Daily Papers

A large literature suggests that people are intuitive Dualists--they consider the mind ethereal, distinct from the body. Past research also shows that Dualism emerges, in part, via learning (e.g., Barlev & Shtulman, 2021). But whether learning is sufficient to give rise to Dualism is unknown.The evidence from human learners does address this question because humans are endowed not only with general learning capacities but also with core knowledge capacities. And recent results suggest that core knowledge begets Dualism (Berent, Theodore & Valencia, 2021; Berent, 2023). To evaluate the role of learning, here, we probe for a mind-body divide in Davinci--a large language model (LLM) that is devoid of any innate core knowledge. We show that Davinci still leans towards Dualism, and that this bias increases systematically with the learner's inductive potential. Thus, davinci (a GPT-3 model) exhibits mild Dualist tendencies, whereas its descendent, text-davinci-003 (a GPT-3.5 model), shows a full-blown bias. It selectively considers thoughts (epistemic states) as disembodied--as unlikely to show up in the body (in the brain), but not in its absence (after death). While Davinci's performance is constrained by its syntactic limitations, and it differs from humans, its Dualist bias is robust. These results demonstrate that the mind-body divide is partly learnable from experience.They also show how, as LLM's are exposed to human narratives, they induce not only human knowledge but also human biases.

Could the abstract ideas of our minds originate from neuronal interactions within our brains? To address this question, we examine interactions within 'brain-world' (BW) and 'brain-brain' (BB) domains, which represent the brain's physical interactions with its environment and the mental interactions between brains, respectively. BW interactions are characterized as analog - dynamic and continuous, whereas BB interactions are digital - non-dynamic and discrete. This distinction allows BB interactions to facilitate effective, albeit information-limited, communication through categorization. We review existing data showing that cascades of neural circuits can convert between analog and digital signals, thereby linking physical and mental processes. Importantly, we argue that these circuits cannot fully reduce one domain to the other, suggesting that the mind-brain duality can be mapped to the BB-BW duality. Such mapping suggests that the mind's foundation is inherently social, offering a novel explanation for the physical-mental gap while acknowledging the coexistence of the physical body and the non-physical mind.

The DSM-1 was published in 1952, contains 128 diagnostic categories, described in 132 pages. The DSM-5 appeared in 2013, contains 541 diagnostic categories, described in 947 pages. The field of psychology is characterised by a steady proliferation of diagnostic models and subcategories, that seems to be inspired by the principle of "divide and inflate". This approach is in contrast with experimental evidence, which suggests on one hand that traumas of various kind are often present in the anamnesis of patients and, on the other, that the gene variants implicated are shared across a wide range of diagnoses. In this work I propose a holistic approach, built with tools borrowed from the field of Artificial Intelligence. My model is based on two pillars. The first one is trauma, which represents the attack to the mind, is psychological in nature and has its origin in the environment. The second pillar is dissociation, which represents the mind defence in both physiological and pathological conditions, and incorporates all other defence mechanisms. Damages to dissociation can be considered as another category of attacks, that are neurobiological in nature and can be of genetic or environmental origin. They include, among other factors, synaptic over-pruning, abuse of drugs and inflammation. These factors concur to weaken the defence, represented by the neural networks that implement the dissociation mechanism in the brain. The model is subsequently used to interpret five mental conditions: PTSD, complex PTSD, dissociative identity disorder, schizophrenia and bipolar disorder. Ideally, this is a first step towards building a model that aims to explain a wider range of psychopathological affections with a single theoretical framework. The last part is dedicated to sketching a new psychotherapy for psychological trauma.

Minimal Phenomenal Experiences (MPEs) are states of consciousness in which wakefulness is preserved but phenomenal content is low or absent. The Entropic Brain Hypothesis (EBH) is a model of conscious processes that regards the entropy of spontaneous brain activity as a marker of 'phenomenal richness', exemplified by high-content psychedelic experiences (HCPEs). Yet recent human neuroimaging studies of MPEs induced by meditation -- and possibly 5-MeO-DMT -- suggest that these states, defined by their phenomenological simplicity, also show signs of increased neurophysiological entropy. This presents a conundrum for the EBH: brain entropy is elevated with increased and decreased richness of the phenomenal experience. Here, we put forward the Complex Brain Hypothesis (CBH), which proposes that the richness of experience differentiating MPEs from HCPEs is better indexed by complexity than by entropy. We argue that brain complexity is modulated by the grain of inference through which the brain resolves uncertainty: some HCPEs exemplify a fine-grained regime, in which loosened constraints amplify fluctuations into proliferating content, whereas some MPEs exemplify a coarse-grained regime, in which a simpler model dissolves variety into an experience of 'contentless' awareness. Both regimes can be associated with elevated brain entropy, but they diverge in phenomenology and perturbational signatures. By resolving the entropy-content conundrum, the CBH refines the EBH and highlights MPEs as an important test case for computational theories of consciousness.

Changes in body perception influence behavior and emotion and can be induced through multisensory feedback. Auditory feedback to one's actions can trigger such alterations; however, it is unclear which individual factors modulate these effects. We employ and evaluate SoniWeight Shoes, a wearable device based on literature for altering one's weight perception through manipulated footstep sounds. In a healthy population sample across a spectrum of individuals (n=84) with varying degrees of eating disorder symptomatology, physical activity levels, body concerns, and mental imagery capacities, we explore the effects of three sound conditions (low-frequency, high-frequency and control) on extensive body perception measures (demographic, behavioral, physiological, psychological, and subjective). Analyses revealed an impact of individual differences in each of these dimensions. Besides replicating previous findings, we reveal and highlight the role of individual differences in body perception, offering avenues for personalized sonification strategies. Datasets, technical refinements, and novel body map quantification tools are provided.