Computational models of the "active self" and its disturbances in schizophrenia

The paradox of the self-studying brain

The paradox of a brain trying to study itself presents a conundrum, raising questions about self-reference, consciousness, psychiatric disorders, and the boundaries of scientific inquiry. By which means can this complex organ shift the focus of study towards itself? We aim at unpacking the intricacies of this paradox. Historically, this question has been raised by philosophers under different frameworks. Thanks to the development of novel techniques to study the brain on a functional and structural level - as well as neurostimulation protocols that can modulate its activity in selected areas - we now possess advanced methods to progress this intricate inquiry. Nonetheless, the broader implications of the brain's pursuit of understanding itself remain unclear to this day. Ultimately, the need to employ both perception and introspection has led to different formulations of consciousness. This creates a challenge, as evidence supporting one formulation does not necessarily support the other. By deconstructing the paradoxical nature of self understanding - from a philosophical and neuroscientific point of view - we may gain insights into the human brain, which could lead to improved understanding of self-awareness and consciousness.

Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging

After more than 30 years since its inception, the utility of brain imaging for understanding and diagnosing mental illnesses is in doubt, receiving well-grounded criticisms from clinical practitioners. Symptom-based correlational approaches have struggled to provide psychiatry with reliable brain-imaging metrics. However, the emergence of computational psychiatry has paved a new path not only for understanding the psychopathology of mental illness but also to provide practical tools for clinical practice in terms of computational metrics, specifically computational phenotypes. However, these phenotypes still lack sufficient test-retest reliability. In this review, we describe recent works revealing that mind and brain-related computational phenotypes show structural (not random) variation over time, longitudinal changes. Furthermore, we show that these findings suggest that understanding the causes of these changes will improve the construct validity of the phenotypes with an ensuing increase in test-retest reliability. We propose that the active inference framework offers a general-purpose approach for causally understanding these longitudinal changes by incorporating brain imaging as observations within partially observable Markov decision processes.

[Ethical challenges of telepsychiatry]

Telepsychiatry is shifting the focus of psychiatry and psychotherapy from personal interaction and relationship building to communication with technological mediators in the form of telecommunication, virtual reality (VR), social robots or artificial intelligence (AI). This article discusses the opportunities and risks of new technologies in psychiatric treatment, taking into account the principles of medical ethics. Telepsychiatric treatments can promote self-determination in the home environment as opposed to institutionalisation but carry the risk of uncontrolled data sharing. They harbour risks of potential harm such as social isolation, negative effects on the doctor-patient relationship and long-term changes in patient behaviour through, for example, VR. In terms of justice, demographic and psychopathological factors could result in unequal access to telepsychiatry, with questionable sustainability effects. The anonymity of telepsychiatry threatens alienation, while moral considerations cannot be replaced by AI for the time being. It is obligatory to carefully weigh up the benefits and risks of telepsychiatry, but especially in rural areas they could optimise care.Precisely because of ethical concerns, further intensive research is necessary to weigh up the risks and benefits. The development of telepsychiatric systems requires transdisciplinary co-operation. At a societal level, the significance of technologies as a supplement or replacement for the doctor-patient relationship needs to be discussed.

Synthesizing the temporal self: robotic models of episodic and autobiographical memory

Episodic memories are experienced as belonging to a self that persists in time. We review evidence concerning the nature of human episodic memory and of the sense of self and how these emerge during development, proposing that the younger child experiences a persistent self that supports a subjective experience of remembering. We then explore recent research in cognitive architectures for robotics that has investigated the possibility of forms of synthetic episodic and autobiographical memory. We show that recent advances in generative modeling can support an understanding of the emergence of self and of episodic memory, and that cognitive architectures which include a language capacity are showing progress towards the construction of a narrative self with autobiographical memory capabilities for robots. We conclude by considering the prospects for a more complete model of mental time travel in robotics and the implications of this modeling work for understanding human episodic memory and the self in time. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

The Human Affectome

Over the last decades, theoretical perspectives in the interdisciplinary field of the affective sciences have proliferated rather than converged due to differing assumptions about what human affective phenomena are and how they work. These metaphysical and mechanistic assumptions, shaped by academic context and values, have dictated affective constructs and operationalizations. However, an assumption about the purpose of affective phenomena can guide us to a common set of metaphysical and mechanistic assumptions. In this capstone paper, we home in on a nested teleological principle for human affective phenomena in order to synthesize metaphysical and mechanistic assumptions. Under this framework, human affective phenomena can collectively be considered algorithms that either adjust based on the human comfort zone (affective concerns) or monitor those adaptive processes (affective features). This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope the Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research.

Enhanced Instructed Fear Learning in Delusion-Proneness

Psychosis is associated with distorted perceptions and deficient bottom-up learning such as classical fear conditioning. This has been interpreted as reflecting imprecise priors in low-level predictive coding systems. Paradoxically, overly strong beliefs, such as overvalued beliefs and delusions, are also present in psychosis-associated states. In line with this, research has suggested that patients with psychosis and associated phenotypes rely more on high-order priors to interpret perceptual input. In this behavioural and fMRI study we studied two types of fear learning, i.e., instructed fear learning mediated by verbal suggestions about fear contingencies and classical fear conditioning mediated by low level associative learning, in delusion proneness-a trait in healthy individuals linked to psychotic disorders. Subjects were shown four faces out of which two were coupled with an aversive stimulation (CS+) while two were not (CS-) in a fear conditioning procedure. Before the conditioning, subjects were informed about the contingencies for two of the faces of each type, while no information was given for the two other faces. We could thereby study the effect of both classical fear conditioning and instructed fear learning. Our main outcome variable was evaluative rating of the faces. Simultaneously, fMRI-measurements were performed to study underlying mechanisms. We postulated that instructed fear learning, measured with evaluative ratings, is stronger in psychosis-related phenotypes, in contrast to classical fear conditioning that has repeatedly been shown to be weaker in these groups. In line with our hypothesis, we observed significantly larger instructed fear learning on a behavioural level in delusion-prone individuals (n = 20) compared to non-delusion-prone subjects (n = 23; n = 20 in fMRI study). Instructed fear learning was associated with a bilateral activation of lateral orbitofrontal cortex that did not differ significantly between groups. However, delusion-prone subjects showed a stronger functional connectivity between right lateral orbitofrontal cortex and regions processing fear and pain. Our results suggest that psychosis-related states are associated with a strong instructed fear learning in addition to previously reported weak classical fear conditioning. Given the similarity between nocebo paradigms and instructed fear learning, our results also have an impact on understanding why nocebo effects differ between individuals.

Disembodied Language in Early-Onset Schizophrenia

A recent view on schizophrenia phenomenology underlines the impaired relations between the mind and the body. An aberrant feeling of ipseity may be the real source of suffering of the patients from psychosis and impacts general symptomatology. The disturbed connection between thinking processes and environmental stimuli may lead to language disembodiment. In the study, we aimed to experimentally test the presence of disembodied language and investigate its association with symptoms of psychosis in adolescents diagnosed with early-onset schizophrenia spectrum disorders. Assessment of language embodiment was conducted using the Zabór Verbal Task (ZVT) with concurrent linguistic and clinical assessment using the Thought, Language, and Communication Scale (TLCS) and Positive and Negative Symptoms Scale (PANSS). The study group of patients (n = 31) aged 11-18 years, with the diagnosis of schizophrenia spectrum according to Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) and the International Classification of Diseases (ICD-10) criteria, was compared with a sex- and age-matched healthy control sample (n = 31). Patients with psychosis made more errors in ZVT than healthy controls (p = 0.01) and this parameter did not improve after 6-8 weeks of standard treatment (p = 0.55). A higher number of errors in ZVT were associated with the presence of auditory hallucinations (odds ratio [OR] 1.14; 95% CI 1.02-1.26). ZVT errors coincided with perception disorders, alternatively to the TLCS scores where we observed association with abnormal beliefs. The results of these preliminary studies indicate the value of the phenomenological approach in the diagnosis of schizophrenia spectrum and suggest a potential involvement of language disembodiment in symptomatology.

Regenerative Virtual Therapy: The Use of Multisensory Technologies and Mindful Attention for Updating the Altered Representations of the Bodily Self

The term “regenerative medicine” (RM) indicates an emerging trend in biomedical sciences that aims at replacing, engineering, or regenerating human cells, tissues, or organs to restore or establish normal function. So far, the focus of RM has been the physical body. Neuroscience, however, is now suggesting that mental disorders can be broadly characterized by a dysfunction in the way the brain computes and integrates the representations of the inner and outer body across time [bodily self-consciousness (BSC)]. In this perspective, we proposed a new kind of clinical intervention, i.e., “Regenerative Virtual Therapy” (RVT), which integrates knowledge from different disciplines, from neuroscience to computational psychiatry, to regenerate a distorted or faulty BSC. The main goal of RVT was to use technology-based somatic modification techniques to restructure the maladaptive bodily representations behind a pathological condition. Specifically, starting from a Bayesian model of our BSC (i.e., body matrix), we suggested the use of mindful attention, cognitive reappraisal, and brain stimulation techniques merged with high-rewarding and novel synthetic multisensory bodily experience (i.e., a virtual reality full-body illusion in sync with a low predictabIlity interoceptive modulation) to rewrite a faulty experience of the body and to regenerate the wellbeing of an individual. The use of RVT will also offer an unprecedented experimental overview of the dynamics of our bodily representations, allowing the reverse-engineering of their functioning for hacking them using advanced technologies.