Sensory processing in humans and mice fluctuates between external and internal modes.

Perception is known to cycle through periods of enhanced and reduced sensitivity to external information. Here, we asked whether such slow fluctuations arise as a noise-related epiphenomenon of limited processing capacity or, alternatively, represent a structured mechanism of perceptual inference. Using 2 large-scale datasets, we found that humans and mice alternate between externally and internally oriented modes of sensory analysis. During external mode, perception aligns more closely with the external sensory information, whereas internal mode is characterized by enhanced biases toward perceptual history. Computational modeling indicated that dynamic changes in mode are enabled by 2 interlinked factors: (i) the integration of subsequent inputs over time and (ii) slow antiphase oscillations in the impact of external sensory information versus internal predictions that are provided by perceptual history. We propose that between-mode fluctuations generate unambiguous error signals that enable optimal inference in volatile environments.

Autobiographical Script-Driven Imagery Has No Detectable Effect on Emotion Regulation in Healthy Individuals.

INTRODUCTION: Emotion regulation (ER), the ability to actively modulate one's own emotion reactions, likely depends on the individual's current emotional state. Here, we investigated whether negative emotions induced by an interpersonal autobiographic script affect the neuronal processes underlying ER. METHODS: Twenty healthy participants were recruited and underwent functional magnetic resonance imaging (fMRI) during performance of distancing, a specific ER strategy, while viewing emotionally arousing pictures. Participants were instructed to either naturally experience ("permit" condition) or to actively downregulate ("regulate" condition) their emotional responses to the presented stimuli. Before each of the 4 runs in total, a neutral or negative autobiographical audio script was presented. The negative script comprised an emotionally negative event from childhood or adolescence that represented either emotional abuse or emotional neglect. The second event comprised an everyday neutral situation. We aimed at identifying the neural correlates of ER and their modulation by script-driven imagery. RESULTS: fMRI analyses testing for greater responses in the "regulate" than the "permit" condition replicated previously reported neural correlates of ER in the right dorsolateral prefrontal cortex and the right inferior parietal lobule. A significant ER effect was also observed in the left orbitofrontal cortex. In the amygdala, we found greater responses in the "permit" compared to the "regulate" condition. We did not observe a significant modulation of the ER effects in any of these regions by the negative emotional state induced by autobiographical scripts. Bayesian statistics confirmed the absence of such modulations by providing marginal evidence for null effects. DISCUSSION: While we replicated previously reported neural correlates of ER, we found no evidence for an effect of mood induction with individualized autobiographical scripts on the neural processes underlying ER in healthy participants.

The Neural Correlates of Hierarchical Predictions for Perceptual Decisions.

Sensory information is inherently noisy, sparse, and ambiguous. In contrast, visual experience is usually clear, detailed, and stable. Bayesian theories of perception resolve this discrepancy by assuming that prior knowledge about the causes underlying sensory stimulation actively shapes perceptual decisions. The CNS is believed to entertain a generative model aligned to dynamic changes in the hierarchical states of our volatile sensory environment. Here, we used model-based fMRI to study the neural correlates of the dynamic updating of hierarchically structured predictions in male and female human observers. We devised a crossmodal associative learning task with covertly interspersed ambiguous trials in which participants engaged in hierarchical learning based on changing contingencies between auditory cues and visual targets. By inverting a Bayesian model of perceptual inference, we estimated individual hierarchical predictions, which significantly biased perceptual decisions under ambiguity. Although "high-level" predictions about the cue-target contingency correlated with activity in supramodal regions such as orbitofrontal cortex and hippocampus, dynamic "low-level" predictions about the conditional target probabilities were associated with activity in retinotopic visual cortex. Our results suggest that our CNS updates distinct representations of hierarchical predictions that continuously affect perceptual decisions in a dynamically changing environment.SIGNIFICANCE STATEMENT Bayesian theories posit that our brain entertains a generative model to provide hierarchical predictions regarding the causes of sensory information. Here, we use behavioral modeling and fMRI to study the neural underpinnings of such hierarchical predictions. We show that "high-level" predictions about the strength of dynamic cue-target contingencies during crossmodal associative learning correlate with activity in orbitofrontal cortex and the hippocampus, whereas "low-level" conditional target probabilities were reflected in retinotopic visual cortex. Our findings empirically corroborate theorizations on the role of hierarchical predictions in visual perception and contribute substantially to a longstanding debate on the link between sensory predictions and orbitofrontal or hippocampal activity. Our work fundamentally advances the mechanistic understanding of perceptual inference in the human brain.

Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.

Theoretical accounts suggest that an alteration in the brain's learning mechanisms might lead to overhasty inferences, resulting in psychotic symptoms. Here, we sought to elucidate the suggested link between maladaptive learning and psychosis. Ninety-eight healthy individuals with varying degrees of delusional ideation and hallucinatory experiences performed a probabilistic reasoning task that allowed us to quantify overhasty inferences. Replicating previous results, we found a relationship between psychotic experiences and overhasty inferences during probabilistic reasoning. Computational modelling revealed that the behavioral data was best explained by a novel computational learning model that formalizes the adaptiveness of learning by a non-linear distortion of prediction error processing, where an increased non-linearity implies a growing resilience against learning from surprising and thus unreliable information (large prediction errors). Most importantly, a decreased adaptiveness of learning predicted delusional ideation and hallucinatory experiences. Our current findings provide a formal description of the computational mechanisms underlying overhasty inferences, thereby empirically substantiating theories that link psychosis to maladaptive learning.

Correction: Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.

[This corrects the article DOI: 10.1371/journal.pcbi.1005328.].

A predictive coding account of bistable perception - a model-based fMRI study.

In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together, our current work provides a theoretical framework that allows for the analysis of behavioural and neural data using a predictive coding perspective on bistable perception. In this, our approach posits a crucial role of prediction error signalling for the resolution of perceptual ambiguities.

Frontoparietal cortex mediates perceptual transitions in bistable perception.

During bistable vision, perception oscillates between two mutually exclusive percepts despite constant sensory input. Greater BOLD responses in frontoparietal cortex have been shown to be associated with endogenous perceptual transitions compared with "replay" transitions designed to closely match bistability in both perceptual quality and timing. It has remained controversial, however, whether this enhanced activity reflects causal influences of these regions on processing at the sensory level or, alternatively, an effect of stimulus differences that result in, for example, longer durations of perceptual transitions in bistable perception compared with replay conditions. Using a rotating Lissajous figure in an fMRI experiment on 15 human participants, we controlled for potential confounds of differences in transition duration and confirmed previous findings of greater activity in frontoparietal areas for transitions during bistable perception. In addition, we applied dynamic causal modeling to identify the neural model that best explains the observed BOLD signals in terms of effective connectivity. We found that enhanced activity for perceptual transitions is associated with a modulation of top-down connectivity from frontal to visual cortex, thus arguing for a crucial role of frontoparietal cortex in perceptual transitions during bistable perception.

Where is the ghost in the shell?

The neurobiology of conscious experience is one of the fundamental mysteries in science. New evidence suggests that transcranial magnetic stimulation of the parietal cortex does not modulate bistable perception. What does this mean for the neural correlates of consciousness, and how should we search for them?

Dynamic predictive templates in perception.

Hallucinations are vivid and transient experiences of objects, such as images or sounds, that occur in the absence of a corresponding stimulus.1,2,3,4,5,6,7,8,9 To understand the neurocomputational mechanisms of hallucinations, cognitive neuroscience has focused on experiments that induce false alarms (FAs) in healthy participants,1,2,3,4,5,9 psychosis-prone individuals,1,3,4 and patients diagnosed with schizophrenia.5 FAs occur when participants make decisions about difficult-to-detect stimuli and indicate the presence of a signal that was, in fact, not presented. Since FAs are, at heart, reports, they must meet two criteria to serve as an experimental proxy for hallucinations: first, FAs should reflect perceptual states that are characterized by specific contents10,11,12 (criterion 1). Second, FAs should occur on a timescale compatible with the temporal dynamics of hallucinations13,14 (criterion 2). In this work, we combined a classification image approach15 with hidden Markov models16 to show that FAs can match the perceptual and temporal characteristics of hallucinations. We asked healthy human participants to discriminate visual stimuli from noise and found that FAs were more likely to occur during an internal mode of sensory processing, a minute-long state of the brain during which perception is strongly biased toward previous experiences17 (serial dependency). Our results suggest that hallucinations are driven by dynamic predictive templates that transform noise into transient, coherent, and meaningful perceptual experiences.

Neural correlates of hierarchical predictive processes in autistic adults.

Bayesian theories of autism spectrum disorders (ASD) suggest that atypical predictive mechanisms could underlie the autistic symptomatology, but little is known about their neural correlates. Twenty-six neurotypical (NT) and 26 autistic adults participated in an fMRI study where they performed an associative learning task in a volatile environment. By inverting a model of perceptual inference, we characterized the neural correlates of hierarchically structured predictions and prediction errors in ASD. Behaviorally, the predictive abilities of autistic adults were intact. Neurally, predictions were encoded hierarchically in both NT and ASD participants and biased their percepts. High-level predictions were following activity levels in a set of regions more closely in ASD than NT. Prediction errors yielded activation in shared regions in NT and ASD, but group differences were found in the anterior cingulate cortex and putamen. This study sheds light on the neural specificities of ASD that might underlie atypical predictive processing.

Sparse models for predicting psychosocial impairments in patients with PTSD: An empirical Bayes approach.

OBJECTIVE: Posttraumatic stress disorder (PTSD) is associated with psychosocial impairments, which represent a relevant focus for therapy. Previous results on the clinical predictors of these psychosocial impairments were inconsistent. The data analyzed in these contexts often suffer from a high number of correlated predictors and small sample sizes, entailing the risk of model overfitting. In Bayesian regression, the problem of overfitting can be mitigated by usage of specific zero-centered (regularizing) prior distributions. In this study, we used the 2 most common Bayesian regression models, the Bayesian Ridge and the Bayesian Lasso, to predict psychosocial impairments in 192 patients of a day clinic for the treatment of PTSD. METHOD: Predictions were based on specific dimensions of PTSD symptoms previously revealed by factor analyses, as well as posttraumatic cognitions, depressive symptoms, comorbid disorders, and demographics. The variance of the prior distribution was estimated through empirical Bayes (maximum marginal likelihood) and an approximation to the posterior distribution was obtained with stochastic variational inference and with a local approximation (Laplace approximation). RESULTS: Severe psychosocial impairments were mainly related to depressive symptoms and symptoms from the amnesia and numbing dimension of PTSD, while gender, posttraumatic cognitions, and reexperience and avoidance symptoms had no impact. As expected, the model coefficients were shrunken to zero when regularizing prior distributions were used, particularly for the Bayesian Lasso. CONCLUSION: Depressive and numbing symptoms are the main clinical correlates of psychosocial impairments in patients with PTSD. Usage of Bayesian and regularized regression can contribute to the generalizability and interpretability of research results. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Sensory and environmental uncertainty in perceptual decision-making.

In perceptual decision-making, uncertainties regarding both noisy sensory information and changing environmental regularities must be considered. We aimed to clarify the relationship between these two sources of uncertainty using a combined motion discrimination and audiovisual reversal learning task with Bayesian modeling. As predicted, the influence of learned beliefs regarding audiovisual associations on perceptual decisions was greater under high sensory uncertainty. Critically, this modulatory effect was larger under high than low environmental uncertainty. Moreover, the degree to which observers relied on learned beliefs when making perceptual decisions depended on their individual tendency to change beliefs. While these findings suggest that weighting of the available sensory information against learned beliefs is modulated by their respective uncertainties, belief learning was not found to rely on sensory uncertainty. Unraveling of these interactive effects of sensory and environmental uncertainties in perception might aid in the understanding of aberrant perceptual inference in psychopathology such as schizophrenia.

Associative learning under uncertainty in adults with autism: Intact learning of the cue-outcome contingency, but slower updating of priors.

LAY ABSTRACT: We have an internal representation of the world that guides our behavior, helps us predicting what comes next and therefore, reducing uncertainty. For instance, after hearing the noise of a door opening, we usually expect to see a person appearing, whose features differ depending on the context. In this example of associative learning, predictions need to be adjusted if there is a change in the environment (e.g. different person depending on the location). Recent theories suggest that the symptoms encountered in autism could be due to an atypical learning of predictions or to a decreased influence of these expectations on perception. Here, we conducted an experiment assessing whether adults with autism could learn and adjust their predictions in a changing environment. Throughout a behavioral task, participants learned to associate a sound with a visual outcome, but this association could sometimes reverse. Results showed that autistic adults could learn to make predictions that fitted the main sound-vision association, but were slower to adapt their expectations when there was an unannounced change in the environment. We also observed that both adults with and without autism tended to be biased by their expectations, as they reported seeing what they expected to see rather than what was actually shown. Altogether, our results indicate that autistic adults can learn predictions but are more inflexible to adjust these predictions in a changing environment. These results help refining recent theories of autism (called "predictive coding" theories), which intend to identify the core mechanisms underlying the autistic symptomatology.

Bistable perception alternates between internal and external modes of sensory processing.

Perceptual history can exert pronounced effects on the contents of conscious experience: when confronted with completely ambiguous stimuli, perception does not waver at random between diverging stimulus interpretations but sticks with recent percepts for prolonged intervals. Here, we investigated the relevance of perceptual history in situations more similar to everyday experience, where sensory stimuli are usually not completely ambiguous. Using partially ambiguous visual stimuli, we found that the balance between past and present is not stable over time but slowly fluctuates between two opposing modes. For time periods of up to several minutes, perception was either largely determined by perceptual history or driven predominantly by disambiguating sensory evidence. Computational modeling suggested that the construction of unambiguous conscious experiences is modulated by slow fluctuations between internally and externally oriented modes of sensory processing.

Overly Strong Priors for Socially Meaningful Visual Signals Are Linked to Psychosis Proneness in Healthy Individuals.

According to the predictive coding theory of psychosis, hallucinations and delusions are explained by an overweighing of high-level prior expectations relative to sensory information that leads to false perceptions of meaningful signals. However, it is currently unclear whether the hypothesized overweighing of priors (1) represents a pervasive alteration that extends to the visual modality and (2) takes already effect at early automatic processing stages. Here, we addressed these questions by studying visual perception of socially meaningful stimuli in healthy individuals with varying degrees of psychosis proneness (n = 39). In a first task, we quantified participants' prior for detecting faces in visual noise using a Bayesian decision model. In a second task, we measured participants' prior for detecting direct gaze stimuli that were rendered invisible by continuous flash suppression. We found that the prior for detecting faces in noise correlated with hallucination proneness (r = 0.50, p = 0.001, Bayes factor 1/20.1) as well as delusion proneness (r = 0.46, p = 0.003, BF 1/9.4). The prior for detecting invisible direct gaze was significantly associated with hallucination proneness (r = 0.43, p = 0.009, BF 1/3.8) but not conclusively with delusion proneness (r = 0.30, p = 0.079, BF 1.7). Our results provide evidence for the idea that overly strong high-level priors for automatically detecting socially meaningful stimuli might constitute a processing alteration in psychosis.

An active role of inferior frontal cortex in conscious experience.

In the search for the neural correlates of consciousness, it has remained controversial whether prefrontal cortex determines what is consciously experienced or, alternatively, serves only complementary functions, such as introspection or action. Here, we provide converging evidence from computational modeling and two functional magnetic resonance imaging experiments that indicated a key role of inferior frontal cortex in detecting perceptual conflicts caused by ambiguous sensory information. Crucially, the detection of perceptual conflicts by prefrontal cortex turned out to be critical in the process of transforming ambiguous sensory information into unambiguous conscious experiences: in a third experiment, disruption of neural activity in inferior frontal cortex through transcranial magnetic stimulation slowed down the updating of conscious experience that occurs in response to perceptual conflicts. These findings show that inferior frontal cortex actively contributes to the resolution of perceptual ambiguities. Prefrontal cortex is thus causally involved in determining the contents of conscious experience.

Psychotic Experiences in Schizophrenia and Sensitivity to Sensory Evidence.

Perceptual inference depends on an optimal integration of current sensory evidence with prior beliefs about the environment. Alterations of this process have been related to the emergence of positive symptoms in schizophrenia. However, it has remained unclear whether delusions and hallucinations arise from an increased or decreased weighting of prior beliefs relative to sensory evidence. To investigate the relation of this prior-to-likelihood ratio to positive symptoms in schizophrenia, we devised a novel experimental paradigm which gradually manipulates perceptually ambiguous visual stimuli by disambiguating stimulus information. As a proxy for likelihood precision, we assessed the sensitivity of individual participants to sensory evidence. As a surrogate for the precision of prior beliefs in perceptual stability, we measured phase duration in ambiguity. Relative to healthy controls, patients with schizophrenia showed a stronger increment in congruent perceptual states for increasing levels of disambiguating stimulus evidence. Sensitivity to sensory evidence correlated positively with the individual patients' severity of perceptual anomalies and hallucinations. Moreover, the severity of such experiences correlated negatively with phase duration. Our results indicate that perceptual anomalies and hallucinations are associated with a shift of perceptual inference toward sensory evidence and away from prior beliefs. This reduced prior-to-likelihood ratio in sensory processing may contribute to the phenomenon of aberrant salience, which has been suggested to give rise to the false inferences underlying psychotic experiences.

Delusion Proneness is Linked to a Reduced Usage of Prior Beliefs in Perceptual Decisions.

Predictive coding theories state an aberrant weighting of prior beliefs and present sensory information as a core computational pathology in psychosis. Specifically, it has been proposed that the influence of prior beliefs which attenuate improbable sensory information is weakened, resulting in an overweighing of this potentially misleading information. However, it is currently unclear whether this alteration is specific to perceptual processes or whether it represents a more pervasive deficit that extends to cognitive processes. Here, we carried out 2 behavioral experiments that probed the usage of priors during perceptual and cognitive processes, respectively, in 123 healthy individuals with varying degrees of delusion proneness. In an audio-visual perceptual discrimination task, participants had to judge the global motion direction of random dot kinematograms. Prior beliefs were induced by auditory cues that probabilistically predicted the global motion direction of the dot kinematograms, allowing us to measure the impact of prior beliefs on perceptual decision making. A control experiment paralleled the design of the perceptual decision making task in the domain of cognitive decision making. By fitting the participants' responses with a probabilistic decision model, we quantified the impact of prior beliefs on participants' decisions in both tasks. With growing delusion proneness, we found a decreased impact of prior beliefs on perceptual but not on cognitive decision making. Our results show that delusion proneness is linked to a specifically reduced usage of prior beliefs in perceptual decisions, thereby empirically substantiating predictive coding theories of psychosis.

Perceptual Stability of the Lissajous Figure Is Modulated by the Speed of Illusory Rotation.

Lissajous figures represent ambiguous structure-from-motion stimuli rotating in depth and have proven to be a versatile tool to explore the cognitive and neural mechanisms underlying bistable perception. They are generated by the intersection of two sinusoids with perpendicular axes and increasing phase-shift whose frequency determines the speed of illusory 3D rotation. Recently, we found that Lissajous figures of higher shifting frequencies elicited longer perceptual phase durations and tentatively proposed a "representational momentum" account. In this study, our aim was twofold. First, we aimed to gather more behavioral evidence related to the perceptual dynamics of the Lissajous figure by simultaneously varying its shifting frequency and size. Using a conventional analysis, we investigated the effects of our experimental manipulations on transition probability (i.e., the probability that the current percept will change at the next critical stimulus configuration). Second, we sought to test the impact of our experimental factors on the occurrence of transitions in bistable perception by means of a Bayesian approach that can be used to directly quantify the impact of contextual cues on perceptual stability. We thereby estimated the implicit prediction of perceptual stability and how it is modulated by experimental manipulations.

Learning What to See in a Changing World.

Visual perception is strongly shaped by expectations, but it is poorly understood how such perceptual expectations are learned in our dynamic sensory environment. Here, we applied a Bayesian framework to investigate whether perceptual expectations are continuously updated from different aspects of ongoing experience. In two experiments, human observers performed an associative learning task in which rapidly changing expectations about the appearance of ambiguous stimuli were induced. We found that perception of ambiguous stimuli was biased by both learned associations and previous perceptual outcomes. Computational modeling revealed that perception was best explained by a model that continuously updated priors from associative learning and perceptual history and combined these priors with the current sensory information in a probabilistic manner. Our findings suggest that the construction of visual perception is a highly dynamic process that incorporates rapidly changing expectations from different sources in a manner consistent with Bayesian learning and inference.