Cerebral cortex (New York, N.Y. : 1991)
January 8, 2025
Megan A. K. Peters
10 citations
Studying subjective experience is difficult because introspection is often considered unreliable and unverifiable. However, the author argues that these limitations do not prevent building a meaningful psychophysical research program that treats subjective experience as a valid empirical target. By precisely characterizing relationships among environmental variables, brain processes, behavior, and self-reported phenomenology, an 'introspective psychophysics' approach treats introspection's apparent faults as features, not bugs. This approach, echoing recent proposals, aims to establish a powerful tool for building and testing explanatory models of phenomenology across dimensions such as urgency, emotion, clarity, vividness, and confidence.
Open mind : discoveries in cognitive science
January 1, 2024
Pietro Amerio, Matthias Michel, Stephan Goerttler et al.
5 citations
Comparing conscious and unconscious perception is central to consciousness science, but many studies fail to control for criterion biases when assessing awareness. In this study, observers tried to discriminate subjectively invisible offsets of Vernier stimuli, with visibility probed using a bias-free task. Stimuli were made less visible by backward masking or very brief presentation (1-3 milliseconds) using a modern tachistoscope. Some behavioral indicators of perception without awareness appeared, but no conclusive evidence emerged. Bayesian observer model simulations, including models generating visibility judgments alongside type-1 judgments, best fit observers with slightly suboptimal conscious access to sensory evidence. The stimuli and manipulations produced mild blindsight-like behavior, suitable for future investigation.
Philosophy and the Mind Sciences
February 27, 2026
Megan A. K. Peters, Hojjat Azimi Asrari
1 citation
Higher-order representations encode information about an agent's own first-order representations, such as their reliability or structure, and are thought to be critical for metacognition, learning, and consciousness. The authors propose that metacognitive estimates of uncertainty reflect a read-out of higher-order "posteriors" from a Bayesian perspective. These posteriors combine higher-order "likelihoods" (current uncertainty evidence) and "priors" (learned distributions over expected uncertainty). The paper discusses emerging analytical approaches to examine the estimation processes and neural correlates of these under-explored components of experienced uncertainty.
Communications Biology
October 13, 2024
Nora A Bradford, Angela Shen, Brian Odegaard et al.
1 citation
A workshop and subawards program aimed to align United States federal funding mechanisms with consciousness research is described, including its motivation, execution, and outcomes, to encourage similar efforts locally and globally.
The Behavioral and brain sciences
June 25, 2026
Megan A. K. Peters
Conscious vision may stem from a reality monitoring mechanism that evolved to distinguish planning from perception. The standard model assumes a slow, winner-take-all strategy that tags only one representation as real. This paper argues that expectations about environmental stability could conceal alternative decision policies, which would have important consequences for learning and for understanding how consciousness evolved and what it does.
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.
Trends in Cognitive Sciences
March 1, 2024
Tim Bayne, Anil K. Seth, Marcello Massimini et al.
New tests for consciousness (C-tests) are urgently needed to resolve uncertainty about when consciousness arises in human development, when it is lost due to neurological disorders and brain injury, and how it is distributed in nonhuman species. This need is amplified by recent developments in AI, neural organoids, and xenobot technology. Existing C-tests are of limited use, and no tests exist for many critical populations. The paper identifies challenges facing any attempt to develop C-tests, proposes a multidimensional classification of such tests, and identifies strategies for validating them.
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.
Current Biology
August 1, 2023
J. LeDoux, Jonathan Birch, Kristin Andrews et al.
Leading experts in consciousness research address big questions about the relationship between AI and consciousness. The piece presents a multi-perspective discussion, with contributors including philosophers, neuroscientists, and cognitive scientists responding to challenges posed by Joseph LeDoux and Jonathan Birch. The experts explore whether AI systems could ever be conscious, what criteria might be used to assess machine consciousness, and how insights from animal and human consciousness research might inform these debates. No single conclusion is reached; instead the work maps the current landscape of scientific and philosophical positions on AI consciousness.
Neuroscience and Biobehavioral Reviews
November 1, 2022
Megan A. K. Peters
Perceptual metacognition—the ability to reflect on one's own perceptual experiences, such as feeling confident about a decision—offers a promising entry point for studying the neural and computational basis of subjective conscious experience. This review identifies five unique properties of metacognition that make it empirically tractable: it produces subjective feelings, is directed at internal representations, involves recursive computations, is linked to observable behavior, and requires sensitive models because its computations are unobservable yet hierarchically dependent. Computational models of metacognition can help characterize the generative processes that construct qualitative experience, drawing on advances in psychology, neuroscience, and philosophy.
Nature Human Behaviour
February 1, 2019
M. Michel, Diane M Beck, N. Block et al.
Advancing scientific research on consciousness is important for addressing clinical and ethical issues in neurology and mental health. To support this field, funding priorities must be set carefully, and challenges such as job creation and media misrepresentation need to be addressed.
Frontiers in Psychology
November 5, 2018
Matthias Michel, Stephen M Fleming, Hakwan Lau et al.
A survey of 249 participants, mostly in academia and about 40% experts in consciousness research, assessed views on the field's progress, funding, job opportunities, and scientific rigor. 78% of respondents said scientific research on consciousness has been making progress. However, most perceived obtaining funding and getting a job in consciousness research as more difficult than in other neuroscience subfields. Work in consciousness research was seen as less rigorous than other neuroscience subfields, but this perception was not linked to the perceived difficulty in funding and jobs. Global workspace theory was rated most promising overall (about 28%), while among non-experts integrated information theory (IIT) was most popular (about 22%).
Neuroscience of Consciousness
September 6, 2017
Megan A. K. Peters, Robert W. Kentridge, I. Phillips et al.
The authors present a symposium discussion on whether unconscious perception truly exists, highlighting disagreement on definitions and empirical methods for determining unconscious states. They argue that the controversy hinges on what is meant by key terms and how to measure unconsciousness. The piece is organized into four distinct contributions to reflect the range of ideas and evidence on this topic, aiming to foster consensus among consciousness researchers.
Neuroscience of Consciousness
January 1, 2016
Megan A. K. Peters, T. Ro, Hakwan Lau
Response bias can contaminate studies of consciousness when observers report not seeing a stimulus not because they lack subjective experience but because they compare it to other stimuli. While bias-free signal detection measures help avoid this confound, an overemphasis on eliminating criterion effects can mislead research. The authors argue that Balsdon and Azzopardi mistakenly criticized a previous report of 'relative blindsight' for response bias, when the original study never intended to use their bias-removal methods. Dismissing findings solely because they depend on criterion is problematic, as many real effects necessarily involve criterion. The authors conclude that criterion effects are conceptually important in conscious awareness research and should be treated carefully, not avoided thoughtlessly.
Megan A. K. Peters
preprint
Introspection, often dismissed as unreliable and unverifiable, can serve as a valid empirical tool for studying subjective experience. The author argues that its imperfections—imperfect access to brain processes, lack of objective verification, and difficulty isolating from non-subjective processing—do not preclude building a meaningful psychophysical research program. By precisely characterizing relationships among environmental variables, brain processes, behavior, and self-reported phenomenology, a new 'introspective psychophysics' can treat introspection's faults as features, not bugs. This approach, echoing recent calls by Peters, Kammerer and Frankish, and Fleming, aims to build and test precise explanatory models of phenomenology across dimensions like urgency, emotion, clarity, vividness, and confidence.