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A predictive processing theory of sensorimotor contingencies: explaining the puzzle of perceptual presence and its absence in synesthesia.
Open accessAbstract
Normal perception involves experiencing objects within perceptual scenes as real, as existing in the world. This property of "perceptual presence" has motivated "sensorimotor theories" which understand perception to involve the mastery of sensorimotor contingencies. However, the mechanistic basis of sensorimotor contingencies and their mastery has remained unclear. Sensorimotor theory also struggles to explain instances of perception, such as synesthesia, that appear to lack perceptual presence and for which relevant sensorimotor contingencies are difficult to identify. On alternative "predictive processing" theories, perceptual content emerges from probabilistic inference on the external causes of sensory signals, however, this view has addressed neither the problem of perceptual presence nor synesthesia. Here, I describe a theory of predictive perception of sensorimotor contingencies which (1) accounts for perceptual presence in normal perception, as well as its absence in synesthesia, and (2) operationalizes the notion of sensorimotor contingencies and their mastery. The core idea is that generative models underlying perception incorporate explicitly counterfactual elements related to how sensory inputs would change on the basis of a broad repertoire of possible actions, even if those actions are not performed. These "counterfactually-rich" generative models encode sensorimotor contingencies related to repertoires of sensorimotor dependencies, with counterfactual richness determining the degree of perceptual presence associated with a stimulus. While the generative models underlying normal perception are typically counterfactually rich (reflecting a large repertoire of possible sensorimotor dependencies), those underlying synesthetic concurrents are hypothesized to be counterfactually poor. In addition to accounting for the phenomenology of synesthesia, the theory naturally accommodates phenomenological differences between a range of experiential states including dreaming, hallucination, and the like. It may also lead to a new view of the (in)determinacy of normal perception.
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“Hence the agent’s model must be able to entertain ‘counterfactually rich’ representations; i.e., beliefs about several alternative potential actions and the states of affairs that these actions would bring about”
From 'Seeing the Dark': Grounding Phenomenal Transparency and Opacity in Precision Estimation for Active Inference (2018) · cites this paper as Seth, 2014
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“The latter case fits well with Seth’s (2014) related proposal that the perceived ‘realness’ of objects depends on the identification of features invariant to some potential (counterfactual) manipulation of the object.”
From 'Seeing the Dark': Grounding Phenomenal Transparency and Opacity in Precision Estimation for Active Inference (2018) · cites this paper as Seth, 2014
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“Interestingly, it has been suggested that such counterfactual aspects of perceptual prediction may underlie basic properties perceptual experience, such as ‘presence’ or ‘objecthood’ [36].”
From Active interoceptive inference and the emotional brain (2016) · cites this paper as [36]
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“This formulation is reminiscent of certain action-oriented or enactive appro aches to consciousness, such as sensorimotor contingency theory (O'Regan, 2011; O'Regan & Noë, 2001; see also Seth, 2014).”
From Integrated information and predictive processing theories of consciousness: An adversarial collaborative review (2026) · cites this paper as Seth, 2014
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Metadata
- Authors
- Seth, A. K.
- Affiliations
- University of Sussex (GB)
- Corresponding author
- Seth, Anil K.
- ORCID
- Seth, Anil K.
- Venue
- Cognitive Neuroscience
- Publisher
- Informa UK Limited
- Published
- 2014-01-21
- Volume
- 5
- Issue
- 2
- Pages
- 97-118
- ISSN
- 1758-8928 (ISSN-L)
- 1758-8936
- PMC
- PMC4037840
- OpenAlex
- W2080654992
- Cited by
- 346
- References
- 121
- Citations by year
- 2026: 23 · 2025: 38 · 2024: 26 · 2023: 56 · 2022: 25 · 2021: 22 · 2020: 32 · 2019: 29 · 2018: 19 · 2017: 27 · 2016: 14 · 2015: 22 · 2014: 13
- FWCI
- 2.44
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cognitive Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Sussex Research Online (University of Sussex) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Funders
- European Commission (258749)
- Engineering and Physical Sciences Research Council (EP/G007543/1)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Social Sciences › Arts and Humanities › Philosophy › Philosophy and Theoretical Science
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Keywords
- predictive processing, sensorimotor contingencies, perceptual presence, synesthesia, counterfactual reasoning, generative models, dreams, hallucinations, probabilistic inference
- MeSH terms
- Synesthesia, Bayes Theorem, Humans, Perception, Perceptual Disorders, Photic Stimulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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