Computationalism in Cognitive Science

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 (...) cognitive science, we propose to replace the ideal subject with a real and intentional human subject, and we propose to model intentionality by using a problem theory which is an intuitionist set theory where the resolving subject is a computing device. We are intentional because we are living beings, where life is the intention of not to die, so we are embodied intentions designed by evolution. We are real and then we have to compute our resolutions to the survival problem, and fortuitously we are computationally Turing complete, so our language is complete and then full and self referable. In summary, evolutionary subjectivism modeled as problem solving by computing should save cognitive science. Or, in other words, we are proposing to update Kant with Darwin and Turing. (shrink)

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, (...) we describe three different kinds of claim -- labelled mathematical, empirical and general -- routinely made by proponents of the framework, and suggest dialectical links between them. Overall, we aim to increase philosophical understanding of active inference so that it may be more readily evaluated. -/- This is a manuscript draft of the Introduction to the Topical Collection "The Free Energy Principle: From Biology to Cognition", forthcoming in Biology & Philosophy. (shrink)

Many philosophers worry that cognitive scientists apply the concept REPRESENTATION too liberally. For example, William Ramsey argues that scientists often ascribe natural representations according to the “receptor notion,” a causal account with absurd consequences. I rehabilitate the receptor notion by augmenting it with a background condition: that natural representations are ascribed only to systems construed as organisms. This Organism-Receptor account rationalizes our existing conceptual practice, including the fact that scientists in fact reject Ramsey’s absurd consequences. The Organism-Receptor account raises some (...) worrying questions, but as a more faithful characterization of scientific practice it is a better guide to conceptual reform. (shrink)

Many of us consider it uncontroversial that information processing is a natural function of the brain. Since functions in biology are only won through empirical investigation, there should be a significant body of unambiguous evidence that supports this functional claim. Before we can interpret the evidence, however, we must ask what it means for a biological system to process information. Although a concept of information is generally accepted in the neurosciences without critique, in other biological sciences applications of information, despite (...) careful analysis, remain controversial. In this work I will review classical stimulus-response studies in neuroscience and use Claude Shannon’s mathematical information theory as a starting point to interpret information processing as a function of the brain. I will illustrate a disanalogy between Shannon’s communication model (source, encode, channel, receiver, decode) and neural systems, and will argue that the neural code is not very code-like in comparison to genetic and engineered codes. I suggest that we have conflated the act of representing neuroscientific facts—which we do to summarize and communicate our findings with others—with taking experimental facts to be representations. (shrink)

For many years, most AI researchers and cognitive scientists have reserved the topic of consciousness for after dinner conversation. Like "intuition," the idea of consciousness appeared to be too vague or general to be a good starting place for understanding cognition. Work on narrowly-defined problems in specialized domains such as medicine and manufacturing focused our concerns on the nature of representation, memory, strategies for problem-solving, and learning. Some writers, notably Ornstein and Hofstadter, continued to explore the ideas, but implications for (...) cognitive modeling were unclear, suggesting neither experiments, nor new computational mechanisms. (shrink)

Neuroscientists are searching for the engram within the conceptual framework established by John Locke's theory of mind. This framework was elaborated before the development of information theory, before the development of information processing machines and the science of computation, before the discovery that molecules carry hereditary information, before the discovery of the codon code and the molecular machinery for editing the messages written in this code and translating it into transcription factors that mark abstract features of organic structure such as (...) anterior and distal. The search for the engram needs to abandon Locke's conceptual framework and work within a framework informed by these developments. The engram is the medium by which information extracted from past experience is transmitted to the computations that inform future behavior. The information-conveying symbols in the engram are rapidly generated in the course of computations, which implies that they are molecules. (shrink)

Neuroclassicism is the view that cognition is explained by “classical” computing mechanisms in the nervous system that exhibit a clear demarcation between processing machinery and read–write memory. The psychologist C. R. Gallistel has mounted a sophisticated defense of neuroclassicism by drawing from ethology and computability theory to argue that animal brains necessarily contain read–write memory mechanisms. This argument threatens to undermine the “connectionist” orthodoxy in contemporary neuroscience, which does not seem to recognize any such mechanisms. In this paper I argue (...) that the neuroclassicist critique rests on a misunderstanding of how computability theory constrains theorizing about natural computing mechanisms. (shrink)

Since Tolman’s paper in 1948, psychologists and neuroscientists have argued that cartographic representations play an important role in cognition. These empirical findings align with some theoretical works developed by philosophers who promote a pluralist view of representational vehicles, stating that cognitive processes involve representations with different formats. However, the inferential relations between maps and representations with different formats have not been sufficiently explored. Thus, this paper is focused on the inferential relations between cartographic and linguistic representations. To that effect, we (...) appeal to heterogeneous inference with ordinary maps and sentences. In doing so, we aim to build a model to bridge the gap between cartographic and linguistic thought. (shrink)

In this paper, we propose a new mathematical model of retrieval of intentions from long-term memory. We model retrieval as a stochastic race between a plurality of potentially relevant intentions stored in long-term memory. Psychological theories are dominated by two opposing conceptions of the role of memory in temporally extended agency – as when a person has to remember to make a phone call in the afternoon because, in the morning, she promised she would do so. According to the Working (...) Memory conception, remembering to make the phone call is explained in terms of the construction and maintenance of intentions in working-memory. According to the Long-Term Memory conception, we should explain the episode in terms of an ability to store intentions in long-term memory. The two conceptions predict different processing profiles. The aim of this paper is to present a new mathematical model of the type of memory mechanism that could realise the long-term memory representations of intentions necessary for the Long-Term Memory conception. We present and illustrate the formal model and propose a new type of experimental paradigm that could allow us to test which of the two conceptions provides the best explanation of the role of memory in temporally extended agency. (shrink)

Events and event prediction are pivotal concepts across much of cognitive science, as demonstrated by the papers in this special issue. We first discuss how the study of events and the predictive processing framework may fruitfully inform each other. We then briefly point to some links to broader philosophical questions about events.

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 (...) make their problem solving algorithms computationally suboptimal, in contrast with the optimal algorithms studied in the computational approach. Therefore, in order to accurately model the human cognitive tasks involved in mathematical problem solving, we need to expand our methodology to also include aspects relevant to the algorithmic level. This allows us to study algorithms that are cognitively optimal for human problem solvers. Since problem solving methods are not universal, I propose that they should be studied in the framework of enculturation, which can explain the expected cultural variance in the humanly optimal algorithms. While mathematical problem solving is used as the case study, the considerations in this paper concern modeling of cognitive tasks in general. (shrink)

Pragmatic theories and computational models of reference must account for people’s frequent use of redundant color adjectives (e.g., referring to a single triangle as “the blue triangle”). The standard pragmatic view holds that the informativity of a referential expression depends on pragmatic contrast: Color adjectives should be used to contrast competitors of the same kind to preempt an ambiguity (e.g., between several triangles of different colors), otherwise they are redundant. Here we propose an alternative to the standard view, the incremental (...) efficiency hypothesis, according to which the efficiency of a referential expression must be calculated incrementally over the entire visual context. This is the first theoretical account of referential efficiency that is sensitive to the incrementality of language processing, making different cross-linguistic predictions depending on word order. Experiment 1 confirmed that English speakers produced more redundant color adjectives (e.g., “the blue triangle”) than Spanish speakers (e.g., “el triángulo azul”), but both language groups used more redundant color adjectives in denser displays where it would be more efficient. In Experiments 2A and 2B, we used eye tracking to show that pragmatic contrast is not a processing constraint. Instead, incrementality and efficiency determine that English listeners establish color contrast across categories (BLUE SHAPES > TRIANGULAR ONE), whereas Spanish listeners establish color contrast within a category (TRIANGLES > BLUE ONE). Spanish listeners, however, reversed their visual search strategy when tested in English immediately after. Our results show that speakers and listeners of different languages exploit word order to increase communicative efficiency. (shrink)

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 (...) each condition) which each presented the exact same relational complexity (same variables and content) but had distinct logical complexity. Both conditions show the same electromagnetic components (P3, N4) in the early temporal window (250–525 ms) and P6 in the late temporal window (500–775 ms). The significant activity in both valid and invalid conditions is found in sensors from medial prefrontal regions, probably corresponding to the ACC or to the medial prefrontal cortex. The amplitude and intensity of valid deductions is significantly lower in both temporal windows (p = 0.0003). The reaction time was 54.37% slower in the valid condition. Validity leaves a minimal but measurable hypoactive electrical trait in brain processing. The minor electrical demand is attributable to the recursive and automatable character of valid deductions, suggesting a physical indicator of computational deductive properties. It is hypothesized that all valid deductions are recursive and hypoactive. (shrink)

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 (...) neuroscience. Instead, it is more appropriate to view the current developments as making central again approaches that facilitated some of neuroscience’s most significant early achievements, namely, the Hodgkin–Huxley and FitzHugh–Nagumo models. The second aim is primarily critical and defends a version of the “dynamical hypothesis” in neuroscience. Whereas the original version centered on defending a noncomputational and nonrepresentational account of cognition, the version I have in mind is broader and includes both cognition and the neural systems that realize it as well. In view of that, I discuss research on motor control as a paradigmatic example demonstrating that the concepts and methods of dynamical systems theory are increasingly and successfully being applied to neural systems in contemporary neuroscience. More significantly, such applications are motivating a stronger metaphysical claim, that is, understanding neural systems as being dynamical systems, which includes not requiring appeal to representations to explain or understand those phenomena. Taken together, the historical claim and the critical claim demonstrate that the dynamical hypothesis is undergoing a renaissance in contemporary neuroscience. (shrink)

This article compares situated cognition to contemporary Neo-Aristotelian approaches to the mind. The article distinguishes two components in this paradigm: an Aristotelian essentialism which is alien to situated cognition and a Wittgensteinian “capacity approach” to the mind which is not just congenial to it but provides important conceptual and argumentative resources in defending social cognition against orthodox cognitive science. It focuses on a central tenet of that orthodoxy. According to what I call “encephalocentrism,” cognition is primarily or even exclusively a (...) computational process occurring inside the brain. Neo-Aristotelians accuse this claim of committing a “homuncular” or “mereological fallacy”. The article explains why the label “fallacy” is misleading, reconstructs the argument to the effect that encephalocentric applications of psychological predicates to the brain and its parts amount to a category mistake, and defends this argument against objections by Dennett, Searle, and Figdor. At the same time it criticizes the Neo-Aristotelian denial that the brain is the organ of cognition. It ends by suggesting ways in which the capacity approach and situated cognition might be combined to provide a realistic and ecologically sound picture of cognition as a suite of powers that flesh-and-blood animals exercise within their physical and social environments. (shrink)

In the literature on the nature and role of cognitive representation, three positions are taken across the conceptual landscape: robust realism, primitivism, and eliminativism. Recently, a fourth alternative that tries to avoid the shortcomings of traditional views has been proposed: content pragmatism. My aim is to defend pragmatism about content against some recent objections moved against the view. According to these objections, content pragmatism fails to capture the role played by representation in the cognitive sciences; and/or is an unstable view (...) that ends up collapsing into one of the traditional alternatives. I argue that those arguments fail. I show that content pragmatism has as much claim to descriptive adequacy as the traditional theories. Moreover, I defend the robustness of the view by arguing that it does not collapse into any of the traditional positions. Content pragmatism therefore offers a valid and coherent account of the nature of representational content. (shrink)

The book provides a deep account of the use of computational thinking (CT) skills in education, with a focus on individual, social and cultural elements, and dives into issues foregrounding CT skills. Although the chapters of the book provide important educational and practical implications for the reader, methodological choices and the lack of theoretical connections of CT concepts curtail the use of CT skills in education, from a constructionist view.

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 (...) solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI. (shrink)

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) .

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 (...) computational complexity theory and focuses on optimal strategies for completing cognitive tasks. However, human cognitive capacities in mathematical problem solving are essentially characterised by processes that are computationally sub-optimal, because they initially add to the computational complexity of the solutions. Yet, these solutions can be optimal for human cognisers given the acquisition and enactment of mathematical practices. Here we present diagrams and the spatial manipulation of symbols as two examples of problem solving strategies that can be computationally sub-optimal but humanly optimal. These aspects need to be taken into account when analysing competence in mathematical problem solving. Empirically informed considerations on enculturation can help identify, explore, and model the cognitive processes involved in problem solving tasks. The enculturation account of mathematical problem solving strongly suggests that computational-level analyses need to be complemented by considerations on the algorithmic and implementational levels. The emerging research strategy can help develop algorithms that model what we call enculturated cognitive optimality in an empirically plausible and ecologically valid way. (shrink)

It is generally accepted that, in the cognitive and neural sciences, there are both computational and mechanistic explanations. We ask how computational explanations can integrate into the mechanistic hierarchy. The problem stems from the fact that implementation and mechanistic relations have different forms. The implementation relation, from the states of an abstract computational system to the physical, implementing states is a homomorphism mapping relation. The mechanistic relation, however, is that of part/whole; the explaining features in a mechanistic explanation are the (...) components of the explanandum phenomenon and their causal organization. Moreover, each component in one level of mechanism is constituted and explained by components of an underlying level of mechanism. Hence, it seems, computational variables and functions cannot be mechanistically explained by the medium-dependent states and properties that implement them. How then, do the computational and the implementational integrate to create the mechanistic hierarchy? After explicating the general problem, we further demonstrate it through a concrete example, of reinforcement learning, in the cognitive and neural sciences. We then examine two possible solutions. On one solution, the mechanistic hierarchy embeds at the same levels computational and implementational properties. This picture fits with the view that computational explanations are mechanistic sketches. On the other solution, there are two separate hierarchies, one computational and another implementational, which are related by the implementation relation. This picture fits with the view that computational explanations are functional and autonomous explanations. It is less clear how these solutions fit with the view that computational explanations are full-fledged mechanistic explanations. Finally, we argue that both pictures are consistent with the reinforcement learning example, but that scientific practice does not align with the view that computational models are merely mechanistic sketches. (shrink)

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 (...) low-level fundamental concepts in statistical mechanics, thus providing a deeper understanding of these ideas than their standard formulations given in the high-level terms of thermodynamics and cognitive science. Since Landauer’s principle is pivotal in the attempts to derive the universal validity of the second law of thermodynamics in statistical mechanics, our result entails that the multiple-computations theorem has crucial implications with respect to the second law. Finally, our analysis contributes to the understanding of notions, such as “logical irreversibility,” “entropy increase,” “implementing a computation,” in terms of fundamental physics, and to resolving open questions in the literature of both fields, such as: what could it possibly mean that a certain physical process implements a certain computation. (shrink)

In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the development of a line of research (...) aiming at the realisation of artificial systems whose decisions are based on the adoption of powerful shortcut strategies (known as heuristics) based on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the “heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial complexity in real-life situations and still represents an important strategy for the design and implementation of intelligent systems. (shrink)

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 (...) cultura humanista seria. En cualquier caso, una cosa parece clara: la simulación por ordenador de cualquier tipo de materia no es la materia misma. Una simulación por ordenador de una estrella no emite luz ni calor, y del mismo modo tampoco ofrece calor humano (en términos materiales) ni voluntad de vivir una máquina que hipotéticamente pueda contener toda la información sobre el ser humano y simular sus respuestas. -/- English translation: The materialism of the Modern Age describes human beings as machines, comparable to complex mechanical devices. It is then possible to imagine that a non-biological machine can constitute a thinking being as humans are, and one could even think of the possibility of coding a real human mind for its subsequent transfer to an artificial substrate. I think that these latter positions are more typical of the geek culture or of science fiction lovers than of a serious humanist culture. In any case, one thing seems clear: the computer simulation of any type of matter is not the matter itself. A computer simulation of a star does not emit light or heat, and likewise a machine that hypothetically can contain all the information about a human being and simulate his/her responses offers neither human heat (in material terms) nor will to live. (shrink)

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 (...) findings from cognitive science provide evidence about the nature of mental states. We demonstrate that this fact has been overlooked in the ensuing debate, resulting in inconsistency and confusion. Defenders of the semantic approach to Intellectualism engage with the argument from cognitive science in a way that implicitly endorses this naturalistic metaphysics, and even rely on it to claim that cognitive science support Intellectualism. In the course of their arguments, however, they also reject that scientific findings can have metaphysical import. We argue that this situation is preventing productive debate about Intellectualism, which would benefit from both sides being more transparent about their metaphilosophical assumptions. (shrink)

The Language of Thought: A New Philosophical Direction, by SchneiderSusan. Cambridge, MA: MIT Press, 2011. Pp. xii + 259.

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 (...) hecho se entrelazan generosamente con la galimatías filosóficas en cuanto a lo que estos hechos significan. Las claras distinciones que Wittgenstein describió hace unos 80 años entre los asuntos científicos y sus descripciones por varios juegos de idiomas rara vez se toman en consideración, y por lo tanto uno es cautivado alternativamente por la ciencia y consternado por su incoherente Análisis. Así es con este volumen. -/- Si uno es crear una mente más o menos como la nuestra, uno necesita tener una estructura lógica para la racionalidad y una comprensión de los dos sistemas de pensamiento (teoría del proceso dual). Si se trata de filosofar sobre esto, se necesita entender la distinción entre cuestiones científicas de hecho y la cuestión filosófica de cómo funciona el lenguaje en el contexto en cuestión, y de cómo evitar las trampas del reduccionismo y el cientismo, pero Kurzweil, como más estudiantes de conducta, no tiene ni idea. Él está encantado con los modelos, teorías y conceptos, y el impulso de explicar, mientras que Wittgenstein nos mostró que sólo necesitamos describir, y que las teorías, conceptos, etc., son sólo formas de usar el lenguaje (juegos de idiomas) que tienen valor sólo en la medida en que tienen un claro prueba (claro que los creadores de la verdad, o como John Searle (el crítico más famoso de AI) le gusta decir, claras condiciones de satisfacción (COS)). He intentado proporcionar un comienzo en este en mis escritos recientes. -/- Aquellos que deseen un marco completo hasta la fecha para el comportamiento humano de la moderna dos sistemas punta da vista puede consultar mi libro 'La estructura lógica de la filosofía, la psicología, la mente y lenguaje en Ludwig Wittgenstein y John Searle ' 2a ED (2019). Los interesados en más de mis escritos pueden ver 'Monos parlantes--filosofía, psicología, ciencia, religión y política en un planeta condenado--artículos y reseñas 2006-2019 3rd ED (2019) y Delirios utópicos suicidas en el siglo 21 4a Ed (2019) y otras. -/- También, como es habitual en las cuentas "fácticas" de la IA/robótica, no da tiempo a las amenazas muy reales a nuestra privacidad, seguridad e incluso la supervivencia de la creciente ' androidización ' de la sociedad que es prominente en otros autores (Bostrum, Hawking, etc.) y frecuentes en SciFi y películas, así que hago algunos comentarios sobre las delirios utópicos muy posiblemente suicidas de androides ' agradables ', humanoides, inteligencia artificial (IA), la democracia, la diversidad y la ingeniería genética. -/- Me da por sentado que los avances técnicos en electrónica, robótica e IA ocurrirán, resultando en profundos cambios en la sociedad. Sin embargo, creo que los cambios provenientes de la ingeniería genética son al menos tan grandes y potencialmente mucho mayores, ya que nos permitirán cambiar totalmente Quiénes somos. Y será factible hacer servidores SuperSmart/súper fuertes modificando nuestros genes o los de otros monos. Al igual que con otras tecnologías, cualquier país que resista se quedará atrás. Pero, ¿será socialmente y económicamente factible implementar BioBots o superhumanos a gran escala? E incluso si es así, no parece Probable, económica o socialmente, para evitar que el Destrucción de la civilización industrial por la sobrepoblación, el agotamiento de los recursos, el cambio climático y probablemente también la regla tiránica de los siete sociópatas que gobiernan China. -/- Por lo tanto, ignorando los errores filosóficos en este volumen como irrelevantes, y dirigiendo nuestra atención sólo a la ciencia, lo que tenemos aquí es otra ilusión utópica suicida enraizada en el fracaso de captar la biología básica, la psicología y la ecología humana, las mismas ilusiones que están destruyendo Estados Unidos y el mundo. Veo una remota posibilidad de que el mundo se pueda salvar, pero no por Ia robótica, CRISPR, ni por neomarxismo, diversidad y la igualdad. (shrink)

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 (...) de hecho científico de las cuestiones de cómo funciona el lenguaje. Discuto las opiniones de Wittgenstein sobre la incompletitud, la paracoherencia y la indecisión y el trabajo de Wolpert en los límites de la computación. Resumiendo: el universo según Brooklyn---buena ciencia, no tan buena filosofía. Aquellos que deseen un marco completo hasta la fecha para el comportamiento humano de la moderna dos sistemas punto de vista puede consultar mi libros Talking Monkeys 3ª ed (2019), Estructura Logica de Filosofia, Psicología, Mente y Lenguaje en Ludwig Wittgenstein y John Searle 2ª ed (2019), Suicidio pela Democracia 4ª ed (2019), La Estructura Logica del Comportamiento Humano (2019), The Logical Structure de la Conciencia (2019, Entender las Conexiones entre Ciencia, Filosofía, Psicología, Religión, Política y Economía (2019), Delirios Utópicos Suicidas en el siglo 21 5ª ed (2019), Observaciones sobre Imposibilidad, Incompletitud, Paraconsistencia, Indecidibilidad, Aleatoriedad, Computabilidad, Paradoja e Incertidumbre en Chaitin, Wittgenstein, Hofstadter, Wolpert, Doria, da Costa, Godel, Searle, Rodych Berto, Floyd, Moyal-Sharrock y Yanofsky y otros. (shrink)

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 (...) dos genios de la psicología descriptiva. S no hace ninguna referencia a la declaración de la mente presciente de W como mecanismo en TLP, y su destrucción de ella en su trabajo posterior. Desde W, S se ha convertido en el principal deconstructor de estas vistas mecánicas de la conducta, y el psicólogo descriptivo más importante (filósofo), pero no se da cuenta de cómo completamente W lo anticipó ni, en general, hacer otros (pero ver los muchos papeles y libros de Proudfoot y Copeland en W, Turing y AI). El trabajo de S es mucho más fácil de seguir que W, y aunque hay cierta jerga, es sobre todo espectacularmente claro si te acercas desde la dirección correcta. Vea mis reseñas de W S y otros libros para más detalles. En general, MSW es un buen Resumen de los muchos avances sustanciales sobre Wittgenstein resultantes del medio siglo de trabajo de S, pero en mi opinión, W todavía es inigualable para la psicología básica una vez que entiendes lo que está diciendo (ver mis comentarios). Idealmente, deben leerse juntos: Searle para la clara prosa coherente y generalizaciones sobre la operación de S2/S3, ilustrada con los ejemplos perspicaces de W de la operación de S1/S2, y sus brillantes aforismos. Si yo fuera mucho más joven escribiría un libro haciendo exactamente eso. Aquellos que deseen un marco completo hasta la fecha para el comportamiento humano de la moderna dos systems punto de vista puede consultar mi libro 'La estructura lógica de la filosofía, la psicología, la mente y lenguaje nn Ludwig Wittgenstein y John Searle ' 2nd ED (2019). Los interesados en más de mis escritos pueden ver 'Monos parlantes--filosofía, psicología, ciencia, religión y política en un planeta condenado--artículos y reseñas 2006-2019 3rd ED (2019) y delirios utópicos suicidas en el 21St Century 4TH Ed (2019) y otras. (shrink)

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 (...) o jargão filosófico a respeito do que estes fatos significam. As distinções claras que Wittgenstein descreveu cerca de 80 anos atrás entre questões científicas e suas descrições por vários jogos de linguagem são raramente levados em consideração, e assim um é alternadamente impressionados pela ciência e desanimado por sua incoerente Análise. Assim é com este volume. -/- Se alguém é para criar uma mente mais ou menos como a nossa, é preciso ter uma estrutura lógica para a racionalidade e uma compreensão dos dois sistemas de pensamento (teoria do processo dual). Se uma delas é filosofar sobre isso, é preciso entender a distinção entre questões científicas de fato e a questão filosófica de como a linguagem funciona no contexto em questão, e de como evitar as armadilhas do reducionismo e do cientismo, mas Kurzweil, como mais estudantes de comportamento, é em grande parte c sem noção. Ele está encantado com modelos, teorias e conceitos, e o impulso de explicar, enquanto Wittgenstein nos mostrou que só precisamos descrever, e que as teorias, conceitos etc, são apenas maneiras de usar a linguagem (jogos de linguagem) que têm valor apenas na medida em que eles têm uma clara teste (claro que os verdadeiros, ou como John Searle (crítico mais famoso da AI) gosta de dizer, claro condições de satisfação (COS)). Eu tentei fornecer um começo nisto em meus escritos recentes. -/- Aqueles que desejam um quadro até à data detalhado para o comportamento humano da opinião moderna dos dois sistemas consultar meu livros Falando Macacos 3ª Ed (2019), A Estrutura Lógica da Filosofia, Psicologia, Mente e Linguagem em Ludwig Wittgenstein e John Searle 2a Ed (2019), Suicídio Pela Democracia,4aEd(2019), Entendendo as Conexões entre Ciência, Filosofia, Psicologia, Religião, Política e Economia Artigos e Análises 2006-2019 (2019), Ilusões Utópicas Suicidas no 21St século 5a Ed (2019), A Estrutura Lógica do Comportamento Humano (2019), e A Estrutura Lógica da Consciência (2019) y outros. (shrink)

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 (...) not to vehicles, in virtue of the particular causal role that vehicles play in psychological explanations. (shrink)

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 (...) large) is therefore called for, and we briefly outline our take on the problem. (shrink)

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 (...) level of culture (according to P. Bourdieu "cultural capital"). On this basis, D. Bell and several other Western sociologists put forward the idea of a new "service" class. (shrink)

We record the advances made by the eleventh century Persian logician Ibn Sina—known in the West as Avicenna—away from a purely cognitive view of proofs and towards a more computational view, and the kinds of consideration that led him to these advances. Some of Ibn Sina’s new logics, which stand somewhere between Aristotle’s categorical syllogisms and modern first-order logic, can serve as a kind of laboratory for testing what are the differences between Aristotelian and modern logic, and where these differences (...) come from. (shrink)

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 (...) the other way around. (shrink)

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 (...) of Content targets computationalism that relies on semantic notion of computation, claiming that it cannot account for the natural origins of content. The Intentionality Problem is targeted against computationalism using non-semantic accounts of computation, arguing that it fails in explaining intentionality. Theion Problem claims that causal interaction between concrete physical processes and abstract computational properties is problematic. We argue that these a... (shrink)

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 (...) of concepts with the those ascribed to the prototype and exemplars-based theories. (shrink)

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 (...) comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the fi nal part of the paper further directions of research will be explored, trying to address current limitations and future challenges. (shrink)

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 (...) to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

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.

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, (...) serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code. (shrink)

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 (...) computational state. (shrink)

Computational approaches dominate contemporary cognitive science, promising a unified, scientific explanation of how the mind works. However, computational approaches raise major philosophical and scientific questions. In what sense is the mind computational? How do computational approaches explain perception, learning, and decision making? What kinds of challenges should computational approaches overcome to advance our understanding of mind, brain, and behaviour? The Routledge Handbook of the Computational Mind is an outstanding overview and exploration of these issues and the first philosophical collection of (...) its kind. Comprising thirty-five chapters by an international team of contributors from different disciplines, the Handbook is organised into four parts: History and future prospects of computational approaches Types of computational approach Foundations and challenges of computational approaches Applications to specific parts of psychology. Essential reading for students and researchers in philosophy of mind, philosophy of psychology, and philosophy of science, The Routledge Handbook of the Computational Mind will also be of interest to those studying computational models in related subjects such as psychology, neuroscience, and computer science. (shrink)

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 (...) of intractability is solved by recourse to ‘as if’ explanations critically depends, inter alia, on the semantics of the ‘as if’ connective. We examine the five most sensible explications in the literature, and conclude that none of them circumvents the problem. As a result, rational ‘as if’ explanations must obey the minimal computational constraint of tractability. (shrink)

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 (...) the assumption that computational systems are necessarily heteronomous systems, to try and motivate in enactivism a more nuanced and less rigid conception of computational systems, and to demonstrate to computational theorists that they might find some interesting material within the enactivist tradition, despite its historical hostility towards computationalism. (shrink)

Annotated online bibliography on the Language of Thought Hypothesis.

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 (...) view. -/- Part II presents Turing’s universal machine (now known as a Turing machine), which provides a theoretical framework for reasoning about computation. His 1936 paper on this subject is widely seen as providing the starting point for the field of theoretical computer science. -/- Part III presents Turing’s working on codebreaking during World War II. While the War was a disastrous interlude for many, for Turing it provided a nationally important outlet for his creative genius. It is not an overstatement to say that without Turing, the War would probably have lasted longer, and may even have been lost by the Allies. The sensitive nature of Turning’s wartime work meant that much of this has been revealed only relatively recently. -/- Part IV presents Turing’s post-War work on computing, both at the National Physical Laboratory and at the University of Manchester. He made contributions to both hardware design, through the ACE computer at the NPL, and software, especially at Manchester. Part V covers Turing’s contribution to machine intelligence (now known as Artificial Intelligence or AI). Although Turing did not coin the term, he can be considered a founder of this field which is still active today, authoring a seminal paper in 1950. -/- Part VI covers morphogenesis, Turing’s last major scientific contribution, on the generation of seemingly random patterns in biology and on the mathematics behind such patterns. Interest in this area has increased rapidly in recent times in the field of bioinformatics, with Turing’s 1952 paper on this subject being frequently cited. -/- Part VII presents some of Turing’s mathematical influences and achievements. Turing was remarkably free of external influences, with few co-authors – Max Newman was an exception and acted as a mathematical mentor in both Cambridge and Manchester. -/- Part VIII considers Turing in a wider context, including his influence and legacy to science and in the public consciousness. -/- Reflecting Turing’s wide influence, the book includes contributions by authors from a wide variety of backgrounds. Contemporaries provide reminiscences, while there are perspectives by philosophers, mathematicians, computer scientists, historians of science, and museum curators. Some of the contributors gave presentations at Turing Centenary meetings in 2012 in Bletchley Park, King’s College Cambridge, and Oxford University, and several of the chapters in this volume are based on those presentations – some through transcription of the original talks, especially for Turing’s contemporaries, now aged in their 90s. Sadly, some contributors died before the publication of this book, hence its dedication to them. -/- For those interested in personal recollections, Chapters 2, 3, 11, 12, 16, 17, and 36 will be of interest. For philosophical aspects of Turing’s work, see Chapters 6, 7, 26–31, and 41. Mathematical perspectives can be found in Chapters 35 and 37–39. Historical perspectives can be found in Chapters 4, 8, 9, 10, 13–15, 18, 19, 21–25, 34, and 40. With respect to Turing’s body of work, the treatment in Parts II–VI is broadly chronological. We have attempted to be comprehensive with respect to all the important aspects of Turing’s achievements, and the book can be read cover to cover, or the chapters can be tackled individually if desired. There are cross-references between chapters where appropriate, and some chapters will inevitably overlap. -/- We hope that you enjoy this volume as part of your library and that you will dip into it whenever you wish to enter the multifaceted world of Alan Turing. (shrink)