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.
Elife
October 12, 2020
Andrea Alamia, Christopher Timmermann, David Nutt et al.
86 citations
The psychedelic drug DMT rapidly induces a highly immersive state of consciousness with vivid visual imagery. In a study of participants who received DMT or placebo (saline) while keeping their eyes closed, brain electrical activity showed a pattern of cortical travelling waves similar to that normally seen during visual stimulation. The typical alpha-brainwave rhythms of eyes-closed rest were significantly reduced, while forward-directed waves from lower to higher brain regions increased. These findings support the idea that psychedelics reduce the brain's reliance on prior expectations, shifting the balance from top-down to bottom-up information processing, which may be a key mechanism underlying altered states of consciousness.
bioRxiv (Cold Spring Harbor Laboratory)
May 8, 2020
Andrea Alamia, Christopher Timmermann, Rufin Vanrullen et al.
25 citations
preprint
The psychedelic drug DMT rapidly induces an immersive conscious state with vivid visual imagery. EEG recordings showed that DMT alters cortical traveling waves: the typical alpha-band backward wave of eyes-closed rest decreased, while a forward wave similar to that seen during visual stimulation increased. This supports a model where psychedelics reduce the precision-weighting of prior expectations, shifting the balance from top-down to bottom-up information flow. The findings suggest that backward traveling waves are correlates of precision weighting and that reduced backward and increased forward waves are a mechanistic principle of psychedelic-induced altered states.
Elife
November 9, 2020
Andrea Alamia, Christopher Timmermann, David Nutt et al.
7 citations
correction
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Trends in Cognitive Sciences
June 1, 2026
Patrick Butlin, Robert Long, Tim Bayne et al.
A method for assessing whether AI systems might be conscious is presented, drawing on existing neuroscientific theories of consciousness. The approach involves deriving indicators from such theories to inform beliefs about AI consciousness. This method can make progress because computational functionalist theories, which are influential, have empirically testable implications for AI. The work does not claim that any current AI is conscious but outlines a rigorous framework for future assessment.
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.
Trends in Neurosciences
September 1, 2021
Rufin Vanrullen, Ryota Kanai
Deep learning has brought artificial intelligence close to human performance on many tasks, but new brain-inspired architectures are still needed. The Global Workspace Theory describes a large-scale system that integrates and distributes information among specialized modules to support higher-level cognition and awareness. The authors argue it is now time to implement this theory explicitly with deep-learning techniques. They propose a roadmap using unsupervised neural translation between multiple latent spaces—neural networks trained on different tasks or sensory modalities—to create a single, amodal Global Latent Workspace (GLW). The potential functional advantages of GLW and its neuroscientific implications are reviewed.