Integrated Information Theory and the Phenomenal Binding Problem: Challenges and Solutions in a Dynamic Framework

Theories of consciousness grounded in neuroscience must explain the phenomenal binding problem, e.g., how micro-units of information are combined to create the macro-scale conscious experience common to human phenomenology. An example is how single 'pixels' of a visual scene are experienced as a single holistic image in the 'mind's eye', rather than as individual, separate, and massively parallel experiences, corresponding perhaps to individual neuron activations, neural ensembles, or foveal saccades, any of which could conceivably deliver identical functionality from an information processing point of view. There are multiple contested candidate solutions to the phenomenal binding problem. This paper explores how the metaphysical infrastructure of Integrated Information Theory (IIT) v4.0 can provide a distinctive solution. The solution-that particular entities aggregable from multiple units ('complexes') define existence-might work in a static picture, but introduces issues in a dynamic system. We ask what happens to our phenomenal self as the main complex moves around a biological neural network. Our account of conscious entities developing through time leads to an apparent dilemma for IIT theorists between non-local entity transitions and contiguous selves: the 'dynamic entity evolution problem'. As well as specifying the dilemma, we describe three ways IIT might dissolve the dilemma before it gains traction. Clarifying IIT's position on the phenomenal binding problem, potentially underpinned with novel empirical or theoretical research, helps researchers understand IIT and assess its plausibility. We see our paper as contributing to IIT's current research emphasis on the shift from static to dynamic analysis.

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Publication Details

Journal
Entropy
Published
2025-03-25
DOI
https://doi.org/10.3390/e27040338
Citations
2
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
2.86
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article

Integrated Information Theory and the Phenomenal Binding Problem: Challenges and Solutions in a Dynamic Framework

Chris Percy, Andrés Gómez-Emilsson
2 citations
Entropy
Neural dynamics and brain function
2.86
article

Integrated Information Theory and the Phenomenal Binding Problem: Challenges and Solutions in a Dynamic Framework

Chris Percy, Andrés Gómez-Emilsson
article en
2 citations

Abstract

Theories of consciousness grounded in neuroscience must explain the phenomenal binding problem, e.g., how micro-units of information are combined to create the macro-scale conscious experience common to human phenomenology. An example is how single 'pixels' of a visual scene are experienced as a single holistic image in the 'mind's eye', rather than as individual, separate, and massively parallel experiences, corresponding perhaps to individual neuron activations, neural ensembles, or foveal saccades, any of which could conceivably deliver identical functionality from an information processing point of view. There are multiple contested candidate solutions to the phenomenal binding problem. This paper explores how the metaphysical infrastructure of Integrated Information Theory (IIT) v4.0 can provide a distinctive solution. The solution-that particular entities aggregable from multiple units ('complexes') define existence-might work in a static picture, but introduces issues in a dynamic system. We ask what happens to our phenomenal self as the main complex moves around a biological neural network. Our account of conscious entities developing through time leads to an apparent dilemma for IIT theorists between non-local entity transitions and contiguous selves: the 'dynamic entity evolution problem'. As well as specifying the dilemma, we describe three ways IIT might dissolve the dilemma before it gains traction. Clarifying IIT's position on the phenomenal binding problem, potentially underpinned with novel empirical or theoretical research, helps researchers understand IIT and assess its plausibility. We see our paper as contributing to IIT's current research emphasis on the shift from static to dynamic analysis.

EntropyVol. 27(4)
University of Derby (GB), Smith-Kettlewell Eye Research Institute (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 11%
Neural dynamics and brain function
2.86
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