Circular inference in bistable perception

Conspiracy beliefs and perceptual inference in times of political uncertainty

Sociopolitical crises causing uncertainty have accumulated in recent years, providing fertile ground for the emergence of conspiracy ideations. Computational models constitute valuable tools for understanding the mechanisms at play in the formation and rigidification of these unshakeable beliefs. Here, the Circular Inference model was used to capture associations between changes in perceptual inference and the dynamics of conspiracy ideations in times of uncertainty. A bistable perception task and conspiracy belief assessment focused on major sociopolitical events were administered to large populations from three polarized countries. We show that when uncertainty peaks, an overweighting of sensory information is associated with conspiracy ideations. Progressively, this exploration strategy gives way to an exploitation strategy in which increased adherence to conspiracy theories is associated with the amplification of prior information. Overall, the Circular Inference model sheds new light on the possible mechanisms underlying the progressive strengthening of conspiracy theories when individuals face highly uncertain situations.

Bistable perception, precision and neuromodulation

Bistable perception follows from observing a static, ambiguous, (visual) stimulus with two possible interpretations. Here, we present an active (Bayesian) inference account of bistable perception and posit that perceptual transitions between different interpretations (i.e. inferences) of the same stimulus ensue from specific eye movements that shift the focus to a different visual feature. Formally, these inferences are a consequence of precision control that determines how confident beliefs are and change the frequency with which one can perceive-and alternate between-two distinct percepts. We hypothesized that there are multiple, but distinct, ways in which precision modulation can interact to give rise to a similar frequency of bistable perception. We validated this using numerical simulations of the Necker cube paradigm and demonstrate the multiple routes that underwrite the frequency of perceptual alternation. Our results provide an (enactive) computational account of the intricate precision balance underwriting bistable perception. Importantly, these precision parameters can be considered the computational homologs of particular neurotransmitters-i.e. acetylcholine, noradrenaline, dopamine-that have been previously implicated in controlling bistable perception, providing a computational link between the neurochemistry and perception.

Functional connectivity and glutamate levels of the medial prefrontal cortex in schizotypy are related to sensory amplification in a probabilistic reasoning task

The circular inference (CI) computational model assumes a corruption of sensory data by prior information and vice versa, leading at the extremes to 'see what we expect' (through prior amplification) and/or to 'expect what we see' (through sensory amplification). Although a CI mechanism has been reported in a schizophrenia population, it has not been investigated in individuals experiencing psychosis-like experiences, such as people with high schizotypy traits. Furthermore, the neurobiological basis of CI, such as the link between hierarchical amplifications, excitatory neurotransmission, and resting state functional connectivity (RSFC), remains untested. The participants included in the present study consisted of a subsample of those recruited in a study previously published by our group, Kozhuharova et al. (2021b). We included 36 participants with High (n=18) and Low (n=18) levels of schizotypy who completed a probabilistic reasoning task (the Fisher task) for which individual confidence levels were obtained and fitted to the CI model. Participants also underwent a 1H-Magnetic Resonance Spectroscopy (MRS) scan to measure medial prefrontal cortex (mPFC) glutamate metabolite levels, and a functional Magnetic Resonance Imaging (fMRI) scan to measure RSFC of the medial prefrontal cortex (mPFC). People with high levels of schizotypy exhibited changes in CI parameters, altered cortical excitatory neurotransmission and RSFC that were all associated with sensory amplification. Our findings capture a multimodal signature of CI that is observable in people early in the psychosis spectrum.

Circular inference predicts nonuniform overactivation and dysconnectivity in brain-wide connectomes

Schizophrenia is a severe mental disorder whose neural basis remains difficult to ascertain. Among the available pathophysiological theories, recent work has pointed towards subtle perturbations in the excitation-inhibition (E/I) balance within different neural circuits. Computational approaches have suggested interesting mechanisms that can account for both E/I imbalances and psychotic symptoms. Based on hierarchical neural networks propagating information through a message-passing algorithm, it was hypothesized that changes in the E/I ratio could cause a "circular belief propagation" in which bottom-up and top-down information reverberate. This circular inference (CI) was proposed to account for the clinical features of schizophrenia. Under this assumption, this paper examined the impact of CI on network dynamics in light of brain imaging findings related to psychosis. Using brain-inspired graphical models, we show that CI causes overconfidence and overactivation most specifically at the level of connector hubs (e.g., nodes with many connections allowing integration across networks). By also measuring functional connectivity in these graphs, we provide evidence that CI is able to predict specific changes in modularity known to be associated with schizophrenia. Altogether, these findings suggest that the CI framework may facilitate behavioral and neural research on the multifaceted nature of psychosis.

From hallucinations to synaesthesia: A circular inference account of unimodal and multimodal erroneous percepts in clinical and drug-induced psychosis

Psychedelics distort perception and induce visual and multimodal hallucinations as well as synaesthesia. This is in contradiction with the high prevalence of distressing voices in schizophrenia. Here we introduce a unifying account of unimodal and multimodal erroneous percepts based on circular inference. We show that amplification of top-down predictions (descending loops) leads to an excessive reliance on priors and aberrant levels of integration of the sensory representations, resulting in crossmodal percepts and stronger illusions. By contrast, amplification of bottom-up information (ascending loops) results in overinterpretation of unreliable sensory inputs and high levels of segregation between sensory modalities, bringing about unimodal hallucinations and reduced vulnerability to illusions. We delineate a canonical microcircuit in which layer-specific inhibition controls the propagation of information across hierarchical levels: inhibitory interneurons in the deep layers exert control over priors, removing descending loops. Conversely, inhibition in the supragranular layers counterbalances the effects of the ascending loops. Overall, we put forward a multiscale and transnosographic account of erroneous percepts with important theoretical, conceptual and clinical implications.

Top-down resolution of visual ambiguity - knowledge from the future or footprints from the past?

Current theories about visual perception assume that our perceptual system weights the a priori incomplete, noisy and ambiguous sensory information with previous, memorized perceptual experiences in order to construct stable and reliable percepts. These theories are supported by numerous experimental findings. Theories about precognition have an opposite point of view. They assume that information from the future can have influence on perception, thoughts, and behavior. Several experimental studies provide evidence for precognition effects, other studies found no such effects. One problem may be that the vast majority of precognition paradigms did not systematically control for potential effects from the perceptual history. In the present study, we presented ambiguous Necker cube stimuli and disambiguated cube variants and systematically tested in two separate experiments whether perception of a currently observed ambiguous Necker cube stimulus can be influenced by a disambiguated cube variant, presented in the immediate perceptual past (perceptual history effects) and/or in the immediate perceptual future (precognition effects). We found perceptual history effects, which partly depended on the length of the perceptual history trace but were independent of the perceptual future. Results from some individual participants suggest on the first glance a precognition pattern, but results from our second experiment make a perceptual history explanation more probable. On the group level, no precognition effects were statistically indicated. The perceptual history effects found in the present study are in confirmation with related studies from the literature. The precognition analysis revealed some interesting individual patterns, which however did not allow for general conclusions. Overall, the present study demonstrates that any future experiment about sensory or extrasensory perception urgently needs to control for potential perceptual history effects and that temporal aspects of stimulus presentation are of high relevance.

Taking others into account: combining directly experienced and indirect information in schizophrenia

An abnormality in inference, resulting in distorted internal models of the world, has been argued to be a common mechanism underlying the heterogeneous psychopathology in schizophrenia. However, findings have been mixed as to wherein the abnormality lies and have typically failed to find convincing relations to symptoms. The limited and inconsistent findings may have been due to methodological limitations of the experimental design, such as conflating other factors (e.g. comprehension) with the inferential process of interest, and a failure to adequately assess and model the key aspects of the inferential process. Here, we investigated probabilistic inference based on multiple sources of information using a new digital version of the beads task, framed in a social context. Thirty-five patients with schizophrenia or schizoaffective disorder with a wide range of symptoms and 40 matched healthy control subjects performed the task, where they guessed the colour of the next marble drawn from a jar based on a sample from the jar as well as the choices and the expressed confidence of four people, each with their own independent sample (which was hidden from participant view). We relied on theoretically motivated computational models to assess which model best captured the inferential process and investigated whether it could serve as a mechanistic model for both psychotic and negative symptoms. We found that 'circular inference' best described the inference process, where patients over-weighed and overcounted direct experience and under-weighed information from others. Crucially, overcounting of direct experience was uniquely associated with most psychotic and negative symptoms. In addition, patients with worse social cognitive function had more difficulties using others' confidence to inform their choices. This difficulty was related to worse real-world functioning. The findings could not be easily ascribed to differences in working memory, executive function, intelligence or antipsychotic medication. These results suggest hallucinations, delusions and negative symptoms could stem from a common underlying abnormality in inference, where directly experienced information is assigned an unreasonable weight and taken into account multiple times. By this, even unreliable first-hand experiences may gain disproportionate significance. The effect could lead to false perceptions (hallucinations), false beliefs (delusions) and deviant social behaviour (e.g. loss of interest in others, bizarre and inappropriate behaviour). This may be particularly problematic for patients with social cognitive deficits, as they may fail to make use of corrective information from others, ultimately leading to worse social functioning.

Representations of uncertainty: where art thou?

Perception is often described as probabilistic inference requiring an internal representation of uncertainty. However, it is unknown whether uncertainty is represented in a task-dependent manner, solely at the level of decisions, or in a fully Bayesian manner, across the entire perceptual pathway. To address this question, we first codify and evaluate the possible strategies the brain might use to represent uncertainty, and highlight the normative advantages of fully Bayesian representations. In such representations, uncertainty information is explicitly represented at all stages of processing, including early sensory areas, allowing for flexible and efficient computations in a wide variety of situations. Next, we critically review neural and behavioral evidence about the representation of uncertainty in the brain agreeing with fully Bayesian representations. We argue that sufficient behavioral evidence for fully Bayesian representations is lacking and suggest experimental approaches for demonstrating the existence of multivariate posterior distributions along the perceptual pathway.

A functional theory of bistable perception based on dynamical circular inference

When we face ambiguous images, the brain cannot commit to a single percept; instead, it switches between mutually exclusive interpretations every few seconds, a phenomenon known as bistable perception. While neuromechanistic models, e.g., adapting neural populations with lateral inhibition, may account for the dynamics of bistability, a larger question remains unresolved: how this phenomenon informs us on generic perceptual processes in less artificial contexts. Here, we propose that bistable perception is due to our prior beliefs being reverberated in the cortical hierarchy and corrupting the sensory evidence, a phenomenon known as “circular inference”. Such circularity could occur in a hierarchical brain where sensory responses trigger activity in higher-level areas but are also modulated by feedback projections from these same areas. We show that in the face of ambiguous sensory stimuli, circular inference can change the dynamics of the perceptual system and turn what should be an integrator of inputs into a bistable attractor switching between two highly trusted interpretations. The model captures various aspects of bistability, including Levelt’s laws and the stabilizing effects of intermittent presentation of the stimulus. Since it is related to the generic perceptual inference and belief updating mechanisms, this approach can be used to predict the tendency of individuals to form aberrant beliefs from their bistable perception behavior. Overall, we suggest that feedforward/feedback information loops in hierarchical neural networks, a phenomenon that could lead to psychotic symptoms when overly strong, could also underlie perception in nonclinical populations.

In cases of high ambiguity, our perceptual system cannot commit to a single percept and switches between different interpretations, giving rise to bistable perception. In this paper we outline a computational model of bistability based on the notion of circular inference, i.e. a form of suboptimal hierarchical inference in which priors and / or sensory inputs are reverberated and over-counted. We suggest that descending loops (i.e. reverberated priors) transform our perceptual system from a simple accumulator of sensory inputs into a bistable attractor, that switches between two highly-trusted interpretations. Using analytical methods we derive the necessary conditions for bistable perception to occur. We show that our dynamical circular inference model is able to capture many features of bistability, such as Levelt’s laws and the stabilizing effects of intermittent presentation of the stimulus. Finally we make novel predictions about the behavior of psychotic patients.