Integrated information and predictive processing theories of consciousness: An adversarial collaborative review

As neuroscientific theories of consciousness continue to proliferate, the need to assess their similarities and differences – as well as their predictive and explanatory power – becomes ever more pressing. Recently, a number of structured adversarial collaborations have been devised to test the competing predictions of several candidate theories of consciousness. In this review, we compare and contrast three theories being investigated in one such adversarial collaboration: Integrated Information Theory, Neurorepresentationalism, and Active Inference. We begin by presenting the core claims of each theory, before comparing them in terms of the phenomena they seek to explain, the sorts of explanations they avail, and the methodological strategies they endorse. We then consider some of the inherent challenges of theory-testing, and how adversarial collaboration addresses some of these difficulties. The stage is then set for the empirical work to come: first, we outline the key hypotheses to be tested across a series of multi-site experiments; second, we discuss the kinds of observations that would support or challenge each theory; third, we consider how these theories might assimilate or accommodate such observations. Finally, we show how data harvested across disparate experiments (and their replicates) may be formally integrated to provide a quantitative measure of the evidential support accrued under each theory. Besides orienting the reader to the theoretical foundations of our collaboration, this review aims to provide valuable meta-scientific insights into the mechanics of adversarial collaboration and theory-testing in general – including the way theories may be evaluated in terms of the scientific progress they deliver. • Compares core features of integrated information and predictive processing theories • Outlines key hypotheses of each theory to be tested via adversarial collaboration • Describes a mechanism for tracking scientific progress in the adversarial setting

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Publication Details

Journal
Neuroscience & Biobehavioral Reviews
Published
2026-05-01
DOI
https://doi.org/10.1016/j.neubiorev.2026.106742
Primary Topic
Face Recognition and Perception
Type
article
Field-Weighted Citation Impact
0.00

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article

Integrated information and predictive processing theories of consciousness: An adversarial collaborative review

Giulio Tononi, Reinder Dorman, Andrew W. Corcoran, Cyriel M. A. Pennartz et al.
Neuroscience & Biobehavioral Reviews
Face Recognition and Perception
article

Integrated information and predictive processing theories of consciousness: An adversarial collaborative review

Giulio Tononi, Reinder Dorman, Andrew W. Corcoran, Cyriel M. A. Pennartz, Andrew Haun, I. Consortium, Karl J. Friston, TWCF, Karl J. Friston
article en

Abstract

As neuroscientific theories of consciousness continue to proliferate, the need to assess their similarities and differences – as well as their predictive and explanatory power – becomes ever more pressing. Recently, a number of structured adversarial collaborations have been devised to test the competing predictions of several candidate theories of consciousness. In this review, we compare and contrast three theories being investigated in one such adversarial collaboration: Integrated Information Theory, Neurorepresentationalism, and Active Inference. We begin by presenting the core claims of each theory, before comparing them in terms of the phenomena they seek to explain, the sorts of explanations they avail, and the methodological strategies they endorse. We then consider some of the inherent challenges of theory-testing, and how adversarial collaboration addresses some of these difficulties. The stage is then set for the empirical work to come: first, we outline the key hypotheses to be tested across a series of multi-site experiments; second, we discuss the kinds of observations that would support or challenge each theory; third, we consider how these theories might assimilate or accommodate such observations. Finally, we show how data harvested across disparate experiments (and their replicates) may be formally integrated to provide a quantitative measure of the evidential support accrued under each theory. Besides orienting the reader to the theoretical foundations of our collaboration, this review aims to provide valuable meta-scientific insights into the mechanics of adversarial collaboration and theory-testing in general – including the way theories may be evaluated in terms of the scientific progress they deliver. • Compares core features of integrated information and predictive processing theories • Outlines key hypotheses of each theory to be tested via adversarial collaboration • Describes a mechanism for tracking scientific progress in the adversarial setting

Neuroscience & Biobehavioral Reviews
University of Wisconsin-Madison, Harvard University, York University, University College London, European Commission, University of Sussex, University of Glasgow, Monash University, Universiteit van Amsterdam, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Lundbeckfonden, Templeton World Charity Foundation, H. Lundbeck A/S, Weill Cornell Medical College, Horizon 2020 Framework Programme, FP7 Coordination of Research Activities
Openalex Percentile: Top 100%
Face Recognition and Perception
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