In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, I argue that computationalism should not be the view that (human) cognition is computation, but that it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. I also argue that, if semiotic systems are systems that interpret signs, then both humans and computers are (...) semiotic systems. Finally, I suggest that minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers. In doing so, I take issue with Fetzer’s arguments to the contrary. (shrink)

I review a widely accepted argument to the conclusion that the contents of our beliefs, desires and other mental states cannot be causally efficacious in a classical computational model of the mind. I reply that this argument rests essentially on an assumption about the nature of neural structure that we have no good scientific reason to accept. I conclude that computationalism is compatible with wide semantic causal efficacy, and suggest how the computational model might be modified to accommodate this possibility.

3.2.2. The principle of conceptual containment ........................... 116 3.3.3. The physical human subject or the “I” ............................... 119 3.4. The hyperverse and its EDWs – the antimetaphysical foundation of the EDWs perspective ........................................... 150 Part II. Applications Chapter 4. Applications to some notions from philosophy of mind .. 159 4.1. Levels and reduction vs. emergence ............................................. 160 4.2. Qualia, Kant and the “I” ............................................................... 181 4.3. Mental causation and supervenience ............................................ 190 Chapter 5. Applications to some notions from cognitive science (...) ...... 200 5.1. Computationalism ........................................................................ 200 5.2. Connectionism .............................................................................. 211 5.3. The dynamical system approach .................................................. 223 5.4. Robotics ........................................................................................ 232 5.5. Dichotomies concerning the notion of mental representation and processing .............................................................................. 243 5.6. The EDWs perspective and some key elements in cognitive science .......................................................................................... 249 5.7. The relation between key elements and some philosophical distinctions ................................................................................... 264 5.8. Cognitive neuroscience ................................................................ 267 5.9. The status of any living entity ...................................................... 277 Chapter 6. Applications to some notions from philosophy of science and science ............................................................................ 281 6.1. A glance at logical positivism ...................................................... 285 6.2. Carnap’s linguistic frameworks .................................................... 289 6.3. Carnap vs. Gödel or syntactic vs. semantic .................................. 292 6.4. Carnap vs. Quine or rational reconstruction vs. naturalized epistemology ................................................................................ 295 6.5. Quine’s ontological relativity ....................................................... 296 6.6. Goodman’s relativity .................................................................... 298 6.7. Putnam and the rejection of the “thing-in-itself” .......................... 299 6.8. Friedman’s relative constitutive a priori principles....................... 301 6.9. Some notions from quantum mechanics ....................................... 305 6.10. The status of the external non-living epistemologically different entities ....................................................................................... 343 Conclusion .............................................................................................. 361 References .............................................................................................. 369. (shrink)

Philosophers have been contemplating the nature of the mind for centuries and have produced mountains of intricate jargon, thought experiments, and views, that map a landscape of interminable disputes. One such dispute is between philosophers who believe that the mind can be explained as a mechanism and philosophers who insist it cannot. In this paper I take a look at this dispute and argue that it is unique in philosophy and a key to the nature of the mind.

Can animals think? In this paper I address the proposal that many animals, including insects such as honeybees, have genuine thoughts. I consider one prominent version of this view that claims that honeybees can represent and process information about their environments in a way that satisfies the main hallmarks of human conceptual thought. I shall argue, however, that this view fails to provide convincing grounds for accepting that animals possess concepts. More precisely, I suggest that two important aspects of conceptual (...) thought, viz., concept individuation and the generality constraint, are not satisfied. (shrink)

The aim of this paper is to discuss the concept of distributed cognition in the context of classic questions posed by mainstream cognitive science. We support our remarks by appealing to empirical evidence from the fields of cognitive science and ethnography. Particular attention is paid to the structure and functioning of a cognitive system, as well as its external representations. We analyze the problem of how far we can push the study of human cognition without taking into account what is (...) underneath an individual’s skin. In light of our discussion, a distinction between DCog and the extended mind becomes important. (shrink)

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience must literally exist the system being modelled. Computational models are important tools to test a theory about how the collected data has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in (...) the model. While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling that combines dynamical systems theory mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science. (shrink)

A popular argument form uses general theories of cognitive architecture to motivate conclusions about the nature of moral cognition. This paper highlights the possibility for modus tollens reversal of this argument form. If theories of cognitive architecture generate predictions for moral cognition, then tests of moral thinking provide feedback to cognitive science. In certain circumstances, philosophers' introspective attention to their own moral deliberations can provide unique data for these tests. Recognizing the possibility for this sort of feedback helps to illuminate (...) a deep continuity between the disciplines. (shrink)

Models of memory in cognitive science and philosophy have traditionally explained human remembering in terms of storage and retrieval. This tendency has been entrenched by reliance on computationalist explanations over the course of the twentieth century; even research programs that eschew computationalism in name, or attempt the revision of traditional models, demonstrate tacit commitment to computationalist assumptions. It is assumed that memory must be stored by means of an isomorphic trace, that memory processes must divide into conceptually distinct systems and (...) phases, and that human remembering consists in inner, cognitive processes that are implemented by distinct neural processes. This dissertation draws on recent empirical work, and on philosophical arguments from Ludwig Wittgenstein and others, to demonstrate that this latent computationalism in the study of memory is problematic, and that it can and should be eliminated. Cognitive psychologists studying memory have encountered numerous data in recent decades that belie archival models. In cognitive neuroscience, establishing the neural basis of storage and retrieval processes has proven elusive. A number of revised models on offer in memory science, that have taken these issues into account, fail to sufficiently extricate the archival framework. Several impasses in memory science are products of these underlying computationalist assumptions. Wittgenstein and other philosophers offer a number of arguments against the need for, and the efficacy of, the storage and retrieval of traces in human remembering. A study of these arguments clarifies the ways that these computationalist assumptions are presently impeding the science of memory, and provides ways forward in removing them. We can and should characterize and model human memory without invoking the storage and retrieval of traces. A range of work in connectionism, dynamical systems theory, and recent philosophical accounts of memory demonstrate how the science of memory can proceed without these assumptions, toward non-archival models of remembering. (shrink)