Spatial attention, precision, and Bayesian inference: a study of saccadic response speed

Superior Attentional Efficiency of Auditory Cue via the Ventral Auditory-thalamic Pathway

Auditory commands are often executed more efficiently than visual commands. However, empirical evidence on the underlying behavioral and neural mechanisms remains scarce. In two experiments, we manipulated the delivery modality of informative cues and the prediction violation effect and found consistently enhanced RT benefits for the matched auditory cues compared with the matched visual cues. At the neural level, when the bottom-up perceptual input matched the prior prediction induced by the auditory cue, the auditory-thalamic pathway was significantly activated. Moreover, the stronger the auditory-thalamic connectivity, the higher the behavioral benefits of the matched auditory cue. When the bottom-up input violated the prior prediction induced by the auditory cue, the ventral auditory pathway was specifically involved. Moreover, the stronger the ventral auditory-prefrontal connectivity, the larger the behavioral costs caused by the violation of the auditory cue. In addition, the dorsal frontoparietal network showed a supramodal function in reacting to the violation of informative cues irrespective of the delivery modality of the cue. Taken together, the results reveal novel behavioral and neural evidence that the superior efficiency of the auditory cue is twofold: The auditory-thalamic pathway is associated with improvements in task performance when the bottom-up input matches the auditory cue, whereas the ventral auditory-prefrontal pathway is involved when the auditory cue is violated.

Alpha oscillations encode Bayesian belief updating underlying attentional allocation in dynamic environments

In a dynamic environment, expectations of the future constantly change based on updated evidence and affect the dynamic allocation of attention. To further investigate the neural mechanisms underlying attentional expectancies, we employed a modified Central Cue Posner Paradigm in which the probability of cues being valid (that is, accurately indicated the upcoming target location) was manipulated. Attentional deployment to the cued location (α), which was governed by precision of predictions on previous trials, was estimated using a hierarchical Bayesian model and was included as a regressor in the analyses of electrophysiological (EEG) data. Our results revealed that before the target appeared, alpha oscillations (8∼13 Hz) for high-predictability cues (88 % valid) were significantly predicted by precision-dependent attention (α). This relationship was not observed under low-predictability conditions (69 % and 50 % valid cues). After the target appeared, precision-dependent attention (α) correlated with alpha band oscillations only in the valid cue condition and not in the invalid condition. Further analysis under conditions of significant attentional modulation by precision suggested a separate effect of cue orientation. These results provide new insights on how trial-by-trial Bayesian belief updating relates to alpha band encoding of environmentally-sensitive allocation of visual spatial attention.

Attention to space and time: Independent or interactive systems? A narrative review

While there is ample evidence for the ability to selectively attend to where in space and when in time a relevant event might occur, it remains poorly understood whether spatial and temporal attention operate independently or interactively to optimize behavior. To elucidate this important issue, we provide a narrative review of the literature investigating the relationship between the two. The studies were organized based on the attentional manipulation employed (endogenous vs. exogenous) and the type of task (detection vs. discrimination). Although the reviewed findings depict a complex scenario, three aspects appear particularly important in promoting independent or interactive effects of spatial and temporal attention: task demands, attentional manipulation, and their combination. Overall, the present review provides key insights into the relationship between spatial and temporal attention and identifies some critical gaps that need to be addressed by future research.

Investigating how Explicit Contextual Cues Affect Predictive Sensorimotor Control in Autistic Adults

Research suggests that sensorimotor difficulties in autism could be reduced by providing individuals with explicit contextual information. To test this, we examined autistic visuomotor control during a virtual racquetball task, in which participants hit normal and unexpectedly-bouncy balls using a handheld controller. The probability of facing each type of ball was varied unpredictably over time. However, during cued trials, participants received explicit information about the likelihood of facing each uncertain outcome. When compared to neurotypical controls, autistic individuals displayed poorer task performance, atypical gaze profiles, and more restricted swing kinematics. These visuomotor patterns were not significantly affected by contextual cues, indicating that autistic people exhibit underlying differences in how prior information and environmental uncertainty are dynamically modulated during movement tasks.

Eight-Month-Old Infants Meta-Learn by Downweighting Irrelevant Evidence

Infants learn to navigate the complexity of the physical and social world at an outstanding pace, but how they accomplish this learning is still largely unknown. Recent advances in human and artificial intelligence research propose that a key feature to achieving quick and efficient learning is meta-learning, the ability to make use of prior experiences to learn how to learn better in the future. Here we show that 8-month-old infants successfully engage in meta-learning within very short timespans after being exposed to a new learning environment. We developed a Bayesian model that captures how infants attribute informativity to incoming events, and how this process is optimized by the meta-parameters of their hierarchical models over the task structure. We fitted the model with infants' gaze behavior during a learning task. Our results reveal how infants actively use past experiences to generate new inductive biases that allow future learning to proceed faster.

Reaction Time "Mismatch Costs" Change with the Likelihood of Stimulus-Response Compatibility

Dyadic interactions require dynamic correspondence between one's own movements and those of the other agent. This mapping is largely viewed as imitative, with the behavioural hallmark being a reaction-time cost for mismatched actions. Yet the complex motor patterns humans enact together extend beyond direct-matching, varying adaptively between imitation, complementary movements, and counter-imitation. Optimal behaviour requires an agent to predict not only what is likely to be observed but also how that observed action will relate to their own motor planning. In 28 healthy adults, we examined imitation and counter-imitation in a task that varied the likelihood of stimulus-response congruence from highly predictable, to moderately predictable, to unpredictable. To gain mechanistic insights into the statistical learning of stimulus-response compatibility, we compared two computational models of behaviour: (1) a classic fixed learning-rate model (Rescorla-Wagner reinforcement [RW]) and (2) a hierarchical model of perceptual-behavioural processes in which the learning rate adapts to the inferred environmental volatility (hierarchical Gaussian filter [HGF]). Though more complex and hence penalized by model selection, the HGF provided a more likely model of the participants' behaviour. Matching motor responses were only primed (faster) in the most experimentally volatile context. This bias was reversed so that mismatched actions were primed when beliefs about volatility were lower. Inferential statistics indicated that matching responses were only primed in unpredictable contexts when stimuli-response congruence was at 50:50 chance. Outside of these unpredictable blocks the classic stimulus-response compatibility effect was reversed: Incongruent responses were faster than congruent ones. We show that hierarchical Bayesian learning of environmental statistics may underlie response priming during dyadic interactions.

Simultaneous modeling of reaction times and brain dynamics in a spatial cueing task

Understanding how brain activity translates into behavior is a grand challenge in neuroscientific research. Simultaneous computational modeling of both measures offers to address this question. The extension of the dynamic causal modeling (DCM) framework for blood oxygenation level‐dependent (BOLD) responses to behavior (bDCM) constitutes such a modeling approach. However, only very few studies have employed and evaluated bDCM, and its application has been restricted to binary behavioral responses, limiting more general statements about its validity. This study used bDCM to model reaction times in a spatial attention task, which involved two separate runs with either horizontal or vertical stimulus configurations. We recorded fMRI data and reaction times (n= 26) and compared bDCM with classical DCM and a behavioral Rescorla–Wagner model using Bayesian model selection and goodness of fit statistics. Results indicate that bDCM performed equally well as classical DCM when modeling BOLD responses and as good as the Rescorla–Wagner model when modeling reaction times. Although our data revealed practical limitations of the current bDCM approach that warrant further investigation, we conclude that bDCM constitutes a promising method for investigating the link between brain activity and behavior.

An examination of active inference in autistic adults using immersive virtual reality

The integration of prior expectations, sensory information, and environmental volatility is proposed to be atypical in Autism Spectrum Disorder, yet few studies have tested these predictive processes in active movement tasks. To address this gap in the research, we used an immersive virtual-reality racquetball paradigm to explore how visual sampling behaviours and movement kinematics are adjusted in relation to unexpected, uncertain, and volatile changes in environmental statistics. We found that prior expectations concerning ball 'bounciness' affected sensorimotor control in both autistic and neurotypical participants, with all individuals using prediction-driven gaze strategies to track the virtual ball. However, autistic participants showed substantial differences in visuomotor behaviour when environmental conditions were more volatile. Specifically, uncertainty-related performance difficulties in these conditions were accompanied by atypical movement kinematics and visual sampling responses. Results support proposals that autistic people overestimate the volatility of sensory environments, and suggest that context-sensitive differences in active inference could explain a range of movement-related difficulties in autism.

Neural Encoding of the Reliability of Directional Information During the Preparation of Targeted Movements

Visual information about the location of an upcoming target can be used to prepare an appropriate motor response and reduce its reaction time. Here, we investigated the brain mechanisms associated with the reliability of directional information used for motor preparation. We recorded brain activity using magnetoencephalography (MEG) during a delayed reaching task in which a visual cue provided valid information about the location of the upcoming target with 50, 75, or 100% reliability. We found that reaction time increased as cue reliability decreased and that trials with invalid cues had longer reaction times than trials with valid cues. MEG channel analysis showed that during the late cue period the power of the beta-band from left mid-anterior channels, contralateral to the responding hand, correlated with the reliability of the cue. This effect was source localized over a large motor-related cortical and subcortical network. In addition, during invalid-cue trials there was a phasic increase of theta-band power following target onset from left posterior channels, localized to the left occipito-parietal cortex. Furthermore, the theta-beta cross-frequency coupling between left mid-occipital and motor cortex transiently increased before responses to invalid-cue trials. In conclusion, beta-band power in motor-related areas reflected the reliability of directional information used during motor preparation, whereas phasic theta-band activity may have signaled whether the target was at the expected location or not. These results elucidate mechanisms of interaction between attentional and motor processes.

Implementation of the diffusion model on dot-probe task performance in children with behavioral inhibition

Attentional bias to threat, the process of preferentially attending to potentially threatening environmental stimuli over neutral stimuli, is positively associated with behavioral inhibition (BI) and trait anxiety. However, the most used measure of attentional bias to threat, the dot-probe task, has been criticized for demonstrating poor reliability. The present study aimed to assess whether utilizing a sequential sampling model to describe performance could detect adequate test-retest reliability for the dot-probe task, demonstrate stronger cueing effects, and improve the association with neural signals of early attention. One hundred and twenty children aged 9-12 years completed the dot-probe task twice. During the second administration, event-related potentials (ERPs) were obtained as time-sensitive neural markers of attention. BI was not associated with traditional or diffusion model measures of performance. Traditional and diffusion model measures of performance were also not associated with N1, P2, or N2 ERP amplitude. There were main effects of Visit, in which RTs were faster and standard deviation of RT smaller during the second administration due to an increase in drift rate and a decrease in non-decision time. The traditional RT bias score (r = 0.06) and bias scores formed via diffusion model parameters (all r's < 0.40) all demonstrated poor reliability. Results confirm recommendations to move away from using the dot-probe task as the primary or sole index of attentional bias.

Semantically predictable input streams impede gaze-orientation to surprising locations

When available, people use prior knowledge to predict dimensions of future events such as their location and semantic features. However, few studies have examined how multi-dimensional predictions are implemented, and mechanistic accounts are absent. Using eye tracking, we evaluated whether predictions of target-location and target-category interact during the earliest stages of orientation. We presented stochastic series so that across four conditions, participants could predict either the location of the next target-image, its semantic category, both dimensions, or neither. Participants observed images in absence of any task involving their semantic content. We modeled saccade latencies using ELATER, a rise-to-threshold model that accounts for accumulation rate (AR), variance of AR over trials, and variance of decision baseline. The main findings were: 1) AR scaled with the degree of surprise associated with a target's location; 2) predictability of semantic-category hindered saccade latencies, suggesting a bottleneck in implementing joint predictions; 3) saccades to targets that satisfied semantic expectations were associated with greater AR-variance than saccades to semantically-surprising images, consistent with a richer repertoire of early evaluative processes for semantically-expected images. Predictability of target-category also impacted gaze pre-positioning prior to target presentation. The results indicate a strong interaction between foreknowledge of object location and semantics during stimulus-guided saccades, and suggest statistical regularities in an input stream can also impact anticipatory, non-stimulus-guided processes.

A Psychophysical Window onto the Subjective Experience of Compulsion

In this perspective, we follow the idea that an integration of cognitive models with sensorimotor theories of compulsion is required to understand the subjective experience of compulsive action. We argue that cognitive biases in obsessive-compulsive disorder may obscure an altered momentary, pre-reflective experience of sensorimotor control, whose detection thus requires an implicit experimental operationalization. We propose that a classic psychophysical test exists that provides this implicit operationalization, i.e., the intentional binding paradigm. We show how intentional binding can pit two ideas against each other that are fundamental to current sensorimotor theories of compulsion, i.e., the idea of excessive conscious monitoring of action, and the idea that patients with obsessive-compulsive disorder compensate for diminished conscious access to "internal states", including states of the body, by relying on more readily observable proxies. Following these ideas, we develop concrete, testable hypotheses on how intentional binding changes under the assumption of different sensorimotor theories of compulsion. Furthermore, we demonstrate how intentional binding provides a touchstone for predictive coding accounts of obsessive-compulsive disorder. A thorough empirical test of the hypotheses developed in this perspective could help explain the puzzling, disabling phenomenon of compulsion, with implications for the normal subjective experience of human action.

Developmental Differences in Probabilistic Reversal Learning: A Computational Modeling Approach

Cognitive flexibility helps us to navigate through our ever-changing environment and has often been examined by reversal learning paradigms. Performance in reversal learning can be modeled using computational modeling which allows for the specification of biologically plausible models to infer psychological mechanisms. Although such models are increasingly used in cognitive neuroscience, developmental approaches are still scarce. Additionally, though most reversal learning paradigms have a comparable design regarding timing and feedback contingencies, the type of feedback differs substantially between studies. The present study used hierarchical Gaussian filter modeling to investigate cognitive flexibility in reversal learning in children and adolescents and the effect of various feedback types. The results demonstrate that children make more overall errors and regressive errors (when a previously learned response rule is chosen instead of the new correct response after the initial shift to the new correct target), but less perseverative errors (when a previously learned response set continues to be used despite a reversal) adolescents. Analyses of the extracted model parameters of the winning model revealed that children seem to use new and conflicting information less readily than adolescents to update their stimulus-reward associations. Furthermore, more subclinical rigidity in everyday life (parent-ratings) is related to less explorative choice behavior during the probabilistic reversal learning task. Taken together, this study provides first-time data on the development of the underlying processes of cognitive flexibility using computational modeling.

The Computational, Pharmacological, and Physiological Determinants of Sensory Learning under Uncertainty

The ability to represent and respond to uncertainty is fundamental to human cognition and decision-making. Noradrenaline (NA) is hypothesized to play a key role in coordinating the sensory, learning, and physiological states necessary to adapt to a changing world, but direct evidence for this is lacking in humans. Here, we tested the effects of attenuating noradrenergic neurotransmission on learning under uncertainty. We probed the effects of the β-adrenergic receptor antagonist propranolol (40 mg) using a between-subjects, double-blind, placebo-controlled design. Participants performed a probabilistic associative learning task, and we employed a hierarchical learning model to formally quantify prediction errors about cue-outcome contingencies and changes in these associations over time (volatility). Both unexpectedness and noise slowed down reaction times, but propranolol augmented the interaction between these main effects such that behavior was influenced more by prior expectations when uncertainty was high. Computationally, this was driven by a reduction in learning rates, with people slower to update their beliefs in the face of new information. Attenuating the global effects of NA also eliminated the phasic effects of prediction error and volatility on pupil size, consistent with slower belief updating. Finally, estimates of environmental volatility were predicted by baseline cardiac measures in all participants. Our results demonstrate that NA underpins behavioral and computational responses to uncertainty. These findings have important implications for understanding the impact of uncertainty on human biology and cognition.

Difficulties with Speech-in-Noise Perception Related to Fundamental Grouping Processes in Auditory Cortex

In our everyday lives, we are often required to follow a conversation when background noise is present (“speech-in-noise” [SPIN] perception). SPIN perception varies widely—and people who are worse at SPIN perception are also worse at fundamental auditory grouping, as assessed by figure-ground tasks. Here, we examined the cortical processes that link difficulties with SPIN perception to difficulties with figure-ground perception using functional magnetic resonance imaging. We found strong evidence that the earliest stages of the auditory cortical hierarchy (left core and belt areas) are similarly disinhibited when SPIN and figure-ground tasks are more difficult (i.e., at target-to-masker ratios corresponding to 60% rather than 90% performance)—consistent with increased cortical gain at lower levels of the auditory hierarchy. Overall, our results reveal a common neural substrate for these basic (figure-ground) and naturally relevant (SPIN) tasks—which provides a common computational basis for the link between SPIN perception and fundamental auditory grouping.

Large-scale brain networks underlying non-spatial attention updating: Towards understanding the function of the temporoparietal junction

The temporoparietal junction (TPJ) and related areas are activated when a target stimulus appears at unexpected locations in Posner's spatial-cueing paradigm, and also when deviant stimuli are presented within a series of standard events in oddball paradigms. This type of activation corresponds to the ventral attention network (VAN), for regions defined on the basis of the spatial task. However, involvement of the VAN in object-based updating of attention has rarely been examined. In the present study, we used functional magnetic resonance imaging to investigate brain responses to (i) invalid targets after category-cueing and (ii) neutrally cued targets deviating in category from the background series of pictures. Bilateral TPJ activation was observed in response to invalidly cued targets, as compared to neutrally cued targets. Reference to the main large-scale brain networks showed that peaks of this activation located in the angular gyrus and inferior parietal lobule belonged to the default mode (DMN) and fronto-parietal networks (FPN), respectively. We found that VAN regions were involved only for simple detection activity. We conclude that spatial and non-spatial reorienting of attention rely on different network underpinnings. Our data suggest that DMN and FPN activity may support the ability to disengage from contextually irrelevant information.

Sex differences in hemispheric lateralization of attentional networks

Males and females differ in various abilities. However, sex differences in hemispheric lateralization of attentional processing are still not well-understood. Using a lateralized version of the attentional network test that combines the Posner cueing paradigm and visual field methodology, we aimed to examine sex differences in the lateralization of several attentional processes including alerting, executive control, orienting benefit, reorienting, and orienting cost. Fifty-six females and 59 males participated in this study. We found a left visual field (right hemisphere) advantage for alerting defined by the differences between no-cue and center-cue conditions in the male group, but it was mainly attributed to the left visual field advantage in the no-cue condition. In contrast, the female group exhibited a left visual field advantage in the center-cue condition. Both groups showed preferences to the left visual field for reorienting and orienting cost, but females exhibited larger effects. This indicates that the two sexes exhibit similarities in terms of the lateralization of these two attentional processes. Furthermore, the interactions between executive control and reorienting/orienting cost were more efficient in males than in females. The current study highlights sex differences in the hemispheric lateralization of attentional networks and possible underlying neural substrates.

What is optimal in optimal inference?

Inferring hidden structure from noisy observations is a problem addressed by Bayesian statistical learning, which aims to identify optimal models of the process that generated the observations given assumptions that constrain the space of potential solutions. Animals and machines face similar "model-selection" problems to infer latent properties and predict future states of the world. Here we review recent attempts to explain how intelligent agents address these challenges and how their solutions relate to Bayesian principles. We focus on how constraints on available information and resources affect inference and propose a general framework that uses benefit(accuracy) and accuracy(cost) curves to assess optimality under these constraints.

Neural arbitration between social and individual learning systems

Decision making requires integrating knowledge gathered from personal experiences with advice from others. The neural underpinnings of the process of arbitrating between information sources has not been fully elucidated. In this study, we formalized arbitration as the relative precision of predictions, afforded by each learning system, using hierarchical Bayesian modeling. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and/or self-sampled outcomes. Decision confidence, as measured by the number of points participants wagered on their predictions, varied with our definition of arbitration as a ratio of precisions. Functional neuroimaging demonstrated that arbitration signals were independent of decision confidence and involved modality-specific brain regions. Arbitrating in favor of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain, whereas arbitrating in favor of social information engaged the ventromedial prefrontal cortex and the amygdala. These findings indicate that relative precision captures arbitration between social and individual learning systems at both behavioral and neural levels.

Statistical Learning and Inference Is Impaired in the Nonclinical Continuum of Psychosis

Our perceptions result from the brain's ability to make inferences, or predictive models, of sensory information. Recently, it has been proposed that psychotic traits may be linked to impaired predictive processes. Here, we examine the brain dynamics underlying statistical learning and inference in stable and volatile environments, in a population of healthy human individuals (N = 75; 36 males, 39 females) with a range of psychotic-like experiences. We measured prediction error responses to sound sequences with electroencephalography, gauged sensory inference explicitly by behaviorally recording sensory statistical learning errors, and used dynamic causal modeling to tap into the underlying neural circuitry. We discuss the findings that were robust to replication across the two experiments (Discovery dataset, N = 31; Validation dataset, N = 44). First, we found that during stable conditions, participants demonstrated greater precision in their predictive model, reflected in a larger prediction error response to unexpected sounds, and decreased statistical learning errors. Moreover, individuals with attenuated prediction errors in stable conditions were found to make greater incorrect predictions about sensory information. Critically, we show that greater errors in statistical learning and inference are related to increased psychotic-like experiences. These findings link neurophysiology to behavior during statistical learning and prediction formation, as well as providing further evidence for the idea of a continuum of psychosis in the healthy, nonclinical population.SIGNIFICANCE STATEMENT While perceiving the world, we make inferences by learning the statistics present in the sensory environment. It has been argued that psychosis may emerge because of a failure to learn sensory statistics, resulting in an impaired representation of the world. Recently, it has been proposed that psychosis exists on a continuum; however, there is conflicting evidence on whether sensory learning deficits align on the nonclinical end of the psychosis continuum. We found that statistical learning of sensory events is associated with the magnitude of mismatch negativity and, critically, is impaired in healthy people who report more psychotic-like experiences. We replicated these findings in an independent sample, demonstrating strengthened credibility to support the continuum of psychosis that extends into the nonclinical population.

Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

The auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the "Bayesian brain" notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, neurobiological interpretations of predictive coding view perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs), and disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia. Here, we provide empirical evidence for this theory, demonstrating the existence of multiple, hierarchically related PEs in a "roving MMN" paradigm. We applied a hierarchical Bayesian model to single-trial EEG data from healthy human volunteers of either sex who received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207 ms poststimulus), while high-level PEs (about transition probability) are reflected by later components (152-199 and 215-277 ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts the inference on abstract statistical regularities. Our findings suggest that NMDAR dysfunction impairs hierarchical Bayesian inference about the world's statistical structure. Beyond the relevance of this finding for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential pathophysiological mechanisms.

Mild Inflammation in Healthy Males Induces Fatigue Mediated by Changes in Effective Connectivity Within the Insula

BACKGROUND

Systemic inflammation is associated with sickness behaviors such as low mood and fatigue. Activity patterns within the insula are suggested to coordinate these behaviors but have not been modeled. We hypothesized that mild systemic inflammation would result in changes in effective connectivity between the viscerosensory and the visceromotor regions of the insula.

METHODS

We used a double-blind, crossover design to randomize 20 male subjects to receive either a Salmonella typhi vaccine or a placebo saline injection at two separate sessions. All participants underwent a resting-state functional magnetic resonance scan 3 hours after injection. We determined behavioral and inflammatory changes, using the Profile of Mood States questionnaire and interleukin-6 levels. We extracted effective connectivity matrices between bilateral mid/posterior (viscerosensory) and anterior (visceromotor) insular cortices using spectral dynamic causal modeling. We applied parametric empirical Bayes and mediation analysis to determine a vaccination effect on effective connectivity and whether this mediated behavioral changes.

RESULTS

The vaccine condition was associated with greater interleukin-6 levels and greater fatigue 3 hours after the injection. Activity within the right mid/posterior insula increased the activity within the bilateral anterior insular regions. This connectivity was augmented by vaccination over a 99% posterior confidence threshold. The right mid/posterior insula-to-left anterior insula connectivity was significantly associated with fatigue and mediated the association between inflammation and increased fatigue scores.

CONCLUSIONS

These results demonstrate that increased effective connectivity between specific nodes of the insula can model and mediate the association between inflammation and fatigue in males.

Brain dynamics for confidence-weighted learning

Learning in a changing, uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions. However, humans also have a sense of confidence that characterizes the precision of their predictions. Bayesian models use a confidence-weighting principle to regulate learning: for a given surprise, the update is smaller when the confidence about the prediction was higher. Prior behavioral evidence indicates that human learning adheres to this confidence-weighting principle. Here, we explored the human brain dynamics sub-tending the confidence-weighting of learning using magneto-encephalography (MEG). During our volatile probability learning task, subjects’ confidence reports conformed with Bayesian inference. MEG revealed several stimulus-evoked brain responses whose amplitude reflected surprise, and some of them were further shaped by confidence: surprise amplified the stimulus-evoked response whereas confidence dampened it. Confidence about predictions also modulated several aspects of the brain state: pupil-linked arousal and beta-range (15–30 Hz) oscillations. The brain state in turn modulated specific stimulus-evoked surprise responses following the confidence-weighting principle. Our results thus indicate that there exist, in the human brain, signals reflecting surprise that are dampened by confidence in a way that is appropriate for learning according to Bayesian inference. They also suggest a mechanism for confidence-weighted learning: confidence about predictions would modulate intrinsic properties of the brain state to amplify or dampen surprise responses evoked by discrepant observations.

Learning in a changing and uncertain world is difficult. In this context, facing a discrepancy between my current belief and new observations may reflect random fluctuations (e.g. my commute train is unexpectedly late, but it happens sometimes), if so, I should ignore this discrepancy and not change erratically my belief. However, this discrepancy could also denote a profound change (e.g. the train company changed and is less reliable), in this case, I should promptly revise my current belief. Human learning is adaptive: we change how much we learn from new observations, in particular, we promote flexibility when facing profound changes. A mathematical analysis of the problem shows that we should increase flexibility when the confidence about our current belief is low, which occurs when a change is suspected. Here, I show that human learners entertain rational confidence levels during the learning of changing probabilities. This confidence modulates intrinsic properties of the brain state (oscillatory activity and neuromodulation) which in turn amplifies or reduces, depending on whether confidence is low or high, the neural responses to discrepant observations. This confidence-weighting mechanism could underpin adaptive learning.

An Embodied Neurocomputational Framework for Organically Integrating Biopsychosocial Processes: An Application to the Role of Social Support in Health and Disease

OBJECTIVE

Two distinct perspectives-typically referred to as the biopsychosocial and biomedical models-currently guide clinical practice. Although the role of psychosocial factors in contributing to physical and mental health outcomes is widely recognized, the biomedical model remains dominant. This is due in part to (a) the largely nonmechanistic focus of biopsychosocial research and (b) the lack of specificity it currently offers in guiding clinicians to focus on social, psychological, and/or biological factors in individual cases. In this article, our objective is to provide an evidence-based and theoretically sophisticated mechanistic model capable of organically integrating biopsychosocial processes.

METHODS

To construct this model, we provide a narrative review of recent advances in embodied cognition and predictive processing within computational neuroscience, which offer mechanisms for understanding individual differences in social perceptions, visceral responses, health-related behaviors, and their interactions. We also review current evidence for bidirectional influences between social support and health as a detailed illustration of the novel conceptual resources offered by our model.

RESULTS

When integrated, these advances highlight multiple mechanistic causal pathways between psychosocial and biological variables.

CONCLUSIONS

By highlighting these pathways, the resulting model has important implications motivating a more psychologically sophisticated, person-specific approach to future research and clinical application in the biopsychosocial domain. It also highlights the potential for quantitative computational modeling and the design of novel interventions. Finally, it should aid in guiding future research in a manner capable of addressing the current criticisms/limitations of the biopsychosocial model and may therefore represent an important step in bridging the gap between it and the biomedical perspective.

Humans adapt their anticipatory eye movements to the volatility of visual motion properties

Animal behavior constantly adapts to changes, for example when the statistical properties of the environment change unexpectedly. For an agent that interacts with this volatile setting, it is important to react accurately and as quickly as possible. It has already been shown that when a random sequence of motion ramps of a visual target is biased to one direction (e.g. right or left), human observers adapt their eye movements to accurately anticipate the target's expected direction. Here, we prove that this ability extends to a volatile environment where the probability bias could change at random switching times. In addition, we also recorded the explicit prediction of the next outcome as reported by observers using a rating scale. Both results were compared to the estimates of a probabilistic agent that is optimal in relation to the assumed generative model. Compared to the classical leaky integrator model, we found a better match between our probabilistic agent and the behavioral responses, both for the anticipatory eye movements and the explicit task. Furthermore, by controlling the level of preference between exploitation and exploration in the model, we were able to fit for each individual's experimental dataset the most likely level of volatility and analyze inter-individual variability across participants. These results prove that in such an unstable environment, human observers can still represent an internal belief about the environmental contingencies, and use this representation both for sensory-motor control and for explicit judgments. This work offers an innovative approach to more generically test the diversity of human cognitive abilities in uncertain and dynamic environments.

Atypical processing of uncertainty in individuals at risk for psychosis

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Humans at psychosis clinical high risk (CHR) over-estimate environmental volatility.

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Low-level prediction error (PE) signals evoke increased frontal activity in CHR.

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Volatility-related PEs are associated with reduced frontal activity in CHR.

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Frontal cortical activation to low-level PEs reflects impaired clinical functioning.

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Atypical PE learning signal representations may promote delusion formation in CHR.

Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in clinical high risk (CHR) individuals.

Non-medicated CHR individuals (n = 13) and control participants (n = 13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour – with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental ‘volatility’ – and used these computational quantities for analyses of fMRI data.

Computational modelling of CHR individuals’ behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of CHR individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in CHR was negatively associated with clinical measures of global functioning.

Our results suggest a multi-faceted learning abnormality in CHR individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation.

Resting-state Functional Connectivity of the Right Temporoparietal Junction Relates to Belief Updating and Reorienting during Spatial Attention

Although multiple studies characterized the resting-state functional connectivity (rsFC) of the right temporoparietal junction (rTPJ), little is known about the link between rTPJ rsFC and cognitive functions. Given a putative involvement of rTPJ in both reorienting of attention and the updating of probabilistic beliefs, this study characterized the relationship between rsFC of rTPJ with dorsal and ventral attention systems and these two cognitive processes. Twenty-three healthy young participants performed a modified location-cueing paradigm with true and false prior information about the percentage of cue validity to assess belief updating and attentional reorienting. Resting-state fMRI was recorded before and after the task. Seed-based correlation analysis was employed, and correlations of each behavioral parameter with rsFC before the task, as well as with changes in rsFC after the task, were assessed in an ROI-based approach. Weaker rsFC between rTPJ and right intraparietal sulcus before the task was associated with relatively faster updating of the belief that the cue will be valid after false prior information. Moreover, relatively faster belief updating, as well as faster reorienting, were related to an increase in the interhemispheric rsFC between rTPJ and left TPJ after the task. These findings are in line with task-based connectivity studies on related attentional functions and extend results from stroke patients demonstrating the importance of interhemispheric parietal interactions for behavioral performance. The present results not only highlight the essential role of parietal rsFC for attentional functions but also suggest that cognitive processing during a task changes connectivity patterns in a performance-dependent manner.

Doubting what you already know: Uncertainty regarding state transitions is associated with obsessive compulsive symptoms

Obsessive compulsive (OC) symptoms involve excessive information gathering (e.g., checking, reassurance-seeking), and uncertainty about possible, often catastrophic, future events. Here we propose that these phenomena are the result of excessive uncertainty regarding state transitions (transition uncertainty): a computational impairment in Bayesian inference leading to a reduced ability to use the past to predict the present and future, and to oversensitivity to feedback (i.e. prediction errors). Using a computational model of Bayesian learning under uncertainty in a reversal learning task, we investigate the relationship between OC symptoms and transition uncertainty. Individuals high and low in OC symptoms performed a task in which they had to detect shifts (i.e. transitions) in cue-outcome contingencies. Modeling subjects' choices was used to estimate each individual participant's transition uncertainty and associated responses to feedback. We examined both an optimal observer model and an approximate Bayesian model in which participants were assumed to attend (and learn about) only one of several cues on each trial. Results suggested the participants were more likely to distribute attention across cues, in accordance with the optimal observer model. As hypothesized, participants with higher OC symptoms exhibited increased transition uncertainty, as well as a pattern of behavior potentially indicative of a difficulty in relying on learned contingencies, with no evidence for perseverative behavior. Increased transition uncertainty compromised these individuals' ability to predict ensuing feedback, rendering them more surprised by expected outcomes. However, no evidence for excessive belief updating was found. These results highlight a potential computational basis for OC symptoms and obsessive compulsive disorder (OCD). The fact the OC symptoms predicted a decreased reliance on the past rather than perseveration challenges preconceptions of OCD as a disorder of inflexibility. Our results have implications for the understanding of the neurocognitive processes leading to excessive uncertainty and distrust of past experiences in OCD.

A Computational Theory of Mindfulness Based Cognitive Therapy from the "Bayesian Brain" Perspective

Mindfulness Based Cognitive Therapy (MBCT) was developed to combine methods from cognitive behavioral therapy and meditative techniques, with the specific goal of preventing relapse in recurrent depression. While supported by empirical evidence from multiple clinical trials, the cognitive mechanisms behind the effectiveness of MBCT are not well understood in computational (information processing) or biological terms. This article introduces a testable theory about the computational mechanisms behind MBCT that is grounded in "Bayesian brain" concepts of perception from cognitive neuroscience, such as predictive coding. These concepts regard the brain as embodying a model of its environment (including the external world and the body) which predicts future sensory inputs and is updated by prediction errors, depending on how precise these error signals are. This article offers a concrete proposal how core concepts of MBCT-(i) the being mode (accepting whatever sensations arise, without judging or changing them), (ii) decentering (experiencing thoughts and percepts simply as events in the mind that arise and pass), and (iii) cognitive reactivity (changes in mood reactivate negative beliefs)-could be understood in terms of perceptual and metacognitive processes that draw on specific computational mechanisms of the "Bayesian brain." Importantly, the proposed theory can be tested experimentally, using a combination of behavioral paradigms, computational modelling, and neuroimaging. The novel theoretical perspective on MBCT described in this paper may offer opportunities for finessing the conceptual and practical aspects of MBCT.

Role of the dorsal attention network in distracter suppression based on features

Selective attention allows us to prioritize the processing of relevant information and filter out irrelevant information. Human functional neuroimaging and lesion-based studies have highlighted the fronto-parietal dorsal attention network (DAN) as an important network in this process. In this study, we investigated the role of the DAN in distracter suppression by dynamically modifying the priority of visual information (target > high priority distracter > low priority distracter) based on features only. To this end, we collected fMRI data in 24 healthy subjects, who performed a feature-based variant of the sustained attention to response task. Participants had to select one or attend two stream(s) of overlapping digits that differed in color and respond to each digit in the task-relevant stream(s) except to a single non-target digit. Results showed higher DAN activity when a target was co-presented with a high versus low priority distracter. Furthermore, higher DAN activity was observed when selectively attending one (target + high/low priority distracter) versus simultaneously attending two (target + target) stream(s) of digits. In conclusion, our study highlights the contribution of the DAN in the feature-based suppression of task-irrelevant information.

Subjective estimates of uncertainty during gambling and impulsivity after subthalamic deep brain stimulation for Parkinson's disease

Subthalamic deep brain stimulation (DBS) for Parkinson’s disease (PD) may modulate chronometric and instrumental aspects of choice behaviour, including motor inhibition, decisional slowing, and value sensitivity. However, it is not well known whether subthalamic DBS affects more complex aspects of decision-making, such as the influence of subjective estimates of uncertainty on choices. In this study, 38 participants with PD played a virtual casino prior to subthalamic DBS (whilst ‘on’ medication) and again, 3-months postoperatively (whilst ‘on’ stimulation). At the group level, there was a small but statistically significant decrease in impulsivity postoperatively, as quantified by the Barratt Impulsiveness Scale (BIS). The gambling behaviour of participants (bet increases, slot machine switches and double or nothing gambles) was associated with this self-reported measure of impulsivity. However, there was a large variance in outcome amongst participants, and we were interested in whether individual differences in subjective estimates of uncertainty (specifically, volatility) were related to differences in pre- and postoperative impulsivity. To examine these individual differences, we fit a computational model (the Hierarchical Gaussian Filter, HGF), to choices made during slot machine game play as well as a simpler reinforcement learning model based on the Rescorla-Wagner formalism. The HGF was superior in accounting for the behaviour of our participants, suggesting that participants incorporated beliefs about environmental uncertainty when updating their beliefs about gambling outcome and translating these beliefs into action. A specific aspect of subjective uncertainty, the participant’s estimate of the tendency of the slot machine’s winning probability to change (volatility), increased subsequent to DBS. Additionally, the decision temperature of the response model decreased post-operatively, implying greater stochasticity in the belief-to-choice mapping of participants. Model parameter estimates were significantly associated with impulsivity; specifically, increased uncertainty was related to increased postoperative impulsivity. Moreover, changes in these parameter estimates were significantly associated with the maximum post-operative change in impulsivity over a six month follow up period. Our findings suggest that impulsivity in PD patients may be influenced by subjective estimates of uncertainty (environmental volatility) and implicate a role for the subthalamic nucleus in the modulation of outcome certainty. Furthermore, our work outlines a possible approach to characterising those persons who become more impulsive after subthalamic DBS, an intervention in which non-motor outcomes can be highly variable.

The Predictive Processing Model of EMDR

Eye Movement Desensitization and Reprocessing Therapy (EMDR) is an effective treatment for Post-traumatic Stress Disorder (PTSD). The Adaptive Information Processing Model (AIP) guides the development and practice of EMDR. The AIP postulates inadequately processed memory as the foundation of PTSD pathology. Predictive Processing postulates that the primary function of the brain is prediction that serves to anticipate the next moment of experience in order to resist the dissipative force of entropy thus facilitating continued survival. Memory is the primary substrate of prediction, and is optimized by an ongoing process of precision weighted prediction error minimization that refines prediction by updating the memories on which it is based. The Predictive Processing model of EMDR postulates that EMDR facilitates the predictive processing of traumatic memory by overcoming the bias against exploration and evidence accumulation. The EMDR protocol brings the traumatic memory into an active state of re-experiencing. Defensive responding and/or low sensory precision preclude evidence accumulation to test the predictions of the traumatic memory in the present. Sets of therapist guided eye movements repeatedly challenge the bias against evidence accumulation and compel sensory sampling of the benign present. Eye movements reset the theta rhythm organizing the flow of information through the brain, facilitating the deployment of both overt and covert attention, and the mnemonic search for associations. Sampling of sensation does not support the predictions of the traumatic memory resulting in prediction error that the brain then attempts to minimize. The net result is a restoration of the integrity of the rhythmic deployment of attention, a recalibration of sensory precision, and the updating (reconsolidation) of the traumatic memory. Thus one prediction of the model is a decrease in Attention Bias Variability, a core dysfunction in PTSD, following successful treatment with EMDR.

Parietal neurons encode information sampling based on decision uncertainty

In natural behavior animals actively gather information that is relevant for learning or actions, but the mechanisms of active sampling are rarely investigated. We tested parietal neurons involved in oculomotor control in a task in which monkeys made saccades to gather visual information before reporting a decision based on the information. We show that the neurons encode, before the saccade, the information gains (reduction in decision uncertainty) that the saccade was expected to bring, correlating with the monkeys’ efficiency in processing the information in the post-saccadic fixation. Informational sensitivity is independent of the neurons’ reward sensitivity, which is unreliable across task contexts, inconsistent with the view that the cells encode economic utility. Instead, we suggest that parietal cells are involved in implementing active sampling policies, showing uncertainty-dependent boosts of neural gain that facilitate the selection of relevant cues and the efficient use of the information delivered by these cues.

Exploring how material cues drive sensorimotor prediction across different levels of autistic-like traits

Recent research proposes that sensorimotor difficulties, such as those experienced by many autistic people, may arise from atypicalities in prediction. Accordingly, we examined the relationship between non-clinical autistic-like traits and sensorimotor prediction in the material-weight illusion, where prior expectations derived from material cues typically bias one’s perception and action. Specifically, prediction-related tendencies in perception of weight, gaze patterns, and lifting actions were probed using a combination of self-report, eye-tracking, motion-capture, and force-based measures. No prediction-related associations between autistic-like traits and sensorimotor control emerged for any of these variables. Follow-up analyses, however, revealed that greater autistic-like traits were correlated with reduced adaptation of gaze with changes in environmental uncertainty. These findings challenge proposals of gross predictive atypicalities in autistic people, but suggest that the dynamic integration of prior information and environmental statistics may be related to autistic-like traits. Further research into this relationship is warranted in autistic populations, to assist the development of future movement-based coaching methods.

Predictions as a window into learning: Anticipatory fixation offsets carry more information about environmental statistics than reactive stimulus-responses

A core question underlying neurobiological and computational models of behavior is how individuals learn environmental statistics and use them to make predictions. Most investigations of this issue have relied on reactive paradigms, in which inferences about predictive processes are derived by modeling responses to stimuli that vary in likelihood. Here we deployed a novel anticipatory oculomotor metric to determine how input statistics impact anticipatory behavior that is decoupled from target-driven-response. We implemented transition constraints between target locations, so that the probability of a target being presented on the same side as the previous trial was 70% in one condition (pret70) and 30% in the other (pret30). Rather than focus on responses to targets, we studied subtle endogenous anticipatory fixation offsets (AFOs) measured while participants fixated the screen center, awaiting a target. These AFOs were small (<0.4° from center on average), but strongly tracked global-level statistics. Speaking to learning dynamics, trial-by-trial fluctuations in AFO were well-described by a learning model, which identified a lower learning rate in pret70 than pret30, corroborating prior suggestions that pret70 is subjectively treated as more regular. Most importantly, direct comparisons with saccade latencies revealed that AFOs: (a) reflected similar temporal integration windows, (b) carried more information about the statistical context than did saccade latencies, and (c) accounted for most of the information that saccade latencies also contained about inputs statistics. Our work demonstrates how strictly predictive processes reflect learning dynamics, and presents a new direction for studying learning and prediction.

Tracking the Time Course of Bayesian Inference With Event-Related Potentials:A Study Using the Central Cue Posner Paradigm

In this study, we asked whether the event-related potentials associated to cue and target stimuli of a Central Cue Posner Paradigm (CCPP) may encode key parameters of Bayesian inference – prior expectation and surprise – on a trial-by-trial basis. Thirty-two EEG channel were recorded in a sample of 19 young adult subjects while performing a CCPP, in which a cue indicated (validly or invalidly) the position of an incoming auditory target. Three different types of blocks with validities of 50%, 64%, and 88%, respectively, were presented. Estimates of prior expectation and surprise were obtained on a trial-by-trial basis from participants’ responses, using a computational model implementing Bayesian learning. These two values were correlated on a trial-by-trial basis with the EEG values in all the electrodes and time bins. Therefore, a Spearman correlation metrics of the relationship between Bayesian parameters and the EEG was obtained. We report that the surprise parameter was able to classify the different validity blocks. Furthermore, the prior expectation parameter showed a significant correlation with the EEG in the cue-target period, in which the Contingent Negative Variation develops. Finally, in the post-target period the surprise parameter showed a significant correlation in the latencies and electrodes in which different event-related potentials are induced. Our results suggest that Bayesian parameters are coded in the EEG signals; and namely, the CNV would be related to prior expectation, while the post-target components P2a, P2, P3a, P3b, and SW would be related to surprise. This study thus provides novel support to the idea that human electrophysiological neural activity may implement a (Bayesian) predictive processing scheme.

Hierarchical and Nonlinear Dynamics in Prefrontal Cortex Regulate the Precision of Perceptual Beliefs

Actions are shaped not only by the content of our percepts but also by our confidence in them. To study the cortical representation of perceptual precision in decision making, we acquired functional imaging data whilst participants performed two vibrotactile forced-choice discrimination tasks: a fast-slow judgment, and a same-different judgment. The first task requires a comparison of the perceived vibrotactile frequencies to decide which one is faster. However, the second task requires that the estimated difference between those frequencies is weighed against the precision of each percept-if both stimuli are very precisely perceived, then any slight difference is more likely to be identified than if the percepts are uncertain. We additionally presented either pure sinusoidal or temporally degraded "noisy" stimuli, whose frequency/period differed slightly from cycle to cycle. In this way, we were able to manipulate the perceptual precision. We report a constellation of cortical regions in the rostral prefrontal cortex (PFC), dorsolateral PFC (DLPFC) and superior frontal gyrus (SFG) associated with the perception of stimulus difference, the presence of stimulus noise and the interaction between these factors. Dynamic causal modeling (DCM) of these data suggested a nonlinear, hierarchical model, whereby activity in the rostral PFC (evoked by the presence of stimulus noise) mutually interacts with activity in the DLPFC (evoked by stimulus differences). This model of effective connectivity outperformed competing models with serial and parallel interactions, hence providing a unique insight into the hierarchical architecture underlying the representation and appraisal of perceptual belief and precision in the PFC.

Feature-specific prediction errors for visual mismatch

Predictive coding (PC) theory posits that our brain employs a predictive model of the environment to infer the causes of its sensory inputs. A fundamental but untested prediction of this theory is that the same stimulus should elicit distinct precision weighted prediction errors (pwPEs) when different (feature-specific) predictions are violated, even in the absence of attention. Here, we tested this hypothesis using functional magnetic resonance imaging (fMRI) and a multi-feature roving visual mismatch paradigm where rare changes in either color (red, green), or emotional expression (happy, fearful) of faces elicited pwPE responses in human participants. Using a computational model of learning and inference, we simulated pwPE and prediction trajectories of a Bayes-optimal observer and used these to analyze changes in blood oxygen level dependent (BOLD) responses to changes in color and emotional expression of faces while participants engaged in a distractor task. Controlling for visual attention by eye-tracking, we found pwPE responses to unexpected color changes in the fusiform gyrus. Conversely, unexpected changes of facial emotions elicited pwPE responses in cortico-thalamo-cerebellar structures associated with emotion and theory of mind processing. Predictions pertaining to emotions activated fusiform, occipital and temporal areas. Our results are consistent with a general role of PC across perception, from low-level to complex and socially relevant object features, and suggest that monitoring of the social environment occurs continuously and automatically, even in the absence of attention.

Age-related changes in Bayesian belief updating during attentional deployment and motor intention

Predicting upcoming events using past observations is a crucial component of an efficient allocation of attentional resources. Therefore, the deployment of attention is sensitive to different types of cues predicting upcoming events. Here we investigated probabilistic inference abilities in spatial and feature-based attentional, as well as in motor-intentional subsystems, focusing specifically on the age-related changes in these abilities. In two behavioral experiments, younger and older adults (20 younger and 20 older adults for each experiment) performed three versions of a cueing paradigm, where spatial, feature, or motor cues predicted the location, color, or motor response of a target stimulus. The percentage of cue validity (i.e., the probability of the cue being valid) changed over time, thereby creating a volatile environment. A Bayesian hierarchical model was used to estimate trial-wise beliefs concerning the cue validity from reaction times and to derive a subject-specific belief updating parameter ω in each task version. We also manipulated task difficulty: participants performed an easier version of the task in Experiment 1 and a more difficult version in Experiment 2. Results from Experiment 1 suggested a preserved ability of older adults to use the three different cues to generate predictions. However, the increased task demands of Experiment 2 uncovered a difference in belief updating between the two age groups, indicating moderate evidence for a reduction of the ability to update predictions with motor intention cues in older adults. These results point at a distinction of attentional and motor-intentional subsystems, with age-related differences tackling especially the motor-intentional subsystem.

The dog (Canis familiaris) as a translational model of autism: It is high time we move from promise to reality

Selecting appropriate animal models for a particular human phenomenon is a difficult but important challenge. The difficulty lies in finding animal behaviors that are not only sufficiently relevant and analog to the complex human symptoms (face validity) but also have similar underlying biological and etiological mechanisms (translational or construct validity), and have "human-like" responses to treatment (predictive validity). Over the past several years, the domestic dog (Canis familiaris) has become increasingly proposed as a model for comparative and translational neuroscience. In parallel to the recent advances in canine behavior research, dogs have also been proposed as a model of many human neuropsychiatric conditions, including autism spectrum disorder (ASD). In this opinion paper we will shortly discuss the challenging nature of autism research then summarize the different neurocognitive frameworks for ASD making the case for a canine model of autism. The translational value of a dog model stems from the recognition that (a) there is a large inter-individual variability in the manifestation of dogs' social cognitive abilities including both high and low phenotypic extremes; (b) the phenotypic similarity between the dog and human symptoms are much higher than between the rodent and human symptoms; (c) the symptoms are functionally analogous to the human condition; and (d) more likely to have similar etiology. This article is categorized under: Psychology > Comparative Psychology Cognitive Biology > Evolutionary Roots of Cognition.

Attractor-like Dynamics in Belief Updating in Schizophrenia

Subjects with a diagnosis of schizophrenia (Scz) overweight unexpected evidence in probabilistic inference: such evidence becomes "aberrantly salient." A neurobiological explanation for this effect is that diminished synaptic gain (e.g., hypofunction of cortical NMDARs) in Scz destabilizes quasi-stable neuronal network states (or "attractors"). This attractor instability account predicts that (1) Scz would overweight unexpected evidence but underweight consistent evidence, (2) belief updating would be more vulnerable to stochastic fluctuations in neural activity, and (3) these effects would correlate. Hierarchical Bayesian belief updating models were tested in two independent datasets (n = 80 male and n = 167 female) comprising human subjects with Scz, and both clinical and nonclinical controls (some tested when unwell and on recovery) performing the "probability estimates" version of the beads task (a probabilistic inference task). Models with a standard learning rate, or including a parameter increasing updating to "disconfirmatory evidence," or a parameter encoding belief instability were formally compared. The "belief instability" model (based on the principles of attractor dynamics) had most evidence in all groups in both datasets. Two of four parameters differed between Scz and nonclinical controls in each dataset: belief instability and response stochasticity. These parameters correlated in both datasets. Furthermore, the clinical controls showed similar parameter distributions to Scz when unwell, but were no different from controls once recovered. These findings are consistent with the hypothesis that attractor network instability contributes to belief updating abnormalities in Scz, and suggest that similar changes may exist during acute illness in other psychiatric conditions.SIGNIFICANCE STATEMENT Subjects with a diagnosis of schizophrenia (Scz) make large adjustments to their beliefs following unexpected evidence, but also smaller adjustments than controls following consistent evidence. This has previously been construed as a bias toward "disconfirmatory" information, but a more mechanistic explanation may be that in Scz, neural firing patterns ("attractor states") are less stable and hence easily altered in response to both new evidence and stochastic neural firing. We model belief updating in Scz and controls in two independent datasets using a hierarchical Bayesian model, and show that all subjects are best fit by a model containing a belief instability parameter. Both this and a response stochasticity parameter are consistently altered in Scz, as the unstable attractor hypothesis predicts.

Beliefs about bad people are volatile

People form moral impressions rapidly, effortlessly and from a remarkably young age^1-5. Putatively 'bad' agents command more attention and are identified more quickly and accurately than benign or friendly agents^5-12. Such vigilance is adaptive, but can also be costly in environments where people sometimes make mistakes, because incorrectly attributing bad character to good people damages existing relationships and discourages forming new relationships^13-16. The ability to accurately infer the moral character of others is critical for healthy social functioning, but the computational processes that support this ability are not well understood. Here, we show that moral inference is explained by an asymmetric Bayesian updating mechanism in which beliefs about the morality of bad agents are more uncertain (and therefore more volatile) than beliefs about the morality of good agents. This asymmetry seems to be a property of learning about immoral agents in general, as we also find greater uncertainty for beliefs about the non-moral traits of bad agents. Our model and data reveal a cognitive mechanism that permits flexible updating of beliefs about potentially threatening others, a mechanism that could facilitate forgiveness when initial bad impressions turn out to be inaccurate. Our findings suggest that negative moral impressions destabilize beliefs about others, promoting cognitive flexibility in the service of cooperative but cautious behaviour.

Modeling subjective relevance in schizophrenia and its relation to aberrant salience

In schizophrenia, increased aberrant salience to irrelevant events and reduced learning of relevant information may relate to an underlying deficit in relevance detection. So far, subjective estimates of relevance have not been probed in schizophrenia patients. The mechanisms underlying belief formation about relevance and their translation into decisions are unclear. Using novel computational methods, we investigated relevance detection during implicit learning in 42 schizophrenia patients and 42 healthy individuals. Participants underwent functional magnetic resonance imaging while detecting the outcomes in a learning task. These were preceded by cues differing in color and shape, which were either relevant or irrelevant for outcome prediction. We provided a novel definition of relevance based on Bayesian precision and modeled reaction times as a function of relevance weighted unsigned prediction errors (UPE). For aberrant salience, we assessed responses to subjectively irrelevant cue manifestations. Participants learned the contingencies and slowed down their responses following unexpected events. Model selection revealed that individuals inferred the relevance of cue features and used it for behavioral adaption to the relevant cue feature. Relevance weighted UPEs correlated with dorsal anterior cingulate cortex activation and hippocampus deactivation. In patients, the aberrant salience bias to subjectively task-irrelevant information was increased and correlated with decreased striatal UPE activation and increased negative symptoms. This study shows that relevance estimates based on Bayesian precision can be inferred from observed behavior. This underscores the importance of relevance detection as an underlying mechanism for behavioral adaptation in complex environments and enhances the understanding of aberrant salience in schizophrenia.

Schizophrenia patients display deficits in the appropriate attribution of meaningfulness to stimuli; such as aberrantly increased processing of irrelevant and insufficient processing of relevant information. We aimed to investigate the subjective nature of relevance detection and its deficit in schizophrenia and developed an implicit learning paradigm that allowed for parallel learning from relevant and irrelevant information. Based on the idea that subjective relevance might be captured by Bayesian precision we set up different computational models of how subjective relevance guides learning and behavioral adaptation. We found that subjects use Bayesian precision to estimate stimulus relevance in order to integrate multidimensional information and adapt more to the subjectively relevant stimuli. This relevance weighted adaptation correlated with brain activation within the salience network. Further, schizophrenia patients displayed an increased aberrant tendency to irrelevant events which related to decreased striatal coding of the relevant learning signal. To conclude, our findings demonstrate how individual beliefs about relevance can be inferred from computational models. Furthermore, we suggest that aberrant salience observed in patients with schizophrenia reflects an idiosyncratic bias in states of high subjective uncertainty.

Active Inference, Belief Propagation, and the Bethe Approximation

When modeling goal-directed behavior in the presence of various sources of uncertainty, planning can be described as an inference process. A solution to the problem of planning as inference was previously proposed in the active inference framework in the form of an approximate inference scheme based on variational free energy. However, this approximate scheme was based on the mean-field approximation, which assumes statistical independence of hidden variables and is known to show overconfidence and may converge to local minima of the free energy. To better capture the spatiotemporal properties of an environment, we reformulated the approximate inference process using the so-called Bethe approximation. Importantly, the Bethe approximation allows for representation of pairwise statistical dependencies. Under these assumptions, the minimizer of the variational free energy corresponds to the belief propagation algorithm, commonly used in machine learning. To illustrate the differences between the mean-field approximation and the Bethe approximation, we have simulated agent behavior in a simple goal-reaching task with different types of uncertainties. Overall, the Bethe agent achieves higher success rates in reaching goal states. We relate the better performance of the Bethe agent to more accurate predictions about the consequences of its own actions. Consequently, active inference based on the Bethe approximation extends the application range of active inference to more complex behavioral tasks.