Computational Functionalism is a subfield of philosophy of mind most relevant to the subject of cognitive science as well as to artificial intelligence (AI). The analysis of this paper focuses on Hilary Putnam’s and Gualtiero Piccinini’s standpoints regarding the molecular understanding of computation. Finally, Putnam’s argument of the functionalism in notion of the mental states is based on the positive definition of those states by their functions, while Piccinini, and on the other hand suggest that an understanding (...) of the mechanism of computation will go a long way to explaining cognition. This research also points out shortcomings of functionalism in their theories especially in regards to connectivity with physicality of AI structures. Therefore, the paper identifies the following gaps: The first gap holds that although both theorists have important insights, the two approaches put forward by the respective theorists are flawed and a better proposal is required, then a new proposal is suggested In identifying these gaps, the paper seeks to contribute to the literature by arguing for the need to develop a composite of the two theorists. The results show that the big ideas are important in present-day AI debate and analysis, which helps to explain the mind as computation. (shrink)

The concept of the mind in philosophy encompasses a diverse range of theories and perspectives, examining its immaterial nature, unitary function, self-activity, self-consciousness, and persistence despite bodily changes. This paper explores the attributes of the mind, addressing classical materialism, dualism, and behaviorism, along with contemporary theories like functionalism and computational functionalism. Key philosophical debates include the mind-body problem, the subjectivity of mental states, and the epistemological and conceptual challenges in understanding other minds. Contrasting views from Aristotle, Descartes, (...) Wittgenstein, and modern philosophers like U.T. Place, Gilbert Ryle, and Hilary Putnam are analyzed. The paper also discusses the implications of these theories on our understanding of mental phenomena, consciousness, and the nature of human experience. (shrink)

Some philosophers have conflated functionalism and computationalism. I reconstruct how this came about and uncover two assumptions that made the conflation possible. They are the assumptions that (i) psychological functional analyses are computational descriptions and (ii) everything may be described as performing computations. I argue that, if we want to improve our understanding of both the metaphysics of mental states and the functional relations between them, we should reject these assumptions.

The viewpoint that consciousness, including feeling, could be fully expressed by a computational device is known as strong artificial intelligence or strong AI. Here I offer a defense of strong AI based on machine-state functionalism at the quantum level, or quantum-state functionalism. I consider arguments against strong AI, then summarize some counterarguments I find compelling, including Torkel Franzén’s work which challenges Roger Penrose’s claim, based on Gödel incompleteness, that mathematicians have nonalgorithmic levels of “certainty.” Some consequences of (...) strong AI are then considered. A resolution is offered of some problems including John Searle’s Chinese Room problem and the problem of consciousness propagation under isomorphism. (shrink)

The Hypothesis of Extended Cognition (HEC) holds that that not all human cognition is realized inside the head. The related but distinct Hypothesis of Extended Mentality (HEM) holds that not all human mental items are realized inside the head. Clark & Chalmers distinguish between these hypotheses in their original treatment of cognitive extension, yet these two claims are often confused. I distinguish between functionalist theories on which functional roles are individuated according to computational criteria, and those on which functional (...) roles are individuated according to rational criteria. I then present an argument for a modest version of HEC from computational functionalism, based on Clark & Chalmers’ original argument. In doing so I articulate a successor to their parity principle, and review studies by Wayne Gray et al. that provide plausible evidence for actual cognitive extension. I then respond to a new criticism of HEC by Mark Sprevak using the modest account I have developed, arguing that Sprevak conflates HEC and HEM. (shrink)

In the article I discuss functionalist interpretations of Husserlian phenomenology. The first one was coined in the discussion between Hubert Dreyfus and Ronald McIntyre. They argue that Husserl’s phenomenology shares similarities with computational functionalism, and the key similarity is between the concept of noema and the concept of mental representation. I show the weaknesses of that reading and argue that there is another available functionalist reading of Husserlian phenomenology. I propose to shift perspective and approach the relation between (...) phenomenology and functionalism from a methodological perspective, specifically taking into account the functionalist explanatory strategy called functional analysis. I discuss the notion of function in Husserl’s works and Husserl’s idea of functional phenomenology. The key argument I develop is that in functional phenomenology we can find an explanatory strategy which is analogous to the strategy of functional decomposition used in functional analysis. I conclude that the proposed functionalist reading of phenomenology opens a new approach to the integration of phenomenology with cognitive sciences. (shrink)

Computational functionalism posits that consciousness is a computation. Here we show, perhaps surprisingly, that it cannot be a Turing computation. Rather, computational functionalism implies that consciousness is a novel type of computation that has recently been proposed by Geoffrey Hinton, called mortal computation.

This paper integrates type functionalism with the Kairetic account to develop context-specific models for explaining mental states, particularly pain, across different species and systems. By employing context-dependent mapping f_c, we ensure cohesive causal explanations while accommodating multiple realizations of mental states. The framework identifies context subsets C_i and maps them to similarity subspaces S_i, capturing the unique physiological, biochemical, and computational mechanisms underlying pain in different entities such as humans, octopi, and AI systems. This approach highlights the importance (...) of causal relations in defining mental states and preserves their functional roles across diverse contexts. Furthermore, the paper incorporates elements of token functionalism by recognizing species-specific realizations of mental states. By acknowledging the unique representations of mental states within different species and systems, the framework provides a nuanced understanding of how similar functional roles can be fulfilled by diverse physical substrates. This synthesis of type and token functionalism enhances our explanatory power and coherence in addressing the complex nature of mental states. The resulting framework offers a robust tool for analyzing and understanding mental phenomena, with significant implications for cognitive science, philosophy of mind, and artificial intelligence. By maintaining the functional roles of mental states while accommodating their multiple realizations, this approach not only advances theoretical understanding but also opens new avenues for practical applications in cross-species empathy, AI ethics, and the development of context-aware cognitive models. (shrink)

In this article, I argue that if tacit knowledge of grammar is analyzable in functional‐computational terms, then it cannot ground linguistic meaning, structure, or sound. If to know or cognize a grammar is to be in a certain computational state playing a certain functional role, there can be no unique grammar cognized. Satisfying the functional conditions for cognizing a grammar G entails satisfying those for cognizing many grammars disagreeing with G about expressions' semantic, phonetic, and syntactic values. This (...) threatens the Chomskyan view that expressions have such values for speakers because they cognize grammars assigning them those values. For if this is true, semantics, syntax, and phonology must be indeterminate, thanks to the indeterminacy of grammar‐cognizing (qua functional‐computational state). So, the fact that a speaker cognizes a grammar cannot explain the determinate character of their language. (shrink)

In the domain of cognitive science, understanding consciousness through the investigation of neural correlates has been the primary research approach. The exploration of neural correlates of consciousness is focused on identifying these correlates and reducing consciousness to a physical phenomenon, embodying a form of reductionist physicalism. This inevitably leads to challenges in explaining consciousness itself. The computational interpretation of consciousness takes a functionalist view, grounded in physicalism, and models conscious experience as a cognitive function, elucidated through computational means. (...) This paper posits that predictive processing offers a fresh paradigm for comprehending human cognition and serves as a neural foundation for the computational elucidation of consciousness. Additionally, free energy theory, as a mathematical construct explaining the predictive processing model, furnishes a theoretical scaffold for understanding consciousness computationally. (shrink)

The problem of multiple-computations discovered by Hilary Putnam presents a deep difficulty for functionalism (of all sorts, computational and causal). We describe in out- line why Putnam’s result, and likewise the more restricted result we call the Multiple- Computations Theorem, are in fact theorems of statistical mechanics. We show why the mere interaction of a computing system with its environment cannot single out a computation as the preferred one amongst the many computations implemented by the system. We explain (...) why nonreductive approaches to solving the multiple- computations problem, and in particular why computational externalism, are dualistic in the sense that they imply that nonphysical facts in the environment of a computing system single out the computation. We discuss certain attempts to dissolve Putnam’s unrestricted result by appealing to systems with certain kinds of input and output states as a special case of computational externalism, and show why this approach is not workable without collapsing to behaviorism. We conclude with some remarks about the nonphysical nature of mainstream approaches to both statistical mechanics and the quantum theory of measurement with respect to the singling out of partitions and observables. (shrink)

The mainstream view in cognitive science is that computation lies at the basis of and explains cognition. Our analysis reveals that there is no compelling evidence or argument for thinking that brains compute. It makes the case for inverting the explanatory order proposed by the computational basis of cognition thesis. We give reasons to reverse the polarity of standard thinking on this topic, and ask how it is possible that computation, natural and artificial, might be based on cognition and (...) not the other way around. (shrink)

The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of ‘strong’ computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s (Representation & Reality, Bradford Books, Cambridge in 1988 ) monograph, “Representation & Reality”, which if correct, has (...) important implications for turing machine functionalism and the prospect of ‘conscious’ machines. In the paper, instead of seeking to develop Putnam’s claim that, “everything implements every finite state automata”, I will try to establish the weaker result that, “everything implements the specific machine Q on a particular input set ( x )”. Then, equating Q ( x ) to any putative AI program, I will show that conceding the ‘strong AI’ thesis for Q (crediting it with mental states and consciousness) opens the door to a vicious form of panpsychism whereby all open systems, (e.g. grass, rocks etc.), must instantiate conscious experience and hence that disembodied minds lurk everywhere. (shrink)

Architecture often relies on mathematical models, if only to anticipate the physical behavior of structures. Accordingly, mathematical modeling serves to find an optimal form given certain constraints, constraints themselves translated into a language which must be homogeneous to that of the model in order for resolution to be possible. Traditional modeling tied to design and architecture thus appears linked to a topdown vision of creation, of the modernist, voluntarist and uniformly normative type, because usually (mono)functionalist. One available instrument of calculation/representation/prescription (...) orders this conception of architecture: indeed the search for an optimal solution through mathematical calculation of a model itself mathematical, thus homogeneous and simple, is only possible when one or two functions or functional constraints are formulated, never more, and this, on a global level, therefore starting from a unique and homogenizing viewpoint. It is essential to grasp that, even applied to material and its properties or towards a particular esthetic or functional dimension, this viewpoint is thus abstractive and generalizing: disregarding singularity of context, insertion and relationship to the environment or local, social behavior. It leaves aside functional specificity and heterogeneousness – re-contextualized each time – of functions that the object or edifice are required to fulfill and optimize under diverse constraints, in their different parts. The computational turning point today’s digital design and computational architecture embody modifies these instrumental, original prescriptions, rendering them more flexible. Perhaps in light of this turnabout we should retrospectively interpret 20th century calls for modernism, functionalism and even biomorphism as being just as many ex post rationalizations in respect to techniques of strongly prescriptive modeling since our only instrument is a monolithic language, and so being, incites a top-down conception, (naturally weakly reactive to contexts), including forms whose overall appearance resembles in fine a living form. In order to liberate oneself from this and despite everything, emerge as its initiators, one has constructed from ideology and philosophy (of object, habitat, the urban) ex post, even while it is the instrument of modeling and conception that largely determines, normalizes and dictates ex ante, the possibilities and limitations of the creation of forms and living experiments4 in a given time. (shrink)

Multiple realizability (MR) is traditionally conceived of as the feature of computational systems, and has been used to argue for irreducibility of higher-level theories. I will show that there are several ways a computational system may be seen to display MR. These ways correspond to (at least) five ways one can conceive of the function of the physical computational system. However, they do not match common intuitions about MR. I show that MR is deeply interest-related, and for (...) this reason, difficult to pin down exactly. I claim that MR is of little importance for defending computationalism, and argue that it should rather appeal to organizational invariance or substrate neutrality of computation, which are much more intuitive but cannot support strong antireductionist arguments. (shrink)

In his book “Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence”, Matt Carter presents a comprehensive exploration of the philosophical questions surrounding artificial intelligence (AI). Carter argues that the development of AI is not merely a technological challenge but fundamentally a philosophical one. He delves into key issues like the nature of mental states, the limits of introspection, the implications of memory decay, and the functionalist framework that allows for the possibility of AI. Carter contrasts functionalism (...) with reductive materialism, highlighting how the former accommodates the concept of artificial minds. He also emphasizes the significance of computationalism, which combines functionalism with computational theory to provide a robust explanation for mental processes. The book further discusses formal systems, the role of register machines in computation, and the inherent challenges in AI research, particularly in developing natural language processing systems. Carter’s work underscores the limitations of current computational models of cognition while remaining hopeful that advances in neuroscience may lead to stronger AI systems in the future. (shrink)

In this paper, I defend the metalinguistic solution to the problem of mathematical omniscience for the possible-worlds account of propositions by combining it with a computational model of knowledge and belief. The metalinguistic solution states that the objects of belief and ignorance in mathematics are relations between mathematical sentences and what they express. The most pressing problem for the metalinguistic strategy is that it still ascribes too much mathematical knowledge under the standard possible-worlds model of knowledge and belief on (...) which these are closed under entailment. I first argue that Stalnaker's fragmentation strategy is insufficient to solve this problem. I then develop an alternative, computational strategy: I propose a model of mathematical knowledge and belief adapted from the algorithmic model of Halpern et al. which, when combined with the metalinguistic strategy, entails that mathematical knowledge and belief require computational abilities to access metalinguistic information, and thus aren't closed under entailment. As I explain, the computational model generalizes beyond mathematics to a version of the functionalist theory of knowledge and belief that motivates the possible-worlds account in the first place. I conclude that the metalinguistic and computational strategies yield an attractive functionalist, possible-worlds account of mathematical content, knowledge, and inquiry. (shrink)

In this paper, I shall review the reasons that let Putnam to propose functionalism and the reasons that subsequently led him to abandon it. I would like to discuss Putnam's views belonging to early Putnam and later Putnam. First, let us focus on early Putnam. Early Putnam tries to show the possibility of robot consciousness. As a functionalist, Putnam shows that the human being is an autonomous: that is, human mind is a computing machine. Later, he changes his position (...) to demonstrate not just his intellectual confidence, but also the virtue of seeing the bigger picture. Later Putnam realized this because this sort of utopianism is an illustration of what is called ‘scientism’. It is based on speculations regarding scientific possibilities. The problem is that it is completely unclear just what possibility has been envisaged when one speaks of robotic consciousness. While arguing against Turing machine, the later Putnam said that pessimism about the success of artificial intelligence (AI) in simulating human intelligence amounts to pessimism about possibility of describing the functioning of the brain. He raised the question: what connection is there between simulating intelligence and describing the brain? Even if the computer model of the brain is correct, it does not follow that AI will succeed. Simulation is just simulation, it does not mean exact replications of human mind. (shrink)

Espousing non-reductive physicalism, how do we pick out the specific relevant physical notion(s) from physical facts, specifically in relation to phenomenal experience? Beginning with a historical review of Gilbert Ryle’s behaviorism and moving through Hilary Putnam’s machine-state functionalism and Wilfrid Sellars’ inferential framework, up to more contemporaneous computationalist- and cognitivist-functionalism (Gualtiero Piccinini), we survey accounts of mentality that countenance the emergence of mental states vide input- and output-scheme. Ultimately arriving at the conclusion that functionalism cannot account for (...) problems such as no-cognition reports, we see any robust defense of physicalism must appeal to other principles. Thus we move on to the question of emergence, not as it pertains to the hard(er) problem, but to the matter of conceptual externalization of mental properties from physical properties. Accordingly, we navigate Karen Bennett’s compatibilist solution to the exclusion argument against mental causation for the non-reductive physicalist position, according to which the physical effects of mental cases are not overdetermined, demonstrating that this backfires by offering a path for the mind-body interactionist Dualist to claim causal closure by appealing to this same schema. We conclude with a series of conceptual musings regarding rationality which take into account our challenges and findings, querying about whether phenomenal consciousness is a fundamentally private, or socially configured, notion. (shrink)

his article will focus on the mechanistic origins of the computer metaphor, which forms the conceptual framework for the methodology of the cognitive sciences, some areas of artificial intelligence and the philosophy of mind. The connection between the history of computing technology, epistemology and the philosophy of mind is expressed through the metaphorical dictionaries of the philosophical discourse of a particular era. The conceptual clarification of this connection and the substantiation of the mechanistic components of the computer metaphor is the (...) main goal of this article. The statement is substantiated that the invention of mechanical computing devices, having a long history in the European engineering tradition, formed the prerequisites for the emergence of machine functionalism in the modern philosophy of mind. The idea of multiple implementation stems from the principle that a formal symbol system prescribes rules for the use of rational abstractions through the physical architecture of a computational engine. The article considers the reasons for the conceptual shift and reveals the semantic foundations for the metaphorical transfer of the properties of abstract objects from the theory of automata to the field of modern philosophy of mind. The criticism and ways of protecting the philosophical program of machine functionalism are analyzed by changing the content of the metaphor “Mind as machine”. The reasons for the stability of the information-computer approach in cognitive sciences are also disclosed and explained. (shrink)

In his book "The Structure of Behavior", the philosopher Maurice Merleau-Ponty proposes a solution to the mind-body problem. Merleau-Ponty argues that there is a nested hierarchy of three orders—the physical order, the biological order, and the mental order—in which each lower order composes each higher order. Through the structuration or organization of a lower order, a higher order is created. Merleau-Ponty’s solution is promising, but it leaves an explanatory chasm between the biological order and the mental order that cannot be (...) crossed without introducing an intermediary order. I propose just that: the addition of a fourth order—the computational order—to bridge the chasm. (shrink)

Existential Cognition, divided into four parts of three chapters each, argues that the mind “is an essentially embedded entity; one such that analyzing it in isolation from the environmental context in which it functions will be fundamentally misleading”. Disputing internalists who accept, and who reject, information processing accounts of the mind, as well as anti-cognitivists who reject internalism, McClamrock argues for an externalist information processing account of mental states and processes.

Does the assumption of a weak form of computational functionalism, according to which the right form of neural computation is sufficient for consciousness, entail that a digital computational simulation of such neural computations is conscious? Or must this computational simulation be implemented in the right way, in order to replicate consciousness? From the perspective of Karl Friston’s free energy principle, self-organising systems (such as living organisms) share a set of properties that could be realised in artificial (...) systems, but are not instantiated by computers with a classical (von Neumann) architecture. I argue that at least one of these properties, viz. a certain kind of causal flow, can be used to draw a distinction between systems that merely simulate, and those that actually replicate consciousness. (shrink)

The Gestalt psychologists adopted a set of positions on mind-body issues that seem like an odd mix. They sought to combine a version of naturalism and physiological reductionism with an insistence on the reality of the phenomenal and the attribution of meanings to objects as natural characteristics. After reviewing basic positions in contemporary philosophy of mind, we examine the Gestalt position, characterizing it m terms of phenomenal realism and programmatic reductionism. We then distinguish Gestalt philosophy of mind from instrumentalism and (...) computational functionalism, and examine Gestalt attributions of meaning and value to perceived objects. Finally, we consider a metatheoretical moral from Gestalt theory, which commends the search for commensurate description of mental phenomena and their physiological counterparts. (shrink)

In the last decade, Guilio Tononi has developed the Integrated Information Theory (IIT) of consciousness. IIT postulates that consciousness is equal to integrated information (Φ). The goal of this paper is to show that IIT fails in its stated goal of quantifying consciousness. The paper will challenge the theoretical and empirical arguments in support of IIT. The main theoretical argument for the relevance of integrated information to consciousness is the principle of information exclusion. Yet, no justification is given to support (...) this principle. Tononi claims there is significant empirical support for IIT, but this is called into question by the creation of a trivial theory of consciousness with equal explanatory power. After examining the theoretical and empirical evidence for IIT, arguments from philosophy of mind and epistemology will be examined. Since IIT is not a form of computational functionalism, it is vulnerable to fading/dancing qualia arguments. Finally, the limitations of the phenomenological approach to studying consciousness are examined, and it will be shown that IIT is a theory of protoconsciousness rather than a theory of consciousness. (shrink)

Implementationalism maintains that conventional, silicon-based artificial systems are not conscious because they fail to satisfy certain substantive constraints on computational implementation. In this article, we argue that several recently proposed substantive constraints are implausible, or at least are not well-supported, insofar as they conflate intuitions about computational implementation generally and consciousness specifically. We argue instead that the mechanistic account of computation can explain several of the intuitions driving implementationalism and noncomputationalism in a manner which is consistent with artificial (...) consciousness. Our argument provides indirect support for computationalism about consciousness and the view that conventional artificial systems can be conscious. (shrink)

The scientific study of consciousness, also referred to as consciousness science, is a young scientific field devoted to understanding how conscious experiences and the brain relate. It comprises a host of theories, experiments, and analyses that aim to investigate the problem of consciousness empirically, theoretically, and conceptually. This thesis addresses some of the questions that arise in these investigations from a formal and mathematical perspective. These questions concern theories of consciousness, experimental paradigms, methodology, and artificial consciousness. -/- Regarding theories of (...) consciousness, the thesis contributes to the understanding of the mathematical structure that some of the formal theories in the field propose. The work presented here targets the theory of consciousness known as Integrated Information Theory (IIT) and the neuroscientific theory known as Predictive Processing Theory or Free Energy Principle in its Active Inference form (AI-PP). The thesis provides axiomatic definitions of the mathematical structures that constitute these theories, and uses these definitions to address some of the open questions surrounding the theories. For AI-PP, this includes a rigorous derivation of the formula for Active Inference via Free Energy minimisation and a proof of compositionality of Free Energy. For IIT, this includes resolutions of some of the criticisms of IIT's formal scope and applications, but also the identification of new issues that concern the formalism and its derivation. When possible, the definitions are provided in the mathematical framework of category theory. -/- Regarding experiments, the thesis addresses the main paradigm for testing and falsifying theories of consciousness currently applied in the field. This paradigm consists of comparing the conscious experience that a theory predicts with the conscious experience that is inferred from behavioural data or report by use of measures of consciousness. The thesis provides a formal model of this paradigm and shows that under a certain condition—if inference and prediction are independent—, any minimally informative theory of consciousness can always be falsified. This is deeply problematic since the field’s reliance on report or behaviour to infer conscious experiences, in conjunction with the general structure of most contemporary theories of consciousness, implies such independence. This observation provides the exact formal underpinning of the well-known unfolding argument. The thesis analyses the origin of the problem and identifies precisely which changes are required to avoid this problem in future research. The thesis furthermore shows that the problem of falsifying theories of consciousness, and of empirical comparisons of theories of consciousness more generally, follows from a pervasive closure paradigm in consciousness science, that consists of taking a neuroscientific account of the brain as input to a theory of consciousness, so as to explain what consciousness is, without allowing for modifications or adaptations of the neuroscientific account that would accommodate consciousness as part of the brain's functioning. As is shown in the thesis, this paradigm has implications that point to a fundamental need of revision. -/- Regarding methodological and conceptual questions, the thesis contributes to the foundations of structural research in consciousness science. Structural research aims to use mathematical structures or mathematical spaces, instead of verbal descriptions or simple categorisations, to represent conscious experiences scientifically, for example when building theories of consciousness, or when exploring new empirical avenues to measure consciousness. Despite considerable advances in this realm, there was, prior to this thesis, no explicit definition of what a mathematical structure of conscious experience should be; that is, how the attribution of mathematical structure to conscious experiences should be systematically understood. Perhaps the most important contribution of this thesis to the field is to propose such a definition. The definition, a structural concept, extends existing approaches wherever available, and provides a basis for developing a common formal language to study consciousness, bridging developments as far apart as psychophysics and phenomenology. In addition, and independently of this proposal, the thesis offers a critical analysis of which metaphysical premises need to be presumed in structural research, whether the use of particular formal tools (such as structure-preserving mappings or homomorphisms) is justified, and how structural theories of consciousness could otherwise be built in the first place. An attempt to expand the results from consciousness to more general problems in philosophy of science is made in the context of the well-known Newman problem. -/- Regarding the question of artificial consciousness—can AI feel?—, the thesis contributes two results that take the form of no-go theorems, as known from physics. The first no-go theorem shows that if consciousness is relevant for the temporal evolution of a system's states—if it is dynamically relevant—, then contemporary AI systems cannot be conscious. That is because AI systems run on CPUs, GPUs, TPUs or other processors which have been designed and verified to adhere to computational dynamics that systematically preclude or suppress deviations. The second no-go theorem is situated in the context of computational functionalism, a view which posits that consciousness is a computation. The theorem shows that if computational functionalism holds true, consciousness cannot be a Turing computation. Rather, it must be a novel type of computation that has recently been proposed by Geoffrey Hinton, called mortal computation. -/- This thesis is part of a global effort to pioneer a mathematical perspective in consciousness science, now called Mathematical Consciousness Science. The hope behind the research carried out in this PhD is to illustrate the power and usefulness of mathematical approaches in different areas of consciousness science, and in doing so, to lay the foundations for future mathematical work that complements and supports empirical and theoretical work in the further development of this exciting field. (shrink)

Triviality arguments against the computational theory of mind claim that computational implementation is trivial and thus does not serve as an adequate metaphysical basis for mental states. It is common to take computational implementation to consist in a mapping from physical states to abstract computational states. In this paper, I propose a novel constraint on the kinds of physical states that can implement computational states, which helps to specify what it is for two physical states (...) to non-trivially implement the same computational state. (shrink)

The topic of this paper is a presentation of an alternative theory in the field of philosophy of mind, which was created by the development of cognitive sciences and computer technologies of the twentieth century. Of particular importance is Turnig’s machine whose study at the logical level has contributed to new ways of understanding the mental. A theory such as functionalism (Putnam) does not study the mental in the same way as dualists and materialists do in the philosophical tradition. (...) The Mental has to be described in a generally logical manner (“higher level” of description), i.e. its function is described (input-output ratio), and is created by drawing a parallel between man and machine (known as a computer metaphor). (shrink)

In this article I explore a number of questions that have not been adequately investigated in philosophy of mind circles: are minds located in the same place as the brains (or other computing machinery) supporting them? Must they exist at the same location as the body? Must they exist at the same time? Could a single mind be implemented in multiple brains, or multiple minds in a single brain? Under what conditions might a single mind persist despite being implemented successively (...) in different brains? What contributions do features of the computing machinery make to these questions, compared to the contribution made by the body and embedded point of view? Some of these questions have been touched on previously, but there hasn’t been any attempt at a systematic analysis of the various consequences that different approaches in the philosophy of mind have for how the spatiotemporal location, synchronic individuation and diachronic identity of minds relates to the spatiotemporal location, synchronic individuation, and diachronic identity of both the implementing computational machinery and the embodied embedded point of view. I make a first stab at such an analysis by discussing a variety of thought experiments in which such questions of location, individuation, and identity arise, and I explore how various approaches to understanding the mind – identity theoretic, functionalist, contentualist, embodied/embedded/extended, and so forth – would respond to such situations. A number of novel issues emerge, and some surprising affinities are revealed. (shrink)

Pancomputationalism is the view that everything is a computer. This, if true, poses some difficulties to the computational theory of cognition. In particular, the strongest version of it suggested by John Searle seems enough to trivialize computational cognitivists’ core idea on which our cognitive system is a computing system. The aim of this paper is to argue against Searle’s pancomputationalism. To achieve this, I will draw a line between realized computers and unrealized computers. Through this distinction, I expect (...) that it will become evident that Searle’s pancomputationalism should be understood in terms of unrealized computers, while the computational theory of cognition is concerned with realized computers. (shrink)

Although virtue ethics has repeatedly been proposed as a suitable framework for the development of artificial moral agents, it has been proven difficult to approach from a computational perspective. In this work, we present the first technical implementation of artificial virtuous agents in moral simulations. First, we review previous conceptual and technical work in artificial virtue ethics and describe a functionalistic path to AVAs based on dispositional virtues, bottom-up learning, and top-down eudaimonic reward. We then provide the details of (...) a technical implementation in a moral simulation based on a tragedy of the commons scenario. The experimental results show how the AVAs learn to tackle cooperation problems while exhibiting core features of their theoretical counterpart, including moral character, dispositional virtues, learning from experience, and the pursuit of eudaimonia. Ultimately, we argue that virtue ethics provides a compelling path toward morally excellent machines and that our work provides an important starting point for such endeavors. (shrink)

Virtue ethics has many times been suggested as a promising recipe for the construction of artificial moral agents due to its emphasis on moral character and learning. However, given the complex nature of the theory, hardly any work has de facto attempted to implement the core tenets of virtue ethics in moral machines. The main goal of this paper is to demonstrate how virtue ethics can be taken all the way from theory to machine implementation. To achieve this goal, we (...) critically explore the possibilities and challenges for virtue ethics from a computational perspective. Drawing on previous conceptual and technical work, we outline a version of artificial virtue based on moral functionalism, connectionist bottom–up learning, and eudaimonic reward. We then describe how core features of the outlined theory can be interpreted in terms of functionality, which in turn informs the design of components necessary for virtuous cognition. Finally, we present a comprehensive framework for the technical development of artificial virtuous agents and discuss how they can be implemented in moral environments. (shrink)

For Putnam in "Representation and Reality", there cannot be any intentional science, thus dooming cognitive science. His argument is that intentional concepts are functional, and that functionalism cannot explain anything because "everything has every functional organization", providing a proof. Analyzing his proof, we find that Putnam is assuming an ideal interpreting subject who can compute effortlessly and who is not intentional. But the subject doing science is a human being, and we are not that way. Therefore, in order to (...) save cognitive science, we propose to replace the ideal subject with a real and intentional human subject, and we propose to model intentionality by using a problem theory which is an intuitionist set theory where the resolving subject is a computing device. We are intentional because we are living beings, where life is the intention of not to die, so we are embodied intentions designed by evolution. We are real and then we have to compute our resolutions to the survival problem, and fortuitously we are computationally Turing complete, so our language is complete and then full and self referable. In summary, evolutionary subjectivism modeled as problem solving by computing should save cognitive science. Or, in other words, we are proposing to update Kant with Darwin and Turing. (shrink)

According to the Extended Mind thesis, the mind extends beyond the skull or the skin: mental processes can constitutively include external devices, like a computer or a notebook. The Extended Mind thesis has drawn both support and criticism. However, most discussions—including those by its original defenders, Andy Clark and David Chalmers—fail to distinguish between two very different interpretations of this thesis. The first version claims that the physical basis of mental features can be located spatially outside the body. Once we (...) accept that the mind depends on physical events to some extent, this thesis, though not obvious, is compatible with a large variety of views on the mind. The second version applies to standing states only, and has to do with how we conceive the nature of such states. This second version is much more interesting, because it points to a potential tension in our conception of minds or selves. However, without properly distinguishing between the two theses, the significance of the second is obscured by the comparative triviality of the first. (shrink)

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will (...) lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature. (shrink)

The development of cognitive neuroscience allows scientists to locate areas of brain activity corresponding to consciousness activities through advanced technology. The philosophical basis of cognitive neuroscience is physicalism, which reduces the activity of consciousness into the product of biological brain activity, but there are many drawbacks to this strong physicalism reductionism. Mind-brain identity theory in neuroscience and the computational doctrine in cognitive science show that the theory of physical reductionism can never explain the causal relationship between mental properties and (...) physical properties and physicalism contains the tendency of dualism. In addition, dualism combines with materialism to develop a new dualism of naturalistic tendencies and gain new opportunities for development. It is further demonstrated that the rationality of dualism and mental state cannot be wholly reduced to a physical state. (shrink)

In Philosophical Investigations 193–94, Wittgenstein draws a notorious analogy between the working of a machine and the application of a rule. According to the view of rule-following that Wittgenstein is criticizing, the future applications of a rule are completely determined by the rule itself, as the movements of the machine components are completely determined by the machine configuration. On what conception of the machine is such an analogy based? In this paper, I intend to show that Wittgenstein relied on quite (...) a specific scientific tradition very active at the beginning of the twentieth century: the kinematic or the general science of machines. To explain the fundamental tenets of this line of research and its links with Wittgenstein, I focus on Franz Reuleaux, whose works were known to Wittgenstein. The first payoff of this investigation is to help distance the functionalist framework from which this passage is often read: Wittgenstein’s machines are not computers. The second payoff is to explain why Wittgenstein talks about machines at this place in his discussion on rule-following: it is not the machine model in itself that is criticized in PI 193–94, but the “philosophical” temptation to generalize from it. (shrink)

We overview the main historical and technological elements characterising the rise, the fall and the recent renaissance of the cognitive approaches to Artificial Intelligence and provide some insights and suggestions about the future directions and challenges that, in our opinion, this discipline needs to face in the next years.

(June 2013) “The mind-body problem in cognitive neuroscience”, Philosophia Scientiae 17/2, Gabriel Vacariu and Mihai Vacariu (eds.): 1. William Bechtel (Philosophy, Center for Chronobiology, and Interdisciplinary Program in Cognitive Science University of California, San Diego) “The endogenously active brain: the need for an alternative cognitive architecture” 2. Rolls T. Edmund (Oxford Centre for Computational Neuroscience, Oxford, UK) “On the relation between the mind and the brain: a neuroscience perspective” 3. Cees van Leeuwen (University of Leuven, Belgium; Riken Brain Science (...) Institute, Japan) “Brain and mind” 4. Kari Theurer (Trinity College) and John Bickle (Philosophy, Mississippi State University) “What’s old is new again: Kemeny-Oppenheim reduction at work in current molecular neuroscience” 5. Bernard Andrieu (Staps Université de Lorraine) “Sentir son cerveau? Les dispositifs neuro-expérientiels en 1er personne” 6. Corey Maley and Gualtiero Piccinini (Philosophy, University of Missouri – St. Louis) “Get the latest upgrade: Functionalism 6.3.1” 7. Paula Droege (Philosophy, Pennsylvania State University) “Memory and consciousness” 8. Gabriel Vacariu and Mihai Vacariu (Philosophy, University of Bucharest) “Troubles with cognitive neuroscience”. (shrink)

The Emergic Cognitive Model (ECM) is a unified computational model of visual filling-in based on the Emergic Network architecture. The Emergic Network was designed to help realize systems undergoing continuous change. In this thesis, eight different filling-in phenomena are demonstrated under a regime of continuous eye movement (and under static eye conditions as well). -/- ECM indirectly demonstrates the power of unification inherent with Emergic Networks when cognition is decomposed according to finer-grained functions supporting change. These can interact to (...) raise additional emergent behaviours via cognitive re-use, hence the Emergic prefix throughout. Nevertheless, the model is robust and parameter free. Differential re-use occurs in the nature of model interaction with a particular testing paradigm. -/- ECM has a novel decomposition due to the requirements of handling motion and of supporting unified modelling via finer functional grains. The breadth of phenomenal behaviour covered is largely to lend credence to our novel decomposition. -/- The Emergic Network architecture is a hybrid between classical connectionism and classical computationalism that facilitates the construction of unified cognitive models. It helps cutting up of functionalism into finer-grains distributed over space (by harnessing massive recurrence) and over time (by harnessing continuous change), yet simplifies by using standard computer code to focus on the interaction of information flows. Thus while the structure of the network looks neurocentric, the dynamics are best understood in flowcentric terms. Surprisingly, dynamic system analysis (as usually understood) is not involved. An Emergic Network is engineered much like straightforward software or hardware systems that deal with continuously varying inputs. Ultimately, this thesis addresses the problem of reduction and induction over complex systems, and the Emergic Network architecture is merely a tool to assist in this epistemic endeavour. -/- ECM is strictly a sensory model and apart from perception, yet it is informed by phenomenology. It addresses the attribution problem of how much of a phenomenon is best explained at a sensory level of analysis, rather than at a perceptual one. As the causal information flows are stable under eye movement, we hypothesize that they are the locus of consciousness, howsoever it is ultimately realized. (shrink)

Is it sensical to ascribe psychological predicates to AI systems like chatbots based on large language models (LLMs)? People have intuitively started ascribing emotions or consciousness to social AI (‘affective artificial agents’), with consequences that range from love to suicide. The philosophical question of whether such ascriptions are warranted is thus very relevant. This paper advances the argument that LLMs instantiate language users in Ludwig Wittgenstein’s sense but that ascribing psychological predicates to these systems remains a functionalist temptation. Social AIs (...) are not full-blown language users, but rather more like Italo Calvino’s literature machines. The ideas of LLMs as Wittgensteinian language users and Calvino’s literature-producing writing machine are combined. This sheds light on the misguided functionalist temptation inherent in moving from equating the two to the ascription of psychological predicates to social AI. Finally, the framework of mortal computation is used to show that social AIs lack the basic autopoiesis needed for narrative façons de parler and their role in the sensemaking of human (inter)action. Such psychological predicate ascriptions could make sense: the transition ‘from quantity to quality’ can take place, but its route lies somewhere between life and death, not between affective artifacts and emotion approximation by literature machines. (shrink)

The exploration of the mind is a fundamental pursuit spanning philosophy and psychology, with implications reaching into diverse practical realms. This paper delves into the intricacies of mental states, examining historical perspectives from ancient philosophers to modern theorists. Philosophical inquiries into intentionality, consciousness, and the nature of mental phenomena are scrutinized, alongside empirical investigations by psychologists. The discourse navigates through contrasting theories such as dualism, materialism, and functionalism, shedding light on the challenges of reconciling subjective experiences with objective observations. (...) The problem of other minds and the tension between internalism and externalism are dissected, revealing the complex interplay between individual cognition and external influences. Ultimately, this analysis underscores the intricate nature of philosophical inquiries into consciousness and the mind. (shrink)

Self-consciousness relates to important themes, such as sentience and personhood, and is often the cornerstone of moral theories (Warren, 1997). However, not much attention is given to future moral creatures of the earth: robots. This may be due to the unsettled status of their experience, which is why I have chosen to find the necessary features of self-consciousness in them. Philosophy of mind is also my interest which I have developed since I rejected the idea of souls and could not (...) account for my own phenomenal experiences. Among the current answers to the hard problem of consciousness (Chalmers, 1995) one stood out to me – Daniel Dennett’s multiple drafts model of consciousness presented in “Consciousness Explained” (Dennett, 1991) and his other works. Daniel Dennett is a self-described illusionist (Dennett, 2016) and a functionalist. His position is synonymous to holding self-conscious experiences, such as deliberating over one’s pain or reflecting over one’s actions insufficient to postulate existence of a metaphysical self. He believes instead that experiences are functional realizations which do not require any self, and the feeling of being conscious or self-conscious is actually a ”user-illusion” (Dennett, 1991), a term describing the unjustified belief of being in control of one’s actions. In his views, a selfhood as it is popularly conceived is contradictory. Therefore, he rejects realism about mental events, a position also called naïve realism (Siegel, 2011), on the basis that it is unverifiable and there are insufficient grounds to accept mental events (Dennett, 1988). He adopts a similar scepticism towards other theories of minds, hence nearing eliminative materialism in self-consciousness and related theme of pain (Dennett, 1978). His multiple drafts model of consciousness seeks to ground our beliefs about self-consciousness in the specific architecture of mind, which is particular to every species and kind of being. As such it is a particularly flexible theory that examines experiences of non-humans without falling into binary thinking – believing that a being has as rich inner life as humans do or none. This is why it is a great starting point for establishing necessary conditions for consciousness in robots. In this essay, I will often use words computer, programme, robot interchangeably for the purpose of clarity and take as the working definition “autonomous machines capable of sensing their environment, making decision and performing tasks similar to human actions”. I chose to explore self-consciousness instead of committing to the issue of consciousness, because consciousness can be defined in a weak sense, in which even a thermostat is “conscious” of the temperature, and consciousness in a strong sense, which invokes the internalism and externalism debate that is less connected to the nature of experiences than self-consciousness. My exploration pertains first and foremost to the philosophy of mind, of which I will examine the most relevant positions and decide to commit to functionalism (Block, 1980). Then I will advocate for explaining away of some of phenomena of self-consciousness and believing self-consciousness to be a sort of illusion. From there I will conclude that robots could fall for the same illusion of self-consciousness and therefore become self-conscious. I will argue, that self-consciousness can be established using a modified Turing Test, which would determine if a robot believed in a meaningful narrative about itself (Ricoeur, 1991) and hence be self-conscious. This will lead to answering the question of this essay: what are the features necessary for a self-conscious robot in the light of “Consciousness Explained” by Daniel Dennett? (shrink)

The field of machine perception is based on standard informational and computational approaches to perception. But naturalistic informational theories are widely regarded as being inadequate, while purely syntactic computational approaches give no account of perceptual content. Thus there is a significant need for a novel, purely naturalistic perceptual theory not based on informational or computational concepts, which could provide a new paradigm for mechanistic perception. Now specifically evolutionary naturalistic approaches to perception have been—perhaps surprisingly—almost completely neglected for (...) this purpose. Arguably perceptual mechanisms enhance evolutionary fitness by facilitating sensorily mediated causal interactions between an organism Z and items X in its environment. A ‘reflexive’ theory of perception of this kind is outlined, according to which an organism Z perceives an item X just in case X causes a sensory organ zi of Z to cause Z to acquire a disposition toward the very same item X that caused the perception. The rest of the paper shows how an intuitively plausible account of mechanistic perception can be developed and defended in terms of the reflexive theory. Also, a compatibilist option is provided for those who wish to preserve a distinct informational concept of perception. (shrink)

Given the personal acquaintance between Alan M. Turing and W. Ross Ashby and the partial proximity of their research fields, a comparative view of Turing’s and Ashby’s work on modelling “the action of the brain” (letter from Turing to Ashby, 1946) will help to shed light on the seemingly strict symbolic/embodied dichotomy: While it is clear that Turing was committed to formal, computational and Ashby to material, analogue methods of modelling, there is no straightforward mapping of these approaches onto (...) symbol-based AI and embodiment-centered views respectively. Instead, it will be demonstrated that both approaches, starting from a formal core, were at least partly concerned with biological and embodied phenomena, albeit in revealingly distinct ways. (shrink)

THE MIND-BODY PROBLEM: A GUIDE TO THE CURRENT DEBATE (EDITED BY RICHARD WARNER AND TA D E U S Z SZUBKA) contains recent essays by the key players in the the field of the Mind-Body problem: Searle, Fodor, Problem Honderich, Nagel, McGinn, Stich, Rorty and others. But there are a few interesting exceptions, for example Edelman, Popper, Putnam and Dennett. Nevertheless, these thinkers do get a mention here and there, and nearly all the exciting topical issues are dealt with, including (...) externalism, functionalism, intentionality, Turing computational models, and the relationship between these philosophical problems and psychology. I am particularly struck by the tendency to engage in an evasive stratagem when it comes to stating the physicalist thesis. Instead of a clear definitive position about what kind of physical science would achieve the hoped-for reduction, what I found was variations on Lewis's claim that the reduction would be effected by a "unified body of scientific theories of the sort we now accept". Materialism used to be a clear doctrine: a clockwork Universe of impenetrable particles. But materialism transcended itself. First through Hobbes' and Leibniz's criticisms of Descartes, then Newton's demolition of the Cartesian idea that matter was essentially extension by introducing gravity (action at a distance) and then through the field theories of Faraday and Maxwell, and more recently through Einstein's work, which undermined the "substance" view of matter, something that remained permanent while other changes occurred. I contrast the perspectives of the Churchlands with those of Donald T. Campbell, Karl Popper and other evolutionary and emergentist views. -/- . (shrink)

In this paper we introduce three methods to approach philosophical problems informationally: Minimalism, the Method of Abstraction and Constructionism. Minimalism considers the specifications of the starting problems and systems that are tractable for a philosophical analysis. The Method of Abstraction describes the process of making explicit the level of abstraction at which a system is observed and investigated. Constructionism provides a series of principles that the investigation of the problem must fulfil once it has been fully characterised by the previous (...) two methods. For each method, we also provide an application: the problem of visual perception, functionalism, and the Turing Test, respectively. (shrink)

In this essay, I propose a functionalist theory of grounding (functionalist-grounding). Specifically, I argue that grounding is a second-order phenomenon that is realized by relations that play the noncausal explanatoriness role. I also show that functionalist-grounding can deal with a powerful challenge. Appeals to explanatory unificationism have been made to argue that the success of noncausal explanations does not depend on the existence of grounding relations. Against this, I argue that a systematization involving functionalist-grounding is superior to its anti-relational counterpart.

The independence problems for functionalism stem from the worry that if functional properties are defined in terms of their causes and effects then such functional properties seem to be too intimately connected to these purported causes and effects. I distinguish three different ways the independence problems can be filled out – in terms of necessary connections, analytic connections and vacuous explanations. I argue that none of these present serious problems. Instead, they bring out some important and over-looked features of (...) functionalism. (shrink)