The Behavioral and brain sciences
May 30, 2019
Samuel P L Veissière, Axel Constant, Maxwell J D Ramstead et al.
362 citations
A unifying account of how humans acquire shared cultural habits, norms, and expectations is developed by integrating the variational free-energy principle from theoretical neuroscience with concepts of cultural evolution and implicit learning. Humans construct social niches that provide epistemic resources called cultural affordances. Through immersive participation in patterned cultural practices, agents learn by inferring what other people expect—a process termed "thinking through other minds" (TTOM). This makes information about others' expectations the primary statistical regularity humans use to predict and organize behavior. The model aims to resolve debates in cognitive science between internalist and externalist accounts of theory of mind and between dynamical and representational views of enactivism.
Consciousness and cognition
May 1, 2021
Anna Ciaunica, Axel Constant, Hubert Preissl et al.
99 citations
Perceptual experiences are shaped by prior events, as argued by Predictive Processing and Active Inference frameworks. This paper examines how such experiences begin before birth, within the womb. A key but often neglected point is that humans develop inside another human body, a universal experience. The authors focus on the emergence of minimal selfhood in utero as a process of co-embodiment and co-homeostasis, emphasizing their interdependence. They conclude by discussing implications for debates on conscious experience, the minimal self, and social cognition.
Review of philosophy and psychology
January 1, 2022
Maxwell J D Ramstead, Anil K Seth, Casper Hesp et al.
75 citations
A version of neurophenomenology is presented that uses generative modelling techniques from computational neuroscience and biology to formally model descriptions of lived experience from the phenomenological tradition (e.g., Husserl, Merleau-Ponty). The approach, called computational phenomenology, is situated within the broader project of naturalizing phenomenology. Philosophical objections to that project are evaluated, and the generative modelling framework is reviewed. The approach differs from previous uses of generative modelling for consciousness by constructing computational models of inferential or interpretive processes that best explain particular kinds of lived experience.
PsyArXiv
February 23, 2021
Maxwell James Ramstead, Anil Seth, Casper Hesp et al.
21 citations
preprint
A new approach called computational phenomenology uses generative modeling techniques from computational neuroscience to study conscious experience. The paper reviews efforts to naturalize phenomenology, addresses philosophical objections, and explains how generative models can simulate the inferential processes underlying specific types of lived experience. This differs from prior uses of generative modeling for consciousness by focusing on modeling the interpretive process that best accounts for particular phenomenal experiences.
Neuroscience of consciousness
January 1, 2024
Xu Ji, Eric Elmoznino, George Deane et al.
13 citations
Conscious experiences feel rich and hard to fully describe or recall, a puzzle that partly motivates the explanatory gap—the belief that consciousness cannot be reduced to physical processes. This work offers an information-theoretic dynamical systems framework: richness corresponds to the amount of information in a conscious state, and ineffability to information lost during processing. Attractor dynamics in working memory cause impoverished recollections, language's discrete symbolic nature cannot capture high-dimensional experiential structure, and similar cognitive function between individuals improves communicability. The model advances a physicalist explanation of these puzzling aspects, though it may not settle all questions about the explanatory gap.
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.