Levels of Analysis in Cognitive Science

In this essay, it is argued that naturalism of an even moderate sort speaks strongly against a certain widely held thesis about the human mental (and cognitive) architecture: that it is divided into two distinct levels, the personal and the subpersonal, about the former of which we gain knowledge in a manner that effectively insulates such knowledge from the results of scientific research. -/- An empirically motivated alternative is proposed, according to which the architecture is, so to speak, flattened from (...) above. On this flattened view, although the states and processes typically associated with the personal level likely appear in our best models of the production of human behavior, they appear alongside states and processes normally associated with the subpersonal level. Moreover, the success of such models depends nowise on a levels-based distinction between the various causal contributors. It is argued that the flattened view has methodological implications of significant import. (shrink)

Many ideas from Georg Nortoff’s works (published one paper in 2010, mainly his book in 2011, other papers in 2012, 2103, 2014, especially those related to Kant’s philosophy and the notion of the “observer”, the mind-brain problem, default mode network, the self, the mental states and their “correspondence” to the brain) are surprisingly very similar to my ideas published in my article from 2002, 2005 and my book from 2008. In two papers from 2002 (also my paper from 2005 and (...) my book 2008), following Kant’s philosophy, I introduced the notion of the “observer” for the mind-brain problem. After 2010 (mainly his book 2011 and other papers after this book), Nortoff also uses Kant’s philosophy (even if his knowledge about Kant’s philosophy is very superficial!) and the notion of the “observer” for the mind-brain problem in a methodology very similar to my methodology. Moreover, instead of EDWs, Nortoff uses a kind of “transdisciplinary” view, quite close to parallelism – the closest approach to my EDWs! In his works until 2014, Northoff’s conclusion within the unicorn world was different than my conclusion. However, in his book 2014 (two volumes) using notions like “correlations” and even “correspondences” many times, his conclusion is very closed to my EDWs! This dramatic change of framework in 3 years is quite unbelievable!!! Incredible many ideas from this book are very similar to my ideas from 2005 and 2008! (shrink)

A friend of my sent me the address where this paper has been posted by Adam Frank. Reading it, I realized that more than 90% of the main ideas of this paper (about the mind-brain problem, quantum mechanics (microparticles-wave relationship, Schrodinger equation, probabilities, “perceiving subject in physics”, the idea about consciousness and Nagel, etc. etc.) are UNBELIEVABLE similar to my ideas from my paper 2005 or my book 2008!!!

The aim of this expository paper is to give an informal overview of a plausible naturalistic case for free will. I will describe what I take to be the main naturalistically motivated challenges for free will and respond to them by presenting an indispensability argument for free will. The argument supports the reality of free will as an emergent higher-level phenomenon. I will also explain why the resulting picture of free will does not conflict with the possibility that the fundamental (...) laws of nature are deterministic, and I will address some common objections. (shrink)

Expanded reprint of the WIREs Science of Belief paper for Julien Musolino, Joseph Sommer, and Pernille Hemmer's The Science of Beliefs: A Multidisciplinary Approach. Cambridge University Press.

The empirical study of belief is emerging at a rapid clip, uniting work from all corners of cognitive science. Reliance on belief in understanding and predicting behavior is widespread. Examples can be found, inter alia, in the placebo, attribution theory, theory of mind, and comparative psychological literatures. Research on belief also provides evidence for robust generalizations, including about how we fix, store, and change our beliefs. Evidence supports the existence of a Spinozan system of belief fixation: one that is automatic (...) and independent of belief rejection. Independent research supports the existence of a system of fragmented belief storage: one that relies on large numbers of causally isolated, context-sensitive stores of belief in memory. Finally, empirical and observational data support at least two systems of belief change. One system adheres, mostly, to epistemological norms of updating; the other, the psychological immune system, functions to guard our most centrally held beliefs from potential inconsistency with newly formed beliefs. Refining our under- standing of these systems can shed light on pressing real-world issues, such as how fake news, propaganda, and brainwashing exploit our psychology of belief, and how best to construct our modern informational world. (shrink)

The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, linguistic (...) competence in this framework was regarded as being innate, rule-governed, domain-specific, and fundamentally different from performance, i.e., idiosyncrasies and factors governing linguistic behavior. I analyze state-of-the-art connectionist, deep learning models of natural language processing, most notably large language models, to see what they can tell us about linguistic competence. Deep learning is a statistical technique for the classification of patterns through which artificial intelligence researchers train artificial neural networks containing multiple layers that crunch a gargantuan amount of textual and/or visual data. I argue that these models suggest that linguistic competence should be construed as stochastic, pattern-based, and stemming from domain-general mechanisms. Moreover, I distinguish syntactic from semantic competence, and I show for each the ramifications of the endorsement of a connectionist research program as opposed to the traditional symbolic cognitive science and transformational-generative grammar. I provide a unifying front, consisting of usage-based theories, a construction grammar approach, and an embodied approach to cognition to show that the more multimodal and diverse models are in terms of architectural features and training data, the stronger the case is for the connectionist linguistic competence. I also propose to discard the competence vs. performance distinction as theoretically inferior so that a novel and integrative account of linguistic competence originating in connectionism and empiricism that I propose and defend in the dissertation could be put forward in scientific and philosophical literature. (shrink)

Muhammad Ali Khalidi contends that because cognitive science casts a wider net than neuroscience in searching for the causes of cognition, it is in the superior position to discover “real” cognitive kinds. I argue that while Khalidi identifies appropriate norms for individuating cognitive kinds, these norms ground his characterization of taxonomic practices in cognitive science, rather than the other way around. If we instead treat Khalidi's norms not as descriptively accurate characterizations of taxonomic practices in cognitive science, but as a (...) set of best practices for kinding cognition, is cognitive science in and neuroscience definitively out of the cognitive kinding game? (shrink)

The expanding social role and continued development of artificial intelligence (AI) needs theological investigation of its anthropological and moral potential. A pragmatic theological anthropology adapted for AI can characterize moral AI as experiencing its natural, social, and moral world through interpretations of its external reality as well as its self-reckoning. Systems theory can further structure insights into an AI social self that conceptualizes itself within Ignacio Ellacuria’s historical reality and its moral norms through Thomistic ideogenesis. This enables a conceptualization process (...) capable of carrying moral weight and grounded in reality; structures the experience of an AI emergent self into multiple levels of interpretation; and drives a multi-level systems architecture for moral AI. Modeling AI’s interpretive experience and self-reckoning as a causal, sociotechnical, and moral actor can help moral AI identify conflicts between its normative values and develop the practical wisdom (phronesis) needed to apply its general moral models. (shrink)

Recent years have seen a Bayesian revolution in cognitive science. This should be of interest to metaphysicians of science, whose naturalist project involves working out the metaphysical implications of our leading scientific accounts, and in advancing our understanding of those accounts by drawing on the metaphysical frameworks developed by philosophers. Toward these ends, in this paper I develop a metaphysics of the Bayesian mind. My central claim is that the Bayesian approach supports a novel empirical argument for normativism, the thesis (...) that belief has a normative essence, with the norms in question being those of rationality. The argument I develop draws in part on the causal powers subset account of realization defended by Wilson and Shoemaker, with the link being that Bayesians often appeal to Marr’s framework of levels of analysis, which carries with it multiple realizability claims that can be understood in terms of the subset model. I then use the view developed, with its appeal to normativism and subset realization, to explain how Bayesians can accommodate empirical findings of irrationality that are sometimes thought to pose a problem for their view. (shrink)

Each of our sensory modalities—vision, touch, taste, etc.—works on a slightly different timescale, with differing temporal resolutions and processing lag. This raises the question of how, or indeed whether, these sensory streams are co-ordinated or ‘bound’ into a coherent multisensory experience of the perceptual ‘now’. In this paper I evaluate one account of how temporal binding is achieved: the temporal windows hypothesis, concluding that, in its simplest form, this hypothesis is inadequate to capture a variety of multisensory phenomena. Rather, the (...) evidence suggests the existence of a more complex temporal structure in which multiple overlapping windows support distinct functional mechanisms. To aid in the precise formulation of such views, I propose a taxonomy of temporal window types and their characteristics that in turn suggests promising avenues for future empirical and philosophical research. I conclude by examining some philosophical implications of multi-window models for the metaphysics of perception and perceptual experience more generally. (shrink)

We advance an account that grounds cognition, specifically decision-making, in an activity all organisms as autonomous systems must perform to keep themselves viable—controlling their production mechanisms. Production mechanisms, as we characterize them, perform activities such as procuring resources from their environment, putting these resources to use to construct and repair the organism's body and moving through the environment. Given the variable nature of the environment and the continual degradation of the organism, these production mechanisms must be regulated by control mechanisms (...) that select when a production is required and how it should be carried out. To operate on production mechanisms, control mechanisms need to procure information through measurement processes and evaluate possible actions. They are making decisions. In all organisms, these decisions are made by multiple different control mechanisms that are organized not hierarchically but heterarchically. In many cases, they employ internal models of features of the environment with which the organism must deal. Cognition, in the form of decision-making, is thus fundamental to living systems which must control their production mechanisms. (shrink)

Some naturalistic philosophers of mind subscribing to the predictive processing theory of mind have adopted a realist attitude towards the results of Bayesian cognitive science. In this paper, we argue that this realist attitude is unwarranted. The Bayesian research program in cognitive science does not possess special epistemic virtues over alternative approaches for explaining mental phenomena involving uncertainty. In particular, the Bayesian approach is not simpler, more unifying, or more rational than alternatives. It is also contentious that the Bayesian approach (...) is overall better supported by the empirical evidence. So, to develop philosophical theories of mind on the basis of a realist interpretation of results from Bayesian cognitive science is unwarranted. Naturalistic philosophers of mind should instead adopt an anti-realist attitude towards these results and remain agnostic as to whether Bayesian models are true. For continuing on with an exclusive focus and praise of Bayes within debates about the predictive processing theory will impede progress in philosophical understanding of scientific practice in computational cognitive science as well as of the architecture of the mind. (shrink)

An emerging perspective on human cognition and performance sees it as a kind of self-organizing phenomenon involving dynamic coordination across the body, brain and environment. Measuring this coordination faces a major challenge. Time series obtained from such cognitive, behavioral, and physiological coordination are often complicated in terms of non-stationarity and non-linearity, and in terms of continuous vs. categorical scales. Researchers have proposed several analytical tools and frameworks. One method designed to overcome these complexities is recurrence quantification analysis, developed in the (...) study of non-linear dynamics. It has been applied in various domains, including linguistic data or motion data. However, most previous studies have applied recurrence methods individually to categorical or continuous data. To understand how complex coordination works, an integration of these types of behavior is needed. We aimed to integrate these methods to investigate the relationship between language and motion directly. To do so, we added temporal information to categorical data, and applied joint recurrence analysis methods to visualize and quantify speech-motion coordination coupling during a rap performance. We illustrate how new dynamic methods may capture this coordination in a small case-study design on this expert rap performance. We describe a case study suggesting this kind of dynamic analysis holds promise, and end by discussing the theoretical implications of studying complex performances of this kind as a dynamic, coordinated phenomenon. (shrink)

Realists about mental disorder have been hasty about dismissing social explanations of how mental disorder is constituted. However, many social ontologies are realist ontologies. In order to create a meaningful distinction between realism and social metaphysics about mental disorder, I propose that realism about mental disorder is best understood as Individual Trait Realism (ITR) about them. For ITR, mental disorders exist in virtue of traits. I defend the view that ITR is compatible with social metaphysics, arguing that, in asking whether (...) constituents in the social sphere figure the metaphysics of psychopathology, we are asking questions on three different strata of explanation: the strata of demarcation, instantiation, and individual traits. Distinguishing between these strata allows for nuanced realism that need not reject the social constitution of mental disorder. (shrink)

Recent developments in the literature suggest cognitive representation can be conceived of as a kind of mechanism that meets the functional profile set out by the S-representation account. However, this approach is threatened by worries that the S-representation account cannot tell a satisfactory story about content determination at the subpersonal level. One solution is to complement the S-representation account with a traditional etiological theory of content determination. This paper argues such a move is unwarranted and threatens the broader project of (...) understanding representation in mechanism-friendly terms. Once properly situated within a mechanistic framework, the S-representation account can address the issue of content determination without supplementing it with an etiological theory. In turn, this strengthens the notion that combing a mechanistic framework with the S-representation account provides a robust and comprehensive defense of representation’s positive role in cognitive science. (shrink)

This chapter will be a brief survey of the concepts from general philosophy of science for those interested in cognitive science. It covers several major topics in the philosophy of science: scientific explanation and underdetermination, reductionism and levels of nature, and scientific realism. We will discuss the goals of science, the methods of science, and the most plausible interpretations of science. To demonstrate the importance of these topics, the chapter includes cases in which confusion over these issues has led scientists (...) astray. These cases include instances in which cognitive neuroscience has failed to discover adequate explanations for phenomena, when previously established research did not withstand further scrutiny, and the increasingly complex and bewildering interrelationship between the study of the mind and the study of the brain and its parts. These issues are common to many areas of science, but they can be particularly fraught in a field like cognitive neuroscience, as researchers from a wide variety of disciplinary backgrounds and research foci come together to develop a systematic understanding of the mind. (shrink)

This essay presents the conditional probability of co-contribution account of the individuation of cognitive systems (CPC) and argues that CPC provides an attractive basis for a theory of the cognitive self. The argument proceeds in a largely indirect way, by emphasizing empirical challenges faced by an approach that relies entirely on predictive processing (PP) mechanisms to ground a theory of the cognitive self. Given the challenges faced by PP-based approaches, we should prefer a theory of the cognitive self of the (...) sort CPC offers, one that accommodates variety in the kinds of mechanism that, when integrated, constitute a cognitive system (and thus the cognitive self), to a theory according to which the cognitive self is composed of essentially one kind of thing, for instance, prediction-error minimization mechanisms. The final section focuses on one of the central functions of the cognitive self: to engage in conscious reasoning. It is argued that the phenomenon of conscious, deliberate reasoning poses an apparently insoluble problem for a PP-based view, one that seems to rest on a structural limitation of predictive-processing models. In a nutshell, conscious reasoning is a single-stream phenomenon, but, in order for PP to apply, two streams of activity must be involved, a prediction stream and an input stream. Thus, with regard to the question of the nature of the self, PP-based views must yield to an alternative approach, regardless of whether proponents of the predictive processing, as a comprehensive theory of cognition, can handle the various empirical challenges canvassed in the preceding section. (shrink)

People often answer why-questions with what we call experiential explanations: narratives or stories with temporal structure and concrete details. In contrast, on most theories of the epistemic function of explanation, explanations should be abstractive: structured by general relationships and lacking extraneous details. We suggest that abstractive and experiential explanations differ not only in level of abstraction, but also in structure, and that each form of explanation contributes to the epistemic goals of individual learners and of science. In particular, experiential explanations (...) support mental simulation and survive transitions across background theories; as a result, they support learning and help us translate between competing frameworks. Experiential explanations play an irreducible role in human cognition – and perhaps in science. (shrink)

Optogenetics and DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) are important research tools in recent neurobiology. These tools allow unprecedented control over activity in specifically targeted neurons in behaving animals. Two approaches in philosophy of neuroscience, mechanism and ruthless reductionism, provide explicit accounts of experiments and results using tools like these, but each offers a different picture about how levels of mechanisms relate. I argue here that the ruthless reductionist’s direct mind‐to‐cellular/molecular activities linkages “in a single bound” better fits (...) with both the experimental designs using these tools and some of the scientists’ own judgments about their results than does the mechanist’s “nested hierarchies of mechanisms‐within‐mechanisms.” So at least some important work in current neuroscience appears to be ruthlessly reductive. Mechanism may not correctly characterize all current work in neuroscience, despite its recent popularity. (shrink)

Memes have been proposed to explain wide swathes of human culture and language use. I argue that what is really doing the explanatory work in many of these cases is a basic mechanism of information...

Cognitive science began as a multidisciplinary endeavor to understand how the mind works. Since the beginning, cognitive scientists have been asking questions about the right methodologies and levels of explanation to pursue this goal, and make cognitive science a coherent science of the mind. Key questions include: Is there a privileged level of explanation in cognitive science? How do different levels of explanation fit together, or relate to one another? How should explanations at one level inform or constrain explanations at (...) some other level? Can the different approaches to the mind, brain, and culture be unified? The aim of this issue of topiCS is to provide a platform for discussing different answers to such questions and to facilitate a better understanding between the different strands of thinking about the right levels of explanation in cognitive science. (shrink)

Researchers in the cognitive sciences often seek neural correlates of psychological constructs. In this paper, I argue that even when these correlates are discovered, they do not always lead to reductive outcomes. To this end, I examine the psychological construct of a critical period and briefly describe research identifying its neural correlates. Although the critical period is correlated with certain neural mechanisms, this does not imply that there is a reductionist relationship between this psychological construct and its neural correlates. Instead, (...) this case study suggests that there may be many-to-many psychological-neural mappings, not just one-to-one or even one-to-many relations between psychological kinds and types of neural mechanisms. (shrink)

이것은 행동에 유전자/환경 상호 작용의 훌륭한 검토 이며, 조금 일자 임에도 불구 하 고, 쉽고 가치 있는 읽기. 그(것)들은 행동에 유전학의 압도적인 충격을 보여주는 쌍둥이 연구 결과로 시작합니다. 그들은 공유 가정 환경이 행동에 거의 영향을 미치지 않으며 입양 된 자녀가 무작위로 선택된 사람들처럼 의붓 형제 자매와 다르게 자라는 사실을 확장하고 요약하는 주디스 해리스 (Judith Harris)의 점점 더 잘 알려진 연구에 주목합니다. 그들 (그리고 행동 유전학을 토론하는 거의 모든 사람들이) 주의하지 못하는 한 가지 기본 포인트는 우리의 성격의 모든 기초를 포함하여 인간의 (...) 행동 보편적 인 수백 (수천)이 정상의 변화가없는 유전자에 의해 100 % 결정된다는 것입니다. 모든 사람은 나무를 돌이 아닌 나무로 보고, 음식을 찾고 먹고, 화가 나고 질투합니다. 그래서, 그들은 주로 여기에 대해 이야기하는 것은 얼마나 많은 환경 (문화)가 자신의 외모보다는 다양한 특성이 표시되는 정도에 영향을 미칠 수 있습니다. 마지막으로, 그들은 우리와 모든 유기체가 자연의 우생학의 산물이며 의학, 농업 및 문명 전체와 자연 선택을 물리 치려는 시도가 이 일을계속하는 모든 사회에 재앙이라는 것을 주목하지 못하고, 일반적인 정치적으로 올바른 방식으로 우생학에 대해논의합니다. 모든 개념의 50%, 또는 약 1억 년 동안, 어머니가 인식하지 못하고 거의 모든 조기 자발적 낙태로 끝납니다. 결함이 있는 유전자의 이 자연적인 컬링은 진화를 유도하고, 상대적으로 유전적으로 건전하게 유지하고, 사회를 가능하게 합니다. 이형성은 문명을 파괴하기에 충분하지만 인구 과잉은먼저그것을 o것입니다. 현대 의 두 시스템 보기에서인간의 행동에 대한 포괄적 인 최신 프레임 워크를 원하는 사람들은 내 책을 참조 할 수 있습니다'철학의 논리적 구조, 심리학, 민d와 루드비히 비트겐슈타인과 존 Searle의 언어' 그리고 언어 2nd ed (2019). 내 글의 더 많은 관심있는 사람들은 '이야기 원숭이를 볼 수 있습니다 -철학, 심리학, 과학, 운명 행성에 종교와 정치 - 기사 및 리뷰 2006-2017' 3rd ed (2019) 및 기타. (shrink)

पादरी Hofstadter द्वारा कट्टरपंथी प्रकृतिवाद के चर्च से नवीनतम उपदेश. अपने बहुत अधिक प्रसिद्ध (या अपने अथक दार्शनिक त्रुटियों के लिए कुख्यात) काम Godel, Escher, बाख की तरह, यह एक सतही प्रशंसनीयता है, लेकिन अगर एक समझता है कि यह बड़े पैमाने पर वैज्ञानिकता है जो दार्शनिक लोगों के साथ वास्तविक वैज्ञानिक मुद्दों घोला जा सकता है (यानी, केवल असली मुद्दों क्या भाषा का खेल हम खेलना चाहिए रहे हैं) तो लगभग सभी अपनी रुचि गायब हो जाता है. मैं विकासवादी (...) मनोविज्ञान और Wittgenstein के काम में आधारित विश्लेषण के लिए एक रूपरेखा प्रदान (के बाद से मेरे और अधिक हाल ही में लेखन में अद्यतन). आधुनिक दो systems दृश्यसे मानव व्यवहार के लिए एक व्यापक अप करने के लिए तारीख रूपरेखा इच्छुक लोगों को मेरी पुस्तक 'दर्शन, मनोविज्ञान, मिनडी और लुडविगमें भाषा की तार्किक संरचना से परामर्श कर सकते हैं Wittgenstein और जॉन Searle '2 एड (2019). मेरे लेखन के अधिक में रुचि रखने वालों को देख सकते हैं 'बात कर रहेबंदर- दर्शन, मनोविज्ञान, विज्ञान, धर्म और राजनीति पर एक बर्बाद ग्रह --लेख और समीक्षा 2006-2019 3 एड (2019) और आत्मघाती यूटोपियान भ्रम 21st मेंसदी 4वें एड (2019) . (shrink)

Como tantas veces en filosofía, el título no sólo establece la línea de batalla, sino que expone los prejuicios y errores del autor, ya que si podemos o no dar sentido al juego de idiomas 'Ver las cosas como son' y si es posible tener una 'teoría filosófica' de percepción' (que sólo puede ser sobre cómo funciona el lenguaje de la percepción), a diferencia de uno científico, que es una teoría sobre cómo funciona el cerebro, son exactamente los problemas. Este (...) es el Clásico Searle, excelente y probablemente al menos tan bueno como cualquier otro puede producir, pero carente de una comprensión completa de las ideas fundamentales del Wittgenstein posterior y sin comprender los dos sistemas de marco de pensamiento, lo que podría haberlo hecho brillante. Al igual que en su trabajo anterior, Searle evita en gran medida la ciencia, pero hay lapsos frecuentes y no entiende que los problemas son siempre sobre los juegos de idiomas, un fracaso que comparte con casi todo el mundo. Después de proporcionar un marco que consiste en una Tabla de Intencionalidad basada en los dos sistemas de pensamiento y pensamiento y investigación de decisiones, doy un análisis detallado del libro. 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), Comprender las Conexiones entre Ciencia, Filosofía, Psicología, Religión, Política , Economía, Hitoria y Literatura (2020) y 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 (2019) y otras. Al igual que con Wittgenstein (en adelante W), todo lo que Searle (en adelante S) escribe es un tesoro y es maravilloso que permanezca afilado a medida que se acerca a los 80. A diferencia de la mayoría, incluso sus primeros trabajos siguen siendo relevantes y está trabajando en varios otros libros. También sugiero sus más de 100 conferencias y entrevistas en youtube, vimeo, etc., que, aunque inevitablemente un poco repetitivas, contienen muchas declaraciones que no están en sus escritos. He leído casi toda su obra, y he escuchado todas las conferencias, la mayoría de ellas 2 o 3 veces. Estos son de especial interés como (como Wittgenstein) que no lee de las notas, por lo que cada uno es único y no una réplica de un papel, y es un excelente orador extemporáneo que en su mayoría utiliza un lenguaje sin pretensiones (ambos tan diferentes de la mayoría de los demás). Las conferencias recientes dadas en las Universidades Europeas son magníficas, pero no te pierdas las antiguas como la conferencia de la BBC "Una realidad cambiante, la ciencia del comportamiento humano", que da un excelente relato de por qué la causalidad legítima y repetitiva del rápido cerebro sistema automático, no linguístico 1 (S1) es fundamentalmente diferente y no destacable de la misma manera que la complejidad ilimitada de razones que caracterizan el lento sistema deliberativo, consciente de la lengua 2 (S2), que genera una explosión combinatoria no generalmente representable de una manera útil por las leyes científicas. El sistema dual (S1, S2) método de describir el pensamiento utilizado en esta revisión, común a la investigación de razonamiento desde hace unos 20 años, es mío y no de Searle. Dado que recientemente he escrito un artículo de 75p analizando el trabajo de Searle en comparación con el de Wittgenstein (La estructura lógica de la filosofía, la psicología, la mente y el lenguaje como revelado por Ludwig Wittgenstein y John Searle) no lo repetiré y lo repetiré concentrarse en este libro solamente. (shrink)

What follows is a first step towards an ontology of conscious mental processes. We provide a theoretical foundation and characterization of conscious mental processes based on a realist theory of intentionality and using BFO as our top-level ontology. We distinguish three components of intentional mental process: character, directedness, and objective referent, and describe several features of the process character and directedness significant to defining and classifying mental processes. We arrive at the definition of representational mental process as a process that (...) is the bringing into being, sustaining, modifying, or terminating of a mental representation. We conclude by outlining some benefits and applications of this approach. (shrink)

The most prominent argument for the incompatibility of free will and determinism is Peter van Inwagen’s consequence argument. I offer a new diagnosis of what is wrong with this argument. Proponents and critics typically accept the way the argument is framed, and only disagree on whether the premisses and rules of inference are true. I suggest that the argument involves a category mistake: it conflates two different levels of description, namely, the physical level at which we describe the world from (...) the perspective of fundamental physics and the agential level at which we describe agents and their actions. My diagnosis is based on an account of free will as a higher-level phenomenon. I call this account ‘compatibilist libertarianism’, for reasons that will become clear. Although the paper addresses a primarily metaphysical question, it uses tools similar to those employed in philosophy-of-science work on determinism and indeterminism, higher-level phenomena, and dynamical systems. (shrink)

The most prominent argument for the incompatibility of free will and determinism is Peter van Inwagen’s consequence argument. I offer a new diagnosis of what is wrong with this argument. Proponents and critics typically accept the way the argument is framed, and only disagree on whether the premisses and rules of inference are true. I suggest that the argument involves a category mistake: it conflates two different levels of description, namely, the physical level at which we describe the world from (...) the perspective of fundamental physics and the agential level at which we describe agents and their actions. My diagnosis is based on an account of free will as a higher-level phenomenon. I call this account ‘compatibilist libertarianism’, for reasons that will become clear. Although the paper addresses a primarily metaphysical question, it uses tools similar to those employed in philosophy-of-science work on determinism and indeterminism, higher-level phenomena, and dynamical systems. (shrink)

This chapter argues that the only tenable unconscious inferences theories of cognitive achievement are ones that employ a theory internal technical notion of representation, but that once we give cash-value definitions of the relevant notions of representation and inference, there is little left of the ordinary notion of representation. We suggest that the real value of talk of unconscious inferences lies in (a) their heuristic utility in helping us to make fruitful predictions, such as about illusions, and (b) their providing (...) a high-level description of the functional organization of subpersonal faculties that makes clear how they equip an agent to navigate its environment and pursue its goals. (shrink)

Debate about cognitive science explanations has been formulated in terms of identifying the proper level(s) of explanation. Views range from reductionist, favoring only neuroscience explanations, to mechanist, favoring the integration of multiple levels, to pluralist, favoring the preservation of even the most general, high-level explanations, such as those provided by embodied or dynamical approaches. In this paper, we challenge this framing. We suggest that these are not different levels of explanation at all but, rather, different styles of explanation that capture (...) different, cross-cutting patterns in cognitive phenomena. Which pattern is explanatory depends on both the cognitive phenomenon under investigation and the research interests occasioning the explanation. This reframing changes how we should answer the basic questions of which cognitive science approaches explain and how these explanations relate to one another. On this view, we should expect different approaches to offer independent explanations in terms of their different focal patterns and the value of those explanations to partly derive from the broad patterns they feature. (shrink)

Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative mechanistic perspective and concentrates (...) the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration. (shrink)

In fMRI research, the goal of correcting for multiple comparisons is to identify areas of activity that reflect true effects, and thus would be expected to replicate in future studies. Finding an appropriate balance between trying to minimize false positives (Type I error) while not being too stringent and omitting true effects (Type II error) can be challenging. Furthermore, the advantages and disadvantages of these types of errors may differ for different areas of study. In many areas of social neuroscience (...) that involve complex processes and considerable individual differences, such as the study of moral judgment, effects are typically smaller and statistical power weaker, leading to the suggestion that less stringent corrections that allow for more sensitivity may be beneficial, but also result in more false positives. Using moral judgment fMRI data, we evaluated four commonly used methods for multiple comparison correction implemented in SPM12 by examining which method produced the most precise overlap with results from a meta-analysis of relevant studies and with results from nonparametric permutation analyses. We found that voxel-wise thresholding with family-wise error correction based on Random Field Theory provides a more precise overlap (i.e., without omitting too few regions or encompassing too many additional regions) than either clusterwise thresholding, Bonferroni correction, or false discovery rate correction methods. (shrink)

Radical and autopoietic enactivists disagree concerning how to understand the concept of sense-making in enactivist discourse and the extent of its distribution within the organic domain. I situate this debate within a broader conflict of commitments to naturalism on the part of radical enactivists, and to phenomenology on the part of autopoietic enactivists. I argue that autopoietic enactivists are in part responsible for the obscurity of the notion of sense-making by attributing it univocally to sentient and non-sentient beings and following (...) Hans Jonas in maintaining a phenomenological dimension to life-mind continuity among all living beings, sentient or non-sentient. I propose following Merleau-Ponty instead, who offers a properly phenomenological notion of sense-making for which sentience is a necessary condition. Against radicalist efforts to replace sense-making with a deflationary, naturalist conception of intentionality, I discuss the role of the phenomenological notion of sense-making for understanding animal behavior and experience. (shrink)

Recent mechanistic philosophers :1287–1321, 2016) have argued that the cognitive sciences are not autonomous from neuroscience proper. I clarify two senses of autonomy–metaphysical and epistemic—and argue that cognitive science is still autonomous in both senses. Moreover, mechanistic explanation of cognitive phenomena is not therefore an alternative to the view that cognitive science is autonomous of neuroscience. If anything, it’s a way of characterizing just how cognitive processes are implemented by neural mechanisms.

Philosophers of mind commonly draw a distinction between the personal level – the distinctive realm of conscious experience and reasoned deliberation – and the subpersonal level, the domain of mindless mechanism and brute cause and effect. Moreover, they tend to view cognitive science through the lens of this distinction. Facts about the personal level are given a priori, by introspection, or by common sense; the job of cognitive science is merely to investigate the mechanistic basis of these facts. I argue (...) that this view misrepresents the structure of cognitive-scientific enquiry. Taken at face value, cognitive science makes no commitment to the existence of a distinctive level at which persons or selves appear. Thus, in the age of cognitive science, we should not expect to find the self in an ontologically distinct realm. Instead, we should expect to locate it in cognitive-scientific models themselves. In closing, I indicate likely results of this approach. (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)

This chapter provides an overview of the basic research strategies and analytic techniques deployed in computational cognitive neuroscience. On the one hand, “top-down” strategies are used to infer, from formal characterizations of behavior and cognition, the computational properties of underlying neural mechanisms. On the other hand, “bottom-up” research strategies are used to identify neural mechanisms and to reconstruct their computational capacities. Both of these strategies rely on experimental techniques familiar from other branches of neuroscience, including functional magnetic resonance imaging, single-cell (...) recording, and electroencephalography. What sets computational cognitive neuroscience apart, however, is the explanatory role of analytic techniques from disciplines as varied as computer science, statistics, machine learning, and mathematical physics. These techniques serve to describe neural mechanisms computationally, but also to drive the process of scientific discovery by influencing which kinds of mechanisms are most likely to be identified. For this reason, understanding the nature and unique appeal of computational cognitive neuroscience requires not just an understanding of the basic research strategies that are involved, but also of the formal methods and tools that are being deployed, including those of probability theory, dynamical systems theory, and graph theory. (shrink)

One question of the bounds of cognition is that of which things have it. A scientifically relevant debate on this question must explain the persistent and selective use of psychological predicates to report findings throughout biology: for example, that neurons prefer, fruit flies and plants decide, and bacteria communicate linguistically. This paper argues that these claims should enjoy default literal interpretation. An epistemic consequence is that these findings can contribute directly to understanding the nature of psychological capacities.

In approaching the question of whether groups of people can have cognitive capacities that are fundamentally different than the cognitive capacities of the individuals within the group, we lay out a Multiple, Interactive Levels of Cognitive Systems (MILCS) framework. The goal of MILCS is to explain the kinds of cognitive processes typically studied by cognitive scientists, such as perception, attention, memory, categorization, decision making, problem solving, and judgment. Rather than focusing on high-level constructs such as modules in an information processing (...) flow diagram or internal representations, MILCS focuses on mechanisms that allow systems to engage in flexible, adaptive behavior. Examples of these mechanisms are network structure-process pairs that: combine data from perception with top-down theories, achieve consistency and consensus through information exchange, selectively attend to information globally assessed as relevant, and develop specialized units from originally homogeneous networks through competition among the units. Two such systems are considered in some detail -- lateral inhibition within a network for selecting from a candidate set the option that is most attractive, and a diffusion process for accumulating evidence to reach a generally rapid and accurate decision. These system descriptions are aptly applied at multiple levels, including within and across people. These systems provide accounts that unify cognitive processes across multiple levels, can be expressed in a common vocabulary provided by network science, are inductively powerful yet appropriately constrained, and are applicable to a large number of superficially diverse cognitive systems. Given that people are typically highly motivated to participate in strong groups, cognitively resourceful people will tend to form groups that effectively employ cognitive systems at higher levels than the individuals. The impressive cognitive capacities of individual people do not eliminate the need to talk about group cognition. Instead, smart people generally form smart groups. (shrink)

Rational analysis is an influential but contested account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this paper, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances.

This chapter subsumes David Marr’s levels of analysis account of explanation in cognitive science under the framework of mechanistic explanation: Answering the questions that define each one of Marr’s three levels is tantamount to describing the component parts and operations of mechanisms, as well as their organization, behavior, and environmental context. By explicating these questions and showing how they are answered in several different cognitive science research programs, this chapter resolves some of the ambiguities that remain in Marr’s account, and (...) shows that many different areas and traditions of cognitive scientific research can be unified under the mechanistic framework. (shrink)

I challenge the dominant understanding of what it means to say two thoughts are associated. The two views that dominate the current literature treat association as a kind of mechanism that drives sequences of thought. The first, which I call reductive associationism, treats association as a kind of neural mechanism. The second treats association as a feature of the kind of psychological mechanism associative processing. Both of these views are inadequate. I argue that association should instead be seen as a (...) highly abstract filler term, standing in for causal relations between representational states in a system. Associations, so viewed, could be implemented by many different mechanisms. I outline the role that this view gives associative models as part of a top-down characterization of psychological processes of any kind and of any complexity. (shrink)

The new paradigm in the psychology of reasoning draws on Bayesian formal frameworks, and some advocates of the new paradigm think of these formal frameworks as providing a computational-level theory of rational human inference. I argue that Bayesian theories should not be seen as providing a computational-level theory of rational human inference, where by “Bayesian theories” I mean theories that claim that all rational credal states are probabilistically coherent and that rational adjustments of degrees of belief in the light of (...) new evidence must be in accordance with some sort of conditionalization. The problems with the view I am criticizing can best be seen when we look at chains of inferences, rather than single-step inferences. Chains of inferences have been neglected almost entirely within the new paradigm. (shrink)

In her re-analysis of the evidence presented in Klein and Nichols (2012) to support their argument that patient R.B. temporarily lost possessory custody of consciously apprehended objects (in this case, objects that normally would be non-inferentially taken as episodic memory), Professor Roache concludes Klein and Nichols's claims are untenable. I argue that Professor Roache is incorrect in her re-interpretation, and that this is due, in part, to lack of sufficient familiarity with psychological theory on memory as well as clinical literature (...) on felt loss of ownership of one’s intentional objects. (shrink)

We overview the main historical and technological elements characterising the rise, the fall and the recent renaissance of the cognitive approaches to Artificial Intelligence and provide some insights and suggestions about the future directions and challenges that, in our opinion, this discipline needs to face in the next years.

Recent extensions of mechanistic explanation into psychology suggest that cognitive models are only explanatory insofar as they map neatly onto, and serve as scaffolding for more detailed neural models. Filling in those neural details is what these accounts take the integration of cognitive psychology and neuroscience to mean, and they take this process to be seamless. Critics of this view have given up on cognitive models possibly explaining mechanistically in the course of arguing for cognitive models having explanatory value independent (...) of how well they align with neural mechanisms. We can have things both ways, however. The problem with seamless integration accounts is their seamlessness, not that they take cognitive models to be mechanistic. A non-componential view of mechanisms allows for cognitive and neural models that cross cut one another, and for cognitive models that don’t decompose into parts. I illustrate the inadequacy of seamless accounts of integration by contrasting how “filter” models of attention in psychology and of sodium channels in neuroscience developed; by questioning whether the mappings generated by neuroimaging subtraction studies achieve integration; and by reinterpreting the evidence for cognitive models of memory having been successfully integrated with neural models. I argue that the integrations we can realistically expect are more partial, patchy, and full of loose threads than the mosaic unity Craver describes. (shrink)

Bayesian reverse-engineering is a research strategy for developing three-level explanations of behavior and cognition. Starting from a computational-level analysis of behavior and cognition as optimal probabilistic inference, Bayesian reverse-engineers apply numerous tweaks and heuristics to formulate testable hypotheses at the algorithmic and implementational levels. In so doing, they exploit recent technological advances in Bayesian artificial intelligence, machine learning, and statistics, but also consider established principles from cognitive psychology and neuroscience. Although these tweaks and heuristics are highly pragmatic in character and (...) are often deployed unsystematically, Bayesian reverse-engineering avoids several important worries that have been raised about the explanatory credentials of Bayesian cognitive science: the worry that the lower levels of analysis are being ignored altogether; the challenge that the mathematical models being developed are unfalsifiable; and the charge that the terms ‘optimal’ and ‘rational’ have lost their customary normative force. But while Bayesian reverse-engineering is therefore a viable and productive research strategy, it is also no fool-proof recipe for explanatory success. (shrink)

Complex systems are to be seen as typically having multiple levels of organization. For instance, in the behavioural and cognitive sciences, there has been a long lasting trend, promoted by the seminal work of David Marr, putting focus on three distinct levels of analysis: the computational level, accounting for the What and Why issues, the algorithmic and the implementational levels specifying the How problem. However, the tremendous developments in neuroscience knowledge about processes at different scales of organization together with the (...) complexity of today cognitive theories suggest that there will hardly be only three levels of explanation. Instead, there will be many different degrees of commitments corresponding to the different granularities—from high-level (behavioural) models to low-level (neural and molecular) models of the cognitive research program. For instance, Bayesian approaches, that are usually advocated for formalizing Marr’s computational level and rational behaviour, have even been adopted to model synaptic plasticity and axon guidance by molecular gradients. As a result, we can consider the behavioural scientist as dealing with models at a multiplicity of levels. The purpose of this Research Topic in Frontiers in Theoretical and Philosophical Psychology is to promote an approach to the role of the levels and explanation and models which is of interest for cognitive scientists, neuroscientists, psychologists, behavioural scientists, and philosophers of science. (shrink)

In discussions regarding models of cognition, the very mention of “computationalism” often incites reactions against the insufficiency of the Turing machine model, its abstractness, determinism, the lack of naturalist foundations, triviality and the absence of clarity. None of those objections, however, concerns models based on natural computation or computing nature, where the model of computation is broader than symbol manipulation or conventional models of computation. Computing nature consists of physical structures that form layered computational architecture, with computation processes ranging from (...) quantum to chemical, biological/cognitive and social-level computation. It is argued that, on the lower levels of information processing in the brain, finite automata or Turing machines may still be adequate models, while, on the higher levels of whole-brain information processing, natural computing models are necessary. A layered computational architecture of the mind based on the intrinsic computing of physical systems avoids objections against early versions of computationalism in the form of abstract symbols manipulation. (shrink)