The Depressed Brain: An Evolutionary Systems Theory

Digital Being: social media and the predictive mind

Social media is implicated today in an array of mental health concerns. While concerns around social media have become mainstream, little is known about the specific cognitive mechanisms underlying the correlations seen in these studies or why we find it so hard to stop engaging with these platforms when things obviously begin to deteriorate for us. New advances in computational neuroscience, however, are now poised to shed light on this matter. In this paper, we approach the phenomenon of social media addiction through the lens of the active inference framework. According to this framework, predictive agents like us use a 'generative model' of the world to predict our own incoming sense data and act to minimize any discrepancy between the prediction and incoming signal (prediction error). In order to live well and be able to act effectively to minimize prediction error, it is vital that agents like us have a generative model, which not only accurately reflects the regularities of our complex environment but is also flexible and dynamic and able to stay accurate in volatile and turbulent circumstances. In this paper, we propose that some social media platforms are a spectacularly effective way of warping an agent's generative model and of arresting the model's ability to flexibly track and adapt to changes in the environment. We go on to investigate cases of digital tech, which do not have these adverse effects and suggest-based on the active inference framework-some ways to understand why some forms of digital technology pose these risks, while others do not.

Coactivation of Autonomic and Central Nervous Systems During Processing of Socially Relevant Information in Autism Spectrum Disorder: A Systematic Review

Body-brain interaction provides a novel approach to understand neurodevelopmental conditions such as autism spectrum disorder (ASD). In this systematic review, we analyse the empirical evidence regarding coexisting differences in autonomic (ANS) and central nervous system (CNS) responses to social stimuli between individuals with ASD and typically developing individuals. Moreover, we review evidence of deviations in body-brain interaction during processing of socially relevant information in ASD. We conducted systematic literature searches in PubMed, Medline, PsychInfo, PsychArticles, and Cinahl databases (until 12.1.2022). Studies were included if individuals with ASD were compared with typically developing individuals, study design included processing of social information, and ANS and CNS activity were measured simultaneously. Out of 1892 studies identified based on the titles and abstracts, only six fulfilled the eligibility criteria to be included in synthesis. The quality of these studies was assessed using a quality assessment checklist. The results indicated that individuals with ASD demonstrate atypicalities in ANS and CNS signalling which, however, are context dependent. There were also indications for altered contribution of ANS-CNS interaction in processing of social information in ASD. However, the findings must be considered in the context of several limitations, such as small sample sizes and high variability in (neuro)physiological measures. Indeed, the methodological choices varied considerably, calling for a need for unified guidelines to improve the interpretability of results. We summarize the current experimentally supported understanding of the role of socially relevant body-brain interaction in ASD. Furthermore, we propose developments for future studies to improve incremental knowledge building across studies of ANS-CNS interaction involving individuals with ASD.

The aesthetic valve: how aesthetic appreciation may switch emotional states from anxiety to curiosity

Pursuing new knowledge in the entropic environment is pivotal for survival. However, dealing with uncertainty is a costly challenge for the agent surrounded by the stochastic sensory world, giving rise to different epistemic emotions, such as curiosity and anxiety. We recently proposed that aesthetic appreciation may have the role of associating pleasant feedback with the update of predictive representations. According to this idea, aesthetic appreciation and its associated rewarding feeling could drive people to seek new knowledge over anxiety. However, the relationship between aesthetic appreciation, curiosity, and anxiety has been still under-examined in the literature. Here, we explore the relationship between these epistemic emotions in a series of three experiments. In study 1, we examined whether music-induced aesthetic appreciation would influence curiosity in a gambling task. In studies 2a and 2b, we explore the relationship between music-induced aesthetic appreciation and anxiety state. Overall, aesthetic appreciation promoted curiosity-driven behaviour while it was negatively associated with anxiety. These results were consistent with the idea that aesthetic appreciation could act as a 'valve', prompting the individual to perceive curiosity (i.e. to consider novelty as a valuable opportunity to acquire new knowledge) rather than anxiety (i.e. to consider novelty as a risk to be avoided). This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Aesthetic chills mitigate maladaptive cognition in depression

BACKGROUND

Depression is a major global health challenge, affecting over 300 million people worldwide. Current pharmacological and psychotherapeutic interventions have limited efficacy, underscoring the need for novel approaches. Emerging evidence suggests that peak emotional experiences characterized by awe, transcendence, and meaning hold promise for rapidly shifting maladaptive cognitive patterns in depression. Aesthetic chills, a peak positive emotion characterized by physical sensations such as shivers and goosebumps, may influence reward-related neural pathways and hold promise for modifying core maladaptive beliefs rooted in early adverse experiences.

METHODS

We enrolled 96 patients diagnosed with major depressive disorder. A validated database of multimedia known to elicit chills responses (ChillsDB) was used for stimulus presentation. Participants' emotional responses were assessed using the Emotional Breakthrough Inventory (EBI), while shifts in self-schema were measured via the Young Positive Schema Questionnaire (YSPQ).

RESULTS

The study found that chill-inducing stimuli have the potential to positively influence the core schema of individuals with depression, impacting areas of self-related beliefs. The associated phenomenology triggered by chills appears to share similarities with the altered states of consciousness induced by psychedelic substances like psilocybin.

CONCLUSIONS

These preliminary results suggest that the biological processes involved in aesthetic chills could be harnessed as a non-pharmacological intervention for depression. However, further investigation is necessary to comprehensively understand the neurophysiological responses to chills and to evaluate the practicality, effectiveness, and safety of utilizing aesthetic chills as a preventive measure in mental health care.

Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications

Interoception-the perception of internal bodily signals-has emerged as an area of interest due to its implications in emotion and the prevalence of dysfunctional interoceptive processes across psychopathological conditions. Despite the importance of interoception in cognitive neuroscience and psychiatry, its experimental manipulation remains technically challenging. This is due to the invasive nature of existing methods, the limitation of self-report and unimodal measures of interoception, and the absence of standardized approaches across disparate fields. This article integrates diverse research efforts from psychology, physiology, psychiatry, and engineering to address this oversight. Following a general introduction to the neurophysiology of interoception as hierarchical predictive processing, we review the existing paradigms for manipulating interoception (e.g., interoceptive modulation), their underlying mechanisms (e.g., interoceptive conditioning), and clinical applications (e.g., interoceptive exposure). We suggest a classification for interoceptive technologies and discuss their potential for diagnosing and treating mental health disorders. Despite promising results, considerable work is still needed to develop standardized, validated measures of interoceptive function across domains and before these technologies can translate safely and effectively to clinical settings.

Storm on predictive brain: A neurocomputational account of ketamine antidepressant effect

For the past decade, ketamine, an N-methyl-D-aspartate receptor (NMDAr) antagonist, has been considered a promising treatment for major depressive disorder (MDD). Unlike the delayed effect of monoaminergic treatment, ketamine may produce fast-acting antidepressant effects hours after a single administration at subanesthetic dose. Along with these antidepressant effects, it may also induce transient dissociative (disturbing of the sense of self and reality) symptoms during acute administration which resolve within hours. To understand ketamine's rapid-acting antidepressant effect, several biological hypotheses have been explored, but despite these promising avenues, there is a lack of model to understand the timeframe of antidepressant and dissociative effects of ketamine. In this article, we propose a neurocomputational account of ketamine's antidepressant and dissociative effects based on the Predictive Processing (PP) theory, a framework for cognitive and sensory processing. PP theory suggests that the brain produces top-down predictions to process incoming sensory signals, and generates bottom-up prediction errors (PEs) which are then used to update predictions. This iterative dynamic neural process would relies on N-methyl-D-aspartate (NMDAr) and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic receptors (AMPAr), two major component of the glutamatergic signaling. Furthermore, it has been suggested that MDD is characterized by over-rigid predictions which cannot be updated by the PEs, leading to miscalibration of hierarchical inference and self-reinforcing negative feedback loops. Based on former empirical studies using behavioral paradigms, neurophysiological recordings, and computational modeling, we suggest that ketamine impairs top-down predictions by blocking NMDA receptors, and enhances presynaptic glutamate release and PEs, producing transient dissociative symptoms and fast-acting antidepressant effect in hours following acute administration. Moreover, we present data showing that ketamine may enhance a delayed neural plasticity pathways through AMPAr potentiation, triggering a prolonged antidepressant effect up to seven days for unique administration. Taken together, the two sides of antidepressant effects with distinct timeframe could constitute the keystone of antidepressant properties of ketamine. These PP disturbances may also participate to a ketamine-induced time window of mental flexibility, which can be used to improve the psychotherapeutic process. Finally, these proposals could be used as a theoretical framework for future research into fast-acting antidepressants, and combination with existing antidepressant and psychotherapy.

Body as First Teacher: The Role of Rhythmic Visceral Dynamics in Early Cognitive Development

Embodied cognition-the idea that mental states and processes should be understood in relation to one's bodily constitution and interactions with the world-remains a controversial topic within cognitive science. Recently, however, increasing interest in predictive processing theories among proponents and critics of embodiment alike has raised hopes of a reconciliation. This article sets out to appraise the unificatory potential of predictive processing, focusing in particular on embodied formulations of active inference. Our analysis suggests that most active-inference accounts invoke weak, potentially trivial conceptions of embodiment; those making stronger claims do so independently of the theoretical commitments of the active-inference framework. We argue that a more compelling version of embodied active inference can be motivated by adopting a diachronic perspective on the way rhythmic physiological activity shapes neural development in utero. According to this visceral afferent training hypothesis, early-emerging physiological processes are essential not only for supporting the biophysical development of neural structures but also for configuring the cognitive architecture those structures entail. Focusing in particular on the cardiovascular system, we propose three candidate mechanisms through which visceral afferent training might operate: (a) activity-dependent neuronal development, (b) periodic signal modeling, and (c) oscillatory network coordination.

Script-driven imagery of socially salient autobiographical memories in major depressive disorder

Cues of social rejection and affiliation represent proximal risk and protective factors in the onset and maintenance of depression. Such cues are thought to activate an evolutionarily primed neuro-cognitive alarm system, alerting the agent to the benefits of inclusion or the risk of social exclusion within social hierarchies focused on ensuring continued access to resources. In tandem, autobiographical memory is thought to be over-general and negatively biased in Major Depressive Disorder (MDD) which can contribute to maintenance and relapse. How memories of social rejection and affiliation are experienced and processed in MDD remains unexplored. Eighteen participants with recurrent and chronic MDD and 18 never-depressed controls listened to and vividly revisited autobiographical social experiences in an ecologically valid script-driven imagery paradigm using naturalistic memory narratives in an fMRI paradigm. Memories of Social Inclusion and Social Rejection broadly activated a common network of regions including the bilateral insula, thalamus and pre/postcentral gyrus across both groups. However, having a diagnosis of MDD was associated with an increased activation of the right middle frontal gyrus irrespective of memory type. Changes in positive affect were associated with activity in the dorsal ACC in the MDD group and in the insular cortex of the Control group. Our findings add to the evidence for complex representations for both positive and negative social signals in MDD and suggest neural sensitivity in MDD towards any socially salient information as opposed to selective sensitivity towards negative social experiences.

Mental Gravity: Depression as Spacetime Curvature of the Self, Mind, and Brain

The principle of mental gravity contends that the mind uses physical gravity as a mental model or simulacrum to express the relation between the inner self and the outer world in terms of "UP"-ness and "DOWN"-ness. The simulation of increased gravity characterises a continuum of mental gravity which states includes depression as the paradigmatic example of being down, low, heavy, and slow. The physics of gravity can also be used to model spacetime curvature in depression, particularly gravitational time dilation as a property of MG analogous to subjective time dilation (i.e., the slowing of temporal flow in conscious experience). The principle has profound implications for the Temporo-spatial Theory of Consciousness (TTC) with regard to temporo-spatial alignment that establishes a "world-brain relation" that is centred on embodiment and the socialisation of conscious states. The principle of mental gravity provides the TTC with a way to incorporate the structure of the world into the structure of the brain, conscious experience, and thought. In concert with other theories of cognitive and neurobiological spacetime, the TTC can also work towards the "common currency" approach that also potentially connects the TTC to predictive processing frameworks such as free energy, neuronal gauge theories, and active inference accounts of depression. It gives the up/down dimension of space, as defined by the gravitational field, a unique status that is connected to both our embodied interaction with the physical world, and also the inverse, reflective, emotional but still embodied experience of ourselves.

Origins and consequences of mood flexibility: a computational perspective

A stable and neutral mood (euthymia) is commended by both economic and clinical perspectives, because it enables rational decisions and avoids mental illnesses. Here we suggest, on the contrary, that a flexible mood responsive to life events may be more adaptive for natural selection, because it can help adjust the behavior to fluctuations in the environment. In our model (dubbed MAGNETO), mood represents a global expected value that biases decisions to forage for a particular reward. When flexible, mood is updated every time an action is taken, by aggregating incurred costs and obtained rewards. Model simulations show that, across a large range of parameters, flexible agents outperform cold agents (with stable neutral mood), particularly when rewards and costs are correlated in time, as naturally occurring across seasons. However, with more extreme parameters, simulations generate short manic episodes marked by incessant foraging and lasting depressive episodes marked by persistent inaction. The MAGNETO model therefore accounts for both the function of mood fluctuations and the emergence of mood disorders.

Social rejection sensitivity and its role in adolescent emotional disorder symptomatology

BACKGROUND

Most emotional disorders first emerge during adolescence, a time characterized by heightened sensitivity to social information, especially social rejection. Social rejection sensitivity (SRS), then, may be a promising intervention target.

METHODS

To explore this, 357 participants (M (SD) age = 19.40 (4.18), 63% female) completed self-report measures of SRS, its proposed antecedent, perceived parenting style, its proposed behavioral correlate, negative interpretation bias, and its proposed  clinical correlate, emotional disorder symptoms. Participants additionally completed a single session of a social interpretation bias modification task, the ambiguous social scenarios task (ASST).

RESULTS

SRS was associated with perceived parental rejection, while controlling for other types of maladaptive parenting. SRS partially accounted for variance in the relationship between perceived parental rejection and emotional disorder symptomatology, as well as the relationship between negative interpretation bias and emotional disorder symptoms. Learning rates (i.e., change in reaction time across the task) on the ASST differed as a function of age and SRS, such that younger participants with higher SRS showed the slowest rate of learning. Moreover, individual differences in SRS accounted for the magnitude of change in negative interpretation bias before and after the ASST. Individuals with greater SRS showed less change in interpretation bias.

CONCLUSIONS

SRS appears strongly associated with emotional disorder symptoms in adolescents. Importantly, SRS was associated with the malleability of negative interpretation bias, which may help account for the mixed findings on the effectiveness of interpretation-bias-modification-paradigms in adolescents.

Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD.

Synapses, predictions, and prediction errors: A neocortical computational study of MDD using the temporal memory algorithm of HTM

INTRODUCTION

Synapses and spines play a significant role in major depressive disorder (MDD) pathophysiology, recently highlighted by the rapid antidepressant effect of ketamine and psilocybin. According to the Bayesian brain and interoception perspectives, MDD is formalized as being stuck in affective states constantly predicting negative energy balance. To understand how spines and synapses relate to the predictive function of the neocortex and thus to symptoms, we used the temporal memory (TM), an unsupervised machine-learning algorithm. TM models a single neocortical layer, learns in real-time, and extracts and predicts temporal sequences. TM exhibits neocortical biological features such as sparse firing and continuous online learning using local Hebbian-learning rules.

METHODS

We trained a TM model on random sequences of upper-case alphabetical letters, representing sequences of affective states. To model depression, we progressively destroyed synapses in the TM model and examined how that affected the predictive capacity of the network. We found that the number of predictions decreased non-linearly.

RESULTS

Destroying 50% of the synapses slightly reduced the number of predictions, followed by a marked drop with further destruction. However, reducing the synapses by 25% distinctly dropped the confidence in the predictions. Therefore, even though the network was making accurate predictions, the network was no longer confident about these predictions.

DISCUSSION

These findings explain how interoceptive cortices could be stuck in limited affective states with high prediction error. Connecting ketamine and psilocybin's proposed mechanism of action to depression pathophysiology, the growth of new synapses would allow representing more futuristic predictions with higher confidence. To our knowledge, this is the first study to use the TM model to connect changes happening at synaptic levels to the Bayesian formulation of psychiatric symptomatology. Linking neurobiological abnormalities to symptoms will allow us to understand the mechanisms of treatments and possibly, develop new ones.

The neuroanatomy of social trust predicts depression vulnerability

Trust attitude is a social personality trait linked with the estimation of others’ trustworthiness. Trusting others, however, can have substantial negative effects on mental health, such as the development of depression. Despite significant progress in understanding the neurobiology of trust, whether the neuroanatomy of trust is linked with depression vulnerability remains unknown. To investigate a link between the neuroanatomy of trust and depression vulnerability, we assessed trust and depressive symptoms and employed neuroimaging to acquire brain structure data of healthy participants. A high depressive symptom score was used as an indicator of depression vulnerability. The neuroanatomical results observed with the healthy sample were validated in a sample of clinically diagnosed depressive patients. We found significantly higher depressive symptoms among low trusters than among high trusters. Neuroanatomically, low trusters and depressive patients showed similar volume reduction in brain regions implicated in social cognition, including the dorsolateral prefrontal cortex (DLPFC), dorsomedial PFC, posterior cingulate, precuneus, and angular gyrus. Furthermore, the reduced volume of the DLPFC and precuneus mediated the relationship between trust and depressive symptoms. These findings contribute to understanding social- and neural-markers of depression vulnerability and may inform the development of social interventions to prevent pathological depression.

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

Learned uncertainty: The free energy principle in anxiety

Generalized anxiety disorder is among the world’s most prevalent psychiatric disorders and often manifests as persistent and difficult to control apprehension. Despite its prevalence, there is no integrative, formal model of how anxiety and anxiety disorders arise. Here, we offer a perspective derived from the free energy principle; one that shares similarities with established constructs such as learned helplessness. Our account is simple: anxiety can be formalized as learned uncertainty. A biological system, having had persistent uncertainty in its past, will expect uncertainty in its future, irrespective of whether uncertainty truly persists. Despite our account’s intuitive simplicity—which can be illustrated with the mere flip of a coin—it is grounded within the free energy principle and hence situates the formation of anxiety within a broader explanatory framework of biological self-organization and self-evidencing. We conclude that, through conceptualizing anxiety within a framework of working generative models, our perspective might afford novel approaches in the clinical treatment of anxiety and its key symptoms.

[Multidimensional and computational theory of mood]

What is mood? Despite its crucial place in psychiatric nosography and cognitive science, it is still difficult to delimit its conceptual ground. The distinction between emotion and mood is ambiguous: mood is often presented as an affective state that is more prolonged and less intense than emotion, or as an affective polarity distinguishing high and low mood swinging around a baseline. However, these definitions do not match the clinical reality of mood disorders such as unipolar depression and bipolar disorder, and do not allow us to understand the effect of mood on behaviour, perception and cognition. In this paper, we propose a multidimensional and computational theory of mood inspired by contemporary hypotheses in theoretical neuroscience and philosophy of emotion. After suggesting an operational distinction between emotion and mood, we show how a succession of emotions can cumulatively generate congruent mood over time, making mood an emerging state from emotion. We then present how mood determines mental and behavioral states when interacting with the environment, constituting a dispositional state of emotion, perception, belief, and action. Using this theoretical framework, we propose a computational representation of the emerging and dispositional dimensions of mood by formalizing mood as a layer of third-order Bayesian beliefs encoding the precision of emotion, and regulated by prediction errors associated with interoceptive predictions. Finally, we show how this theoretical framework sheds light on the processes involved in mood disorders, the emergence of mood congruent beliefs, or the mechanisms of antidepressant treatments in clinical psychiatry.

Beauty and Uncertainty as Transformative Factors: A Free Energy Principle Account of Aesthetic Diagnosis and Intervention in Gestalt Psychotherapy

Drawing from field theory, Gestalt therapy conceives psychological suffering and psychotherapy as two intentional field phenomena, where unprocessed and chaotic experiences seek the opportunity to emerge and be assimilated through the contact between the patient and the therapist (i.e., the intentionality of contacting). This therapeutic approach is based on the therapist’s aesthetic experience of his/her embodied presence in the flow of the healing process because (1) the perception of beauty can provide the therapist with feedback on the assimilation of unprocessed experiences; (2) the therapist’s attentional focus on intrinsic aesthetic diagnostic criteria can facilitate the modification of rigid psychopathological fields by supporting the openness to novel experiences. The aim of the present manuscript is to review recent evidence from psychophysiology, neuroaesthetic research, and neurocomputational models of cognition, such as the free energy principle (FEP), which support the notion of the therapeutic potential of aesthetic sensibility in Gestalt psychotherapy. Drawing from neuroimaging data, psychophysiology and recent neurocognitive accounts of aesthetic perception, we propose a novel interpretation of the sense of beauty as a self-generated reward motivating us to assimilate an ever-greater spectrum of sensory and affective states in our predictive representation of ourselves and the world and supporting the intentionality of contact. Expecting beauty, in the psychotherapeutic encounter, can help therapists tolerate uncertainty avoiding impulsive behaviours and to stay tuned to the process of change.

Augmenting Human Selves Through Artificial Agents – Lessons From the Brain

Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for functional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI – but typically these tasks do not target higher-level human cognitive abilities, such as consciousness or morality; these are left to the realm of so-called “strong AI” or “artificial consciousness.” In this paper, we focus on how a machine can augment humans rather than do what they do, and we extend this beyond AGI-style tasks to augmenting peculiarly personal human capacities, such as wellbeing and morality. We base this proposal on associating such capacities with the “self,” which we define as the “environment-agent nexus”; namely, a fine-tuned interaction of brain with environment in all its relevant variables. We consider richly adaptive architectures that have the potential to implement this interaction by taking lessons from the brain. In particular, we suggest conjoining the free energy principle (FEP) with the dynamic temporo-spatial (TSD) view of neuro-mental processes. Our proposed integration of FEP and TSD – in the implementation of artificial agents – offers a novel, expressive, and explainable way for artificial agents to adapt to different environmental contexts. The targeted applications are broad: from adaptive intelligence augmenting agents (IA’s) that assist psychiatric self-regulation to environmental disaster prediction and personal assistants. This reflects the central role of the mind and moral decision-making in most of what we do as humans.

Relations between emotion regulation strategies and affect in daily life: A systematic review and meta-analysis of studies using ecological momentary assessments

Emotion regulation (ER) is a central target in the study of psychological and neurobiological processes of emotions for numerous psychological disorders. Ecological momentary assessments, overcoming retrospective self-reports, allow a better understanding of the relation between the use of ER strategies and daily life affective experiences. A systematic review and meta-analyses of studies testing these relations through experience sampling methods (ESM) and daily diaries were conducted. ESM studies showed significant large effect sizes in contemporaneous relations between negative affect (NA) and rumination, suppression, and worry, and in both contemporaneous and prospective relations between positive affect (PA) and reappraisal; medium effect sizes in prospective relations between NA and rumination, and PA and distraction; and a small effect size in the prospective relation between NA and suppression. Daily diary studies showed significant large effect sizes in contemporaneous relations between NA and rumination and suppression, and in both contemporaneous and prospective relations between PA and reappraisal; medium effect sizes in contemporaneous relations between PA and acceptance, and problem-solving; and a small effect size in the prospective relation between NA and reappraisal. These findings shed light on the temporal relations between the use of ER strategies and affective experiences and highlight conceptual and methodological limitations in the field.

Deficient prefrontal-amygdalar connectivity underlies inefficient face processing in adolescent major depressive disorder

Adolescence represents a critical developmental period where the prevalence of major depressive disorder (MDD) increases. Aberrant emotion processing is a core feature of adolescent MDD that has been associated with functional alterations within the prefrontal-amygdala circuitry. In this study, we tested cognitive and neural mechanisms of emotional face processing in adolescents with MDD utilizing a combination of computational modeling and neuroimaging. Thirty adolescents with MDD (age: M = 16.1 SD = 1.4, 20 females) and 33 healthy controls (age: M = 16.2 SD = 1.9, 20 females) performed a dynamic face- and shape-matching task. A linear ballistic accumulator model was fit to the behavioral data to study differences in evidence accumulation. We used dynamic causal modeling (DCM) to study effective connectivity in the prefrontal-amygdala network to reveal the neural underpinnings of cognitive impairments while performing the task. Face processing efficiency was reduced in the MDD group and most pronounced for ambiguous faces with neutral emotional expressions. Critically, this reduction was related to increased deactivation of the subgenual anterior cingulate (sgACC). Connectivity analysis showed that MDD exhibited altered functional coupling in a distributed network spanning the fusiform face area-lateral prefrontal cortex-sgACC and the sgACC-amygdala pathway. Our results suggest that MDD is related to impairments of processing nuanced facial expressions. Distributed dysfunctional coupling in the face processing network might result in inefficient evidence sampling and inappropriate emotional responses contributing to depressive symptomatology. Our study provides novel insights in the characterization of brain function in adolescents with MDD that strongly emphasize the critical role of aberrant prefrontal-amygdala interactions during emotional face processing.

I overthink—Therefore I am not: An active inference account of altered sense of self and agency in depersonalisation disorder

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This paper examines potential mechanisms behind atypical sense of self and agency in Depersonalisation Disorder (DPD).

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Using Active Inference, we argue that aberrant somatosensory attenuation and precision weighting underlies DPD.

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Failures of somatosensory attenuation may disrupt the sense of agency and control over one’s perceptions and actions.

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In DPD, individuals may believe ‘another agent’ is controlling their thoughts, perceptions or actions.

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Unlike psychosis however, in DPD the individual maintains the full insight that the ‘other agent’ is ‘me’ (the self).

This paper considers the phenomenology of depersonalisation disorder, in relation to predictive processing and its associated pathophysiology. To do this, we first establish a few mechanistic tenets of predictive processing that are necessary to talk about phenomenal transparency, mental action, and self as subject. We briefly review the important role of ‘predicting precision’ and how this affords mental action and the loss of phenomenal transparency. We then turn to sensory attenuation and the phenomenal consequences of (pathophysiological) failures to attenuate or modulate sensory precision. We then consider this failure in the context of depersonalisation disorder. The key idea here is that depersonalisation disorder reflects the remarkable capacity to explain perceptual engagement with the world via the hypothesis that “I am an embodied perceiver, but I am not in control of my perception”. We suggest that individuals with depersonalisation may believe that ‘another agent’ is controlling their thoughts, perceptions or actions, while maintaining full insight that the ‘other agent’ is ‘me’ (the self). Finally, we rehearse the predictions of this formal analysis, with a special focus on the psychophysical and physiological abnormalities that may underwrite the phenomenology of depersonalisation.

Piloting the Update: The Use of Therapeutic Relationship for Change - A Free Energy Account

We apply the Free Energy Principle (FEP) to cognitive behavioral therapy (CBT). FEP describes the basic functioning of the brain as a predictive organ and states that any self-organizing system that is in equilibrium with its environment must minimize its free energy. Based on an internal model of the world and the self, predictions-so-called priors-are created, which are matched with the information input. The sum of prediction errors corresponds to the Free Energy, which must be minimized. Internal models can be identified with the cognitive-affective schemas of the individual that has become dysfunctional in patients. The role of CBT in this picture is to help the patient update her/his priors. They have evolved in learning history and no longer provide adaptive predictions. We discuss the process of updating in terms of the exploration-exploitation dilemma. This consists of the extent to which one relies on what one already has, i.e., whether one continues to maintain and "exploit" one's previous priors ("better safe than sorry") or whether one does explore new data that lead to an update of priors. Questioning previous priors triggers stress, which is associated with increases in Free Energy in short term. The role of therapeutic relationship is to buffer this increase in Free Energy, thereby increasing the level of perceived safety. The therapeutic relationship is represented in a dual model of affective alliance and goal attainment alliance and is aligned with FEP. Both forms of alliance support exploration and updating of priors. All aspects are illustrated with the help of a clinical case example.

Integrating Evolutionary, Cultural, and Computational Psychiatry: A Multilevel Systemic Approach

This paper proposes an integrative perspective on evolutionary, cultural and computational approaches to psychiatry. These three approaches attempt to frame mental disorders as multiscale entities and offer modes of explanations and modeling strategies that can inform clinical practice. Although each of these perspectives involves systemic thinking, each is limited in its ability to address the complex developmental trajectories and larger social systemic interactions that lead to mental disorders. Inspired by computational modeling in theoretical biology, this paper aims to integrate the modes of explanation offered by evolutionary, cultural and computational psychiatry in a multilevel systemic perspective. We apply the resulting Evolutionary, Cultural and Computational (ECC) model to Major Depressive Disorder (MDD) to illustrate how this integrative approach can guide research and practice in psychiatry.

Theory of mind in dysphoric and non--dysphoric adults: An ERP study of true-- and false--belief reasoning

Theory of mind (ToM) - the understanding that others' behaviors are connected with internal mental states - is an important part of everyday social cognition. There is increasing behavioral evidence that ToM reasoning can be affected by mood. To gain insight into the ways sad mood may affect the underlying mechanisms of ToM reasoning, we recorded event-related brain potentials (ERPs) as dysphoric (N = 16) and non-dysphoric (N = 24) participants reasoned about a protagonist's true or false beliefs about an object's location. Results showed significant group effects on early components of the ERP - individuals in the dysphoric group showed greater amplitudes for the anterior N1 and N2/P2 components relative to those in the non-dysphoric group. Later in the ERP, non-dysphoric individuals showed evidence of neurocognitive dissociations between true and false belief. Dysphoric individuals, however, did not show evidence for these later dissociations. This evidence suggests that dysphoria may be associated with effortful reasoning about other's mental states, even when that effort is not necessary (i.e., when reasoning about true beliefs). We discuss the implications of these findings for understanding how mood affects ToM reasoning and for how especially deliberative ToM processing in dysphoria may lead to social difficulties.

Instability and Uncertainty Are Critical for Psychotherapy: How the Therapeutic Alliance Opens Us Up

Tschacher and Haken have recently applied a systems-based approach to modeling psychotherapy process in terms of potentially beneficial tendencies toward deterministic as well as chaotic forms of change in the client's behavioral, cognitive and affective experience during the course of therapy. A chaotic change process refers to a greater exploration of the states that a client can be in, and it may have a potential positive role to play in their development. A distinction is made between on the one hand, specific instances of instability which are due to techniques employed by the therapist, and on the other, a more general instability which is due to the therapeutic relationship, and a key, necessary result of a successful therapeutic alliance. Drawing on Friston's systems-based model of free energy minimization and predictive coding, it is proposed here that the increase in the instability of a client's functioning due to therapy can be conceptualized as a reduction in the precisions (certainty) with which the client's prior beliefs about themselves and their world, are held. It is shown how a good therapeutic alliance (characterized by successful interpersonal synchrony of the sort described by Friston and Frith) results in the emergence of a new hierarchical level in the client's generative model of themselves and their relationship with the world. The emergence of this new level of functioning permits the reduction of the precisions of the client's priors, which allows the client to 'open up': to experience thoughts, emotions and experiences they did not have before. It is proposed that this process is a necessary precursor to change due to psychotherapy. A good consilience can be found between this approach to understanding the role of the therapeutic alliance, and the role of epistemic trust in psychotherapy as described by Fonagy and Allison. It is suggested that beneficial forms of instability in clients are an underappreciated influence on psychotherapy process, and thoughts about the implications, as well as situations in which instability may not be beneficial (or potentially harmful) for therapy, are considered.

Environmental Contributions to Anhedonia

Anhedonia is a core feature of psychopathological conditions that have recent exposure to stress and trauma as central to their etiology. Indeed, evolutionary accounts of depression suggest that decreased motivation to pursue reward may be an adaptive strategy in the face of social stress, in particular, as it may serve to defuse interpersonal conflict. Through a review of rodent models and research with humans, we show that exposure to stress, particularly when it is chronic, repeated, and/or involves themes of social rejection or defeat, is consistently associated with reduced hedonic capacity ("liking"), motivation to pursue reward ("wanting"), and ability to learn from reward ("reward learning"). Further, across rodent and human research, there is evidence that females show greater stress-induced blunting of reward processing than males. In humans, this sex difference emerges most strongly when examining individual differences in the stress response rather than group differences in stress exposure. We discuss the implications of these findings for understanding the etiology of, and sex differences in, stress-related psychopathology, including depression and addiction.

The Predictive Dynamics of Happiness and Well-Being

We offer an account of mental health and well-being using the predictive processing framework (PPF). According to this framework, the difference between mental health and psychopathology can be located in the goodness of the predictive model as a regulator of action. What is crucial for avoiding the rigid patterns of thinking, feeling and acting associated with psychopathology is the regulation of action based on the valence of affective states. In PPF, valence is modelled as error dynamics—the change in prediction errors over time . Our aim in this paper is to show how error dynamics can account for both momentary happiness and longer term well-being. What will emerge is a new neurocomputational framework for making sense of human flourishing.

Multidimensional Analysis of Major Depression: Association Between BDNF Methylation, Psychosocial and Cognitive Domains

Major Depression is a complex disorder with a growing incidence worldwide and multiple variables have been associated with its etiology. Nonetheless, its diagnosis is continually changing and the need to understand it from a multidimensional perspective is clear. The purpose of this study was to identify risk factors for depression in a case-control study with 100 depressive inpatients and 87 healthy controls. A multivariate logistic regression analysis was performed including psychosocial factors, cognitive maladaptive schema domains, and specific epigenetic marks (BDNF methylation levels at five CpG sites in promoter IV). A family history of depression, the cognitive schemas of impaired autonomy/performance, impaired limits, other-directedness, and the methylation level of a specific CpG site were identified as predictors. Interestingly, we found a mediating effect of those cognitive schemas in the relationship between childhood maltreatment and depression. Also, we found that depressive patients exhibited hypomethylation in a CpG site of BDNF promoter IV, which adds to the current discussion about the role of methylation in depression. We highlight that determining the methylation of a specific region of a single gene offers the possibility of accessing a highly informative an easily measurable variable, which represents benefits for diagnosis. Following complete replication and validation on larger samples, models like ours could be applicable as additional diagnostic tools in the clinical context.

Differential Impact of Anxious Misery Psychopathology on Multiple Representations of the Functional Connectome

Background

One aim of characterizing dimensional psychopathology is associating different domains of affective dysfunction with brain circuitry. The functional connectome, as measured by functional magnetic resonance imaging, can be modeled and associated with psychopathology through multiple methods; some methods assess univariate relationships while others summarize broad patterns of activity. It remains unclear whether different dimensions of psychopathology require different representations of the connectome to generate reproducible associations.

Methods

Patients experiencing anxious misery symptomology (depression, anxiety, and trauma; n = 192) received resting-state functional magnetic resonance imaging scans. Three modeling approaches (seed-based correlation analysis, edgewise regression, and brain basis set modeling), each relying on increasingly broader representations of the functional connectome, were used to associate connectivity patterns with six data-driven dimensions of psychopathology: anxiety sensitivity, anxious arousal, rumination, anhedonia, insomnia, and negative affect. To protect against overfitting, 50 participants were held out in a testing dataset, leaving 142 participants as training data.

Results

Different modeling approaches varied in the extent to which they could model different symptom dimensions: seed-based correlation analysis failed to reproducibly model any symptoms, subsets of the connectome (edgewise regression) were sufficient to model insomnia and anxious arousal, and broad representations of the entire connectome (brain basis set modeling) were necessary to model negative affect and ruminative thought.

Conclusions

These results indicate that different methods of representing the functional connectome differ in the degree that they can model different symptom dimensions, highlighting the potential sufficiency of subsets of connections for some dimensions and the necessity of connectome-wide approaches in others.

Converging evidence for enduring perceptions of low social status in individuals in remission from depression

BACKGROUND

The risk of depressive relapse and recurrence is associated with social risk factors that may be amplified by a submissive socio-cognitive profile.

METHODS

In Study 1 we aimed to identify perceptions of low social status in a community sample (N = 613) with a self-reported history of mental health difficulties (n = 232) and, more specifically in Study 2 (N = 122), in individuals in clinical remission from depression (n = 18), relative to a never-depressed control group (n = 64), and relative to a group experiencing a current depressive episode (n = 40).

RESULTS

In Study 1, a total of 225 of the 232 participants in the self-reported mental health difficulties group opted to provide further information regarding their mental health history, of whom 153 (68%) reported a history of anxiety, 168 (74.7%) reported a history of depression, and 13 (5.8%) reported an unspecified mental health history. Elevated depressive symptoms were associated with perceptions of low social status which significantly differed between individuals with and without a self-reported history of mental health difficulties. In Study 2 we found enduring perceptions of low social status in remitted depressed individuals.

LIMITATIONS

We were unable to discern between historical or current clinical diagnosis in the community sample of Study 1, as we were reliant on self-report. We were unable to explore the effects of medication or causal relationships between depressive symptoms and social status as the studies were cross-sectional in nature.

CONCLUSIONS

These findings suggest that evolutionarily rooted socio-cognitive profiles could impact affiliative processes and may confer increased vulnerability to future depressive episodes.

Trust as Extended Control: Human-Machine Interactions as Active Inference

In order to interact seamlessly with robots, users must infer the causes of a robot's behavior-and be confident about that inference (and its predictions). Hence, trust is a necessary condition for human-robot collaboration (HRC). However, and despite its crucial role, it is still largely unknown how trust emerges, develops, and supports human relationship to technological systems. In the following paper we review the literature on trust, human-robot interaction, HRC, and human interaction at large. Early models of trust suggest that it is a trade-off between benevolence and competence; while studies of human to human interaction emphasize the role of shared behavior and mutual knowledge in the gradual building of trust. We go on to introduce a model of trust as an agent' best explanation for reliable sensory exchange with an extended motor plant or partner. This model is based on the cognitive neuroscience of active inference and suggests that, in the context of HRC, trust can be casted in terms of virtual control over an artificial agent. Interactive feedback is a necessary condition to the extension of the trustor's perception-action cycle. This model has important implications for understanding human-robot interaction and collaboration-as it allows the traditional determinants of human trust, such as the benevolence and competence attributed to the trustee, to be defined in terms of hierarchical active inference, while vulnerability can be described in terms of information exchange and empowerment. Furthermore, this model emphasizes the role of user feedback during HRC and suggests that boredom and surprise may be used in personalized interactions as markers for under and over-reliance on the system. The description of trust as a sense of virtual control offers a crucial step toward grounding human factors in cognitive neuroscience and improving the design of human-centered technology. Furthermore, we examine the role of shared behavior in the genesis of trust, especially in the context of dyadic collaboration, suggesting important consequences for the acceptability and design of human-robot collaborative systems.

Depression as a Failed Anxiety: The Continuum of Precision-Weighting Dysregulation in Affective Disorders

Depressive, anxiety, and trauma-related disorders have many symptoms in common such as unstable mood, high anxiety, sleep disturbance, impaired concentration among others. This degeneracy creates ambiguity in classifying psychiatric disorders and raises the question of their categorical vs. dimensional nature. Consequently, such ambiguity presents a dilemma for choosing diagnosis-specific vs. trans-diagnostic therapies. In this paper, I build on a theory that considers affective disorders on the continuum of stress response from normative to traumatic. Using an integrative evolutionary-stress response-predictive processing (iESP) model, I arrange affective disorders on a continuum of precision-weighting dysregulation, where depressive, anxiety and trauma-induced disorders have a characteristic pattern of precision-weighting dysregulation. I specifically address the relationship between anxiety and depressive stress responses, exploring the role of anxiety in the dynamics of depressive stress response and the resulting high co-occurrence of anxiety and depression symptoms. Finally, I discuss the model's relevance for therapy of depression.

Altered hedonic, novelty-, stress- and D-amphetamine-induced response due to social isolation in peripuberty

Reduction in direct social contact with peers during early adolescence is thought to be a risk factor for an increase in depressive symptoms, but there is still no clear evidence to suggest early behavioral manifestations and their association with the later outcome of social distancing during this period. To address this question, we used social isolation paradigm in peripubertal rats as the rodent model of adolescence. The litter was an experimental unit. On postnatal day 29, each litter gave group-housed and single-housed males, which were reared and tested one week and two weeks thereafter. Psychomotor/emotional response to novelty in exploration-based tasks, behavioral and neuronal responses to the drug reward (D-amphetamine), motivation/hedonic behavior, physiological and response to physiological stress were examined. Social isolation in peripubertal rats manifested through: hyper-reactivity/agitation and the state anxiety/risk-taking at an early stage; reduced behavioral response to D-amphetamine and altered neural processing of this stimulus, at a later stage; consummatory hypohedonia that deepened over time without changing the motivation to eat; unchanged body weight gain and resting blood corticosterone, cortisol and glucose levels over time; altered blood biochemistry (silenced corticosterone and increased glucose) due to overnight fasting only at an early stage. Our results highlight that the outcome of reduced direct social contact with peers during peripuberty is dynamic, with the cluster of atypical early symptoms that evolve into the syndrome that is delicate for assessment through routinely measurable behavior and biomarkers of stress, but with progressive consummatory hypohedonia and unaffected motivation to eat as stable marks.

Why Depressed Mood is Adaptive: A Numerical Proof of Principle for an Evolutionary Systems Theory of Depression

We provide a proof of principle for an evolutionary systems theory (EST) of depression. This theory suggests that normative depressive symptoms counter socioenvironmental volatility by increasing interpersonal support via social signalling and that this response depends upon the encoding of uncertainty about social contingencies, which can be targeted by neuromodulatory antidepressants. We simulated agents that committed to a series of decisions in a social two-arm bandit task before and after social adversity, which precipitated depressive symptoms. Responses to social adversity were modelled under various combinations of social support and pharmacotherapy. The normative depressive phenotype responded positively to social support and simulated treatments with antidepressants. Attracting social support and administering antidepressants also alleviated anhedonia and social withdrawal, speaking to improvements in interpersonal relationships. These results support the EST of depression by demonstrating that following adversity, normative depressed mood preserved social inclusion with appropriate interpersonal support or pharmacotherapy.

Facilitating improvements in young people's social relationships to prevent or treat depression: A review of empirically supported interventions

Interpersonal difficulties are often implicated in the onset of depressive disorders, and typically exacerbate depressive symptoms. This is particularly true for young people, given rapid changes in, and the increased importance of, their social relationships. The purpose of this narrative review was to identify empirically supported interventions that aim to prevent or treat depression in young people by facilitating improvements in their social environment. We conducted a search of controlled trials, systematic reviews and meta-analyses of such interventions, published between 1980 and June 2020. Our literature search and interpretation of results was informed by consultations with clinical experts and youth consumers and advocates. A number of promising approaches were identified with respect to prevention and treatment. Preliminary evidence was identified suggesting that school- and Internet-based approaches present a viable means to prevent the worsening of depressive symptoms in young people. Notably, delivering interpersonal psychotherapy-adolescent skills training (IPT-AST) in schools appears to be a promising early intervention strategy for young people at risk of full-threshold depressive disorder. In terms of treating depressive disorders in young people, there is strong evidence for the efficacy of interpersonal psychotherapy for adolescents (IPT-A), and preliminary evidence in favour of attachment-based family therapy (ABFT). Results are discussed with respect to recommendations for future research and practice.

Safety learning and the Pavlovian conditioned inhibition of fear in humans: Current state and future directions

Safety learning occurs when an otherwise neutral stimulus comes to signal the absence of threat, allowing organisms to use safety information to inhibit fear and anxiety in nonthreatening environments. Although it continues to emerge as a topic of relevance in biological and clinical psychology, safety learning remains inconsistently defined and under-researched. Here, we analyse the Pavlovian conditioned inhibition paradigm and its application to the study of safety learning in humans. We discuss existing studies; address outstanding theoretical considerations; and identify prospects for its further application. Though Pavlovian conditioned inhibition presents a theoretically sound model of safety learning, it has been investigated infrequently, with decade-long interims between some studies, and notable methodological variability. Consequently, we argue that the full potential of conditioned inhibition as a model for human safety learning remains untapped, and propose that it could be revisited as a framework for addressing timely questions in the behavioural and clinical sciences.

Attentional bias towards interpersonal aggression in depression - an eye movement study

Depressed individuals exhibit an attentional bias towards mood-congruent stimuli, yet evidence for biased processing of threat-related information in human interaction remains scarce. Here, we tested whether an attentional bias towards interpersonally aggressive pictures over interpersonally neutral pictures could be observed to a greater extent in depressed participants than in control participants. Eye movements were recorded while the participants freely viewed visually matched interpersonally aggressive and neutral pictures, which were presented in pairs. Across the groups, participants spent more time looking at neutral pictures than at aggressive pictures, probably reflecting avoidance behavior. When the participants could anticipate the stimulus valence, depressed participants - but not controls - showed an early attentional bias towards interpersonally aggressive pictures, as indexed by their longer first fixation durations on aggressive pictures than on neutral pictures. Our results thus preliminarily suggest both an early attentional bias towards interpersonal aggression, which is present, in depressed participants, also when aggression contents are anticipated, and a later attentional avoidance of aggression. The early depression-related bias in information processing may have maladaptive effects on the way depressed individuals perceive and function in social interaction and can, therefore, maintain depressed mood.

Immunoceptive inference: why are psychiatric disorders and immune responses intertwined?

There is a steadily growing literature on the role of the immune system in psychiatric disorders. So far, these advances have largely taken the form of correlations between specific aspects of inflammation (e.g. blood plasma levels of inflammatory markers, genetic mutations in immune pathways, viral or bacterial infection) with the development of neuropsychiatric conditions such as autism, bipolar disorder, schizophrenia and depression. A fundamental question remains open: why are psychiatric disorders and immune responses intertwined? To address this would require a step back from a historical mind-body dualism that has created such a dichotomy. We propose three contributions of active inference when addressing this question: translation, unification, and simulation. To illustrate these contributions, we consider the following questions. Is there an immunological analogue of sensory attenuation? Is there a common generative model that the brain and immune system jointly optimise? Can the immune response and psychiatric illness both be explained in terms of self-organising systems responding to threatening stimuli in their external environment, whether those stimuli happen to be pathogens, predators, or people? Does false inference at an immunological level alter the message passing at a psychological level (or vice versa) through a principled exchange between the two systems?

Predictive Processing in Cognitive Robotics: A Review

Predictive processing has become an influential framework in cognitive sciences. This framework turns the traditional view of perception upside down, claiming that the main flow of information processing is realized in a top-down, hierarchical manner. Furthermore, it aims at unifying perception, cognition, and action as a single inferential process. However, in the related literature, the predictive processing framework and its associated schemes, such as predictive coding, active inference, perceptual inference, and free-energy principle, tend to be used interchangeably. In the field of cognitive robotics, there is no clear-cut distinction on which schemes have been implemented and under which assumptions. In this letter, working definitions are set with the main aim of analyzing the state of the art in cognitive robotics research working under the predictive processing framework as well as some related nonrobotic models. The analysis suggests that, first, research in both cognitive robotics implementations and nonrobotic models needs to be extended to the study of how multiple exteroceptive modalities can be integrated into prediction error minimization schemes. Second, a relevant distinction found here is that cognitive robotics implementations tend to emphasize the learning of a generative model, while in nonrobotics models, it is almost absent. Third, despite the relevance for active inference, few cognitive robotics implementations examine the issues around control and whether it should result from the substitution of inverse models with proprioceptive predictions. Finally, limited attention has been placed on precision weighting and the tracking of prediction error dynamics. These mechanisms should help to explore more complex behaviors and tasks in cognitive robotics research under the predictive processing framework.

Deeply Felt Affect: The Emergence of Valence in Deep Active Inference

The positive-negative axis of emotional valence has long been recognized as fundamental to adaptive behavior, but its origin and underlying function have largely eluded formal theorizing and computational modeling. Using deep active inference, a hierarchical inference scheme that rests on inverting a model of how sensory data are generated, we develop a principled Bayesian model of emotional valence. This formulation asserts that agents infer their valence state based on the expected precision of their action model-an internal estimate of overall model fitness ("subjective fitness"). This index of subjective fitness can be estimated within any environment and exploits the domain generality of second-order beliefs (beliefs about beliefs). We show how maintaining internal valence representations allows the ensuing affective agent to optimize confidence in action selection preemptively. Valence representations can in turn be optimized by leveraging the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). AC tracks changes in fitness estimates and lends a sign to otherwise unsigned divergences between predictions and outcomes. We simulate the resulting affective inference by subjecting an in silico affective agent to a T-maze paradigm requiring context learning, followed by context reversal. This formulation of affective inference offers a principled account of the link between affect, (mental) action, and implicit metacognition. It characterizes how a deep biological system can infer its affective state and reduce uncertainty about such inferences through internal action (i.e., top-down modulation of priors that underwrite confidence). Thus, we demonstrate the potential of active inference to provide a formal and computationally tractable account of affect. Our demonstration of the face validity and potential utility of this formulation represents the first step within a larger research program. Next, this model can be leveraged to test the hypothesized role of valence by fitting the model to behavioral and neuronal responses.

Representation Wars: Enacting an Armistice Through Active Inference

Over the last 30 years, representationalist and dynamicist positions in the philosophy of cognitive science have argued over whether neurocognitive processes should be viewed as representational or not. Major scientific and technological developments over the years have furnished both parties with ever more sophisticated conceptual weaponry. In recent years, an enactive generalization of predictive processing – known as active inference – has been proposed as a unifying theory of brain functions. Since then, active inference has fueled both representationalist and dynamicist campaigns. However, we believe that when diving into the formal details of active inference, one should be able to find a solution to the war; if not a peace treaty, surely an armistice of a sort. Based on an analysis of these formal details, this paper shows how both representationalist and dynamicist sensibilities can peacefully coexist within the new territory of active inference.

Recent advances in the application of predictive coding and active inference models within clinical neuroscience

Research in clinical neuroscience is founded on the idea that a better understanding of brain (dys)function will improve our ability to diagnose and treat neurological and psychiatric disorders. In recent years, neuroscience has converged on the notion that the brain is a 'prediction machine,' in that it actively predicts the sensory input that it will receive if one or another course of action is chosen. These predictions are used to select actions that will (most often, and in the long run) maintain the body within the narrow range of physiological states consistent with survival. This insight has given rise to an area of clinical computational neuroscience research that focuses on characterizing neural circuit architectures that can accomplish these predictive functions, and on how the associated processes may break down or become aberrant within clinical conditions. Here, we provide a brief review of examples of recent work on the application of predictive processing models of brain function to study clinical (psychiatric) disorders, with the aim of highlighting current directions and their potential clinical utility. We offer examples of recent conceptual models, formal mathematical models, and applications of such models in empirical research in clinical populations, with a focus on making this material accessible to clinicians without expertise in computational neuroscience. In doing so, we aim to highlight the potential insights and opportunities that understanding the brain as a prediction machine may offer to clinical research and practice.

A continuum hypothesis of psychotomimetic rapid antidepressants

Ketamine, classical psychedelics and sleep deprivation are associated with rapid effects on depression. Interestingly, these interventions also have common psychotomimetic actions, mirroring aspects of psychosis such as an altered sense of self, perceptual distortions and distorted thinking. This raises the question whether these interventions might be acute antidepressants through the same mechanisms that underlie some of their psychotomimetic effects. That is, perhaps some symptoms of depression can be understood as occupying the opposite end of a spectrum where elements of psychosis can be found on the other side. This review aims at reviewing the evidence underlying a proposed continuum hypothesis of psychotomimetic rapid antidepressants, suggesting that a range of psychotomimetic interventions are also acute antidepressants as well as trying to explain these common features in a hierarchical predictive coding framework, where we hypothesise that these interventions share a common mechanism by increasing the flexibility of prior expectations. Neurobiological mechanisms at play and the role of different neuromodulatory systems affected by these interventions and their role in controlling the precision of prior expectations and new sensory evidence will be reviewed. The proposed hypothesis will also be discussed in relation to other existing theories of antidepressants. We also suggest a number of novel experiments to test the hypothesis and highlight research areas that could provide further insights, in the hope to better understand the acute antidepressant properties of these interventions.