton magnetic resonance spectroscopy and functional MRI in 63 participants, we find that higher lactate levels in the dmPFC/dACC are associated with reduced motivation for physical effort, a relationship mediated by neural activity within this region. Additionally, plasma and dmPFC/dACC lactate levels correlate, suggesting a systemic influence on brain metabolism. Supported by path analysis, our results highlight lactate's role as a modulator of dmPFC/dACC activity, hinting at a neurometabolic mechanism that integrates both peripheral and central metabolic states with brain function in effort-based decision-making.

Normative evidence weighing and accumulation in correlated environments

The brain forms certain deliberative decisions following normative principles related to how sensory observations are weighed and accumulated over time. Previously we showed that these principles can account for how people adapt their decisions to the temporal dynamics of the observations (Glaze et al., 2015). Here we show that this adaptability extends to accounting for correlations in the observations, which can have a dramatic impact on the weight of evidence provided by those observations. We tested online human participants on a novel visual-discrimination task with pairwise-correlated observations. With minimal training, the participants adapted to uncued, trial-by-trial changes in the correlations and produced decisions based on an approximately normative weighing and accumulation of evidence. The results highlight the robustness of our brain's ability to process sensory observations with respect to not just their physical features but also the weight of evidence they provide for a given decision.

Expectancy-Mood Neural Dynamics Predict Mechanisms of Short- and Long-Term Antidepressant Placebo Effects

BACKGROUND

Acute experimental models of antidepressant placebo effects suggest that expectancies, encoded within the salience network (SN), are reinforced by sensory evidence and mood fluctuations. However, whether these dynamics extend to longer timescales remains unknown. To answer this question, we investigated how SN and default mode network (DMN) functional connectivity during the processing of antidepressant expectancies facilitates the shift from salience attribution to contextual cues in the SN to belief-induced mood responses in the DMN, both acutely and long term.

METHODS

Sixty psychotropic-free patients with major depressive disorder completed an acute antidepressant placebo functional magnetic resonance imaging experiment manipulating placebo-associated expectancies and their reinforcement while assessing trial-by-trial mood improvement before entering an 8-week double-blind, randomized, placebo-controlled trial of a selective serotonin reuptake inhibitor or placebo.

RESULTS

Learned antidepressant expectancies predicted by a reinforcement learning model modulated SN-DMN connectivity. Acutely, greater modulation predicted higher effects of expectancy and reinforcement manipulations on reported expectancies and mood. Over 8 weeks, no significant drug effects on mood improvement were observed. However, participants who believed that they were receiving an antidepressant exhibited significantly greater mood improvement irrespective of the actual treatment received. Moreover, increased SN-DMN connectivity predicted mood improvement, especially in placebo-treated participants who believed that they received a selective serotonin reuptake inhibitor.

CONCLUSIONS

SN-DMN interactions may play a critical role in the evolution of antidepressant response expectancies, drug-assignment beliefs, and their effects on mood.

Efficient value synthesis in the orbitofrontal cortex explains how loss aversion adapts to the ranges of gain and loss prospects

Is irrational behavior the incidental outcome of biological constraints imposed on neural information processing? In this work, we consider the paradigmatic case of gamble decisions, where gamble values integrate prospective gains and losses. Under the assumption that neurons have a limited firing response range, we show that mitigating the ensuing information loss within artificial neural networks that synthetize value involves a specific form of self-organized plasticity. We demonstrate that the ensuing efficient value synthesis mechanism induces value range adaptation. We also reveal how the ranges of prospective gains and/or losses eventually determine both the behavioral sensitivity to gains and losses and the information content of the network. We test these predictions on two fMRI datasets from the OpenNeuro.org initiative that probe gamble decision-making but differ in terms of the range of gain prospects. First, we show that peoples' loss aversion eventually adapts to the range of gain prospects they are exposed to. Second, we show that the strength with which the orbitofrontal cortex (in particular: Brodmann area 11) encodes gains and expected value also depends upon the range of gain prospects. Third, we show that, when fitted to participant's gambling choices, self-organizing artificial neural networks generalize across gain range contexts and predict the geometry of information content within the orbitofrontal cortex. Our results demonstrate how self-organizing plasticity aiming at mitigating information loss induced by neurons' limited response range may result in value range adaptation, eventually yielding irrational behavior.

Human hippocampus and dorsomedial prefrontal cortex infer and update latent causes during social interaction

Latent-cause inference is the process of identifying features of the environment that have caused an outcome. This problem is especially important in social settings where individuals may not make equal contributions to the outcomes they achieve together. Here, we designed a novel task in which participants inferred which of two characters was more likely to have been responsible for outcomes achieved by working together. Using computational modeling, univariate and multivariate analysis of human fMRI, and continuous theta-burst stimulation, we identified two brain regions that solved the task. Notably, as each outcome occurred, it was possible to decode the inference of its cause (the responsible character) from hippocampal activity. Activity in dorsomedial prefrontal cortex (dmPFC) updated estimates of association between cause-responsible character-and the outcome. Disruption of dmPFC activity impaired participants' ability to update their estimate as a function of inferred responsibility but spared their ability to infer responsibility.

Decoding the Temporal Structures and Interactions of Multiple Face Dimensions Using Optically Pumped Magnetometer Magnetoencephalography (OPM-MEG)

Humans possess a remarkable ability to rapidly access diverse information from others' faces with just a brief glance, which is crucial for intricate social interactions. While previous studies using event-related potentials/fields have explored various face dimensions during this process, the interplay between these dimensions remains unclear. Here, by applying multivariate decoding analysis to neural signals recorded with optically pumped magnetometer magnetoencephalography, we systematically investigated the temporal interactions between invariant and variable aspects of face stimuli, including race, gender, age, and expression. First, our analysis revealed unique temporal structures for each face dimension with high test-retest reliability. Notably, expression and race exhibited a dominant and stably maintained temporal structure according to temporal generalization analysis. Further exploration into the mutual interactions among face dimensions uncovered age effects on gender and race, as well as expression effects on race, during the early stage (∼200-300 ms postface presentation). Additionally, we observed a relatively late effect of race on gender representation, peaking ∼350 ms after the stimulus onset. Taken together, our findings provide novel insights into the neural dynamics underlying the multidimensional aspects of face perception and illuminate the promising future of utilizing OPM-MEG for exploring higher-level human cognition.

Test-retest reliability of behavioral and computational measures of advice taking under volatility

The development of computational models for studying mental disorders is on the rise. However, their psychometric properties remain understudied, posing a risk of undermining their use in empirical research and clinical translation. Here we investigated test-retest reliability (with a 2-week interval) of a computational assay probing advice-taking under volatility with a Hierarchical Gaussian Filter (HGF) model. In a sample of 39 healthy participants, we found the computational measures to have largely poor reliability (intra-class correlation coefficient or ICC < 0.5), on par with the behavioral measures of task performance. Further analysis revealed that reliability was substantially impacted by intrinsic measurement noise (indicated by parameter recovery analysis) and to a smaller extent by practice effects. However, a large portion of within-subject variance remained unexplained and may be attributable to state-like fluctuations. Despite the poor test-retest reliability, we found the assay to have face validity at the group level. Overall, our work highlights that the different sources of variance affecting test-retest reliability need to be studied in greater detail. A better understanding of these sources would facilitate the design of more psychometrically sound assays, which would improve the quality of future research and increase the probability of clinical translation.

Semi-orthogonal subspaces for value mediate a binding and generalization trade-off

When choosing between options, we must associate their values with the actions needed to select them. We hypothesize that the brain solves this binding problem through neural population subspaces. Here, in macaques performing a choice task, we show that neural populations in five reward-sensitive regions encode the values of offers presented on the left and right in distinct subspaces. This encoding is sufficient to bind offer values to their locations while preserving abstract value information. After offer presentation, all areas encode the value of the first and second offers in orthogonal subspaces; this orthogonalization also affords binding. Our binding-by-subspace hypothesis makes two new predictions confirmed by the data. First, behavioral errors should correlate with spatial, but not temporal, neural misbinding. Second, behavioral errors should increase when offers have low or high values, compared to medium values, even when controlling for value difference. Together, these results support the idea that the brain uses semi-orthogonal subspaces to bind features.

The neuroscience of mental illness: Building toward the future

Mental illnesses arise from dysfunction in the brain. Although numerous extraneural factors influence these illnesses, ultimately, it is the science of the brain that will lead to novel therapies. Meanwhile, our understanding of this complex organ is incomplete, leading to the oft-repeated trope that neuroscience has yet to make significant contributions to the care of individuals with mental illnesses. This review seeks to counter this narrative, using specific examples of how neuroscientific advances have contributed to progress in mental health care in the past and how current achievements set the stage for further progress in the future.

Pain reflects the informational value of nociceptive inputs

ABSTRACT

Pain perception and its modulation are fundamental to human learning and adaptive behavior. This study investigated the hypothesis that pain perception is tied to pain's learning function. Thirty-one participants performed a threat conditioning task where certain cues were associated with a possibility of receiving a painful electric shock. The cues that signaled potential pain or safety were regularly changed, requiring participants to continually establish new associations. Using computational models, we quantified participants' pain expectations and prediction errors throughout the task and assessed their relationship with pain perception and electrophysiological responses. Our findings suggest that subjective pain perception increases with prediction error, that is, when pain was unexpected. Prediction errors were also related to physiological nociceptive responses, including the amplitude of nociceptive flexion reflex and electroencephalography markers of cortical nociceptive processing (N1-P2-evoked potential and gamma-band power). In addition, higher pain expectations were related to increased late event-related potential responses and alpha/beta decreases in amplitude during cue presentation. These results further strengthen the idea of a crucial link between pain and learning and suggest that understanding the influence of learning mechanisms in pain modulation could help us understand when and why pain perception is modulated in health and disease.

Exploration-Exploitation and Suicidal Behavior in Borderline Personality Disorder and Depression

Importance

Clinical theory and behavioral studies suggest that people experiencing suicidal crisis are often unable to find constructive solutions or incorporate useful information into their decisions, resulting in premature convergence on suicide and neglect of better alternatives. However, prior studies of suicidal behavior have not formally examined how individuals resolve the tradeoffs between exploiting familiar options and exploring potentially superior alternatives.

Objective

To investigate exploration and exploitation in suicidal behavior from the formal perspective of reinforcement learning.

Design, Setting, and Participants

Two case-control behavioral studies of exploration-exploitation of a large 1-dimensional continuous space and a 21-day prospective ambulatory study of suicidal ideation were conducted between April 2016 and March 2022. Participants were recruited from inpatient psychiatric units, outpatient clinics, and the community in Pittsburgh, Pennsylvania, and underwent laboratory and ambulatory assessments. Adults diagnosed with borderline personality disorder (BPD) and midlife and late-life major depressive disorder (MDD) were included, with each sample including demographically equated groups with a history of high-lethality suicide attempts, low-lethality suicide attempts, individuals with BPD or MDD but no suicide attempts, and control individuals without psychiatric disorders. The MDD sample also included a subgroup with serious suicidal ideation.

Main Outcomes and Measures

Behavioral (model-free and model-derived) indices of exploration and exploitation, suicide attempt lethality (Beck Lethality Scale), and prospectively assessed suicidal ideation.

Results

The BPD group included 171 adults (mean [SD] age, 30.55 [9.13] years; 135 [79%] female). The MDD group included 143 adults (mean [SD] age, 62.03 [6.82] years; 81 [57%] female). Across the BPD (χ23 = 50.68; P < .001) and MDD (χ24 = 36.34; P < .001) samples, individuals with high-lethality suicide attempts discovered fewer options than other groups as they were unable to shift away from unrewarded options. In contrast, those with low-lethality attempts were prone to excessive behavioral shifts after rewarded and unrewarded actions. No differences were seen in strategic early exploration or in exploitation. Among 84 participants with BPD in the ambulatory study, 56 reported suicidal ideation. Underexploration also predicted incident suicidal ideation (χ21 = 30.16; P < .001), validating the case-control results prospectively. The findings were robust to confounds, including medication exposure, affective state, and behavioral heterogeneity.

Conclusions and Relevance

The findings suggest that narrow exploration and inability to abandon inferior options are associated with serious suicidal behavior and chronic suicidal thoughts. By contrast, individuals in this study who engaged in low-lethality suicidal behavior displayed a low threshold for taking potentially disadvantageous actions.

Bayesian confidence in optimal decisions

The optimal way to make decisions in many circumstances is to track the difference in evidence collected in favor of the options. The drift diffusion model (DDM) implements this approach and provides an excellent account of decisions and response times. However, existing DDM-based models of confidence exhibit certain deficits, and many theories of confidence have used alternative, nonoptimal models of decisions. Motivated by the historical success of the DDM, we ask whether simple extensions to this framework might allow it to better account for confidence. Motivated by the idea that the brain will not duplicate representations of evidence, in all model variants decisions and confidence are based on the same evidence accumulation process. We compare the models to benchmark results, and successfully apply four qualitative tests concerning the relationships between confidence, evidence, and time, in a new preregistered study. Using computationally cheap expressions to model confidence on a trial-by-trial basis, we find that a subset of model variants also provide a very good to excellent account of precise quantitative effects observed in confidence data. Specifically, our results favor the hypothesis that confidence reflects the strength of accumulated evidence penalized by the time taken to reach the decision (Bayesian readout), with the penalty applied not perfectly calibrated to the specific task context. These results suggest there is no need to abandon the DDM or single accumulator models to successfully account for confidence reports. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Distractor effects in decision making are related to the individual's style of integrating choice attributes

Humans make irrational decisions in the presence of irrelevant distractor options. There is little consensus on whether decision making is facilitated or impaired by the presence of a highly rewarding distractor, or whether the distractor effect operates at the level of options' component attributes rather than at the level of their overall value. To reconcile different claims, we argue that it is important to consider the diversity of people's styles of decision making and whether choice attributes are combined in an additive or multiplicative way. Employing a multi-laboratory dataset investigating the same experimental paradigm, we demonstrated that people used a mix of both approaches and the extent to which approach was used varied across individuals. Critically, we identified that this variability was correlated with the distractor effect during decision making. Individuals who tended to use a multiplicative approach to compute value, showed a positive distractor effect. In contrast, a negative distractor effect (divisive normalisation) was prominent in individuals tending towards an additive approach. Findings suggest that the distractor effect is related to how value is constructed, which in turn may be influenced by task and subject specificities. This concurs with recent behavioural and neuroscience findings that multiple distractor effects co-exist.

Modeling the Heterogeneity of Post-Stroke Gait Control in Free-Living Environments Using a Personalized Causal Network

Post-stroke gait control is a complex, often fail to account for the heterogeneity and continuity of gait in existing gait models. Precisely evaluating gait speed adjustability and gait instability in free-living environments is important to understand how individuals with post-stroke gait dysfunction approach diverse environments and contexts. This study aimed to explore individual causal interactions in the free-living gait control of persons with stroke. To this end, fifty persons with stroke wore an accelerometer on the fifth lumbar vertebra (L5) for 24 h in a free-living environment. Individually directed acyclic graphs (DAGs) were generated based on the spatiotemporal gait parameters at contemporaneous and temporal points calculated from the acceleration data. Spectral clustering and Bayesian model comparison were used to characterize the DAGs. Finally, the DAG patterns were interpreted via Bayesian logistic analysis. Spectral clustering identified three optimal clusters from the DAGs. Cluster 1 included persons with moderate stroke who showed high gait asymmetry and gait instability and primarily adjusted gait speed based on cadence. Cluster 2 included individuals with mild stroke who primarily adjusted their gait speed based on step length. Cluster 3 comprised individuals with mild stroke who primarily adjusted their gait speed based on both step length and cadence. These three clusters could be accurately classified based on four variables: Ashman's D for step velocity, Fugl-Meyer Assessment, step time asymmetry, and step length. The diverse DAG patterns of gait control identified suggest the heterogeneity of gait patterns and the functional diversity of persons with stroke. Understanding the theoretical interactions between gait functions will provide a foundation for highly tailored rehabilitation.

Multisensory perceptual and causal inference is largely preserved in medicated post-acute individuals with schizophrenia

Hallucinations and perceptual abnormalities in psychosis are thought to arise from imbalanced integration of prior information and sensory inputs. We combined psychophysics, Bayesian modeling, and electroencephalography (EEG) to investigate potential changes in perceptual and causal inference in response to audiovisual flash-beep sequences in medicated individuals with schizophrenia who exhibited limited psychotic symptoms. Seventeen participants with schizophrenia and 23 healthy controls reported either the number of flashes or the number of beeps of audiovisual sequences that varied in their audiovisual numeric disparity across trials. Both groups balanced sensory integration and segregation in line with Bayesian causal inference rather than resorting to simpler heuristics. Both also showed comparable weighting of prior information regarding the signals' causal structure, although the schizophrenia group slightly overweighted prior information about the number of flashes or beeps. At the neural level, both groups computed Bayesian causal inference through dynamic encoding of independent estimates of the flash and beep counts, followed by estimates that flexibly combine audiovisual inputs. Our results demonstrate that the core neurocomputational mechanisms for audiovisual perceptual and causal inference in number estimation tasks are largely preserved in our limited sample of medicated post-acute individuals with schizophrenia. Future research should explore whether these findings generalize to unmedicated patients with acute psychotic symptoms.

Direct stimulation of anterior insula and ventromedial prefrontal cortex disrupts economic choices

Neural activity within the ventromedial prefrontal cortex (vmPFC) and anterior insula (aIns) is often associated with economic choices and confidence. However, it remains unclear whether these brain regions are causally related to these processes. To address this issue, we leveraged intracranial electrical stimulation (iES) data obtained from patients with epilepsy performing an economic choice task. Our results reveal opposite effects of stimulation on decision-making depending on its location along a dorso-ventral axis within each region. Specifically, stimulation of the ventral subregion within aIns reduces risk-taking by increasing participants' sensitivity to potential losses, whereas stimulation of the dorsal subregion of aIns and the ventral portion of the vmPFC increases risk-taking by reducing participants' sensitivity to losses. Moreover, stimulation of the aIns consistently decreases participants' confidence, regardless of its location within the aIns. These findings suggest the existence of functionally dissociated neural subregions and circuits causally involved in accepting or avoiding challenges.

Distinct alterations in probabilistic reversal learning across at-risk mental state, first episode psychosis and persistent schizophrenia

We used a probabilistic reversal learning task to examine prediction error-driven belief updating in three clinical groups with psychosis or psychosis-like symptoms. Study 1 compared people with at-risk mental state and first episode psychosis (FEP) to matched controls. Study 2 compared people diagnosed with treatment-resistant schizophrenia (TRS) to matched controls. The design replicated our previous work showing ketamine-related perturbations in how meta-level confidence maintained behavioural policy. We applied the same computational modelling analysis here, in order to compare the pharmacological model to three groups at different stages of psychosis. Accuracy was reduced in FEP, reflecting increased tendencies to shift strategy following probabilistic errors. The TRS group also showed a greater tendency to shift choice strategies though accuracy levels were not significantly reduced. Applying the previously-used computational modelling approach, we observed that only the TRS group showed altered confidence-based modulation of responding, previously observed under ketamine administration. Overall, our behavioural findings demonstrated resemblance between clinical groups (FEP and TRS) and ketamine in terms of a reduction in stabilisation of responding in a noisy environment. The computational analysis suggested that TRS, but not FEP, replicates ketamine effects but we consider the computational findings preliminary given limitations in performance of the model.

Callousness, exploitativeness, and tracking of cooperation incentives in the human default network

Human cognitive capacities that enable flexible cooperation may have evolved in parallel with the expansion of frontoparietal cortical networks, particularly the default network. Conversely, human antisocial behavior and trait antagonism are broadly associated with reduced activity, impaired connectivity, and altered structure of the default network. Yet, behaviors like interpersonal manipulation and exploitation may require intact or even superior social cognition. Using a reinforcement learning model of decision-making on a modified trust game, we examined how individuals adjusted their cooperation rate based on a counterpart's cooperation and social reputation. We observed that learning signals in the default network updated the predicted utility of cooperation or defection and scaled with reciprocal cooperation. These signals were weaker in callous (vs. compassionate) individuals but stronger in those who were more exploitative (vs. honest and humble). Further, they accounted for associations between exploitativeness, callousness, and reciprocal cooperation. Separately, behavioral sensitivity to prior reputation was reduced in callous but not exploitative individuals and selectively scaled with responses of the medial temporal subsystem of the default network. Overall, callousness was characterized by blunted behavioral and default network sensitivity to cooperation incentives. Exploitativeness predicted heightened sensitivity to others' cooperation but not social reputation. We speculate that both compassion and exploitativeness may reflect cognitive adaptations to social living, enabled by expansion of the default network in anthropogenesis.

A measure of reliability convergence to select and optimize cognitive tasks for individual differences research

Surging interest in individual differences has faced setbacks in light of recent replication crises in psychology, for example in brain-wide association studies exploring brain-behavior correlations. A crucial component of replicability for individual differences studies, which is often assumed but not directly tested, is the reliability of the measures we use. Here, we evaluate the reliability of different cognitive tasks on a dataset with over 250 participants, who each completed a multi-day task battery. We show how reliability improves as a function of number of trials, and describe the convergence of the reliability curves for the different tasks, allowing us to score tasks according to their suitability for studies of individual differences. We further show the effect on reliability of measuring over multiple time points, with tasks assessing different cognitive domains being differentially affected. Data collected over more than one session may be required to achieve trait-like stability.

Connected in Bad Times and in Good Times: Empathy Induces Stable Social Closeness

Humans need social closeness to prosper. There is evidence that empathy can induce social closeness. However, it remains unclear how empathy-related social closeness is formed and how stable it is as time passes. We applied an acquisition-extinction paradigm combined with computational modeling and fMRI, to investigate the formation and stability of empathy-related social closeness. Female participants observed painful stimulation of another person with high probability (acquisition) and low probability (extinction) and rated their closeness to that person. The results of two independent studies showed increased social closeness in the acquisition block that resisted extinction in the extinction block. Providing insights into underlying mechanisms, reinforcement learning modeling revealed that the formation of social closeness is based on a learning signal (prediction error) generated from observing another's pain, whereas maintaining social closeness is based on a learning signal generated from observing another's pain relief. The results of a reciprocity control study indicate that this feedback recalibration is specific to learning of empathy-related social closeness. On the neural level, the recalibration of the feedback signal was associated with neural responses in anterior insula and adjacent inferior frontal gyrus and the bilateral superior temporal sulcus/temporoparietal junction. Together, these findings show that empathy-related social closeness generated in bad times, that is, empathy with the misfortune of another person, transfers to good times and thus may form one important basis for stable social relationships.

CHRFAM7A diversifies human immune adaption through Ca2+ signalling and actin cytoskeleton reorganization

BACKGROUND

Human restricted genes contribute to human specific traits in the immune system. CHRFAM7A, a uniquely human fusion gene, is a negative regulator of the α7 nicotinic acetylcholine receptor (α7 nAChR), the highest Ca2+ conductor of the ACh receptors implicated in innate immunity. Understanding the mechanism of how CHRFAM7A affects the immune system remains unexplored.

METHODS

Two model systems are used, human induced pluripotent stem cells (iPSC) and human primary monocytes, to characterize α7 nAChR function, Ca2+ dynamics and decoders to elucidate the pathway from receptor to phenotype.

FINDINGS

CHRFAM7A/α7 nAChR is identified as a hypomorphic receptor with mitigated Ca2+ influx and prolonged channel closed state. This shifts the Ca2+ reservoir from the extracellular space to the endoplasmic reticulum (ER) leading to Ca2+ dynamic changes. Ca2+ decoder small GTPase Rac1 is then activated, reorganizing the actin cytoskeleton. Observed actin mediated phenotypes include cellular adhesion, motility, phagocytosis and tissue mechanosensation.

INTERPRETATION

CHRFAM7A introduces an additional, human specific, layer to Ca2+ regulation leading to an innate immune gain of function. Through the actin cytoskeleton it drives adaptation to the mechanical properties of the tissue environment leading to an ability to invade previously immune restricted niches. Human genetic diversity predicts profound translational significance as its understanding builds the foundation for successful treatments for infectious diseases, sepsis, and cancer metastasis.

FUNDING

This work is supported in part by the Community Foundation for Greater Buffalo (Kinga Szigeti) and in part by NIH grant R01HL163168 (Yongho Bae).

Value Estimation versus Effort Mobilization: A General Dissociation between Ventromedial and Dorsomedial Prefrontal Cortex

Deciding on a course of action requires both an accurate estimation of option values and the right amount of effort invested in deliberation to reach sufficient confidence in the final choice. In a previous study, we have provided evidence, across a series of judgment and choice tasks, for a dissociation between the ventromedial prefrontal cortex (vmPFC), which would represent option values, and the dorsomedial prefrontal cortex (dmPFC), which would represent the duration of deliberation. Here, we first replicate this dissociation and extend it to the case of an instrumental learning task, in which 24 human volunteers (13 women) choose between options associated with probabilistic gains and losses. According to fMRI data recorded during decision-making, vmPFC activity reflects the sum of option values generated by a reinforcement learning model and dmPFC activity the deliberation time. To further generalize the role of the dmPFC in mobilizing effort, we then analyze fMRI data recorded in the same participants while they prepare to perform motor and cognitive tasks (squeezing a handgrip or making numerical comparisons) to maximize gains or minimize losses. In both cases, dmPFC activity is associated with the output of an effort regulation model, and not with response time. Taken together, these results strengthen a general theory of behavioral control that implicates the vmPFC in the estimation of option values and the dmPFC in the energization of relevant motor and cognitive processes.

Neurocomputational mechanisms involved in adaptation to fluctuating intentions of others

Humans frequently interact with agents whose intentions can fluctuate between competition and cooperation over time. It is unclear how the brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled. Here, we use model-based fMRI and a task in which participants thought they were playing with another player. In fact, they played with an algorithm that alternated without signaling between cooperative and competitive strategies. We show that a neurocomputational mechanism with arbitration between competitive and cooperative experts outperforms other learning models in predicting choice behavior. At the brain level, the fMRI results show that the ventral striatum and ventromedial prefrontal cortex track the difference of reliability between these experts. When attributing competitive intentions, we find increased coupling between these regions and a network that distinguishes prediction errors related to competition and cooperation. These findings provide a neurocomputational account of how the brain arbitrates dynamically between cooperative and competitive intentions when making adaptive social decisions.

Parkinson's disease impairs cortical sensori-motor decision-making cascades

The transformation from perception to action requires a set of neuronal decisions about the nature of the percept, identification and selection of response options and execution of the appropriate motor response. The unfolding of such decisions is mediated by distributed representations of the decision variables-evidence and intentions-that are represented through oscillatory activity across the cortex. Here we combine magneto-electroencephalography and linear ballistic accumulator models of decision-making to reveal the impact of Parkinson's disease during the selection and execution of action. We used a visuomotor task in which we independently manipulated uncertainty in sensory and action domains. A generative accumulator model was optimized to single-trial neurophysiological correlates of human behaviour, mapping the cortical oscillatory signatures of decision-making, and relating these to separate processes accumulating sensory evidence and selecting a motor action. We confirmed the role of widespread beta oscillatory activity in shaping the feed-forward cascade of evidence accumulation from resolution of sensory inputs to selection of appropriate responses. By contrasting the spatiotemporal dynamics of evidence accumulation in age-matched healthy controls and people with Parkinson's disease, we identified disruption of the beta-mediated cascade of evidence accumulation as the hallmark of atypical decision-making in Parkinson's disease. In frontal cortical regions, there was inefficient processing and transfer of perceptual information. Our findings emphasize the intimate connection between abnormal visuomotor function and pathological oscillatory activity in neurodegenerative disease. We propose that disruption of the oscillatory mechanisms governing fast and precise information exchanges between the sensory and motor systems contributes to behavioural changes in people with Parkinson's disease.

How do humans give confidence? A comprehensive comparison of process models of perceptual metacognition

Humans have the metacognitive ability to assess the accuracy of their decisions via confidence judgments. Several computational models of confidence have been developed but not enough has been done to compare these models, making it difficult to adjudicate between them. Here, we compare 14 popular models of confidence that make various assumptions, such as confidence being derived from postdecisional evidence, from positive (decision-congruent) evidence, from posterior probability computations, or from a separate decision-making system for metacognitive judgments. We fit all models to three large experiments in which subjects completed a basic perceptual task with confidence ratings. In Experiments 1 and 2, the best-fitting model was the lognormal meta noise (LogN) model, which postulates that confidence is selectively corrupted by signal-dependent noise. However, in Experiment 3, the positive evidence (PE) model provided the best fits. We evaluated a new model combining the two consistently best-performing models-LogN and the weighted evidence and visibility (WEV). The resulting model, which we call logWEV, outperformed its individual counterparts and the PE model across all data sets, offering a better, more generalizable explanation for these data. Parameter and model recovery analyses showed mostly good recoverability but with important exceptions carrying implications for our ability to discriminate between models. Finally, we evaluated each model's ability to explain different patterns in the data, which led to additional insight into their performances. These results comprehensively characterize the relative adequacy of current confidence models to fit data from basic perceptual tasks and highlight the most plausible mechanisms underlying confidence generation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Characterizing Human Habits in the Lab

Habits pose a fundamental puzzle for those aiming to understand human behavior. They pervade our everyday lives and dominate some forms of psychopathology but are extremely hard to elicit in the lab. In this Registered Report, we developed novel experimental paradigms grounded in computational models, which suggest that habit strength should be proportional to the frequency of behavior and, in contrast to previous research, independent of value. Specifically, we manipulated how often participants performed responses in two tasks varying action repetition without, or separately from, variations in value. Moreover, we asked how this frequency-based habitization related to value-based operationalizations of habit and self-reported propensities for habitual behavior in real life. We find that choice frequency during training increases habit strength at test and that this form of habit shows little relation to value-based operationalizations of habit. Our findings empirically ground a novel perspective on the constituents of habits and suggest that habits may arise in the absence of external reinforcement. We further find no evidence for an overlap between different experimental approaches to measuring habits and no associations with self-reported real-life habits. Thus, our findings call for a rigorous reassessment of our understanding and measurement of human habitual behavior in the lab.

Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space

Primates exploring and exploiting a continuous sensorimotor space rely on dynamic maps in the dorsal stream. Two complementary perspectives exist on how these maps encode rewards. Reinforcement learning models integrate rewards incrementally over time, efficiently resolving the exploration/exploitation dilemma. Working memory buffer models explain rapid plasticity of parietal maps but lack a plausible exploration/exploitation policy. The reinforcement learning model presented here unifies both accounts, enabling rapid, information-compressing map updates and efficient transition from exploration to exploitation. As predicted by our model, activity in human frontoparietal dorsal stream regions, but not in MT+, tracks the number of competing options, as preferred options are selectively maintained on the map, while spatiotemporally distant alternatives are compressed out. When valuable new options are uncovered, posterior β1/α oscillations desynchronize within 0.4 to 0.7 s, consistent with option encoding by competing β1-stabilized subpopulations. Together, outcomes matching locally cached reward representations rapidly update parietal maps, biasing choices toward often-sampled, rewarded options.

Disentangling sensory precision and prior expectation of change in autism during tactile discrimination

Predictive coding theories suggest that core symptoms in autism spectrum disorders (ASD) may stem from atypical mechanisms of perceptual inference (i.e., inferring the hidden causes of sensations). Specifically, there would be an imbalance in the precision or weight ascribed to sensory inputs relative to prior expectations. Using three tactile behavioral tasks and computational modeling, we specifically targeted the implicit dynamics of sensory adaptation and perceptual learning in ASD. Participants were neurotypical and autistic adults without intellectual disability. In Experiment I, tactile detection thresholds and adaptation effects were measured to assess sensory precision. Experiments II and III relied on two-alternative forced choice tasks designed to elicit a time-order effect, where prior knowledge biases perceptual decisions. Our results suggest a subtler explanation than a simple imbalance in the prior/sensory weights, having to do with the dynamic nature of perception, that is the adjustment of precision weights to context. Compared to neurotypicals, autistic adults showed no difference in average performance and sensory sensitivity. Both groups managed to implicitly learn and adjust a prior that biased their perception. However, depending on the context, autistic participants showed no, normal or slower adaptation, a phenomenon that computational modeling of trial-to-trial responses helped us to associate with a higher expectation for sameness in ASD, and to dissociate from another observed robust difference in terms of response bias. These results point to atypical perceptual learning rather than altered perceptual inference per se, calling for further empirical and computational studies to refine the current predictive coding theories of ASD.

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response

Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN. However, direct comparisons of these two main hypotheses are scarce, and previous modeling studies suffered from methodological limitations. Based on reports indicating spatial and temporal dissociation of physiological mechanisms within the timecourse of mismatch responses in animals, we hypothesized that different computational models would best fit different temporal phases of the human MMN. Using electroencephalographic data from two independent studies of a simple auditory oddball task (n = 82), we compared adaptation and Bayesian learning models' ability to explain the sequential dynamics of auditory deviance detection in a time-resolved fashion. We first ran simulations to evaluate the capacity of our design to dissociate the tested models and found that they were sufficiently distinguishable above a certain level of signal-to-noise ratio (SNR). In subjects with a sufficient SNR, our time-resolved approach revealed a temporal dissociation between the two model families, with high evidence for adaptation during the early MMN window (from 90 to 150-190 ms post-stimulus depending on the dataset) and for Bayesian learning later in time (170-180 ms or 200-220ms). In addition, Bayesian model averaging of fixed-parameter models within the adaptation family revealed a gradient of adaptation rates, resembling the anatomical gradient in the auditory cortical hierarchy reported in animal studies.

Risky decisions are influenced by individual attributes as a function of risk preference

It has long been assumed in economic theory that multi-attribute decisions involving several attributes or dimensions - such as probabilities and amounts of money to be earned during risky choices - are resolved by first combining the attributes of each option to form an overall expected value and then comparing the expected values of the alternative options, using a unique evidence accumulation process. A plausible alternative would be performing independent comparisons between the individual attributes and then integrating the results of the comparisons afterwards. Here, we devise a novel method to disambiguate between these types of models, by orthogonally manipulating the expected value of choice options and the relative salience of their attributes. Our results, based on behavioral measures and drift-diffusion models, provide evidence in favor of the framework where information about individual attributes independently impacts deliberation. This suggests that risky decisions are resolved by running in parallel multiple comparisons between the separate attributes - possibly alongside an additional comparison of expected value. This result stands in contrast with the assumption of standard economic theory that choices require a unique comparison of expected values and suggests that at the cognitive level, decision processes might be more distributed than commonly assumed. Beyond our planned analyses, we also discovered that attribute salience affects people of different risk preference type in different ways: risk-averse participants seem to focus more on probability, except when monetary amount is particularly high; risk-neutral/seeking participants, in contrast, seem to focus more on monetary amount, except when probability is particularly low.

Judging the difficulty of perceptual decisions

Deciding how difficult it is going to be to perform a task allows us to choose between tasks, allocate appropriate resources, and predict future performance. To be useful for planning, difficulty judgments should not require completion of the task. Here, we examine the processes underlying difficulty judgments in a perceptual decision-making task. Participants viewed two patches of dynamic random dots, which were colored blue or yellow stochastically on each appearance. Stimulus coherence (the probability, pblue, of a dot being blue) varied across trials and patches thus establishing difficulty, |pblue -0.5|. Participants were asked to indicate for which patch it would be easier to decide the dominant color. Accuracy in difficulty decisions improved with the difference in the stimulus difficulties, whereas the reaction times were not determined solely by this quantity. For example, when the patches shared the same difficulty, reaction times were shorter for easier stimuli. A comparison of several models of difficulty judgment suggested that participants compare the absolute accumulated evidence from each stimulus and terminate their decision when they differed by a set amount. The model predicts that when the dominant color of each stimulus is known, reaction times should depend only on the difference in difficulty, which we confirm empirically. We also show that this model is preferred to one that compares the confidence one would have in making each decision. The results extend evidence accumulation models, used to explain choice, reaction time, and confidence to prospective judgments of difficulty.

Corrective feedback guides human perceptual decision-making by informing about the world state rather than rewarding its choice

Corrective feedback received on perceptual decisions is crucial for adjusting decision-making strategies to improve future choices. However, its complex interaction with other decision components, such as previous stimuli and choices, challenges a principled account of how it shapes subsequent decisions. One popular approach, based on animal behavior and extended to human perceptual decision-making, employs "reinforcement learning," a principle proven successful in reward-based decision-making. The core idea behind this approach is that decision-makers, although engaged in a perceptual task, treat corrective feedback as rewards from which they learn choice values. Here, we explore an alternative idea, which is that humans consider corrective feedback on perceptual decisions as evidence of the actual state of the world rather than as rewards for their choices. By implementing these "feedback-as-reward" and "feedback-as-evidence" hypotheses on a shared learning platform, we show that the latter outperforms the former in explaining how corrective feedback adjusts the decision-making strategy along with past stimuli and choices. Our work suggests that humans learn about what has happened in their environment rather than the values of their own choices through corrective feedback during perceptual decision-making.

Reward and punishment learning deficits among bipolar disorder subtypes

BACKGROUND

Reward sensitivity is an essential dimension related to mood fluctuations in bipolar disorder (BD), but there is currently a debate around hypersensitivity or hyposensitivity hypotheses to reward in BD during remission, probably related to a heterogeneous population within the BD spectrum and a lack of reward bias evaluation. Here, we examine reward maximization vs. punishment avoidance learning within the BD spectrum during remission.

METHODS

Patients with BD-I (n = 45), BD-II (n = 34) and matched (n = 30) healthy controls (HC) were included. They performed an instrumental learning task designed to dissociate reward-based from punishment-based reinforcement learning. Computational modeling was used to identify the mechanisms underlying reinforcement learning performances.

RESULTS

Behavioral results showed a significant reward learning deficit across BD subtypes compared to HC, captured at the computational level by a lower sensitivity to rewards compared to punishments in both BD subtypes. Computational modeling also revealed a higher choice randomness in BD-II compared to BD-I that reflected a tendency of BD-I to perform better during punishment avoidance learning than BD-II.

LIMITATIONS

Our patients were not naive to antipsychotic treatment and were not euthymic (but in syndromic remission) according to the International Society for Bipolar Disorder definition.

CONCLUSIONS

Our results are consistent with the reward hyposensitivity theory in BD. Computational modeling suggests distinct underlying mechanisms that produce similar observable behaviors, making it a useful tool for distinguishing how symptoms interact in BD versus other disorders. In the long run, a better understanding of these processes could contribute to better prevention and management of BD.

Neural and computational underpinnings of biased confidence in human reinforcement learning

While navigating a fundamentally uncertain world, humans and animals constantly evaluate the probability of their decisions, actions or statements being correct. When explicitly elicited, these confidence estimates typically correlates positively with neural activity in a ventromedial-prefrontal (VMPFC) network and negatively in a dorsolateral and dorsomedial prefrontal network. Here, combining fMRI with a reinforcement-learning paradigm, we leverage the fact that humans are more confident in their choices when seeking gains than avoiding losses to reveal a functional dissociation: whereas the dorsal prefrontal network correlates negatively with a condition-specific confidence signal, the VMPFC network positively encodes task-wide confidence signal incorporating the valence-induced bias. Challenging dominant neuro-computational models, we found that decision-related VMPFC activity better correlates with confidence than with option-values inferred from reinforcement-learning models. Altogether, these results identify the VMPFC as a key node in the neuro-computational architecture that builds global feeling-of-confidence signals from latent decision variables and contextual biases during reinforcement-learning.

Mood fluctuations shift cost-benefit tradeoffs in economic decisions

Mood effects on economic choice seem blatantly irrational, but might rise from mechanisms adapted to natural environments. We have proposed a theory in which mood helps adapting the behaviour to statistical dependencies in the environment, by biasing the expected value of foraging actions (which involve taking risk, spending time and making effort to get more reward). Here, we tested the existence of this mechanism, using an established mood induction paradigm combined with independent economic choices that opposed small but uncostly rewards to larger but costly rewards (involving either risk, delay or effort). To maximise the sensitivity to mood fluctuations, we developed an algorithm ensuring that choice options were continuously adjusted to subjective indifference points. In 102 participants tested twice, we found that during episodes of positive mood (relative to negative mood), choices were biased towards better rewarded but costly options, irrespective of the cost type. Computational modelling confirmed that the incidental mood effect was best explained by a bias added to the expected value of costly options, prior to decision making. This bias is therefore automatically applied even in artificial environments where it is not adaptive, allowing mood to spill over many sorts of decisions and generate irrational behaviours.

Human thalamic low-frequency oscillations correlate with expected value and outcomes during reinforcement learning

Reinforcement-based adaptive decision-making is believed to recruit fronto-striatal circuits. A critical node of the fronto-striatal circuit is the thalamus. However, direct evidence of its involvement in human reinforcement learning is lacking. We address this gap by analyzing intra-thalamic electrophysiological recordings from eight participants while they performed a reinforcement learning task. We found that in both the anterior thalamus (ATN) and dorsomedial thalamus (DMTN), low frequency oscillations (LFO, 4-12 Hz) correlated positively with expected value estimated from computational modeling during reward-based learning (after outcome delivery) or punishment-based learning (during the choice process). Furthermore, LFO recorded from ATN/DMTN were also negatively correlated with outcomes so that both components of reward prediction errors were signaled in the human thalamus. The observed differences in the prediction signals between rewarding and punishing conditions shed light on the neural mechanisms underlying action inhibition in punishment avoidance learning. Our results provide insight into the role of thalamus in reinforcement-based decision-making in humans.

A primer on Variational Laplace (VL)

This article details a scheme for approximate Bayesian inference, which has underpinned thousands of neuroimaging studies since its introduction 15 years ago. Variational Laplace (VL) provides a generic approach to fitting linear or non-linear models, which may be static or dynamic, returning a posterior probability density over the model parameters and an approximation of log model evidence, which enables Bayesian model comparison. VL applies variational Bayesian inference in conjunction with quadratic or Laplace approximations of the evidence lower bound (free energy). Importantly, update equations do not need to be derived for each model under consideration, providing a general method for fitting a broad class of models. This primer is intended for experimenters and modellers who may wish to fit models to data using variational Bayesian methods, without assuming previous experience of variational Bayes or machine learning. Accompanying code demonstrates how to fit different kinds of model using the reference implementation of the VL scheme in the open-source Statistical Parametric Mapping (SPM) software package. In addition, we provide a standalone software function that does not require SPM, in order to ease translation to other fields, together with detailed pseudocode. Finally, the supplementary materials provide worked derivations of the key equations.

Involuntary shifts of spatial attention contribute to distraction-Evidence from oscillatory alpha power and reaction time data

Imagine you are focusing on the traffic on a busy street to ride your bike safely when suddenly you hear the siren of an ambulance. This unexpected sound involuntarily captures your attention and interferes with ongoing performance. We tested whether this type of distraction involves a spatial shift of attention. We measured behavioral data and magnetoencephalographic alpha power during a cross-modal paradigm that combined an exogenous cueing task and a distraction task. In each trial, a task-irrelevant sound preceded a visual target (left or right). The sound was usually the same animal sound (i.e., standard sound). Rarely, it was replaced by an unexpected environmental sound (i.e., deviant sound). Fifty percent of the deviants occurred on the same side as the target, and 50% occurred on the opposite side. Participants responded to the location of the target. As expected, responses were slower to targets that followed a deviant compared to a standard. Crucially, this distraction effect was mitigated by the spatial relationship between the targets and the deviants: responses were faster when targets followed deviants on the same versus different side, indexing a spatial shift of attention. This was further corroborated by a posterior alpha power modulation that was higher in the hemisphere ipsilateral (vs. contralateral) to the location of the attention-capturing deviant. We suggest that this alpha power lateralization reflects a spatial attention bias. Overall, our data support the contention that spatial shifts of attention contribute to deviant distraction.

Semi-orthogonal subspaces for value mediate a tradeoff between binding and generalization

When choosing between options, we must associate their values with the action needed to select them. We hypothesize that the brain solves this binding problem through neural population subspaces. To test this hypothesis, we examined neuronal responses in five reward-sensitive regions in macaques performing a risky choice task with sequential offers. Surprisingly, in all areas, the neural population encoded the values of offers presented on the left and right in distinct subspaces. We show that the encoding we observe is sufficient to bind the values of the offers to their respective positions in space while preserving abstract value information, which may be important for rapid learning and generalization to novel contexts. Moreover, after both offers have been presented, all areas encode the value of the first and second offers in orthogonal subspaces. In this case as well, the orthogonalization provides binding. Our binding-by-subspace hypothesis makes two novel predictions borne out by the data. First, behavioral errors should correlate with putative spatial (but not temporal) misbinding in the neural representation. Second, the specific representational geometry that we observe across animals also indicates that behavioral errors should increase when offers have low or high values, compared to when they have medium values, even when controlling for value difference. Together, these results support the idea that the brain makes use of semi-orthogonal subspaces to bind features together.

Introducing tomsup: Theory of mind simulations using Python

Theory of mind (ToM) is considered crucial for understanding social-cognitive abilities and impairments. However, verbal theories of the mechanisms underlying ToM are often criticized as under-specified and mutually incompatible. This leads to measures of ToM being unreliable, to the extent that even canonical experimental tasks do not require representation of others' mental states. There have been attempts at making computational models of ToM, but these are not easily available for broad research application. In order to help meet these challenges, we here introduce the Python package tomsup: Theory of mind simulations using Python. The package provides a computational eco-system for investigating and comparing computational models of hypothesized ToM mechanisms and for using them as experimental stimuli. The package notably includes an easy-to-use implementation of the variational recursive Bayesian k-ToM model developed by (Devaine, Hollard, & Daunizeau, 2014b) and of simpler non-recursive decision models, for comparison. We provide a series of tutorials on how to: (i) simulate agents relying on the k-ToM model and on a range of simpler types of mechanisms; (ii) employ those agents to generate online experimental stimuli; (iii) analyze the data generated in such experimental setup, and (iv) specify new custom ToM and heuristic cognitive models.

Decision-making under risk and ambiguity in adults with Tourette syndrome

BACKGROUND

Tourette syndrome (TS) as well as its most common comorbidities are associated with a higher propensity for risky behaviour in everyday life. However, it is unclear whether this increased risk propensity in real-life contexts translates into a generally increased attitude towards risk. We aimed to assess decision-making under risk and ambiguity based on prospect theory by considering the effects of comorbidities and medication.

METHODS

Fifty-four individuals with TS and 32 healthy controls performed risk and ambiguity decision-making tasks under both gains and losses conditions. Behavioural and computational parameters were evaluated using (i) univariate analysis to determine parameters difference taking independently; (ii) supervised multivariate analysis to evaluate whether our parameters could jointly account for between-group differences (iii) unsupervised multivariate analysis to explore the potential presence of sub-groups.

RESULTS

Except for general 'noisier' (less consistent) decisions in TS, we showed no specific risk-taking behaviour in TS or any relation with tics severity or antipsychotic medication. However, the presence of comorbidities was associated with distortion of decision-making. Specifically, TS with obsessive-compulsive disorder comorbidity was associated with a higher risk-taking profile to increase gain and a higher risk-averse profile to decrease loss. TS with attention-deficit hyperactivity disorder comorbidity was associated with risk-seeking in the ambiguity context to reduce a potential loss.

CONCLUSIONS

Impaired valuation of risk and ambiguity was not related to TS per se. Our findings are important for clinical practice: the involvement of individuals with TS in real-life risky situations may actually rather result from other factors such as psychiatric comorbidities.

Strengths of social ties modulate brain computations for third-party punishment

Costly punishment of social norm transgressors by third-parties has been considered as a decisive stage in the evolution of human cooperation. An important facet of social relationship knowledge concerns the strength of the social ties between individuals, as measured by social distance. Yet, it is unclear how the enforcement of social norms is influenced by the social distance between a third-party and a norm violator at the behavioral and the brain system levels. Here, we investigated how social distance between punishers and norm-violators influences third-party punishment. Participants as third-party punished norm violators more severely as social distance between them increased. Using model-based fMRI, we disentangled key computations contributing to third-party punishment: inequity aversion, social distance between participant and norm violator and integration of the cost to punish with these signals. Inequity aversion increased activity in the anterior cingulate cortex and bilateral insula, and processing social distance engaged a bilateral fronto-parietal cortex brain network. These two brain signals and the cost to punish were integrated in a subjective value signal of sanctions that modulated activity in the ventromedial prefrontal cortex. Together, our results reveal the neurocomputational underpinnings of third-party punishment and how social distance modulates enforcement of social norms in humans.

Frontopolar cortex represents complex features and decision value during choice between environments

Important decisions often involve choosing between complex environments that define future item encounters. Despite its importance for adaptive behavior and distinct computational challenges, decision-making research primarily focuses on item choice, ignoring environment choice altogether. Here we contrast previously studied item choice in ventromedial prefrontal cortex with lateral frontopolar cortex (FPl) linked to environment choice. Furthermore, we propose a mechanism for how FPl decomposes and represents complex environments during decision making. Specifically, we trained a choice-optimized, brain-naive convolutional neural network (CNN) and compared predicted CNN activation with actual FPl activity. We showed that the high-dimensional FPl activity decomposes environment features to represent the complexity of an environment to make such choice possible. Moreover, FPl functionally connects with posterior cingulate cortex for guiding environment choice. Further probing FPl's computation revealed a parallel processing mechanism in extracting multiple environment features.