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  1. Near-Decomposability and the Timescale Relativity of Causal Representations.Naftali Weinberger - 2020 - Philosophy of Science 87 (5):841-856.
    A common strategy for simplifying complex systems involves partitioning them into subsystems whose behaviors are roughly independent of one another at shorter timescales. Dynamic causal models clarify how doing so reveals a system’s nonequilibrium causal relationships. Here I use these models to elucidate the idealizations and abstractions involved in representing a system at a timescale. The models reveal that key features of causal representations—such as which variables are exogenous—may vary with the timescale at which a system is considered. This has (...)
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  • On the role of contextual factors in cognitive neuroscience experiments: a mechanistic approach.Abel Wajnerman-Paz & Daniel Rojas-Líbano - 2022 - Synthese 200 (5):1-26.
    Experiments in cognitive neuroscience build a setup whose set of controlled stimuli and rules elicits a cognitive process in a participant. This setup requires researchers to decide the value of quite a few parameters along several dimensions. We call ‘’contextual factors’’ the parameters often assumed not to change the cognitive process elicited and are free to vary across the experiment’s repetitions. Against this assumption, empirical evidence shows that many of these contextual factors can significantly influence cognitive performance. Nevertheless, it is (...)
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  • Beyond the Platonic Brain: Facing the Challenge of Individual Differences in Function-Structure Mapping.Marco Viola - 2020 - Synthese 199 (1-2):2129-2155.
    In their attempt to connect the workings of the human mind with their neural realizers, cognitive neuroscientists often bracket out individual differences to build a single, abstract model that purportedly represents every human being’s brain. In this paper I first examine the rationale behind this model, which I call ‘Platonic Brain Model’. Then I argue that it is to be surpassed in favor of multiple models allowing for patterned inter-individual differences. I introduce the debate on legitimate ways of mapping neural (...)
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  • Descriptive Multiscale Modeling in Data-Driven Neuroscience.Philipp Haueis - 2022 - Synthese 200 (2):1-26.
    Multiscale modeling techniques have attracted increasing attention by philosophers of science, but the resulting discussions have almost exclusively focused on issues surrounding explanation. In this paper, I argue that besides explanation, multiscale techniques can serve important exploratory functions when scientists model systems whose organization at different scales is ill-understood. My account distinguishes explanatory and descriptive multiscale modeling based on which epistemic goal scientists aim to achieve when using multiscale techniques. In explanatory multiscale modeling, scientists use multiscale techniques to select information (...)
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  • The Dynamical Renaissance in Neuroscience.Luis H. Favela - 2020 - Synthese 199 (1-2):2103-2127.
    Although there is a substantial philosophical literature on dynamical systems theory in the cognitive sciences, the same is not the case for neuroscience. This paper attempts to motivate increased discussion via a set of overlapping issues. The first aim is primarily historical and is to demonstrate that dynamical systems theory is currently experiencing a renaissance in neuroscience. Although dynamical concepts and methods are becoming increasingly popular in contemporary neuroscience, the general approach should not be viewed as something entirely new to (...)
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  • Reflex Theory, Cautionary Tale: Misleading Simplicity in Early Neuroscience.M. Chirimuuta - 2021 - Synthese 199 (5-6):12731-12751.
    This paper takes an integrated history and philosophy of science approach to the topic of "simplicity out of complexity". The reflex theory was a framework within early twentieth century psychology and neuroscience which aimed to decompose complex behaviours and neural responses into simple reflexes. It was controversial in its time, and did not live up to its own theoretical and empirical ambitions. Examination of this episode poses important questions about the limitations of simplifying strategies, and the relationship between simplification and (...)
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  • Contents, Vehicles, and Complex Data Analysis in Neuroscience.Daniel C. Burnston - 2020 - Synthese 199 (1-2):1617-1639.
    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. Still, I am (...)
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