What is consciousness, and could machines have it?

By Stanislas Dehaene, H. Lau, Sid Kouider. Science, 2017

623 citations recorded by the source corpus.

The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the human brain. We suggest that the word “consciousness” conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense). We argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. We review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7f6acbbeabcbd585ef52105749705ca9cc3854bc

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In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Coordination Among Neural Modules Through a Shared Global Workspace (2021, methodology): “In cognitive neuroscience, the Global Workspace Theory (GWT) (Baars, 1993; Dehaene et al., 2017) suggests an architecture allowing specialist modules to interact.”
Conscious Processing and the Global Neuronal Workspace Hypothesis (2020, background): “In the future, it will be fascinating to see whether some of the present ideas can be made precise enough to be implemented in an actual computational device (Dehaene et al., 2017).”
Inductive biases for deep learning of higher-level cognition (2020, methodology): “This is at the heart of the Global Workspace Theory (or GWT) from Baars (1993) and its extension, the Global Neuronal Workspace model (Dehaene et al., 2017).”
Deep learning for AI (2021, background): “The nature of system 2 processing and cognitive neuroscience theories for them 5,30 suggests several such inductive biases and architectures, 11,45 which may be exploited to de-sign novel deep learning systems.”
The Consciousness Prior (2017, methodology): “…language, inspired by modern theories of consciousness such as the global workspace theory [Baars, 1988, 1997, 2002, Dehaene and Naccache, 2001, Dehaene et al., 2017] as a form of awareness [van Gulick, 2004], i.e., as deﬁned by Locke, consciousness is “the perception of what passes in a man’s…”
On the link between conscious function and general intelligence in humans and machines (2022, background): “A similar attempt has been made in the global workspace literature, with Dehaene et al. (2017) proposing that information processing in the brain can be divided into unconscious processing as well as two diﬀerent kinds of conscious processing.”
Open multi-center intracranial electroencephalography dataset with task probing conscious visual perception (2025, unknown): “…paradigm and associated data shared here were collected as part of a large-scale international adversarial collaboration testing key contradictory predictions of two prominent theories of Consciousness: 14 Global Neuronal Workspace Theory (GNWT) 15–19 and Integrated Information Theory (IIT) 20–23 .”