Journal of Cognitive Neuroscience
April 15, 2016
Maxine T. Sherman, Ryota Kanai, Anil K. Seth et al.
134 citations
Spontaneous alpha-band neural oscillations in the brain periodically transmit prior expectations to the visual cortex, biasing both objective decisions and subjective confidence before a stimulus appears. In a detection task with scalp EEG, prestimulus occipital alpha phase predicted the weighting of expectations on yes/no decisions and on confidence judgments, independent of attention. These findings suggest that alpha oscillations change the baseline from which evidence accumulation begins, shaping early visual processing and informing how expectations influence perception at the neural level.
May 18, 2023
Arthur Juliani, Adam Safron, Ryota Kanai
9 citations
preprint
Psychedelic therapy shows promise for treating various mental disorders. The REBUS model proposes that psychedelics help by relaxing overly rigid, maladaptive beliefs. The CANAL model extends this by suggesting that canalization—the development of excessively rigid belief structures—may underlie psychopathology. This paper refines the CANAL model by drawing on learning theory from deep neural networks, distinguishing two separate optimization landscapes for belief representation. Each landscape can develop pathologies from either too much or too little canalization, indicating a non-linear relationship with psychopathology. The refined model generates novel predictions about which psychopathologies might respond to psychedelic therapy and which forms of therapy may benefit specific individuals.
Neuroscience of Consciousness
January 1, 2024
Arthur Juliani, Adam Safron, Ryota Kanai
8 citations
Psychedelic therapy shows promise for treating mental disorders, and the "RElaxed Beliefs Under pSychedelics" (REBUS) model explains this by suggesting psychedelics loosen maladaptive high-level beliefs. The newer "CANAL" model proposes that overly rigid belief landscapes (canalization) contribute to psychopathology. This work uses deep neural network learning theory to refine the CANAL model, distinguishing two separate optimization landscapes for belief representation in the brain. Each can develop unique pathologies from either too much or too little canalization, indicating that canalization's link to psychopathology is not simply linear. The refined model makes novel predictions about which aspects of psychopathology psychedelic therapy may treat and which therapy forms might benefit a given individual.
Neuroscience of Consciousness
January 1, 2024
Ryota Kanai, Ippei Fujisawa
The paper introduces 'Universality' as a desirable property for theories of consciousness, borrowed from physics, where fundamental laws apply consistently everywhere. Universality requires that a theory can determine whether any fully described dynamical system is conscious or non-conscious, based on intrinsic properties rather than external interpretation. Most current theories lack this property, as they focus on neural correlates of consciousness in brain-centric systems. The authors argue that functionalist theories could become universal by specifying mathematical formulations of their concepts. While neurobiological and functionalist theories remain useful, a universal theory is needed to fully explain why certain systems possess consciousness.
arXiv Preprint Archive
August 17, 2023
Patrick Butlin, Robert Long, Eric Elmoznino et al.
No current AI systems are conscious, but there are no obvious technical barriers to building ones that might be, according to an analysis grounded in neuroscientific theories of consciousness. The report surveys prominent theories—recurrent processing, global workspace, higher-order, predictive processing, and attention schema—and derives computational indicator properties from them. Applying these indicators to recent AI systems yields no evidence of consciousness, but the authors argue that future systems could potentially implement the necessary properties.
arXiv Preprint Archive
September 28, 2019
Acer Y. C. Chang, Martin Biehl, Yen Yu et al.
Conscious experience corresponds to information encoded in coarse-grained neural states, such as the firing patterns of neuronal populations, rather than in the noisy activity of individual neurons or in macro-level interactions like interpersonal communication. The authors introduce Information Closure Theory of Consciousness (ICT), which hypothesizes that conscious processes form non-trivial informational closure (NTIC) with respect to the environment at certain coarse-grained levels. This closure confines conscious experience to those levels. ICT provides quantitative definitions of conscious content and conscious level, offering explanations and predictions for various consciousness phenomena and reconciling issues in existing theories.