Modeling Working Memory to Identify Computational Correlates of Consciousness

James A. Reggia, Garrett E. Katz, Gregory P. Davis

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Recent advances in philosophical thinking about consciousness, such as cognitive phenomenology and mereological analysis, provide a framework that facilitates using computational models to explore issues surrounding the nature of consciousness. Here we suggest that, in particular, studying the computational mechanisms of working memory and its cognitive control is highly likely to identify computational correlates of consciousness and thereby lead to a deeper understanding of the nature of consciousness. We describe our recent computational models of human working memory and propose that three computational correlates of consciousness follow from the results of this work: Itinerant attractor sequences, top-down gating, and very fast weight changes. Our current investigation is focused on evaluating whether these three correlates are sufficient to create more complex working memory models that encompass compositionality and basic causal inference. We conclude that computational models of working memory are likely to be a fruitful approach to advancing our understanding of consciousness in general and in determining the long-term potential for development of an artificial consciousness specifically.

Original languageEnglish (US)
Pages (from-to)252-269
Number of pages18
JournalOpen Philosophy
Issue number1
StatePublished - Jan 1 2019


  • artificial consciousness
  • cognitive control
  • cognitive phenomenology
  • computational correlates
  • computational explanatory gap
  • machine consciousness
  • mereology
  • mind-brain problem
  • working memory

ASJC Scopus subject areas

  • Philosophy


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