A suitable project for the new Millenium is to radically reconfigure our image of human rationality. Such a project is already underway, within the Cognitive Sciences, under the umbrellas of work in Situated Cognition, Distributed and De-centralized Cogition, Real-world Robotics and Artificial Life1. Such approaches, however, are often criticized for giving certain aspects of rationality too wide a berth. They focus their attention on on such superficially poor cousins as.

I argue for two claims. First I argue against the consensus view that accurate behavioral prediction based on accurate representation of cognitive states, i.e. mind reading , is the sustaining function of propositional attitude ascription. This practice cannot have been selected in evolution and cannot persist, in virtue of its predictive utility, because there are principled reasons why it is inadequate as a tool for behavioral prediction. Second I give reasons that favor an alternative account of the sustaining function of (...) propositional attitude ascription. I argue that it serves a mind-shaping function. Roughly, propositional attitude ascription enables human beings to set up regulative ideals that function to mold behavior so as to make it easier to coordinate with. (shrink)

The target article sketched and explored a mechanism (action-oriented predictive processing) most plausibly associated with core forms of cortical processing. In assessing the attractions and pitfalls of the proposal we should keep that element distinct from larger, though interlocking, issues concerning the nature of adaptive organization in general.

Jerry Fodor has consistently cited the persistence of illusions--especially the M.

What features will something have if it counts as an explanation? And will something count as an explanation if it has those features? In the second half of the 20th century, philosophers of science set for themselves the task of answering such questions, just as a priori conceptual analysis was generally falling out of favor. And as it did, most philosophers of science just moved on to more manageable questions about the varieties of explanation and discipline-specific scientific explanation. Often, such (...) shifts are sound strategies for problem-solving. But leaving fallow certain basic conceptual issues can also result in foundational debates. (shrink)

Recent work in cognitive science highlights the importance of exem- plar-based know-how in supporting human expertise. Influenced by this model, certain accounts of moral knowledge now stress exemplar- based, non-sentential know-how at the expense of rule-and-principle based accounts. I shall argue, however, that moral thought and reason cannot be understood by reference to either of these roles alone. Moral cognition – like other forms of ‘advanced’ cognition – depends crucially on the subtle interplay and interaction of multiple factors and forces (...) and especially (or so I argue) between the use of linguistic tools and formulations and more biologically basic forms of thought and reason. (shrink)

Davidson's principle of the anomalousness of the mental was instrumental in discrediting once-popular versions of mind-brain reductionism. In this essay I argue that a novel account of intertheoretic reduction, which does not require the sort of cross-theoretic bridge laws that Davidson's principle rules out, allows a version of mind-brain reductionism which is immune from Davidson's challenge. In the final section, I address a second worry about reductionism, also based on Davidson's principle, that survives this response. I argue that new reductionists (...) should revise some significant details of the program, particularly the conception of theories, to circumvent this more potent Davidson-inspired worry. (shrink)

The article began life as, and retains the character of, spoken argument for not allowing the neurosciences to shape the agenda of the history of the human sciences. This argument is then used to suggest purposes and content for the journal, History of the Human Sciences. The style is rhetorical, even polemical, but open-ended. I challenge two clichés about the neurosciences, that they intellectually challenge other areas of knowledge, and that they are reconfiguring the human with the notion of ‘brainhood’. (...) The suggestion is that the real challenges lie elsewhere; specifically with understanding the relations of different forms of knowledge and making it conceivable by political action, or simply mode of life, to implement one way of being human rather than another. The conclusion re-asserts the value of the heading, ‘history of the human sciences’, and of the value of the journal with this name, as a forum in which to reflect on the identity and relations of forms of knowledge about ‘the human’ in all their variety. (shrink)

Opinionated state of the art paper on scientific explanation. Analysis and discussion of the most relevant models and theories in the contemporary literature, namely, the deductive-nomological model, the models of inductive-statistical and statistical relevance, the pragmatic theory of why questions, the unifying theory of standard arguments, and the causal/non-causal counterfactual theory.

Paul Feyerabend recommended the methodological policy of proliferating competing theories as a means to uncovering new empirical data, and thus as a means to increase the empirical constraints that all theories must confront. Feyerabend's policy is here defended as a clear consequence of connectionist models of explanatory understanding and learning. An earlier connectionist "vindication" is criticized, and a more realistic and penetrating account is offered in terms of the computationally plastic cognitive profile displayed by neural networks with a recurrent architecture.

In this review, we deal with two central questions of consciousness how and why, and we outline their possible future development. The question how refers to the empirical endeavor to reveal the neural correlates and mechanisms that form consciousness. On the other hand, the question why generally refers to the “hard problem” of consciousness, which claims that empirical science will always fail to provide a satisfactory answer to the question why is there conscious experience at all. Unfortunately, the hard problem (...) of consciousness will probably never completely disappear because it will always have its most committed supporters. However, there is a good chance that its weight and importance will be highly reduced by empirically tackling consciousness in the near future. We expect that future empirical endeavor of consciousness will be based on a unifying brain theory and will answer the question as to what is the function of conscious experience, which will in turn replace the implications of the hard problem. The candidate of such a unifying brain theory is predictive coding, which will have to explain both perceptual consciousness and conscious mind-wandering in order to become the truly unifying theory of brain functioning. (shrink)

An account of scientific explanation is presented according to which (1) scientific explanation consists in solving “insight” problems (Metcalfe and Wiebe 1984) and (2) understanding is the result of solving such problems. The theory is pragmatic; it draws upon van Fraassen’s (1977, 1980) insights, avoids the objections to pragmatic accounts offered by Kitcher and Salmon (1987), and relates scientific explanation directly to understanding. The theory also accommodates cases of explanatory asymmetry and intuitively legitimate rejections of explanation requests.

The article explores the transformative impact on human and social sciences in response to anticipated societal shifts driven by the forthcoming proliferation of artificial systems, whose intelligence will match human capabilities. Initially, it was posited that artificial intelligence (AI) would excel beyond human abilities in computational tasks and algorithmic operations, leaving creativity and humanities as uniquely human domains. However, recent advancements in large language models have significantly challenged these conventional beliefs about AI’s limitations and strengths. It is projected that, in (...) the near future, generative AI models will adeptly replicate individual qualities, desires, beliefs, opinions, and the essence of human identity and consciousness to a degree that is nearly indistinguishable from that of humans. This lends support to the connectionist approach to understanding consciousness, suggesting an inherent similarity between biological and artificial neural networks. The discussion posits two innovative areas of scientific inquiry: “mathematical anthropology” and “multi-dimensional calculus of value.” The former suggests that viewing human nature through a mathematical and calculative lens not only preserves but enriches our understanding of the complexity of anthropological experience and its perceived contradictions. The latter hypothesis explores how mathematical models could facilitate various social interactions. The advanced simulation capabilities of neural networks suggest that traditional social and political frameworks face a growing vulnerability to AI-driven manipulations. This trend underscores the urgency of developing social interaction models that incorporate explicitly defined calculative rules. In conclusion, the paper advocates for a paradigm shift in how mathematics is perceived – not merely as a tool for computation but as a foundational science for crafting sophisticated models. In conclusion, the article advocates for transitioning from perceiving mathematics solely as a science of computation to viewing it as a discipline dedicated to constructing various formalized models, thereby deepening our insight into the complexities of human and social phenomena. (shrink)

The article provides an analysis of Paul and Patricia Churchland’s eliminative materialism. I will distinguish two lines of argument in their eliminativism: one seeking to eliminate folk psychology and the second criticising Jerry Fodor’s language of thought hypothesis. Then I will closely examine the second line of argument, and show that it represents the main motive of Churchland’s work since the end of 1980s and demonstrate why the success of the argument against the language of thought hypothesis does not constitute (...) a reason for the elimination of folk psychology. Finally, I will examine the consequences of this approach for the role of folk psychology in the study of mind and show that the weakened eliminativist position still fulfils the original aim of Churchland’s program. (shrink)

The argument I present here is an example of the manner in which naturalizing epistemology can help address fairly traditional epistemological issues. I develop one argument against coherentist epistemologies of empirical knowledge. In doing so, I draw on BonJour (1985), for that account seems to me to indicate the direction in which any plausible coherentist account would need to be developed, at least insofar as such accounts are to conceive of justification in terms of an agent (minimally) possessing articul able (...) reasons and arguments, as is standard. I end by indicating important elements of coherentist epistemology that can be salvaged in the face of my argument, provided we are willing to drop the traditional commitment to characterizing justification in terms of the structure of articulable argument. (shrink)

In this paper the issue of drawing inferences about biological cognitive systems on the basis of connectionist simulations is addressed. In particular, the justification of inferences based on connectionist models trained using the backpropagation learning algorithm is examined. First it is noted that a justification commonly found in the philosophical literature is inapplicable. Then some general issues are raised about the relationships between models and biological systems. A way of conceiving the role of hidden units in connectionist networks is then (...) introduced. This, in combination with an assumption about the way evolution goes about solving problems, is then used to suggest a means of justifying inferences about biological systems based on connectionist research. (shrink)