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  1.  6
    Causality in the Sciences of the Mind and Brain.Lise Marie Andersen, Jonas Fogedgaard Christensen, Samuel Schindler & Asbjørn Steglich-Petersen - 2018 - Minds and Machines 28 (2):237-241.
  2.  15
    Intervening on the Causal Exclusion Problem for Integrated Information Theory.Matthew Baxendale & Garrett Mindt - 2018 - Minds and Machines 28 (2):331-351.
    In this paper, we examine the causal framework within which integrated information theory of consciousness makes it claims. We argue that, in its current formulation, IIT is threatened by the causal exclusion problem. Some proponents of IIT have attempted to thwart the causal exclusion problem by arguing that IIT has the resources to demonstrate genuine causal emergence at macro scales. In contrast, we argue that their proposed solution to the problem is damagingly circular as a result of inter-defining information and (...)
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  3.  16
    Discovering Brain Mechanisms Using Network Analysis and Causal Modeling.Matteo Colombo & Naftali Weinberger - 2018 - Minds and Machines 28 (2):265-286.
    Mechanist philosophers have examined several strategies scientists use for discovering causal mechanisms in neuroscience. Findings about the anatomical organization of the brain play a central role in several such strategies. Little attention has been paid, however, to the use of network analysis and causal modeling techniques for mechanism discovery. In particular, mechanist philosophers have not explored whether and how these strategies incorporate information about the anatomical organization of the brain. This paper clarifies these issues in the light of the distinction (...)
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  4.  6
    Intervention and Identifiability in Latent Variable Modelling.Jan-Willem Romeijn & Jon Williamson - 2018 - Minds and Machines 28 (2):243-264.
    We consider the use of interventions for resolving a problem of unidentified statistical models. The leading examples are from latent variable modelling, an influential statistical tool in the social sciences. We first explain the problem of statistical identifiability and contrast it with the identifiability of causal models. We then draw a parallel between the latent variable models and Bayesian networks with hidden nodes. This allows us to clarify the use of interventions for dealing with unidentified statistical models. We end by (...)
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  5.  8
    Reduction Without Elimination: Mental Disorders as Causally Efficacious Properties.Gottfried Vosgerau & Patrice Soom - 2018 - Minds and Machines 28 (2):311-330.
    We argue that any account of mental disorders that meets the desideratum of assigning causal efficacy to mental disorders faces the so-called “causal exclusion problem”. We argue that fully reductive accounts solve this problem but run into the problem of multiple realizability. Recently advocated symptom-network approaches avoid the problem of multiple realizability, but they also run into the causal exclusion problem. Based on a critical analysis of these accounts, we will present our own account according to which mental disorders are (...)
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  6.  5
    Interaction-Dominant Causation in Mind and Brain, and Its Implication for Questions of Generalization and Replication.Sebastian Wallot & Damian G. Kelty-Stephen - 2018 - Minds and Machines 28 (2):353-374.
    The dominant assumption about the causal architecture of the mind is, that it is composed of a stable set of components that contribute independently to relevant observables that are employed to measure cognitive activity. This view has been called component-dominant dynamics. An alternative has been proposed, according to which the different components are not independent, but fundamentally interdependent, and are not stable basic properties of the mind, but rather an emergent feature of the mind given a particular task context. This (...)
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  7.  23
    Rethinking Causality in Biological and Neural Mechanisms: Constraints and Control.Jason Winning & William Bechtel - 2018 - Minds and Machines 28 (2).
    Existing accounts of mechanistic causation are not suited for understanding causation in biological and neural mechanisms because they do not have the resources to capture the unique causal structure of control heterarchies. In this paper, we provide a new account on which the causal powers of mechanisms are grounded by time-dependent, variable constraints. Constraints can also serve as a key bridge concept between the mechanistic approach to explanation and underappreciated work in theoretical biology that sheds light on how biological systems (...)
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  8.  20
    Still Autonomous After All.Andrew Knoll - 2018 - Minds and Machines 28 (1):7-27.
    Recent mechanistic philosophers :1287–1321, 2016) have argued that the cognitive sciences are not autonomous from neuroscience proper. I clarify two senses of autonomy–metaphysical and epistemic—and argue that cognitive science is still autonomous in both senses. Moreover, mechanistic explanation of cognitive phenomena is not therefore an alternative to the view that cognitive science is autonomous of neuroscience. If anything, it’s a way of characterizing just how cognitive processes are implemented by neural mechanisms.
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  9.  16
    Toward Analog Neural Computation.Corey J. Maley - 2018 - Minds and Machines 28 (1):77-91.
    Computationalism about the brain is the view that the brain literally performs computations. For the view to be interesting, we need an account of computation. The most well-developed account of computation is Turing Machine computation, the account provided by theoretical computer science which provides the basis for contemporary digital computers. Some have thought that, given the seemingly-close analogy between the all-or-nothing nature of neural spikes in brains and the binary nature of digital logic, neural computation could be a species of (...)
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  10.  50
    Towards a Cognitive Neuroscience of Intentionality.Alex Morgan & Gualtiero Piccinini - 2018 - Minds and Machines 28 (1):119-139.
    We situate the debate on intentionality within the rise of cognitive neuroscience and argue that cognitive neuroscience can explain intentionality. We discuss the explanatory significance of ascribing intentionality to representations. At first, we focus on views that attempt to render such ascriptions naturalistic by construing them in a deflationary or merely pragmatic way. We then contrast these views with staunchly realist views that attempt to naturalize intentionality by developing theories of content for representations in terms of information and biological function. (...)
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  11.  9
    No-Report Paradigmatic Ascription of the Minimally Conscious State: Neural Signals as a Communicative Means for Operational Diagnostic Criteria.Hyungrae Noh - 2018 - Minds and Machines 28 (1):173-189.
    The minimally conscious sta te (MCS) is usually ascribed when a patientwith brain damage exhibits obser vable volitional behaviors that predict recovery ofcognitive funct ions. Nevertheless, a patient with brain damage who lacks motorcapacit y might nonetheless be in MCS. For this reason, some clinicians use neuralsignals as a communicative means for MCS ascription. For instance, a vegetativestate patient is diagnosed with MCS if activity in the motor area is observed whenthe instruction to imagine wiggling toes is given. The validi (...)
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  12.  19
    Computation and Representation in Cognitive Neuroscience.Gualtiero Piccinini - 2018 - Minds and Machines 28 (1):1-6.
  13.  12
    Neural Representations Beyond “Plus X”.Alessio Plebe & Vivian M. De La Cruz - 2018 - Minds and Machines 28 (1):93-117.
    In this paper we defend structural representations, more specifically neural structural representation. We are not alone in this, many are currently engaged in this endeavor. The direction we take, however, diverges from the main road, a road paved by the mathematical theory of measure that, in the 1970s, established homomorphism as the way to map empirical domains of things in the world to the codomain of numbers. By adopting the mind as codomain, this mapping became a boon for all those (...)
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  14.  12
    A Theory of Resonance: Towards an Ecological Cognitive Architecture.Vicente Raja - 2018 - Minds and Machines 28 (1):29-51.
    This paper presents a blueprint for an ecological cognitive architecture. Ecological psychology, I contend, must be complemented with a story about the role of the CNS in perception, action, and cognition. To arrive at such a story while staying true to the tenets of ecological psychology, it will be necessary to flesh out the central metaphor according to which the animal perceives its environment by ‘resonating’ to information in energy patterns: what is needed is a theory of resonance. I offer (...)
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  15.  12
    The Brain as an Input–Output Model of the World.Oron Shagrir - 2018 - Minds and Machines 28 (1):53-75.
    An underlying assumption in computational approaches in cognitive and brain sciences is that the nervous system is an input–output model of the world: Its input–output functions mirror certain relations in the target domains. I argue that the input–output modelling assumption plays distinct methodological and explanatory roles. Methodologically, input–output modelling serves to discover the computed function from environmental cues. Explanatorily, input–output modelling serves to account for the appropriateness of the computed function to the explanandum information-processing task. I compare very briefly the (...)
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  16.  36
    Neural Representations Observed.Eric Thomson & Gualtiero Piccinini - 2018 - Minds and Machines 28 (1):191-235.
    The historical debate on representation in cognitive science and neuroscience construes representations as theoretical posits and discusses the degree to which we have reason to posit them. We reject the premise of that debate. We argue that experimental neuroscientists routinely observe and manipulate neural representations in their laboratory. Therefore, neural representations are as real as neurons, action potentials, or any other well-established entities in our ontology.
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  17.  27
    Predictive Processing and the Representation Wars.Daniel Williams - 2018 - Minds and Machines 28 (1):141-172.
    Clark has recently suggested that predictive processing advances a theory of neural function with the resources to put an ecumenical end to the “representation wars” of recent cognitive science. In this paper I defend and develop this suggestion. First, I broaden the representation wars to include three foundational challenges to representational cognitive science. Second, I articulate three features of predictive processing’s account of internal representation that distinguish it from more orthodox representationalist frameworks. Specifically, I argue that it posits a resemblance-based (...)
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