Turing and Computationalism

Many important lines of argumentation have been presented during the last decades claiming that machines cannot think like people. Yet, it has been possible to construct devices and information systems, which replace people in tasks which have previously been occupied by people as the tasks require intelligence. The long and versatile discourse over, what machine intelligence is, suggests that there is something unclear in the foundations of the discourse itself. Therefore, we critically studied the foundations of used theory languages. By looking critically some of the main arguments of machine thinking, one can find unifying factors. Most of them are based on the fact that computers cannot perform sense-making selections without human support and supervision. This calls attention to mathematics and computation itself as a representational constructing language and as a theory language in analysing human mentality. It is possible to notice that selections must be based on relevance, i.e., on why some elements of sets belong to one class and others do not. Since there is no mathematical justification to such selection, it is possible to say that relevance and related concepts are beyond the power of expression of mathematics and computation. Consequently, Turing erroneously assumed that mathematics and formal language is equivalent with natural languages. He missed the fact that mathematics cannot express relevance, and for this reason, mathematical representations cannot be complete models of the human mind. Preface This paper is of a programmatic nature. We fully acknowledge the enormous achievements of modern science, engineering and design by calling on the most advanced machine models, as we always did in the past and will continue into the future. We will not primarily discuss the physical limitations (Markov 2014), but instead focus on the conceptual constraints, the intuitive assumptions of underlying theories, resp. their foundations (Saariluoma 1997). We try to understand and find an answer to the fundamental question: Is the human mind capable of understanding itself beyond computability? We question the mainstream assumption that everything is (or at least should be) 'computational' (Chatelin 2012, Chalmers 1996, Sun, Wilson, and Lynch 2016). We argue that the foundations of such an assumption are (still) not fully justifiable. Therefore, we imitate Kant's (1781/1922) famous 'Copernican Revolution' from a kind of Wittgensteinian (1921/1974) perspective and ask whether the properties of the theory of language itself used in discourse can explain why the problems have proven to be so hard. In other words, we ask whether formal theory languages (i.e., logic, mathematics and computation) are powerful enough to express problems of human thinking and represent thoughts. Since many of the foundational issues concentrate on one theoretical construct, Turing machines (TM), we must once again consider whether people 'think like machines'.

Science & Technology Studies, 2023

As part of ongoing research bridging ethnomethodology and computer science, in this article we offer an alternate reading of Alan Turing's 1936 paper, "On Computable Numbers". Following through Turing's machinic respecification of computation, we hope to contribute to a deflationary position on AI by showing that the activities attributed to AIs are achieved in the course of methodic hands-on work with computational systems and not in isolation by them. Turing's major innovation was a demonstration that mathematical and logical operations could be broken down into elementary, mechanically executable operations, devoid of intellectual content. Drawing out lessons from a re-enactment of Turing's methods as a means of reflecting on contemporary artificial intelligence (AI), including the way those methods disappear into the technology, we will suggest the interesting question raised in "On Computable Numbers" is less about the possibilities of designing machines that "can think" (cf. Turing, 1950), but the practical work we do, and which is made possible, when we ourselves set out to think like machines.

The proper treatment of computationalism, as the thesis that cognition is computable, is presented and defended. Some arguments of James H. Fetzer against computationalism are examined and found wanting, and his positive theory of minds as semiotic systems is shown to be consistent with computationalism. An objection is raised to an argument of Selmer Bringsjord against one strand of computationalism, namely, that Turing-Test± passing artifacts are persons, it is argued that, whether or not this objection holds, such artifacts will inevitably be persons.

Cognitive Science, 1998

Turing's analysis of computation is a fundamental part of the background of cognitive science. In this paper it is argued that a re-interpretation of Turing's work is required to underpin theorizing about cognitive architecture. It is claimed that the symbol systems view of the mind, which is the conventional way of understanding how Turing's work impacts on cognitive science, is deeply flawed. There is an alternative interpretation that is more faithful to Turing's original insights, avoids the criticisms made of the symbol systems approach and is compatible with the growing interest in agent-environment interaction. It is argued that this interpretation should form the basis for theories of cognitive architecture.

The emergence of cognitive science as a multi-disciplinary investigation into the nature of mind has historically revolved around the core assumption that the central ‘cognitive’ aspects of mind are computational in character. Although there is some disagreement and philosophical speculation concerning the precise formulation of this ‘core assumption’ it is generally agreed that computationalism in some form lies at the heart of cognitive science as it is currently conceived. Von Eckardt’s recent work on this topic is useful in enabling us to get a sense of the scope of the computational assumption. She makes clear that there are two rather different ways in which we could understand cognitive science’s commitment to computationalism and hence two ways to understand the claim that the ‘mind is a computer’, by appeal to either (1) A mathematical theory of computability or (2) A theory of data-processing or information-processing. Importantly, she also argues that although there are many aspects of claim that the ‘mind is a computer’ that can be nicely captured by Boyd’s account of the way scientific metaphors are employed, not to direct attention to the hitherto unnoticed, but to encourage investigation of the unknown. Nonetheless, cognitive scientists are not making the claim that the ‘mind is a computer’ in a metaphorical sense. If Von Eckhardt is correct, when cognitive scientists assume the ‘mind is a computer’ and give a sense to the notion of the computer in the sense of (2) above, they are making a literal claim about the nature of mind (Von Eckardt, 1993, p. 116). And as she points out that if one reads (2) in a theoretically committed way then there is no a priori reason to exclude the organic brain from the list of entities that might fall under the description of being a ‘computer’. Important, we can truly describe it as a data-processing (or information-processing) device. What is useful about Von Eckardt’s general analysis of computationalism’s core assumption is that it provides a clear angle from which to view the flaws of computationalism. This paper defends the claim that if there is an account of information adequate to capture those aspects of mind that we regard as essential to mentality it is one that requires us to surrender the idea that the mind is a computer.

Isonomia, 2014

The article deals with some ideas by Turing concerning the background and the birth of the well-known Turing Test, showing the evolution of the main question proposed by Turing on thinking machine. The notions he used, especially that one of imitation, are not so much exactly defined and shaped, but for this very reason they have had a deep impact in artificial intelligence and cognitive science research from an epistemological point of view. Then, it is suggested that the fundamental concept involved in Turing's imitation game, conceived as a test for detecting the presence of intelligence in an artificial entity, is the concept of interaction, that Turing adopts in a wider, more intuitive and more fruitful sense than the one that is proper to the current research in interactive computing.

"Classical computationalism considers the Turing Machine to be a psychologically implausible model of human computation. In this paper, I will first elaborate on Andrew Wells' thesis that the claim of psychological implausibility derives from a wrong interpretation of the TM as originally conceived by Turing. Then, I will show how Turing's original interpretation of the TM could be useful to construct cognitive models of simple phenomena of human computation, such as counting using our fingers or performing arithmetical operations using paper and pencil."

Turing starts on an equivocation. We know now that what he will go on to consider is not whether or not machines can think, but whether or not machines can do what thinkers like us can do--and if so, how. Doing is performance capacity, empirically observable. Thinking (or cognition) is an internal state, its correlates empirically observable as neural activity (if we only knew which neural activity corresponds to thinking!) and its associated quality introspectively observable as our own mental state when we are thinking.

In this paper, I review the objections against the claim that brains are computers, or, to be precise, information-processing mechanisms. By showing that practically all the popular objections are based on uncharitable (or simply incorrect) interpretations of the claim, I argue that the claim is likely to be true, relevant to contemporary cognitive (neuro)science, and non-trivial. The computational theory of mind, or computationalism, has been fruitful in cognitive research. The main tenet of the computational theory of mind is that the brain is a kind of information-processing mechanism, and that information-processing is necessary for cognition; it is non-trivial and is generally accepted in cognitive science. The positive view will not be developed here, in particular the account of physical computation, because it has already been elucidated in book-length accounts (Fresco, 2014; Miłkowski, 2013; Piccinini, 2015). Instead, a review of objections is offered here, as no comprehensive survey is available. The survey suggests that the majority of objections fail just because they make computationalism a straw man. Some of them, however, have shown that stronger versions of the computational theory of mind are untenable, as well. Historically, they have helped to shape the theory and methodology of computational modeling. In particular, a number of objections show that cognitive systems are not only computers, or that computation is not the sole condition of cognition; no objection, however, establishes that there might be cognition without computation.