Marcin Miłkowski

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.

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.

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.

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.

The goal of the article is to show that a complete answer to the title question can be given only in the context of the natural sciences. We believe that the group of cognitive sciences are the most reliable source of information about cognitive mental processes is. Making use of their achievements, we present a series of criteria for possessing a mind. We distinguish between many kinds of minds. We attempt to outline the conditions that must be fulfilled by an adequate model of the mind. In our opinion, such a model must make use of all available empirical data and of scientific theories constructed on the basis of such data. From the point of view of philosophy, the requirements placed upon such theories by ontology are especially important. Their reconstruction can be a prolegomenon to a future integrated ontology of the mind. We emphasize that the mind is not an independent thing. In speaking about the mind we have in mind states, events, processes, functions, and dispositions that are derivative with respect to processes of a lower order. We assume that an adequate model of the mind is multi-dimensional, taking into account several mutually interacting levels of organization. We interpret the psychophysical problem as one of the relation between levels of organization, a relation that is constitutive for the actualization of mental states. Psychophysical relations turn out to be a particular case of the broader issue of relations between levels. In carrying out a preliminary conceptualization we make use of the notion of emergence; this is why our position, which is mainly in opposition to substantial dualism, may be termed emergent monism or naturalism

The paper defends the claim that the mechanistic explanation of information processing is the fundamental kind of explanation in cognitive science. These mechanisms are complex organized systems whose functioning depends on the orchestrated interaction of their component parts and processes. A constitutive explanation of every mechanism must include both appeal to its environment and to the role it plays in it. This role has been traditionally dubbed competence. To fully explain how this role is played it is necessary to explain the information processing inside the mechanism embedded in the environment. The most usual explanation on this level has a form of a computational model, for example a software program or a trained artificial neural network. However, this is not the end of the explanatory chain. What is left to be explained is how the program is realized (or what processes are responsible for information processing in the artificial neural network). By using two dramatically different examples from the history of cognitive science I show the multi-level structure of explanations in cognitive science. These examples are (1) the explanation of human process solving as proposed by A. Newell & H. Simon; (2) the explanation of cricket phonotaxis via robotic models by B. Webb.

The paper proposes an empirical method to investigate linguistic prescriptions as inherent corrective behaviors. The behaviors in question may but need not necessarily be supported by any explicit knowledge of rules. It is possible to gain insight into them, for example by extracting information about corrections from revision histories of texts (or by analyzing speech corpora where users correct themselves or one another). One easily available source of such information is the revision history of Wikipedia. As is shown, the most frequent and short corrections are limited to linguistic errors such as typos (and editorial conventions adopted in Wikipedia). By perusing an automatically generated revision corpus, one gains insight into the prescriptive nature of language empirically. At the same time, the prescriptions offered are not reducible to descriptions of the most frequent linguistic use.

Explanations in cognitive science and computational neuroscience rely predominantly on computational modeling. Although the scientific practice is systematic, and there is little doubt about the empirical value of numerous models, the methodological account of computational explanation is not up-to-date. The current chapter offers a systematic account of computational explanation in cognitive science and computational neuroscience within a mechanistic framework. The account is illustrated with a short case study of modeling of the mirror neuron system in terms of predictive coding.

In Darwin’s Dangerous Idea, Daniel Dennett claims that evolution is algorithmic. On Dennett’s analysis, evolutionary processes are trivially algorithmic because he assumes that all natural processes are algorithmic. I will argue that there are more robust ways to understand algorithmic processes that make the claim that evolution is algorithmic empirical and not conceptual. While laws of nature can be seen as compression algorithms of information about the world, it does not follow logically that they are implemented as algorithms by physical processes. For that to be true, the processes have to be part of computational systems. The basic difference between mere simulation and real computing is having proper causal structure. I will show what kind of requirements this poses for natural evolutionary processes if they are to be computational.

Artificial models of cognition serve different purposes, and their use determines the way they should be evaluated. There are also models that do not represent any particular biological agents, and there is controversy as to how they should be assessed. At the same time, modelers do evaluate such models as better or worse. There is also a widespread tendency to call for publicly available standards of replicability and benchmarking for such models. In this paper, I argue that proper evaluation ofmodels does not depend on whether they target real biological agents or not; instead, the standards of evaluation depend on the use of models rather than on the reality of their targets. I discuss how models are validated depending on their use and argue that all-encompassing benchmarks for models may be well beyond reach

The contributors to this volume engage with issues of normativity within naturalised philosophy. The issues are critical to naturalism as most traditional notions in philosophy, such as knowledge, justification or representation, are said to involve normativity. Some of the contributors pursue the question of the correct place of normativity within a naturalised ontology, with emergentist and eliminativist answers offered on neighbouring pages. Others seek to justify particular norms within a naturalised framework, the more surprising ones including naturalist takes on the a priori and intuitions. Finally, yet others examine concrete examples of the application of norms within particular epistemic endeavours, such as psychopathology and design. The overall picture is that of an intimate engagement with issues of normativity on the part of naturalist philosophers – questioning some of the fundamentals at the same time as they try to work out many of the details.

In this paper, I argue that even if the Hard Problem of Content, as identified by Hutto and Myin, is important, it was already solved in natu- ralized semantics, and satisfactory solutions to the problem do not rely merely on the notion of information as covariance. I point out that Hutto and Myin have double standards for linguistic and mental representation, which leads to a peculiar inconsistency. Were they to apply the same standards to basic and linguistic minds, they would either have to embrace representationalism or turn to semantic nihilism, which is, as I argue, an unstable and unattractive position. Hence, I conclude, their book does not offer an alternative to representation- alism. At the same time, it reminds us that representational talk in cognitive science cannot be taken for granted and that information is different from men- tal representation. Although this claim is not new, Hutto and Myin defend it forcefully and elegantly.

Autor artykułu broni tezy, że niektóre systemy obliczeniowe mogą mieć własności semantyczne. Wskazana została klasa systemów obliczeniowych, w których reprezentacje mogą mieć przynajmniej dwie własności: własność odnoszenia się do obiektów (desygnowanie) i własność wspomagania rozpoznawania obiektów oznaczanych przez daną reprezentację (konotowanie). Autor argumentuje także, że własności semantyczne reprezentacji nie zależą wyłącznie od architektury systemów obliczeniowych, w których te reprezentacje występują. Konkretna architektura obliczeniowa nie jest czynnikiem kluczowym, a bodaj najmniej istotne są same rodzaje struktur danych, które mają mieć własności desygnowania czy konotowania. Własność desygnowania czy konotowania nie musi być zlokalizowana w samych reprezentacjach, może być własnością wyższego rzędu, powstającą w mechanizmie wyższego poziomu. Własności semantyczne reprezentacji mogą być wielorako realizowane. Systemy klasyczne, koneksjonistyczne czy też hybrydowe mogą równie dobrze mieć własności semantyczne, jak ich nie mieć