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      Sources of richness and ineffability for phenomenally conscious states

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          Abstract

          Conscious states—state that there is something it is like to be in—seem both rich or full of detail and ineffable or hard to fully describe or recall. The problem of ineffability, in particular, is a longstanding issue in philosophy that partly motivates the explanatory gap: the belief that consciousness cannot be reduced to underlying physical processes. Here, we provide an information theoretic dynamical systems perspective on the richness and ineffability of consciousness. In our framework, the richness of conscious experience corresponds to the amount of information in a conscious state and ineffability corresponds to the amount of information lost at different stages of processing. We describe how attractor dynamics in working memory would induce impoverished recollections of our original experiences, how the discrete symbolic nature of language is insufficient for describing the rich and high-dimensional structure of experiences, and how similarity in the cognitive function of two individuals relates to improved communicability of their experiences to each other. While our model may not settle all questions relating to the explanatory gap, it makes progress toward a fully physicalist explanation of the richness and ineffability of conscious experience—two important aspects that seem to be part of what makes qualitative character so puzzling.

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          Most cited references119

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          A Mathematical Theory of Communication

          C. Shannon (1948)
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            Neural population dynamics during reaching

            Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from an analogous approach to primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well that analogy holds. Single-neuron responses in motor cortex appear strikingly complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. We found that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behavior. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate unexpected yet surprisingly simple structure in the population response. That underlying structure explains many of the confusing features of individual-neuron responses.
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              Nonlinear dimensionality reduction by locally linear embedding.

              Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.
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                Author and article information

                Contributors
                Journal
                Neurosci Conscious
                Neurosci Conscious
                nconsc
                Neuroscience of Consciousness
                Oxford University Press (UK )
                2057-2107
                2024
                01 March 2024
                01 March 2024
                : 2024
                : 1
                : niae001
                Affiliations
                departmentMila - Quebec AI Institute, Montreal , Quebec H2S 3H1, Canada
                departmentDepartment of Computer Science and Operations Research‌, University of Montreal , Pavillon André-Aisenstadt 2920, chemin de la Tour, Montreal, Quebec H3T 1J4, Canada
                departmentMila - Quebec AI Institute, Montreal , Quebec H2S 3H1, Canada
                departmentDepartment of Computer Science and Operations Research‌, University of Montreal , Pavillon André-Aisenstadt 2920, chemin de la Tour, Montreal, Quebec H3T 1J4, Canada
                departmentDepartment of Philosophy , University of Montreal, Pavillon 2910, boul. Édouard-Montpetit, Montreal, Quebec H3C 3J7, Canada
                departmentSchool of Engineering and Informatics, University of Sussex , Sussex House, Falmer, East Sussex BN1 9RH, United Kingdom
                departmentMila - Quebec AI Institute, Montreal , Quebec H2S 3H1, Canada
                departmentDepartment of Psychiatry and Addiction, University of Montreal , Pavillon Roger-Gaudry 2900, boul. Édouard-Montpetit, Montreal, Quebec H3T 1J4, Canada
                departmentMila - Quebec AI Institute, Montreal , Quebec H2S 3H1, Canada
                departmentDepartment of Mathematics and Statistics , University of Montreal, Pavillon André-Aisenstadt (AA-5190) 2920, chemin de la Tour, Montreal, Quebec H3T 1J4, Canada
                departmentDepartment of Philosophy , University of Montreal, Pavillon 2910, boul. Édouard-Montpetit, Montreal, Quebec H3C 3J7, Canada
                departmentMila - Quebec AI Institute, Montreal , Quebec H2S 3H1, Canada
                departmentDepartment of Computer Science and Operations Research‌, University of Montreal , Pavillon André-Aisenstadt 2920, chemin de la Tour, Montreal, Quebec H3T 1J4, Canada
                departmentCIFAR - Canadian Institute for Advanced Research , MaRS Centre, West Tower 661 University Ave., Suite 505, Toronto, Ontario M5G 1M1, Canada
                Author notes
                *Correspondence address. Mila - Quebec Artificial Intelligence Institute, Artificial Intelligence, Montreal, Canada. E-mail: xu.ji@ 123456mila.quebec
                Author information
                https://orcid.org/0000-0001-6769-6944
                https://orcid.org/0000-0002-4608-9712
                https://orcid.org/0000-0003-2418-8282
                https://orcid.org/0000-0002-2253-1844
                Article
                niae001
                10.1093/nc/niae001
                10939345
                38487679
                83b14390-8fe8-45b8-b451-81d6269dca39
                © The Author(s) 2024. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 23 January 2024
                : 03 January 2024
                : 01 May 2023
                : 08 January 2024
                : 01 March 2024
                Page count
                Pages: 18
                Categories
                Research Article
                AcademicSubjects/SCI01870
                AcademicSubjects/SCI01880
                AcademicSubjects/SCI01950
                AcademicSubjects/SCI02120
                AcademicSubjects/SCI02139

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