What Kind of Information is Brain Information?

Rathkopf, Charles

doi:10.1007/s11245-017-9512-6

Cited by 11 publications

(8 citation statements)

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“…Second, representations are used to understand the organization of the system that implements those relationships. As Rupert (2018) puts it, representational posits are valid when they "play an explanatory role in an architecture" (p. 205) implementing cognitive processes, including the neural architecture (Rathkopf 2017).…”

Section: Setupmentioning

confidence: 99%

Contents, vehicles, and complex data analysis in neuroscience

Burnston

2020

Synthese

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The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call "algorithmic homuncularism," individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea (2018). Still, I am skeptical about algorithmic homuncularism, and I argue against it by focusing on recent methods for complex data analysis in systems neuroscience. I claim that analyses such as principle components analysis and linear discriminant analysis prevent individuating vehicles as algorithmic homuncularism recommends. Rather, each individual part contributes to a global state space, trajectories of which vary with important task parameters. I argue that, while homuncularism is false, this view still supports a kind of "vehicle realism," and I apply this view to debates about the explanatory role of representation.

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Section: Setupmentioning

confidence: 99%

Contents, vehicles, and complex data analysis in neuroscience

Burnston

2020

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“…Cronbach (1955) shares Shannon’s concerns arguing against the indiscriminate application of information theory to topics in psychology (for a review, see Sayood, 2018 ). While Shannon shows lucid reasoning, the assumption of information (and computations over it) in the brain is pervasive ( Reinagel, 2000 ; Stone, 2018 ; Nizami, 2019 ; Luppi and Stamatakis, 2021 ), the debates revolving around what kind of information is brain information ( Rathkopf, 2020 ): semantic, Shannon, or something in between? As noted by Shannon, applying information theory to the brain would be a simplistic description that is nevertheless widespread, preventing the progress in a theory of the mind and cognitive and brain science.…”

Section: Philosophy Of Mind and Its Computational Dashes Of Realismmentioning

confidence: 99%

Cognition Without Neural Representation: Dynamics of a Complex System

Hipólito

2022

Front. Psychol.

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This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS).

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“…Conversely, the division introduced in footnote 12 between physical/Shannon and semantic information (Lombardi et al, 2015(Lombardi et al, , 2016Mingers & Standing, 2018;Ball et al, 2020) has recently been challenged in various ways (for example by Isaac, 2019;Figdor, 2020;Rathkopf, 2020;Sprevak, 2020), raising the prospect for a more unified interpretation.…”

Section: Appendix Systems and Informationmentioning

confidence: 99%

The metaphysics of perception: enactivism, phenomenology and information.

Rose¹

2021

Preprint

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Enactivism is a major research programme in the philosophy of perception. Yet its metaphysical status is unclear, since it is claimed to avoid both idealism and realism yet still has aspects of both within it. One attempt to solve this conundrum is based on the fusion of enactivism with phenomenology and the mathematical concept of symmetry breaking (Moss Brender, 2013). I suggest this is not entirely successful and propose it needs the addition of a multi-level, non-reductive metaphysics (for example, Informational Structural Realism). The processes we commonly call ‘perception’ are causal transfers of information at certain levels in the hierarchy of meaningful structures that comprise physical reality. Phenomenologists could use the word ‘perception’ metaphorically across all levels, although realists need not do so.

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What Kind of Information is Brain Information?

Cited by 11 publications

References 20 publications

Contents, vehicles, and complex data analysis in neuroscience

Contents, vehicles, and complex data analysis in neuroscience

Cognition Without Neural Representation: Dynamics of a Complex System

The metaphysics of perception: enactivism, phenomenology and information.

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