The practice of self-diagnosing, amplified by the spread of psychiatric knowledge through social media, has grown rapidly. Yet, the motivations behind this trend, and, critically, its psychological repercussions remain poorly understood. Self-ascribing a psychiatric label always occurs within a broader narrative context, with narratives serving as essential interpretive tools for understanding oneself and others.In this paper, we identify four principal motivators for people pursuing self-diagnosis, pertaining to 1. waiting time and cost of mental health resources, 2. recognition, 3. identity formation, (...) and 4. destigmatization. We compare these motivators against the backdrops of psychiatric narratives, including DSM-based (operationalism), bio-medical, and Neurodiversity narratives, to evaluate the psychological implications of self-ascriptions. We show that while self-ascription aligns with many motivations, it also carries the risk of essentialism across all analyzed narratives, which can over-simplify and sometimes misrepresent people’s attempts to find meaning through psychiatric labels. Essentialism makes narratives less comprehensive, responsive, and resourceful.We contend that self-ascriptions are not inherently problematic; instead, they show the need for greater focus and understanding in psychiatric practices and the importance of incorporating people’s experiences and perspectives in clinical settings. However, both the narratives and the social climate from which they emerge may need adjustments to make self-ascriptions meaningful, comprehensive, and resourceful tools. (shrink)

Computational psychiatry is a quickly evolving discipline that aims to understand psychopathology in terms of computational, hence algorithmic processes. While cognitive phenomena, especially beliefs or ways of “reasoning”, can more easily be formalized, meaning re-described in mathematical terms and then entered computational models, there is speculation as to whether phenomenology might be formalizable too. In other words, there are speculations in terms of what aspects of the human experience, rather than specific cognitive processes alone, can enter computational models. Here, we (...) explore the possibility of formalizing and modeling a phenomenological account of schizophrenia, using the concepts of “minimal self” and “intentionality”. To test the applicability of these concepts for formalization and modeling, we first aim to clarify some misunderstandings around the very nature of minimal self and intentionality, namely: whether these concepts entail a “minimal” sense of self, or might be better described in “transparent” sensory-integration and information processing terms. We then try to apply the concepts to a computational logic based on Marr’s levels of description, a fundamental account for understanding the rationale of computational psychiatry. Overall, we are asking via what conditions phenomenology can enter a computational logic. (shrink)

The computational approach to psychiatric disorders, including delusions, promises explanation and treatment. Here, we argue that an information processing approach might be misleading to understand psychopathology and requires further refinement. We explore the claim of computational psychiatry being a bridge between phenomenology and physiology while focussing on the ontological commitments and corresponding methodology computational psychiatry is based on. Interconnecting ontological claims and methodological practices, the paper illustrates the structure of theory-building and testing in computational psychiatry.First, we will explain the ontological (...) commitments computational psychiatry is grounded in, theBayesian Brain hypothesis(BBH) of unconscious inference, paired with normative deontic approaches applied to gauge psychopathology. We then turn to the steps taken in empirical paradigms, from definitions, which are used as starting points, to the operationalisation and isolation of cognitive processes and hypothesis testing based on algorithmic models, to consecutive interpretations regarding the aetiology of psychiatric disorders. We outline how experimental paradigms in computational psychiatry are specifically designed to confirm aberrations in assumed inferential processes, which are thought of as being the underlyingcore invariant features.We will illustrate a gap between the ontological commitments of computational psychiatry and the operationalisation and testing of the cognition assumed to be relevant for psychopathology. This conceptual gap is of utmost importance when designing computational paradigms and may impede a crisp understanding of the approach. Lastly, in evaluating the conceptual gap, it becomes apparent that the information processing formalism used in computational psychiatry is still grounded in rational cognitive psychology. (shrink)