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)

There is a general conception of levels in philosophy which says that the world is arrayed into a hierarchy of levels and that there are different modes of analysis that correspond to each level of this hierarchy, what can be labelled the ‘Hierarchical Correspondence View of Levels” (or HCL). The trouble is that despite its considerable lineage and general status in philosophy of science and metaphysics the HCL has largely escaped analysis in specific domains of inquiry. The goal of (...) this paper is to take up a recent call to domain-specificity by examining the role of the HCL in cognitive science. I argue that the HCL is, in fact, a conception of levels that has been employed in cognitive science and that cognitive scientists should avoid its use where possible. The argument is that the HCL is problematic when applied to cognitive science specifically because it fails to distinguish two important kinds of shifts used when analysing information processing systems: shifts in grain and shifts in analysis. I conclude by proposing a revised version of the HCL which accommodates the distinction. (shrink)

There are two distinct approaches to Bayesian modelling in cognitive science. Black-box approaches use Bayesian theory to model the relationship between the inputs and outputs of a cognitive system without reference to the mediating causal processes; while mechanistic approaches make claims about the neural mechanisms which generate the outputs from the inputs. This paper concerns the relationship between these two approaches. We argue that the dominant trend in the philosophical literature, which characterizes the relationship between black-box and (...) mechanistic approaches to Bayesian cognitive science in terms of the dichotomy between instrumentalism and realism, is misguided. We propose that the two distinctions are orthogonal: black-box and mechanistic approaches to Bayesian modelling can each be given either an instrumentalist or a realist interpretation. We argue that the current tendency to conflate black-box approaches with instrumentalism and mechanistic approaches with realism stems from unwarranted assumptions about the nature of scientific explanation, the ontological commitments of scientific theories, and the role of abstraction and idealization in scientific models. We challenge each of these assumptions to reframe the debates over Bayesian modelling in cognitive science. (shrink)

Descriptions of bodies within the literature of the enactive approach to cognitive science exhibit an interesting dialectical tension. On the one hand, a body is considered to be a unity which instantiates an identity, forming an intrinsic basis for value. On the other, a living body is in a reciprocally defining relationship with the environment, and is therefore immersed and entangled with, rather than distinct from, its environment. In this paper I examine this tension, and its implications for (...) the enactive approach, particularly the enactive conceptions of life and bodies. Following the lead of others, I argue that enactive cognitive science can benefit from a deeper reading and integration with extant work on the complexity and multiplicity of the living body within feminist philosophy and feminist science studies. (shrink)

Over the past half century, our best scientific understanding of the immune system has been transformed. The immune system has turned out to be extremely sophisticated, densely connected to the central nervous system and cognitive capacities, deeply involved in the production of behavior, and responsive to different kinds of psychosocial event. Such results have rendered the immune system part of the subject-matter of psychology and cognitive science. I argue that such results, alongside the history of psychoneuroimmunology, give (...) us good reason to be skeptical about the characterization of cognitive science and psychology as studying the mind and the mental. (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)

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 scientists use computational models to represent the results of their experimental work and to guide further research. Neither of these claims is particularly controversial, but the philosophical and evidentiary statuses of these models are hotly debated. To clarify the issues, I return to Newell and Simon’s 1972 exposition on the computational approach; they herald its ability to describe mental operations despite that the neuroscience of the time could not. Using work on visual imagery (cf. imagination) as a guide, (...) I examine the extent to which this holds true today. Does contemporary neuroscience contain mechanisms capable of describing experimental results in imagery? I argue that it does not, first by exploring foundational achievements in imagery research then by showing that their neural basis cannot be specified. Newell and Simon’s methodological position accordingly stands, even 50 years later. Computational — as opposed to physiological — descriptions must be retained to characterize and study mental phenomena, even as we learn high-level details of their implementation via brain data. (shrink)

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.

In this article, I show the role that the philosopher of cognitive science can cur-rently play in cognitive science research. I argue for the important, and not yet considered, role of the philosophy of cognitive science in cognitive science, that is, the importance of cooperation between philosophers of science with cogni-tive scientists in investigating the research methods and theoretical assump-tions of cognitive science. At the beginning of the paper I (...) point out, how the philosopher of science, here, the philosopher of cognitive science, can participate in interdisciplinary research. I am opting of the cooperation in investigating the so-called reflective problems. Then, I discuss four examples of issues important for the cognitive science, in which the competences possessed by the philosopher are useful. At the ending I point out wider landscape of possible cooperation of philosophers with cognitive scientists. (shrink)

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)

Philosophical accounts of narrative fiction can be loosely divided into two types. Participant accounts argue that some sort of simulation, or 1st person perspective taking plays a critical role in our engagement with narratives. Observer accounts argue to the contrary that we primarily engage narrative fictions from a 3rd person point of view, as either side participants or outside observers. Recent psychological research suggests a means to evaluate this debate. The perception of distance and slope is influenced by the energetic (...) (e.g., task difficulty) and emotional (e.g., anxiety) costs of actions. These effects are limited to increases in the costs of actions agents intend to perform themselves, generalize to cases where participants imagine acting, and demonstrate a role for tacit motor simulation in action planning. If participant accounts are sound, one should, therefore, find similar effects across changes in the interpretation of the costs of actions depicted in static images. We asked people to copy the rough spatial layout of two paintings across different interpretations of the costs of the actions they depicted. We predicted that increasing costs would cause participants to draw distances as longer and hills as steeper. Our results confirm this prediction for the energetic, but not the emotional, costs of actions. (shrink)

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)

In this paper, I argue that explaining cognitive behavior can be achieved through what I call hybrid explanatory inferences: inferences that posit mechanisms, but also draw on observed regularities. Moreover, these inferences can be used to achieve unification, in the sense developed by Allen Newel in his work on cognitive architectures. Thus, it seems that explanatory pluralism and unification do not rule out each other in cognitive science, but rather that the former represents a way to (...) achieve the latter. (shrink)

The doctrine of the Intelligent Design offers an intuitive explanation of why the ordering in the Universe is authored by an intentional agency. Due to its appeal to common-sense perception, this doctrine is endorsed even by scientifically literate circles despite of its obvious contradiction with the discoveries of science. In this article, an attempt to apply the tools of the cognitive science of religion to the appraisal of the methodological and epistemic status of the ID doctrine is (...) presented. It is concluded that the ID doctrine may serve as means of the real-time metaphorical reassurance of the creative power of God while it cannot give grounds to any literal inferences on the nature of the Divine action. (shrink)