Is predictive processing a theory of perceptual consciousness?

Marvan, Tomáš; Havlík, Marek

doi:10.1016/j.newideapsych.2020.100837

Cited by 19 publications

(13 citation statements)

References 70 publications

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“…It is for this reason that we retain the workspace label. However, unlike the initial conceptual account of the PGNW introduced by Whyte (2019) which, as Marvan and Tomáš (2020) point out, relies on explanatory machinery external to the active inference framework to explain conscious access, here we identify the global availability of information with temporally deep processing, and conscious access with the posterior confidence threshold required for report (broadly construed as goal-directed verbal report, button presses, saccades or any other method of goal-directed reporting of subjective content). As such the PGNW explains conscious access exclusively in terms of the explanatory tools of active inference.…”

Section: Relationship To Other Modelsmentioning

confidence: 93%

The predictive global neuronal workspace: A formal active inference model of visual consciousness

Whyte

Smith

2021

Progress in Neurobiology

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The global neuronal workspace (GNW) model has inspired over two decades of hypothesis driven research on the neural basis consciousness. However, recent studies have reported findings that appear inconsistent with the predictions of the model. Further, the macroanatomical focus of current GNW research has limited the specificity of predictions afforded by the model. In this paper we present a neurocomputational model -based on the active inference framework -that captures central architectural elements of the GNW and that can address these limitations. The resulting 'predictive global workspace' casts neuronal dynamics as approximating Bayesian inference, allowing precise, testable predictions at both the behavioural and neural levels of description. We report simulations demonstrating the model's ability to reproduce: 1) the electrophysiological and behaviour results observed in previous studies of inattentional blindness, and 2) the previously described four-way taxonomy predicted by the GNW, which describes the relationship between consciousness, attention, and sensory signal strength. We then illustrate how our model can reconcile/explain (apparently) conflicting findings, extend the GNW taxonomy to include the influence of prior expectations, and inspire novel paradigms to test associated behavioural and neural predictions..

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Section: Relationship To Other Modelsmentioning

confidence: 93%

The predictive global neuronal workspace: A formal active inference model of visual consciousness

Whyte

Smith

2021

Progress in Neurobiology

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“…Before turning to the discussion of the MUM, one important objection needs to be noted. 16 In a recent paper, Marvan and Havlík (2021) analyze some of the accounts of consciousness discussed here. Their goal is to argue that PP does not by itself provide a theory of consciousness but requires external explanatory machinery provided by established theories of consciousness to do so.…”

Section: Discussionmentioning

confidence: 99%

“…While all accounts discussed above assign a strong explanatory role to precision and complexity, each of them provides further specifications and limitations with regard to relevant properties. This in fact has been the subject of the criticisms put forward by Marvan and Havlík (2021) . The WH account is closest to establishing precision optimization as sufficient for conscious access.…”

Section: Precision and Complexity As A Minimal Unifying Modelmentioning

confidence: 99%

Bayesian theories of consciousness: a review in search for a minimal unifying model

Rorot

2021

Neuroscience of Consciousness

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The goal of the paper is to review existing work on consciousness within the frameworks of Predictive Processing, Active Inference, and Free Energy Principle. The emphasis is put on the role played by the precision and complexity of the internal generative model. In the light of those proposals, these two properties appear to be the minimal necessary components for the emergence of conscious experience—a Minimal Unifying Model of consciousness.

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“…In this section we will review some examples of these proposals. We will only discuss those that exclusively use conceptual tools from active inference, but see Marvan and Havlík (2021) for an overview of proposals that mix active inference with mechanisms from other theories. We will also omit theories of specific aspects of consciousness (e.g., the phenomenological property of conscious presence, or the self) that are based on predictive coding mechanisms alone rather than active inference (e.g.…”

Section: Active Inference As a Modelling Framework For Building A Process Theorymentioning

confidence: 99%

Active Inference as a Computational Framework for Consciousness

2021

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Recently, the mechanistic framework of active inference has been put forward as a principled foundation to develop an overarching theory of consciousness which would help address conceptual disparities in the field (Wiese 2018; Hohwy and Seth 2020). For that promise to bear out, we argue that current proposals resting on the active inference scheme need refinement to become a process theory of consciousness. One way of improving a theory in mechanistic terms is to use formalisms such as computational models that implement, attune and validate the conceptual notions put forward. Here, we examine how computational modelling approaches have been used to refine the theoretical proposals linking active inference and consciousness, with a focus on the extent and success to which they have been developed to accommodate different facets of consciousness and experimental paradigms, as well as how simulations and empirical data have been used to test and improve these computational models. While current attempts using this approach have shown promising results, we argue they remain preliminary in nature. To refine their predictive and structural validity, testing those models against empirical data is needed i.e., new and unobserved neural data. A remaining challenge for active inference to become a theory of consciousness is to generalize the model to accommodate the broad range of consciousness explananda; and in particular to account for the phenomenological aspects of experience. Notwithstanding these gaps, this approach has proven to be a valuable avenue for theory advancement and holds great potential for future research.

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Is predictive processing a theory of perceptual consciousness?

Cited by 19 publications

References 70 publications

The predictive global neuronal workspace: A formal active inference model of visual consciousness

The predictive global neuronal workspace: A formal active inference model of visual consciousness

Bayesian theories of consciousness: a review in search for a minimal unifying model

Active Inference as a Computational Framework for Consciousness

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