arXiv (Cornell University)
April 18, 2024
Michele Farisco, Kathinka Evers, Jean-Pierre Changeux
From an evolutionary perspective, several structural and functional features of the human brain appear key for human-like conscious experience. Current AI research is limited in emulating human consciousness for both intrinsic (structural and architectural) and extrinsic (current scientific and technological knowledge) reasons, but taking inspiration from brain characteristics that enable conscious processing is a promising strategy. It is theoretically possible that AI could develop partial or alternative forms of consciousness qualitatively different from human, possibly more or less sophisticated. The authors recommend neuroscience-inspired caution, proposing to clearly specify what is common and what differs in AI conscious processing from full human conscious experience.
Trends in Cognitive Sciences
June 1, 2026
Jean-Pierre Changeux, Michele Farisco
The Global Neuronal Workspace (GNW) theory, often conflated with functionalist computational theories, actually describes a multilevel architecture of conscious processing that extends from cellular and molecular mechanisms to large-scale network dynamics. The paper argues that GNW is not a functionalist computational theory, unlike the Global Workspace theory with which it is frequently confused.
Neural networks : the official journal of the International Neural Network Society
December 1, 2024
Michele Farisco, Kathinka Evers, Jean-Pierre Changeux
Analyzing the question of developing artificial consciousness from an evolutionary perspective, using the evolution of the human brain and its relation with consciousness as a benchmark, reveals several structural and functional features of the human brain that appear key for human-like complex conscious experience. Current AI research should take these into account. Even if AI is limited in emulating human consciousness for intrinsic and extrinsic reasons, taking inspiration from brain characteristics that make human-like conscious processing possible is a promising strategy.
BMC Medical Ethics
March 27, 2022
Michele Farisco, Cyriel M A Pennartz, Jitka Annen et al.
A recently introduced list of operational indicators of consciousness, originally developed for challenging cases like non-human animals and artificial intelligence, may help address the high misdiagnosis rate among patients with disorders of consciousness. These indicators are particular capacities deduced from behavior, cognitive performance, or neural correlates, and they do not define a hard threshold for consciousness but allow graded inference based on consistency across indicators. Applying these indicators to disorders of consciousness could inspire new strategies for reducing misdiagnosis, establishing a gold standard for detecting consciousness, and refining the taxonomy of these disorders.
Frontiers in Systems Neuroscience
January 1, 2019
Cyriel M A Pennartz, Michele Farisco, Kathinka Evers
Consciousness likely evolved to provide animals with a multimodal, situational awareness of the world and body, supporting complex decision-making and goal-directed behavior. This review proposes six observable indicators—goal-directed behavior and model-based learning; brain substrates for integrative multimodal representations; psychometrics and metacognition; episodic memory; susceptibility to illusions and multistable perception; and specific visuospatial behaviors—that together can be used to assess consciousness in non-human animals and intelligent artifacts. Rather than a single hard threshold, consistency across these indicators yields a graded assessment, similar to the Glasgow Coma Scale. Current deep learning neural networks and agile robots show no indication of consciousness; assessing machine consciousness requires ethological, longitudinal study of flexible, improvisational behaviors.
Frontiers in Psychology
January 1, 2018
Michele Farisco, Jeanette H Kotaleski, Kathinka Evers
Computer models and simulations are increasingly used to describe, explain, and predict brain function, aiming to integrate fragmented neuroscientific knowledge. This paper examines whether simulation technologies could plausibly emulate consciousness and assesses their potential clinical impact on disorders of consciousness such as coma, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state. Despite technical limitations, the authors suggest that simulating neural correlates of consciousness may offer new solutions to practical clinical problems, particularly improving treatments for patients with these disorders.