According to the traditional nomological-deductive methodology of physics and chemistry [Hempel and Oppenheim, 1948], explaining a phenomenon means subsuming it under a law. Logic becomes then the glue of explanation and laws the primary explainers. Thus, the scientific study of a system would consist in the development of a logically sound model of it, once the relevant observables (state variables) are identified and the general laws governing their change (expressed as differential equations, state transition rules, maximization/minimization principles,. . . ) (...) are well determined, together with the initial or boundary conditions for each particular case. Often this also involves making a set of assumptions about the elementary components of the system (e.g., their structural and dynamic properties) and modes of local interaction. In this framework, predictability becomes the main goal and that is why research is carried out through the construction of accurate mathematical models. Thus, physics and chemistry have made most progress so far by focusing on systems that, either due to their intrinsic properties or to the conditions in which they are investigated, allow for very strong simplifying assumptions, under which, nevertheless, those highly idealized models of reality are deemed to be in good correspondence with reality itself. Despite the enormous success that this methodology has had, the study of living and cognitive phenomena had to follow a very different road, because these phenomena are produced by systems whose underlying material structure and organization do not permit such crude approximations. Seen from the perspective of physics or chemistry, biological and cognitive systems are made of an enormous number of parts or elements interacting in non-linear and selective ways, which makes very difficult their tractability through mathematical models. In addition, many of those interacting elements are hierarchically organized, in a way that the “macroscopic” (observable) parts behave according to rules that cannot be, in practice, derived from simple principles at the level of their “microscopic” dynamics.. (shrink)

We show that the so-called Multiple-Computations Theorem in cognitive science and philosophy of mind challenges Landauer’s Principle in physics. Since the orthodox wisdom in statistical physics is that Landauer’s Principle is implied by, or is the mechanical equivalent of, the Second Law of thermodynamics, our argument shows that the Multiple-Computations Theorem challenges the universal validity of the Second Law of thermodynamics itself. We construct two examples of computations carried out by one and the same dynamical process with respect to (...) which Landauer’s principle implies contradictory predictions concerning the entropy increase. Our two examples are based on a weak version of the Multiple-Computations Theorem, which is quite uncontroversial, and therefore they amount to a clear refutation of the universal validity of Landauer’s Principle. We consider some responses to this argument that do not attempt to single out one computation over the others, and we show that they do not work. We further consider ways out of the argument by externalist approaches supporting the computational theory of the mind who propose that the interaction of a computing system with the environment is enough to select a single computation over the others. We show on physical grounds that this approach fails too. We then reverse the direction of our challenge and formulate a dilemma for supporters of the computational theory of the mind: they must reject the causal closure of physic; or else they must accept on a priori grounds that Landauer’s Principle and the Second Law of thermodynamics are not universally valid. Finally, we present our version of a type–type mind-brain identity theory called Flat Physicalism, which is based on the paradigm case of statistical mechanics, and we show that it circumvents the challenge from Landauer’s Principle and the Multiple-Computations Theorem and does not fall prey to our dilemma. (shrink)

Vādanyāya is one of the representative works of Dharmakīrti. It is concerned with debate logic and deals with win-or-lose reasoning rules in the broad sense of logic. In this paper, we will concentrate our discussion on Dharmakīrti’s theory of nigrahasthāna (fault) in his debate logic, a key issue in Vādanyāya. First, we point out that the justification of three logical reasons as proof conditions of debate constitutes the rational point of departure for Dharmakīrti’s debate logic. Second, we analyze the differences (...) between Dharmakīrti’s and the Naiyāyika’s notion of debate and point out that Dharmakīrti’s ‘debate’ involves two parties (the disputant and the opponent) attempting to rationally refute the other’s opinion to persuade the other to change their wrong ideas. Last, based on the discussion of Dharmakīrti’s concept of debate logic, we identify seven types of nigrahasthāna in Vādanyāya with regard to the two parties of a debate (the disputant and the opponent). (shrink)

If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.

Since its origins in the mid-Twentieth Century, Cognitive Science has almost exclusively operated within the philosophical frames provided by Cartesian Representationalism. In recent years, alternative, phenomenological and pragmatist, frames have served as a resource for the emergence of non-representational approaches to mind and cognition. These have been gathered under the label ‘4E cognition’, indicating their Embodied, Extended, Enacted and Embedded nature. This chapter examines one version of 4E cognition, which builds upon Ecological Psychology, and argues that it fails to (...) pass the Evaluation Test: it cannot account for the central role of normativity and evaluation in our responsiveness to loci of significance in the lifeworld, while failing to provide an argument for the exclusion of evaluative and normative aspects. (shrink)

This book gives a convincing philosophical explanation for the strong persistence of our diverse folk psychological intuitions about the self. For this purpose it introduces, on the one hand, the distinction between subject and self model as proposed by Metzinger, on the other hand, the distinction between a social/normative and a cognitive/organic perspective on the self. The book argues that one needs to take into account both distinctions, if one wants to answer notoriously difficult questions like the one that gives (...) the book its title: Is the self real? (shrink)

8.6 Issues Raised by the Algorithms Proposed.

Cognitive Science is a major new guide to the central theories and problems in the study of the mind and brain. The authors clearly explain how and why cognitive science aims to understand the brain as a computational system that manipulates representations. They identify the roots of cognitive science in Descartes - who argued that all knowledge of the external world is filtered through some sort of representation - and examine the present-day role of Artificial Intelligence, computing, (...) psychology, linguistics and neuroscience. Throughout, the key building blocks of cognitive science are clearly illustrated: perception, memory, attention, emotion, language, control of movement, learning, understanding and other important mental phenomena. Cognitive Science: presents a clear, collaborative introduction to the subject is the first textbook to bring together all the different strands of this new science in a unified approach includes illustrations and exercises to aid the student. (shrink)

Cognitive Science, Volume 46, Issue 8, August 2022.

It's an exciting story, no doubt, and I hate to be a spoilsport. But there are a few problems. One is that virtually every reference to me or to co-workers around the world, and to the areas in which we work, is fanciful, sometimes even bringing to mind Pauli 's famous observation "not even wrong." I'll review what seems to be a fair sample.

We suggest a common ground for alternative proposals In different domains of cognitive science which have previously seemed to have little in common. The underlying common theme is associated with a redefinition of the basic unit of analysis in each domain of thought. Our framework suggests a definition of unity which is based not on inherent properties of the elements constituting the unit, but rather on dynamic patterns of correlation across the elements. We introduce a set of features that (...) characterize the new dynamic units, and distinguish them from traditional units of analysis. A conceptual connection is made with the identification of elementary units in modern physics theories, as well as with concepts and structures in the study of complex dynamical systems and connectionism. The paper analyses the evolution of the concept of unit in different domains of thought in cognitive science, and examines the proposed framework of “dynamic unity” with regard to various theoretical problems within each domain. The particular cognitive science issues discussed in the paper include: (1) the binding problem in the brain; (2) the mental unit of conceptual organization; (3) defining ‘wordhood’ in linguistics; and (4) identifying the unit of cognition in the natural environment. (shrink)

This paper investigates whether AI can possess wisdom, a complex and deeply human capacity. We adopt an interdisciplinary approach, drawing on philosophy, 4E cognition, Material Engagement Theory, and engaged epistemology, to argue that wisdom is a dynamic force unfolding through meaningful life experiences and resonant interactions with the world. Central to our discussion is Rosa's concept of resonance, essential for fostering personal growth, emotional empathy, as well as existential and bodily connectedness to the world and its unfolding into an uncertain (...) future. Despite the advanced capabilities of AI/Large Language Models (LLMs), we demonstrate their inability to embody wisdom due to their lack of deep sense-making, embodied interaction, and meaningful engagement with the world and future potentials. We emphasize the need for cultivating human wisdom to ensure technological advancements enrich rather than diminish the human experience, advocating for a balanced and reflective human-technology relationship. (shrink)

Núñez et al (2019) argue (1) that the field of Cognitive Science has failed, in that it has not arrived at a cohesive theory, and (2) that this is contrary to the intentions of the founders. Their survey of publication and citation patterns bears out the lack of a cohesive theory and also provides corroboration for (3) the concern that the field is becoming unbalanced, with psychology overweighted (Gentner, 2010). I will argue against points (1) and (2), but agree (...) with point (3). My central claim is that cognitive science was never meant to have one unified theoretical framework, nor should it have. (shrink)

Cognitive Science, Literature, and the Arts is the first student-friendly introduction to the uses of cognitive science in the study of literature, written specifically for the non-scientist. Patrick Colm Hogan guides the reader through all of the major theories of cognitive science, focusing on those areas that are most important to fostering a new understanding of the production and reception of literature. This accessible volume provides a strong foundation of the basic principles of cognitive science, and (...) allows us to begin to understand how the brain works and makes us feel as we read. (shrink)

This article has been retracted. Please see the retraction notice for more detail: 10.1007/s40961-018-0159-5.

The thesis develops a naturalist theory of phenomenal consciousness. The argument proceeds in three broad steps. The first consists in a defense of a representationalist view of consciousness. The second part argues that the relevant form of mental representation can be explained in terms of the predictive processing approach to brain function. The final part consists in an attack on metaphysical realism inspired by Bayesian approaches to cognition and a discussion of the implications of metaphysical anti-realism for the hard problem (...) of consciousness. (shrink)