Bayesian Predictive Coding Research Articles

Reviving Bistable Perception in Patients With Depression by Decreasing the Overestimation of Prior Precision.

Slower perceptual alternations, a notable perceptual effect observed in psychiatric disorders, can be alleviated by antidepressant therapies that affect serotonin levels in the brain. While these phenomena have been well documented, the underlying neurocognitive mechanisms remain to be elucidated. Our study bridges this gap by employing a computational cognitive approach within a Bayesian predictive coding framework to explore these mechanisms in depression. We fitted a prediction error (PE) model to behavioral data from a binocular rivalry task, uncovering that significantly higher initial prior precision and lower PE led to a slower switch rate in patients with depression. Furthermore, serotonin-targeting antidepressant treatments significantly decreased the prior precision and increased PE, both of which were predictive of improvements in the perceptual alternation rate of depression patients. These findings indicated that the substantially slower perception switch rate in patients with depression was caused by the greater reliance on top-down priors and that serotonin treatment's efficacy was in its recalibration of these priors and enhancement of PE. Our study not only elucidates the cognitive underpinnings of depression, but also suggests computational modeling as a potent tool for integrating cognitive science with clinical psychology, advancing our understanding and treatment of cognitive impairments in depression.

Placebo Effects Are Small on Average in the 7.5% CO2 Inhalational Model of Generalized Anxiety.

Anxiety disorders are highly prevalent and socio-economically costly. Novel pharmacological treatments for these disorders are needed because many patients do not respond to current agents or experience unwanted side effects. However, a barrier to treatment development is the variable and large placebo response rate seen in trials of novel anxiolytics. Despite this, the mechanisms that drive placebo responses in anxiety disorders have been little investigated, possibly due to low availability of convenient experimental paradigms. We aimed to develop and test a novel protocol for inducing placebo anxiolysis in the 7.5% CO2 inhalational model of generalized anxiety in healthy volunteers. Following a baseline 20-minute CO2 challenge, 32 healthy volunteers were administered a placebo intranasal spray labelled as either the anxiolytic "lorazepam" or "saline." Following this, participants surreptitiously underwent a 20-minute inhalation of normal air. Post-conditioning, a second dose of the placebo was administered, after which participants completed another CO2 challenge. Participants administered sham "lorazepam" reported significant positive expectations of reduced anxiety (P = .001), but there was no group-level placebo effect on anxiety following CO2 challenge post-conditioning (Ps > .350). Surprisingly, we found many participants exhibited unexpected worsening of anxiety, despite positive expectations. Contrary to our hypothesis, our novel paradigm did not induce a placebo response, on average. It is possible that effects of 7.5% CO2 inhalation on prefrontal cortex function or behavior in line with a Bayesian predictive coding framework attenuated the effect of expectations on subsequent placebo response. Future studies are needed to explore these possibilities.

Long COVID as a functional somatic symptom disorder caused by abnormally precise prior expectations during Bayesian perceptual processing: A new hypothesis and implications for pandemic response.

This review proposes a model of Long-COVID where the constellation of symptoms are in fact genuinely experienced persistent physical symptoms that are usually functional in nature and therefore potentially reversible, that is, Long-COVID is a somatic symptom disorder. First, we describe what is currently known about Long-COVID in children and adults. Second, we examine reported "Long-Pandemic" effects that create a risk for similar somatic symptoms to develop in non-COVID-19 patients. Third, we describe what was known about somatization and somatic symptom disorder before the COVID-19 pandemic, and suggest that by analogy, Long-COVID may best be conceptualized as one of these disorders, with similar symptoms and predisposing, precipitating, and perpetuating factors. Fourth, we review the phenomenon of mass sociogenic (functional) illness, and the concept of nocebo effects, and suggest that by analogy, Long-COVID is compatible with these descriptions. Fifth, we describe the current theoretical model of the mechanism underlying functional disorders, the Bayesian predictive coding model for perception. This model accounts for moderators that can make symptom inferences functionally inaccurate and therefore can explain how to understand common predisposing, precipitating, and perpetuating factors. Finally, we discuss the implications of this framework for improved public health messaging during a pandemic, with recommendations for the management of Long-COVID symptoms in healthcare systems. We argue that the current public health approach has induced fear of Long-COVID in the population, including from constant messaging about disabling symptoms of Long-COVID and theorizing irreversible tissue damage as the cause of Long-COVID. This has created a self-fulfilling prophecy by inducing the very predisposing, precipitating, and perpetuating factors for the syndrome. Finally, we introduce the term "Pandemic-Response Syndrome" to describe what previously was labeled Long-COVID. This alternative perspective aims to stimulate research and serve as a lesson learned to avoid a repeat performance in the future.

Functional neurological disorder and placebo and nocebo effects: shared mechanisms

Functional neurological disorder (FND) is characterized by neurological symptoms that cannot be explained by a structural neurological cause. Among the different aetiological models that have been proposed for FND, of note is the Bayesian predictive coding model, which posits that perception relies on top-down cortical predictions (priors) to infer the source of incoming sensory information. This model can also apply to non-pathological experiences, such as placebo and nocebo effects, wherein sensory information is shaped by prior expectations and learning. To date, most studies of the relationship between placebo and nocebo effects and FND have focused on the use of placebos for diagnosis and treatment of FND. Here, we propose that this relationship might go beyond diagnosis and therapy. We develop a framework in which shared cognitive, personality and neuroanatomical factors justify the consideration of a deeper link between FND and placebo and nocebo effects. This new perspective might offer guidance for clarification of the pathogenesis of FND and for the identification of potential biomarkers and therapeutic targets.

State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning

Anxiety influences how the brain estimates and responds to uncertainty. The consequences of these processes on behaviour have been described in theoretical and empirical studies, yet the associated neural correlates remain unclear. Rhythm-based accounts of Bayesian predictive coding propose that predictions in generative models of perception are represented in alpha (8–12 Hz) and beta oscillations (13–30 Hz). Updates to predictions are driven by prediction errors weighted by precision (inverse variance) encoded in gamma oscillations (>30 Hz) and associated with the suppression of beta activity. We tested whether state anxiety alters the neural oscillatory activity associated with predictions and precision-weighted prediction errors (pwPE) during learning. Healthy human participants performed a probabilistic reward-based learning task in a volatile environment. In our previous work, we described learning behaviour in this task using a hierarchical Bayesian model, revealing more precise (biased) beliefs about the tendency of the reward contingency in state anxiety, consistent with reduced learning in this group. The model provided trajectories of predictions and pwPEs for the current study, allowing us to assess their parametric effects on the time-frequency representations of EEG data. Using convolution modelling for oscillatory responses, we found that, relative to a control group, state anxiety increased beta activity in frontal and sensorimotor regions during processing of pwPE, and in fronto-parietal regions during encoding of predictions. No effects of state anxiety on gamma modulation were found. Our findings expand prior evidence on the oscillatory representations of predictions and pwPEs into the reward-based learning domain. The results suggest that state anxiety modulates beta-band oscillatory correlates of pwPE and predictions in generative models, providing insights into the neural processes associated with biased belief updating and poorer learning.

Structural and contextual priors affect visual search in children with and without autism.

Bayesian predictive coding theories of autism spectrum disorder propose that impaired acquisition or a broader shape of prior probability distributions lies at the core of the condition. However, we still know very little about how probability distributions are learned and encoded by children, let alone children with autism. Here, we take advantage of a recently developed distribution learning paradigm to characterize how children with and without autism acquire information about probability distributions. Twenty-four autistic and 25-matched neurotypical children searched for an odd-one-out target among a set of distractor lines with orientations sampled from a Gaussian distribution repeated across multiple trials to allow for learning of the parameters (mean and variance) of the distribution. We could measure the width (variance) of the participant's encoded distribution by introducing a target-distractor role-reversal while varying the similarity between target and previous distractor mean. Both groups performed similarly on the visual search task and learned the distractor distribution to a similar extent. However, the variance learned was much broader than the one presented, consistent with less informative priors in children irrespective of autism diagnosis. These findings have important implications for Bayesian accounts of perception throughout development, and Bayesian accounts of autism specifically. LAY SUMMARY: Recent theories about the underlying cognitive mechanisms of autism propose that the way autistic individuals estimate variability or uncertainty in their perceptual environment may differ from how typical individuals do so. Children had to search an oddly tilted line in a set of lines pointing in different directions, and based on their response times we examined how they learned about the variability in a set of objects. We found that autistic children learn variability as well as typical children, but both groups learn with less precision than typical adults do on the same task.

A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding

Wireless sensor networks (WSNs) generate a variety of continuous data streams. To reduce data storage and transmission cost, compression is recommended to be applied to the data streams from every single sensor node. Local compression falls into two categories: lossless and lossy. Lossy compression techniques are generally preferable for sensors in commercial nodes than the lossless ones as they provide a better compression ratio at a lower computational cost. However, the traditional approaches for data compression in WSNs are sensitive to sensor accuracy. They are less efficient when there are abnormal and faulty measurements or missing data. This paper proposes a new lossy compression approach using the Bayesian predictive coding (BPC). Instead of the original signals, predictive coding transmits the error terms which are calculated by subtracting the predicted signals from the actual signals to the receiving node. Its compression performance depends on the accuracy of the adopted prediction technique. BPC combines the Bayesian inference with the predictive coding. Prediction is made by the Bayesian inference instead of regression models as in traditional predictive coding. In this way, it can utilize prior information and provide inferences that are conditional on the data without reliance on asymptotic approximation. Experimental tests show that the BPC is the same efficient as the linear predictive coding when handling independent signals which follow a stationary probability distribution. More than that, the BPC is more robust toward occasionally erroneous or missing sensor data. The proposed approach is based on the physical knowledge of the phenomenon in applications. It can be considered as a complementary approach to the existing lossy compression family for WSNs.

Autism and psychosis as diametrical disorders of embodiment.

Humans have evolved an elaborate system of self-consciousness, self-identity, self-agency, and self-embodiment that is grounded in specific neurological structures including an expanded insula. Instantiation of the bodily self has been most-extensively studied via the ‘rubber hand illusion’, whereby parallel stimulation of a hidden true hand, and a viewed false hand, leads to the felt belief that the false hand is one’s own. Autism and schizophrenia have both long been regarded as conditions centrally involving altered development of the self, but they have yet to be compared directly with regard to the self and embodiment. Here, we synthesize the embodied cognition literature for these and related conditions, and describe evidence that these two sets of disorders exhibit opposite susceptibilities from typical individuals to the rubber hand illusion: reduced on the autism spectrum and increased in schizophrenia and other psychotic-affective conditions. Moreover, the opposite illusion effects are mediated by a consilient set of associated phenomena, including empathy, interoception, anorexia risk and phenotypes, and patterns of genetic correlation. Taken together, these findings: (i) support the diametric model of autism and psychotic-affective disorders, (ii) implicate the adaptive human system of self-embodiment, and its neural bases, in neurodevelopmental disorders, and suggest new therapies and (iii) experimentally ground Bayesian predictive coding models with regard to autism compared with psychosis.Lay summary: Humans have evolved a highly developed sense of self and perception of one’s own body. The ‘rubber hand illusion’ can be used to test individual variation in sense of self, relative to connection with others. We show that this illusion is reduced in autism spectrum disorders, and increased in psychotic and mood disorders. These findings have important implications for understanding and treatment of mental disorders.

Auditory predictions shape the neural responses to stimulus repetition and sensory change

Perception is a highly active process relying on the continuous formulation of predictive inferences using short-term sensory memory templates, which are recursively adjusted based on new input. According to this idea, earlier studies have shown that novel stimuli preceded by a higher number of repetitions yield greater novelty responses, indexed by larger mismatch negativity (MMN). However, it is not clear whether this MMN memory trace effect is driven by more adapted responses to prior stimulation or rather by a heightened processing of the unexpected deviant, and only few studies have so far attempted to characterize the functional neuroanatomy of these effects. Here we implemented a modified version of the auditory frequency oddball paradigm that enables modeling the responses to both repeated standard and deviant stimuli. Fifteen subjects underwent functional magnetic resonance imaging (fMRI) while their attention was diverted from auditory stimulation. We found that deviants with longer stimulus history of standard repetitions yielded a more robust and widespread activation in the bilateral auditory cortex. Standard tones repetition yielded a pattern of response entangling both suppression and enhancement effects depending on the predictability of upcoming stimuli. We also observed that regularity encoding and deviance detection mapped onto spatially segregated cortical subfields. Our data provide a better understanding of the neural representations underlying auditory repetition and deviance detection effects, and further support that perception operates through the principles of Bayesian predictive coding.

The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective

In this paper, we argue for a theoretical separation of the free-energy principle from Helmholtzian accounts of the predictive brain. The free-energy principle is a theoretical framework capturing the imperative for biological self-organization in information-theoretic terms. The free-energy principle has typically been connected with a Bayesian theory of predictive coding, and the latter is often taken to support a Helmholtzian theory of perception as unconscious inference. If our interpretation is right, however, a Helmholtzian view of perception is incompatible with Bayesian predictive coding under the free-energy principle. We argue that the free energy principle and the ecological and enactive approach to mind and life make for a much happier marriage of ideas. We make our argument based on three points. First we argue that the free energy principle applies to the whole animal–environment system, and not only to the brain. Second, we show that active inference, as understood by the free-energy principle, is incompatible with unconscious inference understood as analagous to scientific hypothesis-testing, the main tenet of a Helmholtzian view of perception. Third, we argue that the notion of inference at work in Bayesian predictive coding under the free-energy principle is too weak to support a Helmholtzian theory of perception. Taken together these points imply that the free energy principle is best understood in ecological and enactive terms set out in this paper.

Neural dynamics of prediction and surprise in infants.

Prior expectations shape neural responses in sensory regions of the brain, consistent with a Bayesian predictive coding account of perception. Yet, it remains unclear whether such a mechanism is already functional during early stages of development. To address this issue, we study how the infant brain responds to prediction violations using a cross-modal cueing paradigm. We record electroencephalographic responses to expected and unexpected visual events preceded by auditory cues in 12-month-old infants. We find an increased response for unexpected events. However, this effect of prediction error is only observed during late processing stages associated with conscious access mechanisms. In contrast, early perceptual components reveal an amplification of neural responses for predicted relative to surprising events, suggesting that selective attention enhances perceptual processing for expected events. Taken together, these results demonstrate that cross-modal statistical regularities are used to generate predictions that differentially influence early and late neural responses in infants.

Direct social perception, mindreading and Bayesian predictive coding.

Mindreading accounts of social cognition typically claim that we cannot directly perceive the mental states of other agents and therefore have to exercise certain cognitive capacities in order to infer them. In recent years this view has been challenged by proponents of the direct social perception (DSP) thesis, who argue that the mental states of other agents can be directly perceived. In this paper we show, first, that the main disagreement between proponents of DSP and mindreading accounts has to do with the so-called 'sandwich model' of social cognition. Although proponents of DSP are critical of this model, we argue that they still seem to accept the distinction between perception, cognition and action that underlies it. Second, we contrast the sandwich model of social cognition with an alternative theoretical framework that is becoming increasingly popular in the cognitive neurosciences: Bayesian Predictive Coding (BPC). We show that the BPC framework renders a principled distinction between perception, cognition and action obsolete, and can accommodate elements of both DSP and mindreading accounts.