If the Trade Descriptions Act were applied to academic labels, cognitive scientists would be in trouble. For what they do is much wider than the name suggests—and wider, too, than most philosophers assume. They give you more for your money than you may have expected.

In addition to his famous Chinese Room argument, John Searle has posed a more radical problem for views on which minds can be understood as programs. Even his wall, he claims, implements the WordStar program according to the standard definition of implementation because there is some ‘‘pattern of molecule movements’’ that is isomorphic to the formal structure of WordStar. Program implementation, Searle charges, is merely observer-relative and thus not an intrinsic feature of the world. I argue, first, that analogous charges (...) involving other concepts (motion and meaning) lead to consequences no one accepts. Second, I show that Searle’s treatment of computation is incoherent, yielding the consequence that nothing computes anything: even our standard personal computers fail to run any programs on this account. I propose an alternative account, one that accords with the way engineers, programmers, and cognitive scientists use the concept of computation in their empirical work. This alternative interpretation provides the basis of a philosophical analysis of program implementation, one that may yet be suitable for a computational theory of the mind. (shrink)

Arguments to the effect that Church's thesis is intrinsically unprovable because proof cannot relate an informal, intuitive concept to a mathematically defined one are unconvincing, since other 'theses' of this kind have indeed been proved, and Church's thesis has been proved in one direction. However, though evidence for the truth of the thesis in the other direction is overwhelming, it does not yet amount to proof.

Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, automated credit-scoring processes (...) “discriminate” on gender, etc.—there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “... all the impressive achievements of deep learning amount to just curve fitting.” The key, as Pearl suggests, is to replace “reasoning by association” with “causal reasoning” —the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for theNew York Times: “we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets—often using an approach known as ‘Deep Learning’—and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space, and causality.” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all. (shrink)

The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give rise to (...) disagreement is articulated. The goal here is to provide something like a ‘geography’ of the various notions of ‘symbol’ that have appeared in the various literatures, so as to highlight the key issues and to permit the focusing of attention upon the important dimensions. In particular, the relationship between ‘tokens’ and ‘symbols’ is addressed. The issue of designation is discussed in some detail. The distinction between simple and complex symbols is clarified and an apparently necessary condition for a system to be potentially symbol, or token bearing, is introduced. (shrink)

Digital physics claims that the entire universe is, at the very bottom, made out of bits; as a result, all physical processes are intrinsically computational. For that reason, many digital physicists go further and affirm that the universe is indeed a giant computer. The aim of this article is to make explicit the ontological assumptions underlying such a view. Our main concern is to clarify what kind of properties the universe must instantiate in order to perform computations. We analyse the (...) logical form of the two models of computation traditionally adopted in digital physics, namely, cellular automata and Turing machines. These models are computationally equivalent, but we show that they support different ontological commitments about the fundamental properties of the universe. In fact, cellular automata are compatible with a rather traditional form of physicalism, whereas Turing machines support a dualistic ontology, which could be understood as a realism about the laws of nature or, alternatively, as a kind of panpsychism. (shrink)

This introduction begins by posing the question that this Special Issue addresses and briefly considers historical precedents and why the issue is important. The discussion then moves on to the consideration of important milestones in the history of computing, up until the present time. A brief specification of the essential components of computational systems is then offered. The final section introduces the papers that are included in this volume.

Dual Process Theory is currently a popular theory for explaining why we show bounded rationality in reasoning and decision-making tasks. This theory proposes there must be a sharp distinction in thinking to explain two clusters of correlational features. One cluster describes a fast and intuitive process, while the other describes a slow and reflective one. A problem for this theory is identifying a common principle that binds these features together, explaining why they form a unity, the unity problem. To solve (...) it, a hypothesis is developed combining embodied predictive processing with symbolic classical approaches. The hypothesis, simplified, states that Type 1 processes are bound together because they rely on embodied predictive processing whereas Type 2 processes form a unity because they are accomplished by symbolic classical cognition. To show that this is likely the case, the features of Dual Process Theory are discussed in relation to these frameworks. (shrink)

Computer simulations and experiments share many important features. One way of explaining the similarities is to say that computer simulations just are experiments. This claim is quite popular in the literature. The aim of this paper is to argue against the claim and to develop an alternative explanation of why computer simulations resemble experiments. To this purpose, experiment is characterized in terms of an intervention on a system and of the observation of the reaction. Thus, if computer simulations are experiments, (...) either the computer hardware or the target system must be intervened on and observed. I argue against the first option using the non-observation argument, among others. The second option is excluded by e.g. the over-control argument, which stresses epistemological differences between experiments and simulations. To account for the similarities between experiments and computer simulations, I propose to say that computer simulations can model possible experiments and do in fact often do so. (shrink)

We have begun a theory of measurement in which an experimenter and his or her experimental procedure are modeled by algorithms that interact with physical equipment through a simple abstract interface. The theory is based upon using models of physical equipment as oracles to Turing machines. This allows us to investigate the computability and computational complexity of measurement processes. We examine eight different experiments that make measurements and, by introducing the idea of an observable indicator, we identify three distinct forms (...) of measurement process and three types of measurement algorithm. We give axiomatic specifications of three forms of interfaces that enable the three types of experiment to be used as oracles to Turing machines, and lemmas that help certify an experiment satisfies the axiomatic specifications. For experiments that satisfy our axiomatic specifications, we give lower bounds on the computational power of Turing machines in polynomial time using nonuniform complexity classes. These lower bounds break the barrier defined by the Church-Turing Thesis. (shrink)

Cognitive psychologists, like biologists, frequently describe mechanisms when explaining phenomena. Unlike biologists, who can often trace material transformations to identify operations, psychologists face a more daunting task in identifying operations that transform information. Behavior provides little guidance as to the nature of the operations involved. While not itself revealing the operations, identification of brain areas involved in psychological mechanisms can help constrain attempts to characterize the operations. In current memory research, evidence that the same brain areas are involved in what (...) are often taken to be different memory phenomena or in other cognitive phenomena is playing such a heuristic function. †To contact the author, please write to: Department of Philosophy, 0119, University of California, San Diego, La Jolla, CA 92093‐0119; e‐mail: [email protected]. (shrink)

Philosophy of science is positioned to make distinctive contributions to cognitive science by providing perspective on its conceptual foundations and by advancing normative recommendations. The philosophy of science I embrace is naturalistic in that it is grounded in the study of actual science. Focusing on explanation, I describe the recent development of a mechanistic philosophy of science from which I draw three normative consequences for cognitive science. First, insofar as cognitive mechanisms are information-processing mechanisms, cognitive science needs an account of (...) how the representations invoked in cognitive mechanisms carry information about contents, and I suggest that control theory offers the needed perspective on the relation of representations to contents. Second, I argue that cognitive science requires, but is still in search of, a catalog of cognitive operations that researchers can draw upon in explaining cognitive mechanisms. Last, I provide a new perspective on the relation of cognitive science to brain sciences, one which embraces both reductive research on neural components that figure in cognitive mechanisms and a concern with recomposing higher-level mechanisms from their components and situating them in their environments. (shrink)

Chaos-related obstructions to predictability have been used to challenge accounts of theory validation based on the agreement between theoretical predictions and experimental data. These challenges are incomplete in two respects: they do not show that chaotic regimes are unpredictable in principle and, as a result, that there is something conceptually wrong with idealized expectations of correct predictions from acceptable theories, and they do not explore whether chaos-induced predictive failures of deterministic models can be remedied by stochastic modeling. In this paper (...) we appeal to an asymptotic analysis of state space trajectories and their numerical approximations to show that chaotic regimes are deterministically unpredictable even with unbounded resources. Additionally, we explain why stochastic models of chaotic systems, while predictively successful in some cases, are in general predictively as limited as deterministic ones. We conclude by suggesting that the way in which scientists deal with such principled obstructions to predictability calls for a more comprehensive approach to theory validation, on which experimental testing is augmented by a multifaceted mathematical analysis of theoretical models, capable of identifying chaos-related predictive failures as due to principled limitations which the world itself imposes on any less-than-omniscient epistemic access to some natural systems. (shrink)

One of the most important contributions of A. Church to logic is his invention of the lambda calculus. We present the genesis of this theory and its two major areas of application: the representation of computations and the resulting functional programming languages on the one hand and the representation of reasoning and the resulting systems of computer mathematics on the other hand.

This paper reviews Benjamin Noys’ recent attempt in Malign Velocities: Accelerationism and Capitalism to mount a critique of accelerationism. The book, persuasive in certain respects, bypasses the institutional dynamics of accelerationism’s theoretical progenitors, viz. Nick Land and the Cybernetic Culture Research Unit of Warwick University, and instead portrays it as a “defeatist strategy” of the post-’68 conjuncture of “Deleuzian Thatcherism.” Such portrayal is debatable to the extent that it exhibits a questionable appropriation of “theory” in the strict sense of the (...) term; a term the paper attempts to define with reference to Matthew Arnold, Louis Althusser and Gilles Grelet. (shrink)

Given two infinite binary sequences A,B we say that B can compress at least as well as A if the prefix-free Kolmogorov complexity relative to B of any binary string is at most as much as the prefix-free Kolmogorov complexity relative to A, modulo a constant. This relation, introduced in Nies [14] and denoted by A≤LKB, is a measure of relative compressing power of oracles, in the same way that Turing reducibility is a measure of relative information. The equivalence classes (...) induced by ≤LK are called LK degrees and there is a least degree containing the oracles which can only compress as much as a computable oracle, also called the ‘low for K’ sets. A well-known result from Nies [14] states that these coincide with the K-trivial sets, which are the ones whose initial segments have minimal prefix-free Kolmogorov complexity. We show that with respect to ≤LK, given any non-trivial sets X,Y there is a computably enumerable set A which is not K-trivial and it is below X,Y. This shows that the local structures of and Turing degrees are not elementarily equivalent to the corresponding local structures in the LK degrees. It also shows that there is no pair of sets computable from the halting problem which forms a minimal pair in the LK degrees; this is sharp in terms of the jump, as it is known that there are sets computable from which form a minimal pair in the LK degrees. We also show that the structure of LK degrees below the LK degree of the halting problem is not elementarily equivalent to the or structures of LK degrees. The proofs introduce a new technique of permitting below a set that is not K-trivial, which is likely to have wider applications. (shrink)

Computability theory concerns information with a causal–typically algorithmic–structure. As such, it provides a schematic analysis of many naturally occurring situations. Emil Post was the first to focus on the close relationship between information, coded as real numbers, and its algorithmic infrastructure. Having characterised the close connection between the quantifier type of a real and the Turing jump operation, he looked for more subtle ways in which information entails a particular causal context. Specifically, he wanted to find simple relations on reals (...) which produced richness of local computability-theoretic structure. To this extent, he was not just interested in causal structure as an abstraction, but in the way in which this structure emerges in natural contexts. Post’s programme was the genesis of a more far reaching research project. In this article we will firstly review the history of Post’s programme, and look at two interesting developments of Post’s approach. The first of these developments concerns the extension of the core programme, initially restricted to the Turing structure of the computably enumerable sets of natural numbers, to the Ershov hierarchy of sets. The second looks at how new types of information coming from the recent growth of research into randomness, and the revealing of unexpected new computability-theoretic infrastructure. We will conclude by viewing Post’s programme from a more general perspective. We will look at how algorithmic structure does not just emerge mathematically from information, but how that emergent structure can model the emergence of very basic aspects of the real world. (shrink)

Several papers (e.g. [7, 23, 42]) have recently sought to give general frameworks for online structures and algorithms ([4]), and seeking to connect, if only by analogy, online and computable structure theory. These initiatives build on earlier work on online colouring and other combinatorial algorithms by Bean [10], Kierstead, Trotter et al. [48, 54, 57] and others, as we discuss below. In this paper we will look at such frameworks and illustrate them with examples from the first author’s MSc Thesis (...) ([58]). In this thesis, Askes looks at online, adversarial online and strongly online algorithms and structures. Additionally, we prove some new theorems about online algorithms on graphs of bounded pathwidth as well as some new results on punctually 1-decidable structures. (shrink)

Gottfried Wilhelm Leibniz is the self-proclaimed inventor of the binary system and is considered as such by most historians of mathematics and/or mathematicians. Really though, we owe the groundwork of today’s computing not to Leibniz but to the Englishman Thomas Harriot and the Spaniard Juan Caramuel de Lobkowitz, whom Leibniz plagiarized. This plagiarism has been identified on the basis of several facts: Caramuel’s work on the binary system is earlier than Leibniz’s, Leibniz was acquainted—both directly and indirectly—with Caramuel’s work and (...) Leibniz had a natural tendency to plagiarize scientific works. (shrink)

We analyse Kreisel’s notion of human-effective computability. Like Kreisel, we relate this notion to a concept of informal provability, but we disagree with Kreisel about the precise way in which this is best done. The resulting two different ways of analysing human-effective computability give rise to two different variants of Church’s thesis. These are both investigated by relating them to transfinite progressions of formal theories in the sense of Feferman.

Turing was an exceptional mathematician with a peculiar and fascinating personality and yet he remains largely unknown. In fact, he might be considered the father of the von Neumann architecture computer and the pioneer of Artificial Intelligence. And all thanks to his machines; both those that Church called “Turing machines” and the a-, c-, o-, unorganized- and p-machines, which gave rise to evolutionary computations and genetic programming as well as connectionism and learning. This paper looks at all of these and (...) at why he is such an often overlooked and misunderstood figure. (shrink)

This paper sheds light on the shift that is taking place from the practice of ‘coding’, namely developing programs as conventional in the software community, to the practice of ‘curing’, an activity that has emerged in the last few years in Deep Learning (DL) and that amounts to curing the data regime to which a DL model is exposed during training. Initially, the curing paradigm is illustrated by means of a study-case on autonomous vehicles. Subsequently, the shift from coding to (...) curing is analysed taking into consideration the epistemological notions, central in the philosophy of computer science, of function, implementation, and correctness. First, it is illustrated how, in the curing paradigm, the functions performed by the trained model depend much more on dataset curation rather than on the model algorithms which, in contrast with the coding paradigm, do not comply with requested specifications. Second, it is highlighted how DL models cannot be considered implementations according to any of the available definitions of implementation that follow an intentional theory of functions. Finally, it is argued that DL models cannot be evaluated in terms of their correctness but rather in their experimental computational validity. (shrink)

The most recent tool for acting on the world, the exosomatization of cognitive activities, is often considered an autonomous and objective replacement of knowledge construction. We show the intrinsic limits of the mechanistic myths in AI, from classical to Deep Learning techniques, and its relation to the human construction of sense. Human activities in a changing ecosystem – in their somatic and sensible dimensionalities proper to any living experiences – are at the core of our analysis. By this, we stress (...) the key role of the knowing subject, far away from any allegedly objective big collections of data. The production of organized structures of physics, biology and in societal analysis will be compared and distinguished by trying to set on more robust grounds the constructive as well as the disruptive roles of entropic, negentropic, anti-entropic dynamics that are different concepts in different domains, to be handled with care: the use of machine learning and optimization methods as tools and models to analyse and manage human activities in view of their scientific and political ideology of technoscientific governance. They suppose that which they try to produce is objective, that is, standardized and controllable behaviours. In this dialogue we stress a mirror symmetry between the lack of theoretical interpretation of scientific data and the lack of democracy in this fiction of neutrality. Moreover, bad analogies constitute an obstacle to grasp the anteriority of biological and ecological constraints which enable and limit all artificial products of human intelligence. We will thus stress biological specificity, the role of normativity and constraints in evolution, of labour in structuring the human historical construction of sense by common activities. (shrink)

This essay discusses Peirce’s appeal to logical machines as an argument against psychologism. It also contends that some of Peirce’s anti-psychologistic remarks on logic contain interesting premonitions arising from his perception of the asymmetry of proof complexity in monadic and relational logical calculi that were only given full formulation and explication in the early twentieth century through Church’s Theorem and Hilbert’s broad-ranging Entscheidungsproblem.

Recently, many philosophers have been inclined to ascribe mentality to animals on the main grounds that they possess certain complex computational abilities. In this paper I contend that this view is misleading, since it wrongly assumes that those computational abilities demand a psychological explanation. On the contrary, they can be just characterised from a computational level of explanation, which picks up a domain of computation and information processing that is common to many computing systems but is autonomous from the domain (...) of psychology. Thus, I propose that it is possible to conceive insects and other animals as mere computing agents, without having any commitment to ascribe mentality to them. I conclude by sketching a proposal about how to draw the line between mere computing and genuine mentality. (shrink)

The popular debates about the future organization of work through artificial intelligence technologies focus on the replacement of human beings by novel technologies. In this essay, we oppose this statement by closely following what has been developed as AI technologies and analyzing how they work, specifically focusing on research that may impact work organizations. We develop this argument by showing that the recent research and developments in AI technologies focus on developing accurate and precise performance models, which in turn shapes (...) organizational patterns of work. We propose that the increased interest in the relationship between human cognition and performance will shortly bring human cognition to the focus on AI systems in workplaces. More specifically, we claim that the cognitive load measurement will shape human performance in manufacturing systems shortly. (shrink)

In this paper I argue that whether or not a computer can be built that passes the Turing test is a central question in the philosophy of mind. Then I show that the possibility of building such a computer depends on open questions in the philosophy of computer science: the physical Church-Turing thesis and the extended Church-Turing thesis. I use the link between the issues identified in philosophy of mind and philosophy of computer science to respond to a prominent argument (...) against the possibility of building a machine that passes the Turing test. Finally, I respond to objections against the proposed link between questions in the philosophy of mind and philosophy of computer science. (shrink)

Recent literature on the role of pictorial representation in the life sciences has focused on the relationship between detailed representations of empirical data and more abstract, formal representations of theory. The standard argument is that in both a historical and epistemic sense, this relationship is a directional one: beginning with raw, unmediated images and moving towards diagrams that are more interpreted and more theoretically rich. Using the neural network diagrams of Warren McCulloch and Walter Pitts as a case study, I (...) argue that while in the empirical sciences, pictorial representation tends to move from data to theory, in areas of the life sciences that are predominantly theoretical, when abstraction occurs at the outset, the relationship between detail and abstraction in pictorial representations can be of a different character. (shrink)

Exactly how do the sign/symbol/token systems of endo- and exo-biosemiosis differ from those of cognitive semiosis? Do the biological messages that integrate metabolism have conceptual meaning? Semantic information has two subsets: Descriptive and Prescriptive. Prescriptive information instructs or directly produces nontrivial function. In cognitive semiosis, prescriptive information requires anticipation and “choice with intent” at bona fide decision nodes. Prescriptive information either tells us what choices to make, or it is a recordation of wise choices already made. Symbol systems allow recordation (...) of deliberate choices and the transmission of linear digital prescriptive information. Formal symbol selection can be instantiated into physicality using physical symbol vehicles (tokens). Material symbol systems (MSS) formally assign representational meaning to physical objects. Even verbal semiosis instantiates meaning into physical sound waves using an MSS. Formal function can also be incorporated into physicality through the use of dynamically-inert (dynamically-incoherent or -decoupled) configurable switch-settings in conceptual circuits. This paper examines the degree to which biosemiosis conforms to the essential formal criteria of prescriptive semiosis and cybernetic management. (shrink)

Modern computing is generally taken to consist primarily of symbol manipulation. But symbols are abstract, and computers are physical. How can a physical device manipulate abstract symbols? Neither Church nor Turing considered this question. My answer is that the bit, as a hardware-implemented abstract data type, serves as a bridge between materiality and abstraction. Computing also relies on three other primitive—but more straightforward—abstractions: Sequentiality, State, and Transition. These physically-implemented abstractions define the borderline between hardware and software and between physicality and (...) abstraction. At a deeper level, asking how a physical device can interact with abstract symbols is the wrong question. The relationship between symbols and physical devices begins with the realization that human beings already know what it means to manipulate symbols. We build and program computers to do what we understand to be symbol manipulation. To understand what that means, consider a light switch. A light switch doesn’t turn a light on or off. Those are abstractions. Light switches don’t operate with abstractions. We build light switches, so that when flipped, the world is changed in such a way that we understand the light to be on or off. Similarly, we build computers to perform operations that we understand as manipulating symbols. (shrink)

The article presents a conceptual framework for distinguishing different sorts of heterogeneous digital materials. The hypothesis is that a wide range of heterogeneous data resources can be characterized and classified due to their particular configurations of hypertext features such as scripts, links, interactive processes, and time scalings, and that the hypertext configuration is a major but not sole source of the messiness of big data. The notion of hypertext will be revalidated, placed at the center of the interpretation of networked (...) digital media, and used in the analysis of the fast-growing amounts of heterogeneous digital collections, assemblages, and corpora. The introduction summarizes the wider background of a fast-changing data landscape. (shrink)

Quantum computing is of high interest because it promises to perform at least some kinds of computations much faster than classical computers. Arute et al. 2019 (informally, “the Google Quantum Team”) report the results of experiments that purport to demonstrate “quantum supremacy” – the claim that the performance of some quantum computers is better than that of classical computers on some problems. Do these results close the debate over quantum supremacy? We argue that they do not. In the following, we (...) provide an overview of the Google Quantum Team’s experiments, then identify some open questions in the quest to demonstrate quantum supremacy. (shrink)

Can the machines that play board games or recognize images only in the comfort of the virtual world be intelligent? To become reliable and convenient assistants to humans, machines need to learn how to act and communicate in the physical reality, just like people do. The authors propose two novel ways of designing and building Artificial General Intelligence (AGI). The first one seeks to unify all participants at any instance of the Turing test – the judge, the machine, the human (...) subject as well as the means of observation instead of building a separating wall. The second one aims to design AGI programs in such a way so that they can move in various environments. The authors of the article thoroughly discuss four areas of interaction for robots with AGI and introduce a new idea of techno-umwelt bridging artificial intelligence with biology in a new way. (shrink)

This paper discusses the epistemic status of biology from the standpoint of the systemic approach to living systems based on the notion of biological autonomy. This approach aims to provide an understanding of the distinctive character of biological systems and this paper analyses its theoretical and epistemological dimensions. The paper argues that, considered from this perspective, biological systems are examples of emergent phenomena, that the biological domain exhibits special features with respect to other domains, and that biology as a discipline (...) employs some core concepts, such as teleology, function, regulation among others, that are irreducible to those employed in physics and chemistry. It addresses the claim made by Jacques Monod that biology as a science is marginal. It argues that biology is general insofar as it constitutes a paradigmatic example of complexity science, both in terms of how it defines the theoretical object of study and of the epistemology and heuristics employed. As such, biology may provide lessons that can be applied more widely to develop an epistemology of complex systems. (shrink)

The Turing machine is one of the simple abstract computational devices that can be used to investigate the limits of computability. In this paper, they are considered from several points of view that emphasize the importance and the relativity of mathematical languages used to describe the Turing machines. A deep investigation is performed on the interrelations between mechanical computations and their mathematical descriptions emerging when a human (the researcher) starts to describe a Turing machine (the object of the study) by (...) different mathematical languages (the instruments of investigation). Together with traditional mathematical languages using such concepts as ‘enumerable sets’ and ‘continuum’ a new computational methodology allowing one to measure the number of elements of different infinite sets is used in this paper. It is shown how mathematical languages used to describe the machines limit our possibilities to observe them. In particular, notions of observable deterministic and non-deterministic Turing machines are introduced and conditions ensuring that the latter can be simulated by the former are established. (shrink)

Cognitive modules are internal mental structures. Some theorists and empirical researchers hypothesize that the human mind is either partially or massively comprised of structures that are modular in nature. Modules are also invoked to explain cognitive capacities associated with the performance of specific functional tasks. Jerry Fodor (1983) considered that modules are useful only for explaining relatively low-level systems (input systems). These are the systems involved in capacities like perception and language. For Fodor, the central (high-level) systems of mind — (...) those involved in capacities like judgment (the “fixation of belief” in Fodor’s jargon), planning (practical reasoning), decision-making, and so forth — are not explainable in terms of modular mechanisms. However, some other philosophers as well as proponents and practitioners of evolutionary psychology consider that it is sensible — and even necessary — to invoke modules for explaining these systems, too. Indeed, the debate over modularity is mainly a debate over the modularity of central systems (high-level cognitive capacities). Admittedly, it is also possible to raise doubts about the modularity of peripheral systems (Prinz, 2006), but this kind of skepticism is not widespread in the literature. In contrast, both the empirical and the theoretical (a priori) cases in favor of central modularity are usually contested on multiple fronts and in tough terms. The case for the modularity of central systems is the core of the case for the massive modularity of mind hypothesis (viz. the hypothesis that consists in asserting that both the peripheral and the central systems of human mind are largely composed of modules). Granted, the massive modularity of mind hypothesis is an empirical statement and, as such, its truth should be ultimately decided in an empirical way, not a priori (Sperber, 1994, 2001). Nonetheless, much of the debate over massive modularity has taken place on theoretical grounds. This is due to an alleged underdetermination of the hypotheses regarding the modularity of central systems by data. In such conditions, the remaining open option has been to advance theoretical considerations in favor of the plausibility — and not directly in favor of the empirical truth or truthlikeness — of the massive modularity of mind generally, and of the modularity of some central systems in particular. These theoretical considerations are mostly based on an adaptationist view of evolution cum a classical computationalist approach to mind. They include arguments about the nature and evolution of hierarchically ordered complex systems (the evolvability of complexity argument), a presumption of optimality expressed in the apparent design of phenotypic traits shaped by natural selection (the task-specificity argument) and dismissing nonmodular mental architectures as computationally intractable (the tractability argument). Is the massive modularity of mind hypothesis a cogent view about the ontological nature of human mind or is it, rather, an effective/ineffective adaptationist discovery heuristic for generating predictively successful hypothesis about both heretofore unknown psychological traits and unknown properties of already identified psychological traits? Considering the inadequacies of the case in favor of massive modularity as an ontological hypothesis, I suggest approaching and valuing massive modularity as an adaptationist discovery heuristic. (shrink)

This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism – from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost complete (...) abandonment of its assumptions in the study of cognition. Criticisms have mostly pointed to its explanatory inadequacy as a theory of cognition or to its biological implausibility as a theory of implementation, and critics often focused on specific shortcomings of some connectionist models and argued that they apply to connectionism in general. In this paper, we want to show that both types of critique are based on the assumption that the only valid explanations in cognitive science are instances of homuncular functionalism and that by removing this assumption and by adopting an alternative methodology – exploratory mechanistic strategy, we can reject most objections to connectionism as irrelevant, explain the progress of connectionist models despite their shortcomings and sketch the trajectory of their future development. By adopting mechanistic explanations and by criticizing functionalism, we will reject the objections of explanatory inadequacy, by characterizing connectionist models as generic rather than concrete mechanisms, we will reject the objections of biological implausibility, and by attributing the exploratory character to connectionist models we will show that practice of generalizing current to general failures of connectionism is unjustified. (shrink)

A practical viewpoint links reality, representation, and language to calculation by the concept of Turing (1936) machine being the mathematical model of our computers. After the Gödel incompleteness theorems (1931) or the insolvability of the so-called halting problem (Turing 1936; Church 1936) as to a classical machine of Turing, one of the simplest hypotheses is completeness to be suggested for two ones. That is consistent with the provability of completeness by means of two independent Peano arithmetics discussed in Section I. (...) Many modifications of Turing machines cum quantum ones are researched in Section II for the Halting problem and completeness, and the model of two independent Turing machines seems to generalize them. Then, that pair can be postulated as the formal definition of reality therefore being complete unlike any of them standalone, remaining incomplete without its complementary counterpart. Representation is formal defined as a one-to-one mapping between the two Turing machines, and the set of all those mappings can be considered as “language” therefore including metaphors as mappings different than representation. Section III investigates that formal relation of “reality”, “representation”, and “language” modeled by (at least two) Turing machines. The independence of (two) Turing machines is interpreted by means of game theory and especially of the Nash equilibrium in Section IV. Choice and information as the quantity of choices are involved. That approach seems to be equivalent to that based on set theory and the concept of actual infinity in mathematics and allowing of practical implementations. (shrink)

A set theory model of reality, representation and language based on the relation of completeness and incompleteness is explored. The problem of completeness of mathematics is linked to its counterpart in quantum mechanics. That model includes two Peano arithmetics or Turing machines independent of each other. The complex Hilbert space underlying quantum mechanics as the base of its mathematical formalism is interpreted as a generalization of Peano arithmetic: It is a doubled infinite set of doubled Peano arithmetics having a remarkable (...) symmetry to the axiom of choice. The quantity of information is interpreted as the number of elementary choices (bits). Quantum information is seen as the generalization of information to infinite sets or series. The equivalence of that model to a quantum computer is demonstrated. The condition for the Turing machines to be independent of each other is reduced to the state of Nash equilibrium between them. Two relative models of language as game in the sense of game theory and as ontology of metaphors (all mappings, which are not one-to-one, i.e. not representations of reality in a formal sense) are deduced. (shrink)

The present PhD dissertation aims to examine the relation between modality and change in Aristotle’s metaphysics. -/- On the one hand, Aristotle supports his modal realism (i.e., worldly objects have modal properties - potentialities and essences - that ground the ascriptions of possibility and necessity) by arguing that the rejection of modal realism makes change inexplicable, or, worse, banishes it from the realm of reality. On the other hand, the Stagirite analyses processes by means of modal notions (‘change is the (...) actuality of what is virtual insofar as it is virtual’). In other words: to grasp what change is, one has to resort to the modal idiom of potentialities, while the fact that there is change is indicative of the fact that nature is full of modal properties. -/- Aristotle’s modal and kinetic realism finds a negative in the figure of the Megaric Diodorus Kronus. The polemical situation of Greek philosophy has indeed the dialectical advantage of not opposing the Aristotelian position to a sui generis straw man. Both in its reduction of modal judgements to temporal quantifications and in its dissolution of the reality of the state-of-being-in-motion in favour of a cinematographic view of processes, the philosophical figure of reductionist antirealism embodied by Diodorus constitutes an alternative that takes the opposite view of Aristotle’s realism. -/- The present study is structured as a discussion between these two positions. In the face of Diodorus’ antirealist challenges, Aristotle articulates modal and kinetic considerations: the answers he gives to Diodorus’ puzzles thus provide valuable insight into his metaphysics. Moreover, the examination of Aristotle’s and Diodorus’ metaphysics, because of their insight and originality, will not fail to interest the philosopher concerned with the metaphysical foundations of modern physics, insofar as the entanglement between modalities and processes is nowadays at the core of mechanics (phase spaces, path integrals, etc.). (shrink)

Quantum information is discussed as the universal substance of the world. It is interpreted as that generalization of classical information, which includes both finite and transfinite ordinal numbers. On the other hand, any wave function and thus any state of any quantum system is just one value of quantum information. Information and its generalization as quantum information are considered as quantities of elementary choices. Their units are correspondingly a bit and a qubit. The course of time is what generates choices (...) by itself, thus quantum information and any item in the world in final analysis. The course of time generates necessarily choices so: The future is absolutely unorderable in principle while the past is always well-ordered and thus unchangeable. The present as the mediation between them needs the well-ordered theorem equivalent to the axiom of choice. The latter guarantees the choice even among the elements of an infinite set, which is the case of quantum information. The concrete and abstract objects share information as their common base, which is quantum as to the formers and classical as to the latter. The general quantities of matter in physics, mass and energy can be considered as particular cases of quantum information. The link between choice and abstraction in set theory allows of “Hume’s principle” to be interpreted in terms of quantum mechanics as equivalence of “many” and “much” underlying quantum information. Quantum information as the universal substance of the world calls for the unity of physics and mathematics rather than that of the concrete and abstract objects and thus for a form of quantum neo-Pythagoreanism in final analysis. (shrink)

This work presents a post-positivist research framework to explain any surprising fact in the evolutionary path of a complex, dynamic and contingent social phenomenon. Primarily, it reconciles the ontological and epistemological assumptions of Critical Realism with the principles of American Pragmatism. Then, the research approach is presented: theoretical propositions about a social structure are translated into a set of grammar rules that acknowledges a pattern of sequences of events of either individual action or social interaction between actors within a real (...) social system. The result is a discrete mathematical model for a concrete category of social process based on these rules. Finally, data-grounded refinement of the theory is possible by the comparison between cases belonging to the same category, but differing about some contingent pattern of sequences of event outcomes. Consequently, their grammars differ in some pairs of context-sensitive rules that can explain this surprising fact, such that the derivation of this alternative historical trajectory of event outcomes becomes an extension to the early category of social process. In this sense, the proposed framework suggests there is a hierarchy of classes of grammars for middle-range explanations based upon the ontological assumption of the generative nature of social reality. (shrink)

The Aristotelian origins of formal systems are outlined, together with Aristotle's use of causal terms in describing syllogisms. The precision and exactness of a formalism, based on the projection of logical forms into perceptive signs, is contrasted with foundational, abstract concepts, independent of any formalism, which are presupposed for the understanding of a formal language. The definition of a formal system by means of a Turing machine is put in the context of Wittgenstein's general considerations of a machine understood as (...) a sign. A modification of Łukasiewicz's logic Ł3 with the inclusion of justification terms is proposed in order to formally analyse some features of formalistic reasoning as a mechanical, causal affair within a wider context of "indeterminacy". (shrink)