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  1. The Intention of Intention.Ramón Casares - manuscript
    For Putnam in "Representation and Reality", there cannot be any intentional science, thus dooming cognitive science. His argument is that intentional concepts are functional, and that functionalism cannot explain anything because "everything has every functional organization", providing a proof. Analyzing his proof, we find that Putnam is assuming an ideal interpreting subject who can compute effortlessly and who is not intentional. But the subject doing science is a human being, and we are not that way. Therefore, in order to save (...)
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  2. Are algorithms always arbitrary? Three types of arbitrariness and ways to overcome the computationalist’s trilemma.C. Percy - manuscript
    Implementing an algorithm on part of our causally-interconnected physical environment requires three choices that are typically considered arbitrary, i.e. no single option is innately privileged without invoking an external observer perspective. First, how to delineate one set of local causal relationships from the environment. Second, within this delineation, which inputs and outputs to designate for attention. Third, what meaning to assign to particular states of the designated inputs and outputs. Having explained these types of arbitrariness, we assess their relevance for (...)
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  3. The psychology of implicit knowledge.Zoe Drayson - forthcoming - In Kurt Sylvan, Ernest Sosa, Jonathan Dancy & Matthias Steup (eds.), The Blackwell Companion to Epistemology, 3rd edition. Wiley Blackwell.
    Explicit knowledge is consciously accessible to the knower: the person can introspect what it is that they know and articulate it in the form of a statement (Dummett 1991, Davies 2015, Thompson 2023). If a person possesses some knowledge which they are unable to articulate to themselves or others, this knowledge is said to be implicit rather than explicit. Standard examples of implicit knowledge include a speaker’s knowledge of language, or practical knowledge such as how to ride a bike. The (...)
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  4. Function-Theoretic Explanation and Neural Mechanisms.Frances Egan - forthcoming - In David M. Kaplan (ed.), Integrating Mind and Brain Science: Mechanistic Perspectives and Beyond. Oxford University Press.
    A common kind of explanation in cognitive neuroscience might be called function-theoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes (in the system’s normal environment) the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it reveals (...)
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  5. Troubles with mathematical contents.Marco Facchin - forthcoming - Philosophical Psychology.
    To account for the explanatory role representations play in cognitive science, Egan’s deflationary account introduces a distinction between cognitive and mathematical contents. According to that account, only the latter are genuine explanatory posits of cognitive-scientific theories, as they represent the arguments and values cognitive devices need to represent to compute. Here, I argue that the deflationary account suffers from two important problems, whose roots trace back to the introduction of mathematical contents. First, I will argue that mathematical contents do not (...)
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  6. Intelligence. And what computers still can’t do.Jobst Landgrebe & Barry Smith - 2024 - Cosmos+Taxis 12 (5+6):104-114.
    We comment on the collection of papers inspired by our book Why Machines Will Never Rule the World published in volume 12 (5+6) of the journal Cosmos+Taxis. We summarize the arguments made by the contributors about what we say in the book, and then show where we disagree.
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  7. Intelligence, from Natural Origins to Artificial Frontiers - Human Intelligence vs. Artificial Intelligence.Nicolae Sfetcu - 2024 - Bucharest, Romania: MultiMedia Publishing.
    The parallel history of the evolution of human intelligence and artificial intelligence is a fascinating journey, highlighting the distinct but interconnected paths of biological evolution and technological innovation. This history can be seen as a series of interconnected developments, each advance in human intelligence paving the way for the next leap in artificial intelligence. Human intelligence and artificial intelligence have long been intertwined, evolving in parallel trajectories throughout history. As humans have sought to understand and reproduce intelligence, AI has emerged (...)
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  8. Modelling Thought Versus Modelling the Brain.Orly Shenker - 2024 - Human Arenas 1 (1):1.
    What is the connection between modelling thought and modelling the brain? In a model (as understood here), we strip away from the modelled system some non-essential features and retain some essential ones. What are the essential features of thought that are to be re- tained in the model, and conversely, what are its inessential features, that may be stripped away in the model? According to a prevalent view in contemporary science and philoso- phy, thought is a computation, and therefore its (...)
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  9. Is Intelligence Non-Computational Dynamical Coupling?Jonathan Simon - 2024 - Cosmos+Taxis 12 (5+6):23-36.
    Is the brain really a computer? In particular, is our intelligence a computational achievement: is it because our brains are computers that we get on in the world as well as we do? In this paper I will evaluate an ambitious new argument to the contrary, developed in Landgrebe and Smith (2021a, 2022). Landgrebe and Smith begin with the fact that many dynamical systems in the world are difficult or impossible to model accurately (inter alia, because it is intractable to (...)
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  10. Mind and Machine: A Philosophical Examination of Matt Carter’s “Minds & Computers: An Introduction to the Philosophy of Artificial Intelligence”.R. L. Tripathi - 2024 - Open Access Journal of Data Science and Artificial Intelligence 2 (1):3.
    In his book “Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence”, Matt Carter presents a comprehensive exploration of the philosophical questions surrounding artificial intelligence (AI). Carter argues that the development of AI is not merely a technological challenge but fundamentally a philosophical one. He delves into key issues like the nature of mental states, the limits of introspection, the implications of memory decay, and the functionalist framework that allows for the possibility of AI. Carter contrasts functionalism with (...)
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  11. Time-consciousness in computational phenomenology: a temporal analysis of active inference.Juan Diego Bogotá & Zakaria Djebbara - 2023 - Neuroscience of Consciousness 2023 (1):niad004.
    Time plays a significant role in science and everyday life. Despite being experienced as a continuous flow, computational models of consciousness are typically restricted to a sequential temporal structure. This difference poses a serious challenge for computational phenomenology—a novel field combining phenomenology and computational modelling. By analysing the temporal structure of the active inference framework, we show that an integrated continuity of time can be achieved by merging Husserlian temporality with a sequential order of time. We also show that a (...)
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  12. The language-of-thought hypothesis as a working hypothesis in cognitive science.Jake Quilty-Dunn, Nicolas Porot & Eric Mandelbaum - 2023 - Behavioral and Brain Sciences 46:e292.
    The target article attempted to draw connections between broad swaths of evidence by noticing a common thread: Abstract, symbolic, compositional codes, that is, language-of-thoughts (LoTs). Commentators raised concerns about the evidence and offered fascinating extensions to areas we overlooked. Here we respond and highlight the many specific empirical questions to be answered in the next decade and beyond.
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  13. Massive Modularity: An Ontological Hypothesis or an Adaptationist Discovery Heuristic?David Villena - 2023 - International Studies in the Philosophy of Science 36 (4):317-334.
    Cognitive modules are internal mental structures. Some theorists and empirical researchers hypothesise that the human mind is either partially or massively comprised of structures that are modular in nature. Is the massive modularity of mind hypothesis a cogent view about the ontological nature of human mind or is it, rather, an effective/ineffective adaptationist discovery heuristic for generating predictively successful hypotheses about both heretofore unknown psychological traits and unknown properties of already identified psychological traits? Considering the inadequacies of the case in (...)
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  14. Formal thought disorder and logical form: A symbolic computational model of terminological knowledge.Luis M. Augusto & Farshad Badie - 2022 - Journal of Knowledge Structures and Systems 3 (4):1-37.
    Although formal thought disorder (FTD) has been for long a clinical label in the assessment of some psychiatric disorders, in particular of schizophrenia, it remains a source of controversy, mostly because it is hard to say what exactly the “formal” in FTD refers to. We see anomalous processing of terminological knowledge, a core construct of human knowledge in general, behind FTD symptoms and we approach this anomaly from a strictly formal perspective. More specifically, we present here a symbolic computational model (...)
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  15. Mind as Machine: The Influence of Mechanism on the Conceptual Foundations of the Computer Metaphor.Pavel Baryshnikov - 2022 - RUDN Journal of Philosophy 26 (4):755-769.
    his article will focus on the mechanistic origins of the computer metaphor, which forms the conceptual framework for the methodology of the cognitive sciences, some areas of artificial intelligence and the philosophy of mind. The connection between the history of computing technology, epistemology and the philosophy of mind is expressed through the metaphorical dictionaries of the philosophical discourse of a particular era. The conceptual clarification of this connection and the substantiation of the mechanistic components of the computer metaphor is the (...)
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  16. Everything and More: The Prospects of Whole Brain Emulation.Eric Mandelbaum - 2022 - Journal of Philosophy 119 (8):444-459.
    Whole Brain Emulation has been championed as the most promising, well-defined route to achieving both human-level artificial intelligence and superintelligence. It has even been touted as a viable route to achieving immortality through brain uploading. WBE is not a fringe theory: the doctrine of Computationalism in philosophy of mind lends credence to the in-principle feasibility of the idea, and the standing of the Human Connectome Project makes it appear to be feasible in practice. Computationalism is a popular, independently plausible theory, (...)
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  17. Free energy: a user’s guide.Stephen Francis Mann, Ross Pain & Michael D. Kirchhoff - 2022 - Biology and Philosophy 37 (4):1-35.
    Over the last fifteen years, an ambitious explanatory framework has been proposed to unify explanations across biology and cognitive science. Active inference, whose most famous tenet is the free energy principle, has inspired excitement and confusion in equal measure. Here, we lay the ground for proper critical analysis of active inference, in three ways. First, we give simplified versions of its core mathematical models. Second, we outline the historical development of active inference and its relationship to other theoretical approaches. Third, (...)
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  18. Brain electrical traits of logical validity.F. Salto - 2021 - Scientific Reports 11 (7892).
    Neuroscience has studied deductive reasoning over the last 20 years under the assumption that deductive inferences are not only de jure but also de facto distinct from other forms of inference. The objective of this research is to verify if logically valid deductions leave any cerebral electrical trait that is distinct from the trait left by non-valid deductions. 23 subjects with an average age of 20.35 years were registered with MEG and placed into a two conditions paradigm (100 trials for (...)
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  19. Social Ontology: Time to Compute.Igor Mikhailov - 2020 - Vestnik Tomskogo Gosudarstvennogo Universiteta. Filosofiya, Sotsiologiya, Politologiya 1 (55):36-46.
    Discussions on the alleged methodological specificity of social knowledge are fueled to not the least extent by a kind of retarded position of the latter against technological advancements of natural and information science based on exact methods and formal or quantitative languages. It is more or less obvious that applicability of exact scientific methods to social disciplines is highly dependent on a chosen conception of social reality, i. e., on social ontology. In the article, the author critically approaches the ontological (...)
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  20. AI-Completeness: Using Deep Learning to Eliminate the Human Factor.Kristina Šekrst - 2020 - In Sandro Skansi (ed.), Guide to Deep Learning Basics. Springer. pp. 117-130.
    Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...)
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  21. 类人猿或安卓会毁灭地球吗?*雷·库兹韦尔(2012年)关于如何创造心灵的评论 (Will Hominoids or Androids Destroy the Earth? —A Review of How to Create a Mind by Ray Kurzweil (2012)) (2019年修订版).Michael Richard Starks - 2020 - In 欢迎来到地球上的地狱 婴儿,气候变化,比特币,卡特尔,中国,民主,多样性,养成基因,平等,黑客,人权,伊斯兰教,自由主义,繁荣,网络,混乱。饥饿,疾病,暴力,人工智能,战争. Las Vegas, NV USA: Reality Press. pp. 146-158.
    几年前,我通常可以从书名中分辨出什么,或者至少从章节标题中看出,会犯什么样的哲学错误,以及错误的频率。就名义上的科学著作而言,这些可能在很大程度上局限于某些章节,这些章节具有哲学意义或试图得出关于该作 品的意义或长期意义的一般性结论。然而,通常情况下,事实的科学问题慷慨地与哲学的胡言乱语,这些事实意味着什么。维特根斯坦在大约80年前描述的科学问题与各种语言游戏所描述的明确区别很少被考虑,因此人们交替 地被科学所震惊,并因它的不连贯而感到沮丧。分析。因此,这是与这个卷。 如果一个人要创造一个或多或少像我们一样的头脑,一个人需要有一个理性的逻辑结构,并理解两种思想体系(双过程理论)。如果一个人要对此进行哲学思考,就需要理解科学事实问题与语言如何在问题语境中工作,以及如何 避免还原主义和科学主义的陷阱的哲学问题之间的区别,但Kurzweil,如最学生的行为,基本上都是无知的。他被模型、理论和概念所陶醉,以及解释的冲动,而维特根斯坦向我们表明,我们只需要描述,理论、概念等 只是使用语言(语言游戏)的方式,只有它们有明确的价值测试(清晰的真理制造者,或约翰西尔(AI最著名的批评家)喜欢说,明确的满意条件(COS))。我试图在我最近的著作中对此作一个开端。 那些希望从现代两个系统的观点来看为人类行为建立一个全面的最新框架的人,可以查阅我的书《路德维希的哲学、心理学、Mind 和语言的逻辑结构》维特根斯坦和约翰·西尔的《第二部》(2019年)。那些对我更多的作品感兴趣的人可能会看到《会说话的猴子——一个末日星球上的哲学、心理学、科学、宗教和政治——文章和评论2006-201 9年第3次(2019年)和自杀乌托邦幻想21篇世纪4日 (2019) .
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  22. O "Frame Problem": a sensibilidade ao contexto como um desafio para teorias representacionais da mente.Carlos Barth - 2019 - Dissertation, Federal University of Minas Gerais
    Context sensitivity is one of the distinctive marks of human intelligence. Understanding the flexible way in which humans think and act in a potentially infinite number of circumstances, even though they’re only finite and limited beings, is a central challenge for the philosophy of mind and cognitive science, particularly in the case of those using representational theories. In this work, the frame problem, that is, the challenge of explaining how human cognition efficiently acknowledges what is relevant from what is not (...)
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  23. The quantization error in a Self-Organizing Map as a contrast and color specific indicator of single-pixel change in large random patterns.Birgitta Dresp-Langley - 2019 - Neural Networks 120:116-128..
    The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series and in time series of satellite images. Here, the functional properties of the quantization error in SOM are explored further to show that the metric is capable of reliably discriminating between the finest differences in local contrast intensities and contrast signs. While this capability of the QE is (...)
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  24. A fresh look at research strategies in computational cognitive science: The case of enculturated mathematical problem solving.Regina E. Fabry & Markus Pantsar - 2019 - Synthese 198 (4):3221-3263.
    Marr’s seminal distinction between computational, algorithmic, and implementational levels of analysis has inspired research in cognitive science for more than 30 years. According to a widely-used paradigm, the modelling of cognitive processes should mainly operate on the computational level and be targeted at the idealised competence, rather than the actual performance of cognisers in a specific domain. In this paper, we explore how this paradigm can be adopted and revised to understand mathematical problem solving. The computational-level approach applies methods from (...)
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  25. The physics of implementing logic: Landauer's principle and the multiple-computations theorem.Meir Hemmo & Orly Shenker - 2019 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 68:90-105.
    This paper makes a novel linkage between the multiple-computations theorem in philosophy of mind and Landauer’s principle in physics. The multiple-computations theorem implies that certain physical systems implement simultaneously more than one computation. Landauer’s principle implies that the physical implementation of “logically irreversible” functions is accompanied by minimal entropy increase. We show that the multiple-computations theorem is incompatible with, or at least challenges, the universal validity of Landauer’s principle. To this end we provide accounts of both ideas in terms of (...)
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  26. Del hombre-máquina a la máquina-hombre: materialismo, mecanicismo y transhumanismo.Martín López Corredoira - 2019 - Naturaleza y Libertad. Revista de Estudios Interdisciplinares 12:179-190.
    El materialismo de la Edad Moderna nos describe al hombre como una máquina, comparable a un complejo artilugio mecánico. Cabe entonces imaginar que una máquina no-biológica pueda constituir un ser pensante como lo son los seres humanos, e incluso cabría pensar en la posibilidad de codificación de una mente humana real para su posterior trasvase a un sustrato artificial. Considero que estas últimas posiciones son más propias de la cultura friki o de amantes de la ciencia ficción que de una (...)
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  27. Cognitive and Computational Complexity: Considerations from Mathematical Problem Solving.Markus Pantsar - 2019 - Erkenntnis 86 (4):961-997.
    Following Marr’s famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage in cognitive processes that (...)
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  28. Intellectualism and the argument from cognitive science.Arieh Schwartz & Zoe Drayson - 2019 - Philosophical Psychology 32 (5):662-692.
    Intellectualism is the claim that practical knowledge or ‘know-how’ is a kind of propositional knowledge. The debate over Intellectualism has appealed to two different kinds of evidence, semantic and scientific. This paper concerns the relationship between Intellectualist arguments based on truth-conditional semantics of practical knowledge ascriptions, and anti-Intellectualist arguments based on cognitive science and propositional representation. The first half of the paper argues that the anti-Intellectualist argument from cognitive science rests on a naturalistic approach to metaphysics: its proponents assume that (...)
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  29. Reseña de ' Los Límites Exteriores de la Razón '(The Outer Limits of Reason) por Noson Yanofsky 403p (2013) (revision revisada 2019).Michael Richard Starks - 2019 - In Delirios Utópicos Suicidas en el Siglo 21 La filosofía, la naturaleza humana y el colapso de la civilización Artículos y reseñas 2006-2019 4TH Edición. Reality Press. pp. 283-298.
    Doy una revisión detallada de ' los límites externos de la razón ' por Noson Yanofsky desde una perspectiva unificada de Wittgenstein y la psicología evolutiva. Yo indiqué que la dificultad con cuestiones como la paradoja en el lenguaje y las matemáticas, la incompletitud, la indeterminación, la computabilidad, el cerebro y el universo como ordenadores, etc., surgen de la falta de mirada cuidadosa a nuestro uso del lenguaje en el adecuado contexto y, por tanto, el Error al separar los problemas (...)
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  30. Reseña de ‘Hacer el Mundo Social’ (‘Making the Social World’) por John Searle (2010) (revisión revisada 2019).Michael Richard Starks - 2019 - In Delirios Utópicos Suicidas en el Siglo 21 La filosofía, la naturaleza humana y el colapso de la civilización Artículos y reseñas 2006-2019 4TH Edición. Reality Press. pp. 102-123.
    Antes de comentar detalladamente sobre Haciendo lo Mundo Social (MSW) primero voy a ofrecer algunos comentarios sobre la filosofía (psicología descriptiva) y su relación con la investigación psicológica contemporánea como ejemplificado en las obras de Searle (S) y Wittgenstein (W), ya que siento que esta es la mejor manera de lugar Searle o cualquier comentarista sobre el comportamiento, en la perspectiva adecuada. Será de gran ayuda para ver mis comentarios de PNC, TLP, PI, OC, TARW y otros libros de estos (...)
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  31. Será que Hominoids ou Androids Destroem a Terra? — uma revisão de Como Criar Uma Mente (How to Create a Mind) por Ray Kurzweil (2012) (revisão revisada 2019).Michael Richard Starks - 2019 - In Delírios Utópicos Suicidas no Século XXI - Filosofia, Natureza Humana e o Colapso da Civilization - Artigos e Comentários 2006-2019 5ª edição. Las Vegas, NV USA: Reality Press. pp. 155-167.
    Alguns anos atrás, cheguei ao ponto onde eu normalmente pode dizer a partir do título de um livro, ou pelo menos a partir dos títulos do capítulo, que tipos de erros filosóficos serão feitas e com que freqüência. No caso de obras nominalmente científicas, estas podem ser largamente restritas a certos capítulos que enceram filosóficos ou tentam tirar conclusões gerais sobre o significado ou significado a longo prazo do trabalho. Normalmente entretanto as matérias científicas do fato são misturado generosa com (...)
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  32. ¿Los hominoides o androides destruirán la tierra? — Una revisión de ‘Cómo Crear una Mente’ (How to Create a Mind) por Ray Kurzweil (2012) (revisión revisada 2019).Michael Richard Starks - 2019 - In Delirios Utópicos Suicidas en el Siglo 21 La filosofía, la naturaleza humana y el colapso de la civilización Artículos y reseñas 2006-2019 4TH Edición. Reality Press. pp. 250-262.
    Hace algunos años, Llegué al punto en el que normalmente puedo decir del título de un libro, o al menos de los títulos de los capítulos, qué tipos de errores filosóficos se harán y con qué frecuencia. En el caso de trabajos nominalmente científicos, estos pueden estar en gran parte restringidos a ciertos capítulos que enceran filosóficos o tratan de sacar conclusiones generales sobre el significado o significado a largo plazo de la obra. Normalmente, sin embargo, las cuestiones científicas de (...)
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  33. The realizers and vehicles of mental representation.Zoe Drayson - 2018 - Studies in History and Philosophy of Science Part A 68:80-87.
    The neural vehicles of mental representation play an explanatory role in cognitive psychology that their realizers do not. In this paper, I argue that the individuation of realizers as vehicles of representation restricts the sorts of explanations in which they can participate. I illustrate this with reference to Rupert’s (2011) claim that representational vehicles can play an explanatory role in psychology in virtue of their quantity or proportion. I propose that such quantity-based explanatory claims can apply only to realizers and (...)
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  34. In the interest of saving time: a critique of discrete perception.Tomer Fekete, Sander Van de Cruys, Vebjørn Ekroll & Cees van Leeuwen - 2018 - Neuroscience of Consciousness 2018 (1):1-8.
    A recently proposed model of sensory processing suggests that perceptual experience is updated in discrete steps. We show that the data advanced to support discrete perception are in fact compatible with a continuous account of perception. Physiological and psychophysical constraints, moreover, as well as our awake-primate imaging data, imply that human neuronal networks cannot support discrete updates of perceptual content at the maximal update rates consistent with phenomenology. A more comprehensive approach to understanding the physiology of perception (and experience at (...)
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  35. The Phenomenon of "Virtualization" of the World in Modern Society.Nikitin Grigory & Danilova Marina - 2018 - Astra Salvensis (12):661-663.
    Post-industrial society is also defined as a "post-class" society, reflecting the breakdown of the stable social structures and identities characteristic of industrial society. If before the status of an individual in a society was determined by his place in the economic structure, that is, by the class affiliation to which all other social characteristics were subordinated, now the status characteristic of the individual is determined by a multitude of factors, among which the increasing role is played by education and the (...)
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  36. The Cognitive Basis of Computation: Putting Computation in Its Place.Daniel D. Hutto, Erik Myin, Anco Peeters & Farid Zahnoun - 2018 - In Mark Sprevak & Matteo Colombo (eds.), The Routledge Handbook of the Computational Mind. Routledge. pp. 272-282.
    The mainstream view in cognitive science is that computation lies at the basis of and explains cognition. Our analysis reveals that there is no compelling evidence or argument for thinking that brains compute. It makes the case for inverting the explanatory order proposed by the computational basis of cognition thesis. We give reasons to reverse the polarity of standard thinking on this topic, and ask how it is possible that computation, natural and artificial, might be based on cognition and not (...)
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  37. Making too many enemies: Hutto and Myin’s attack on computationalism.Jesse Kuokkanen & Anna-Mari Rusanen - 2018 - Philosophical Explorations 21 (2):282-294.
    We analyse Hutto & Myin's three arguments against computationalism [Hutto, D., E. Myin, A. Peeters, and F. Zahnoun. Forthcoming. “The Cognitive Basis of Computation: Putting Computation In Its Place.” In The Routledge Handbook of the Computational Mind, edited by M. Sprevak, and M. Colombo. London: Routledge.; Hutto, D., and E. Myin. 2012. Radicalizing Enactivism: Basic Minds Without Content. Cambridge, MA: MIT Press; Hutto, D., and E. Myin. 2017. Evolving Enactivism: Basic Minds Meet Content. Cambridge, MA: MIT Press]. The Hard Problem (...)
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  38. Heterogeneous Proxytypes Extended: Integrating Theory-like Representations and Mechanisms with Prototypes and Exemplars.Antonio Lieto - 2018 - In Advances in Intelligent Systems and Computing, Springer. Springer.
    The paper introduces an extension of the proposal according to which conceptual representations in cognitive agents should be intended as heterogeneous proxytypes. The main contribution of this paper is in that it details how to reconcile, under a heterogeneous representational perspective, different theories of typicality about conceptual representation and reasoning. In particular, it provides a novel theoretical hypothesis - as well as a novel categorization algorithm called DELTA - showing how to integrate the representational and reasoning assumptions of the theory-theory (...)
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  39. The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments.Antonio Lieto, Christian Lebiere & Alessandro Oltramari - 2018 - Cognitive Systems Research:1-42.
    In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build arti cial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the (...)
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  40. Objections to Computationalism: A Survey.Marcin Miłkowski - 2018 - Roczniki Filozoficzne 66 (3):57-75.
    In this paper, the Author reviewed the typical objections against the claim that brains are computers, or, to be more precise, information-processing mechanisms. By showing that practically all the popular objections are based on uncharitable interpretations of the claim, he argues that the claim is likely to be true, relevant to contemporary cognitive science, and non-trivial.
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  41. From Computer Metaphor to Computational Modeling: The Evolution of Computationalism.Marcin Miłkowski - 2018 - Minds and Machines 28 (3):515-541.
    In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...)
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  42. Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail.Marcin Miłkowski, Witold M. Hensel & Mateusz Hohol - 2018 - Journal of Computational Neuroscience 3 (45):163-172.
    Replicability and reproducibility of computational models has been somewhat understudied by “the replication movement.” In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, (...)
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  43. Reasoning, Rules and Representation.Paul Robinson & Richard Samuels - 2018 - In Sorin Bangu (ed.), Naturalizing Logico-Mathematical Knowledge: Approaches From Psychology and Cognitive Science. New York: Routledge. pp. 30-51.
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  44. The Swapping Constraint.Henry Ian Schiller - 2018 - Minds and Machines 28 (3):605-622.
    Triviality arguments against the computational theory of mind claim that computational implementation is trivial and thus does not serve as an adequate metaphysical basis for mental states. It is common to take computational implementation to consist in a mapping from physical states to abstract computational states. In this paper, I propose a novel constraint on the kinds of physical states that can implement computational states, which helps to specify what it is for two physical states to non-trivially implement the same (...)
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  45. Rational analysis, intractability, and the prospects of ‘as if’-explanations.Iris van Rooij, Johan Kwisthout, Todd Wareham & Cory Wright - 2018 - Synthese 195 (2):491-510.
    Despite their success in describing and predicting cognitive behavior, the plausibility of so-called ‘rational explanations’ is often contested on the grounds of computational intractability. Several cognitive scientists have argued that such intractability is an orthogonal pseudoproblem, however, since rational explanations account for the ‘why’ of cognition but are agnostic about the ‘how’. Their central premise is that humans do not actually perform the rational calculations posited by their models, but only act as if they do. Whether or not the problem (...)
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  46. Enactive autonomy in computational systems.Mario Villalobos & Joe Dewhurst - 2018 - Synthese 195 (5):1891-1908.
    In this paper we will demonstrate that a computational system can meet the criteria for autonomy laid down by classical enactivism. The two criteria that we will focus on are operational closure and structural determinism, and we will show that both can be applied to a basic example of a physically instantiated Turing machine. We will also address the question of precariousness, and briefly suggest that a precarious Turing machine could be designed. Our aim in this paper is to challenge (...)
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  47. Five-Year-Olds’ Systematic Errors in Second-Order False Belief Tasks Are Due to First-Order Theory of Mind Strategy Selection: A Computational Modeling Study.Burcu Arslan, Niels A. Taatgen & Rineke Verbrugge - 2017 - Frontiers in Psychology 8.
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  48. The Turing Guide.Jack Copeland, Jonathan Bowen, Robin Wilson & Mark Sprevak (eds.) - 2017 - Oxford: Oxford University Press.
    This volume celebrates the various facets of Alan Turing (1912–1954), the British mathematician and computing pioneer, widely considered as the father of computer science. It is aimed at the general reader, with additional notes and references for those who wish to explore the life and work of Turing more deeply. -/- The book is divided into eight parts, covering different aspects of Turing’s life and work. -/- Part I presents various biographical aspects of Turing, some from a personal point of (...)
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  49. (3 other versions)Function-Theoretic Explanation and the Search for Neural Mechanisms.Frances Egan - 2017 - In David Michael Kaplan (ed.), Explanation and Integration in Mind and Brain Science. Oxford, United Kingdom: Oxford University Press. pp. 145-163.
    A common kind of explanation in cognitive neuroscience might be called functiontheoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes (in the system’s normal environment) the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it reveals (...)
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  50. (3 other versions)Function-Theoretic Explanation and the Search for Neural Mechanisms.Frances Egan - 2017 - In David Michael Kaplan (ed.), Explanation and Integration in Mind and Brain Science. Oxford, United Kingdom: Oxford University Press. pp. 145-163.
    A common kind of explanation in cognitive neuroscience might be called function-theoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes the exercise of the cognitive capacity (in the system's normal environment). Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it reveals (...)
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