Marcin Miłkowski

Is there a field of social intelligence? Many various disciplines approach the subject and it may only seem natural to suppose that different fields of study aim at explaining different phenomena; in other words, there is no special field of study of social intelligence. In this paper, I argue for an opposite claim. Namely, there is a way to integrate research on social intelligence, as long as one accepts the mechanistic account to explanation. Mechanistic integration of different explanations, however, comes at a cost: mechanism requires explanatory models to be fairly complete and realistic, and this does not seem to be the case for many models concerning social intelligence, especially models of economical behavior. Such models need either be made more realistic, or they would not count as contributing to the same field. I stress that the focus on integration does not lead to ruthless reductionism; on the contrary, mechanistic explanations are best understood as explanatorily pluralistic.

In this chapter, I sketch the history of mechanistic models of the mental, as related to the technological project of trying to build mechanical minds, and discuss the contemporary debates on psychological and cognitive explanations. In the first section, I introduce the Cartesian notion of mechanism, which has shaped the debate in the centuries to follow. Early mechanistic proposals are also connected with early attempts to formulate the computational account of thinking and reasoning, upheld notably by Hobbes and Leibniz. In the second section, associationist and behaviorist models of the mind are sketched, along with attempts to understand the neural system in terms of connections and associations. Early robotic models, built mostly by behaviorists and other students of animal behavior, are also introduced. In the third section, the focus is on early computational and cybernetic models of the mind. In the fourth section, I deal with computational models of mental mechanisms as proposed by students of artificial intelligence and cognitive science. Then, I sketch the contemporary debate over the question whether computational and psychological models are best understood as offering functional, or maybe mechanistic explanations.

Naturalism is currently the most vibrantly developing approach to philosophy, with naturalised methodologies being applied across all the philosophical disciplines. One of the areas naturalism has been focussing upon is the mind, traditionally viewed as a topic hard to reconcile with the naturalistic worldview. A number of questions have been pursued in this context. What is the place of the mind in the world? How should we study the mind as a natural phenomenon? What is the significance of cognitive science research for philosophical debates? In this book, philosophical questions about the mind are asked in the context of recent developments in cognitive science, evolutionary theory, psychology, and the project of the naturalisation. Much of the focus is upon what we have learned by studying natural mental mechanisms as well as designing artificial ones. In the case of natural mental mechanisms, this includes consideration of such issues as the significance of deficits in these mechanisms for psychiatry. The significance of the evolutionary context for mental mechanisms as well as questions regarding rationality and wisdom is also explored. Mechanistic and functional models of the mind are used to throw new light on discussions regarding issues of explanation, reduction and the realisation of mental phenomena. Finally, naturalistic approaches are used to look anew at such traditional philosophical issues as the correspondence of mind to world and presuppositions of scientific research.

In this paper, an account of theoretical integration in cognitive (neuro)science from the mechanistic perspective is defended. It is argued that mechanistic patterns of integration can be better understood in terms of constraints on representations of mechanisms, not just on the space of possible mechanisms, as previous accounts of integration had it. This way, integration can be analyzed in more detail with the help of constraintsatisfaction account of coherence between scientific representations. In particular, the account has resources to talk of idealizations and research heuristics employed by researchers to combine separate results and theoretical frameworks. The account is subsequently applied to an example of successful integration in the research on hippocampus and memory, and to a failure of integration in the research on mirror neurons as purportedly explanatory of sexual orientation.

In this paper, I show how semantic factors constrain the understanding of the computational phenomena to be explained so that they help build better mechanistic models. In particular, understanding what cognitive systems may refer to is important in building better models of cognitive processes. For that purpose, a recent study of some phenomena in rats that are capable of ‘entertaining’ future paths (Pfeiffer and Foster 2013) is analyzed. The case shows that the mechanistic account of physical computation may be complemented with semantic considerations, and in many cases, it actually should.

Recent work on skin-brain thesis suggests the possibility of empirical evidence that empiricism is false. It implies that early animals need no traditional sensory receptors to be engaged in cognitive activity. The neural structure required to coordinate extensive sheets of contractile tissue for motility provides the starting point for a new multicellular organized form of sensing. Moving a body by muscle contraction provides the basis for a multicellular organization that is sensitive to external surface structure at the scale of the animal body. In other words, the nervous system first evolved for action, not for receiving sensory input. Thus, sensory input is not required for minimal cognition; only action is. The whole body of an organism, in particular its highly specific animal sensorimotor organization, reflects the bodily and environmental spatiotemporal structure. The skin-brain thesis suggests that, in contrast to empiricist claims that cognition is constituted by sensory systems, cognition may be also constituted by action-oriented feedback mechanisms. Instead of positing the reflex arc as the elementary building block of nervous systems, it proposes that endogenous motor activity is crucial for cognitive processes. In the paper, I discuss the issue whether the skin-brain thesis and its supporting evidence can be really used to overthrow the main tenet of empiricism empirically, by pointing out to cognizing agents that fail to have any sensory apparatus.

Deleuze uważa, ze nie można pogodzić Hegla i Nietzschego. Hegel jest wedle niego abstrakcyjny, a Nietzsche - konkretny. Tymczasem pojęcia "konkret" i "abstrakcja" należą do ideologicznego arsenału konserwatyzmu. Rozpatruję nie tyle prawdziwość tezy Deleuza, co jej genealogię. Hegel i Nietzsche kontynuują oświeceniowe poszukiwania "człowieka konkretnego". "Człowiek konkretny" to wytwór drugiej fazy oświecenia (rodzaj "kompensacji" w znaczeniu Marquarda): przekształcenie parenetyki w filozofię historii i kultury (wzgl. społeczną). "Wielki bohater historii" i "nadczłowiek" są próbami ujęcia konkretu społeczno-historycznego. Rzut oka na strukturalną pozycję kategorii Hegla w jego systemie oraz ideału Nietzschego w jego myśli (mediatyzacja a problem wątpliwej konkretności nadczłowieka) uprawdopodabnia następujący wniosek: Nadczłowiek stanowi abstrakcyjny projekt uchwycenia konkretu. Nie można ukryć trudności w ocenie stosunku Hegla i Nietzschego wobec konserwatyzmu romantycznego. Przyczyna leży m.in. w wykorzystaniu arsenału myśli konserwatywnej przy jednoczesnym jej przekształceniu. Stąd trudno orzec, czy "konserwatyzm romantyczny" zniesiony (Hegel) lub przekształcony w ekstatyczno-chiliastyczną filozofię przyszłości (Nietzsche) jest bardziej konserwatywny, czy raczej liberalny itp. Jeden przykład z "warsztatu filozofowania" obu myślicieli wykazuje swoiste odniesienie się do konkretu, a także problematyczność tego odniesienia. Co więcej, opozycja konkret-abstrakt przy zmieniającym się jej nacechowaniu ideologicznym obejmuje również "empiryzm transcendentalny" samego Deleuze'a, a także - jak się zdaje - sporą część filozofii kontynuującej Nietzschego w intencji sprzeciwienia się Heglowi i jego metanarracjom. Wieloznaczność problemu konkretu ukrywa to, że jest pseudoproblemem lub - w najlepszym wypadku - mylącym wyróżnikiem stanowiska filozoficznego.

Many philosophers use “physicalism” and “naturalism” interchangeably. In this paper, I will distinguish ontological naturalism from physicalism. While broad versions of physicalism are compatible with naturalism, naturalism doesn't have to be committed to strong versions of physical reductionism, so it cannot be defined as equivalent to it. Instead of relying on the notion of ideal physics, naturalism can refer to the notion of ideal natural science that doesn't imply unity of science. The notion of ideal natural science, as well as the notion of ideal physics, will be vindicated. I will shortly explicate the notion of ideal natural science, and define ontological naturalism based on it.

Is there a field of social intelligence? Many various disciplines ap-proach the subject and it may only seem natural to suppose that different fields of study aim at explaining different phenomena; in other words, there is no spe-cial field of study of social intelligence. In this paper, I argue for an opposite claim. Namely, there is a way to integrate research on social intelligence, as long as one accepts the mechanistic account to explanation. Mechanistic inte-gration of different explanations, however, comes at a cost: mechanism requires explanatory models to be fairly complete and realistic, and this does not seem to be the case for many models concerning social intelligence, especially models of economical behavior. Such models need either be made more realistic, or they would not count as contributing to the same field. I stress that the focus on integration does not lead to ruthless reductionism; on the contrary, mechanistic explanations are best understood as explanatorily pluralistic.

It would be hard to find a more fervent advocate of the position that computers are of profound significance to philosophy than Aaron Sloman. Yet, he is not a stereotypical proponent of Artificial Intelligence (AI). Far from it; in his writings, he undermines several popular convictions of functionalists. Through his drafts and polemics, Sloman definitely exerts quite substantial influence on the philosophy of Artificial Intelligence. Sloman's paper “Evolution: The Computer Systems Engineer Designing Minds” presents a bold hypothesis that the evolution of the human mind actually involved the development of a several dozen of virtual machines that support various forms of self-monitoring. This, in turn, helps explain different features of our cognitive functioning

In this article, after presenting the basic idea of causal accounts of implementation and the problems they are supposed to solve, I sketch the model of computation preferred by Chalmers and argue that it is too limited to do full justice to computational theories in cognitive science. I also argue that it does not suffice to replace Chalmers’ favorite model with a better abstract model of computation; it is necessary to acknowledge the causal structure of physical computers that is not accommodated by the models used in computability theory. Additionally, an alternative mechanistic proposal is outlined.

Cognitive science is an interdisciplinary conglomerate of various research fields and disciplines, which increases the risk of fragmentation of cognitive theories. However, while most previous work has focused on theoretical integration, some kinds of integration may turn out to be monstrous, or result in superficially lumped and unrelated bodies of knowledge. In this paper, I distinguish theoretical integration from theoretical unification, and propose some analyses of theoretical unification dimensions. Moreover, two research strategies that are supposed to lead to unification are analyzed in terms of the mechanistic account of explanation. Finally, I argue that theoretical unification is not an absolute requirement from the mechanistic perspective, and that strategies aiming at unification may be premature in fields where there are multiple conflicting explanatory models.

The standard objection against naturalised epistemology is that it cannot account for normativity in epistemology (Putnam 1982; Kim 1988). There are different ways to deal with it. One of the obvious ways is to say that the objection misses the point: It is not a bug; it is a feature, as there is nothing interesting in normative principles in epistemology. Normative epistemology deals with norms but they are of no use in prac-tice. They are far too general to be guiding principles of research, up to the point that they even seem vacuous (see Knowles 2003). In this chapter, my strategy will be different and more in spirit of the founding father of naturalized epistemology, Quine, though not faithful to the letter. I focus on methodological prescriptions supplied by cogni-tive science in re-engineering of cognitive architectures. Engineering norms based on mechanism design weren’t treated as seriously as they should in epistemology, and that is why I will develop a sketch of a framework for researching them, starting from analysing cognitive sci-ence as engineering in section 3, then showing functional normativity in section 4, to eventually present functional engineering models of cogni-tive mechanisms as normative in section 5. Yet before showing the kind of engineering normativity specific for these prescriptions, it is worth-while to review briefly the role of normative methodology and the levels of norm complexity in it, and show how it follows Quine’s steps.

I argue that influential purely syntactic views of computation, shared by such philosophers as John Searle and Hilary Putnam, are mistaken. First, I discuss common objections, and during the discussion I mention additional necessary conditions of implementation of computations in physical processes that are neglected in classical philosophical accounts of computation. Then I try to show why realism in regards of physical computations is more plausible, and more coherent with any realistic attitude towards natural science than the received view, and distinguish computational simulation, implementation, and re-engineering. I also point out the sources of confusion about what computation is that seem to stem from disregarding the use/mention distinction.

This paper centers around the notion that internal, mental representations are grounded in structural similarity, i.e., that they are so-called S-representations. We show how S-representations may be causally relevant and argue that they are distinct from mere detectors. First, using the neomechanist theory of explanation and the interventionist account of causal relevance, we provide a precise interpretation of the claim that in S-representations, structural similarity serves as a “fuel of success”, i.e., a relation that is exploitable for the representation using system. Then, we discuss crucial differences between S-representations and indicators or detectors, showing that—contrary to claims made in the literature—there is an important theoretical distinction to be drawn between the two.

The Computational Theory of Mind (CTM) holds that the mind is a computer and that cognition is the manipulation of representations. CTM is commonly viewed as the main hypothesis in cognitive science, with classical CTM (related to the Language of Thought Hypothesis) being the most popular variant. However, other computational accounts of the mind either reject LOTH or do not subscribe to RTM. CTM proponents argue that it clarifies how thought and content are causally relevant in the physical world, and they frame this argument in terms of the physical symbol hypothesis, formalism, or syntactic engines. This article focuses on specific problems with CTM, with four main sections covering the introduction of the three main variants of CTM, the most important conceptions of computational explanation in cognitive science, skeptical arguments against CTM raised by Hilary Putnam, and common objections to CTM.

W artykule przedstawiono argumenty, że konfirmacja tezy, iż istnieją moduły umysłowe wyjaśniające cechy umysłu, jest z kilku powodów kłopotliwa. Po pierwsze, istnieje kilka konkurencyjnych teorii modularności, które zresztą nie zawsze się wykluczają, przez co nie można między nimi rozstrzygać eksperymentalnie. Po drugie, tezy na temat modularności często oparte są na bezzasadnym założeniu, iż wyróżnianie specyficznych dziedzin (semantycznych lub składniowych) działania modułów nie jest problematyczne. Po trzecie, analizując znany z literatury moduł wykrywania oszustów, postulowany przez Cosmides w celu wyjaśnienia rzekomej irracjonalności objawiającej się w tzw. zadaniu Wasona, pokazuję, że wyjaśniane zjawisko nie zostało zdefiniowane dostatecznie dokładnie, a przez to nieostra jest funkcjonalna charakterystyka modułów je wyjaśniających. Co więcej, nie ma powodów sądzić, że zjawisko, które ten moduł miał wyjaśniać, w ogóle istnieje. Wskazuję też kilka problemów metodologicznych związanych ze zbieraniem eksperymentalnych świadectw na rzecz modularności, takich jak zaburzanie wyników eksperymentów przez uśrednianie i brak kontroli nad kluczowymi czynnikami wpływającymi na rezultaty uzyskiwane przez uczestników badania.