mlsanityData to 7 Oct 2026

Sources of Richness and Ineffability for Phenomenally Conscious States

arXiv 2302.06403 ↗First posted 13 Feb 2023q-bio.NC cs.AIcited by 7

Not on the map. Why?

Abstract (arXiv)

Conscious states (states 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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 11 of its 149 references that are in the map. First posted on arXiv 13 Feb 2023.

Builds on

11 of 149 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  3. 2021Transformers in Vision: A Surveycited by 1,009
  4. 2016Deep Variational Information Bottleneckcited by 915
  5. 2020Hopfield Networks is All You Needcited by 461
  6. 2015Attention-Based Models for Speech Recognitioncited by 400
  7. 2020Inductive Biases for Deep Learning of Higher-Level Cognitioncited by 273
  8. 2019Universality and individuality in neural dynamics across large populations of recurrent networkscited by 61
  9. 2021Attractor and integrator networks in the braincited by 60
  10. 2022Designing Ecosystems of Intelligence from First Principlescited by 23
  11. 2023What is a Mathematical Structure of Conscious Experience?cited by 4

Built on by

Cited by year

Citing papers per year: 2023: 4; 2024: 2; 2025: 1.

7 papers.