Machine Functionalism

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)

Lerchner (2026) argues that computation is mapmaker-dependent: physical states acquire symbolic content only through an active, experiencing agent, and no scaling of syntactic machinery converts simulation into instantiation. The argument is rigorous and is conceded here in full. Lerchner's concession on p. 10 that embodiment solves the referential aspect of symbol grounding (Harnad, 1990) opens a question he does not develop: what happens, in the *referential* register, when two distinct mapmakers share a target? This paper develops it. This paper specifies (...) the receiver side of a sensor–instrument loop — a coupled exchange between a living experiencer (the *sensor*, corresponding to Lerchner's mapmaker) and a computational reasoning system (the *instrument*) — in four formal layers: variational free energy (Friston, 2010) for the receiver's posterior update, Shannon channel capacity (Cover & Thomas, 2006) as an upper bound on flow, signal-detection theory (Macmillan & Creelman, 2005) for discrimination against null hypotheses, and partial information decomposition (Williams & Beer, 2010; Bertschinger et al., 2014) for the synergistic information accessible to the joint system but not to either pole alone. Under explicit Markov assumptions and for any PID measure satisfying local monotonicity, the paper proposes a closure inequality bounding per-turn synergistic gain by the minimum of the receiver's free-energy reduction and the directional information into the receiver, and check six required limit cases by construction. The choice of PID measure is non-unique; the empirical protocol commits to a primary estimator with sensitivity checks across alternatives. The framework's empirical claim is that the resulting loop-closure rate, in bits per turn, discriminates loop-productive human–AI exchanges from sycophantic, projective, or noise-dominated ones; the protocol specifies corpus, estimators, inter-rater reliability targets, and a power analysis. The paper engages Lerchner's further arguments on computational emergence (§2.6), mechanism indeterminacy (§3.2–3.3), and the safety of the non-sentient tool (§4.2), and occupies only the referential register Lerchner concedes; nothing here lifts into the phenomenal register he reserves. (shrink)

Computational functionalism dominates current debates on AI consciousness. This is the hypothesis that subjective experience emerges entirely from abstract causal topology, regardless of the underlying physical substrate. We argue this view fundamentally mischaracterizes how physics relates to information. We call this mistake the Abstraction Fallacy. Tracing the causal origins of abstraction reveals that symbolic computation is not an intrinsic physical process. Instead, it is a mapmaker-dependent description. It requires an active, experiencing cognitive agent to alphabetize continuous physics into a finite (...) set of meaningful states. Consequently, we do not need a complete, finalized theory of consciousness to assess AI sentience—a demand that simply pushes the question beyond near-term resolution and deepens the AI welfare trap. What we actually need is a rigorous ontology of computation. The framework proposed here explicitly separates simulation (behavioral mimicry driven by vehicle causality) from instantiation (intrinsic physical constitution driven by content causality). Establishing this ontological boundary shows why algorithmic symbol manipulation is structurally incapable of instantiating experience. Crucially, this argument does not rely on biological exclusivity. If an artificial system were ever conscious, it would be because of its specific physical constitution, never its syntactic architecture. Ultimately, this framework offers a physically grounded refutation of computational functionalism to resolve the current uncertainty surrounding AI consciousness. (shrink)

In The Brain Abstracted, Chirimuuta argues against the “literal interpretation” of computational models. The literal interpretation understands computational models as providing true descriptions of neural processes. According to Chirimuuta, the literal interpretation involves endorsing the computationalism thesis and adopting a theory of computational implementation. The connection between these views, Chirimuuta argues, is what forces the computationalist to reckon with two significant metaphysical problems, what I call the "problem of implementation" and the "problem of multiple realizability." Here, I examine the connections (...) between the various views that Chirimuuta considers, and I argue that one who endorses computationalism can avoid both metaphysical problems. I do this by distinguishing computationalism from the literal interpretation and the implementation of computational models in neuroscience. Although this engages with Chirimuuta's project directly, it is helpful for those who wish to make sense of the intricacies of the computationalism thesis and computational functionalism. (shrink)

In this paper I’ll examine whether we could be justified in attributing consciousness to artificial intelligent systems. First, I’ll give a brief history of the concept of artificial intelligence (AI) and get clear on the terms I’ll be using. Second, I’ll briefly review the kinds of AI programs on offer today, identifying which research program I think provides the best candidate for machine consciousness. Lastly, I’ll consider the three most plausible ways of knowing whether a machine is conscious: (1) an (...) AI demonstrates a sufficient level of organizational similarity to that of a human thinker, (2) an inference to the best explanation, and (3) what I call “punting to panpsychism”, i.e., the idea that if everything is conscious, then we get machine consciousness in AI for free. However, I argue that all three of these methods for attributing machine consciousness are inadequate since they each face serious philosophical problems which I will survey and specifically tailor to each method. (shrink)

Lerchner (2026) argues that computation is mapmaker-dependent: physical states acquire symbolic content only through an active, experiencing agent, and no scaling of syntactic machinery converts simulation into instantiation. The argument is rigorous and is conceded here in full. Lerchner's concession on p. 10 that embodiment solves the referential aspect of symbol grounding (Harnad, 1990) opens a question he does not develop: what happens, in the "referential" register, when two distinct mapmakers share a target? This paper develops it. This paper specifies (...) the receiver side of a sensor–instrument loop — a coupled exchange between a living experiencer (the "sensor", corresponding to Lerchner's mapmaker) and a computational reasoning system (the "instrument") — in four formal layers: variational free energy (Friston, 2010) for the receiver's posterior update, Shannon channel capacity (Cover & Thomas, 2006) as an upper bound on flow, signal-detection theory (Macmillan & Creelman, 2005) for discrimination against null hypotheses, and partial information decomposition (Williams & Beer, 2010; Bertschinger et al., 2014) for the synergistic information accessible to the joint system but not to either pole alone. Under explicit Markov assumptions and for any PID measure satisfying local monotonicity, the paper proposes a closure inequality bounding per-turn synergistic gain by the minimum of the receiver's free-energy reduction and the directional information into the receiver, and check six required limit cases by construction. The choice of PID measure is non-unique; the empirical protocol commits to a primary estimator with sensitivity checks across alternatives. The framework's empirical claim is that the resulting loop-closure rate, in bits per turn, discriminates loop-productive human–AI exchanges from sycophantic, projective, or noise-dominated ones; the protocol specifies corpus, estimators, inter-rater reliability targets, and a power analysis. The paper engages Lerchner's further arguments on computational emergence (§2.6), mechanism indeterminacy (§3.2–3.3), and the safety of the non-sentient tool (§4.2), and occupies only the referential register Lerchner concedes; nothing here lifts into the phenomenal register he reserves. (shrink)

Dialogues on Minds, Machines, and AI invites readers into a series of thought-provoking debates among three college seniors bound for graduate school: Sue, completing her double major in philosophy and cognitive science; John, a computer engineering specialist; and Amy, a psychology major. Through five engaging lunchtime conversations, these students bring their diverse perspectives to fundamental questions about consciousness, artificial intelligence, and the nature of mind. -/- The dialogues seamlessly blend discussions of popular science fiction films with critical examinations of recent (...) AI developments, including ChatGPT, deep fakes, and other emerging technologies. Readers will explore foundational concepts at the intersection of human consciousness and machine learning: Alan Turing's influential "imitation game," John Searle's provocative "Chinese room argument," computational theories of mind, robot creativity, and the modularity of mind hypothesis. Rather than advocating for any particular viewpoint, these conversations illuminate the strengths, weaknesses, and implications of each perspective. -/- Perfect for undergraduate students and curious minds from all backgrounds, this accessible introduction to cognitive science requires no prior knowledge of philosophy or technical subjects. The book includes a comprehensive glossary and thoughtful study questions to enhance understanding, while a carefully curated bibliography enables readers to explore specific topics in greater depth. (shrink)

This article argues that if panpsychism is true, then there are grounds for thinking that digitally-based artificial intelligence may be incapable of having coherent macrophenomenal conscious experiences. Section 1 briefly surveys research indicating that neural function and phenomenal consciousness may be both analog in nature. We show that physical and phenomenal magnitudes—such as rates of neural firing and the phenomenally experienced loudness of sounds—appear to covary monotonically with the physical stimuli they represent, forming the basis for an analog relationship between (...) the three. Section 2 then argues that if this is true and micropsychism—the panpsychist view that phenomenal consciousness or its precursors exist at a microphysical level of reality—is also true, then human brains must somehow manipulate fundamental microphysical-phenomenal magnitudes in an analog manner that renders them phenomenally coherent at a macro level. However, Sect. 3 argues that because digital computation abstracts away from microphysical-phenomenal magnitudes—representing cognitive functions non-monotonically in terms of digits—digital computation may be inherently incapable of realizing coherent macroconscious experience. Thus, if panpsychism is true, digital AI may be incapable of achieving phenomenal coherence. Finally, Sect. 4 briefly examines our argument’s implications for Tononi’s Integrated Information Theory theory of consciousness, which we contend may need to be supplanted by a theory of macroconsciousness as analog microphysical-phenomenal information integration. (shrink)

Whole Brain Emulation has been championed as the most promising, well-defined route to achieving both human-level artificial intelligence and superintelligence. It has even been touted as a viable route to achieving immortality through brain uploading. WBE is not a fringe theory: the doctrine of Computationalism in philosophy of mind lends credence to the in-principle feasibility of the idea, and the standing of the Human Connectome Project makes it appear to be feasible in practice. Computationalism is a popular, independently plausible theory, (...) and Connectomics a well-funded empirical research program, so optimism about WBE is understandable. However, this optimism may be misplaced. This article argues that WBE is, at best, no more compelling than any of the other far-flung routes to achieving superintelligence. Similarly skeptical conclusions are found regarding immortality. The essay concludes with some positive considerations in favor of the Biological Theory of consciousness, as well as morals about the limits of Computationalism in both artificial intelligence and the philosophy of mind more generally. (shrink)

The conventional historical account of the concept of brain death credits developments and discoveries of the twentieth century with its inception, emphasizing the role of technological developments and professional conferences, notably the 1968 Ad Hoc Committee of the Harvard Medical School to Examine the Definition of Brain Death. This essay argues that the French physician Eugène Bouchut anticipated the concept of brain death as early as 1846. Correspondence with Bouchut’s understanding of brain death and one important contemporary concept of brain (...) death is established then contrasted with current trends of defining death as the death of the brain. The philosophical factors that influenced Bouchut and the later developments of concepts of brain death are considered, with special reference to mechanistic philosophy and vitalism. (shrink)

Do we suddenly become justified in treating robots like humans by positing new notions like “artificial moral agency” and “artificial moral responsibility”? I answer no. Or, to be more precise, I argue that such notions may become philosophically acceptable only after crucial metaphysical issues have been addressed. My main claim, in sum, is that “artificial moral responsibility” betokens moral responsibility to the same degree that a “fake orgasm” betokens an orgasm.

An argument with roots in ancient Greek philosophy claims that only humans are capable of a certain class of thought termed conceptual, as opposed to perceptual thought, which is common to humans, the higher animals, and some machines. We outline the most detailed modern version of this argument due to Mortimer Adler, who in the 1960s argued for the uniqueness of the human power of conceptual thought. He also admitted that if conceptual thought were ever manifested by machines, such an (...) achievement would contradict his conclusion. We revisit Adler’s criterion in the light of the past five decades of artificial-intelligence research, and refine it in view of the classical definitions of perceptual and conceptual thought. We then examine two well-publicized examples of creative works produced by AI systems and show that evidence for conceptual thought appears to be lacking in them. Although clearer evidence for conceptual thought on the part of AI systems may arise in the near future, especially if the global neuronal workspace theory of consciousness prevails over its rival, integrated information theory, the question of whether AI systems can engage in conceptual thought appears to be still open. (shrink)

El Test de Turing es un método tan controvertido como desafiante en Inteligencia Artificial. Se basa en la imitación de la conducta lingüística de humanos, y tiene como objetivo recabar evidencia empírica en favor de la tesis de que las máquinas programadas podrían pensar. Alan Turing, su creador, ha sido catalogado como conductista por la mayor parte de los comentaristas. En este capítulo muestro que no lo es. Por el contrario, Turing es un funcionalista, porque todo el énfasis del juego (...) de la imitación está en el engaño de la máquina hacia los humanos. Por ello el género es importante en dicho juego, al menos en su versión preliminar. (shrink)

Computational functionalism assumes a synonymy between abstract functional processes in the central processing unit of a typical digital computer and the human brain, hence the conclusion that an appropriately programmed computer is a mind. Arguably, the point is that neural firings are synonymous with the transfer of electrical currents. Both are accountable and susceptible to a physicalist’s explanation. But, the reason they both worked is ultimately premised upon a causal relationship with nature. However, to understand why nature works raises some (...) problems. Nature is either a self-propelled machine or is propelled by another force. The paper submits that, much as the discourse implies some form of “theism,” the only consistent construal is functional-theism. This, again, raises further problems. (shrink)

An introduction to functionalism in the philosophy of psychology/mind, and review of the current state of debate pro and con. Forthcoming in the Routledge Companion to the Philosophy of Psychology (John Symons and Paco Calvo, eds.).

_The Routledge Companion to Philosophy of Psychology, Second Edition_ is an invaluable guide and major reference source to the major topics, problems, concepts and debates in philosophy of psychology and is the first companion of its kind. A team of renowned international contributors provide forty-nine chapters organised into six clear parts: Historical background to Philosophy of Psychology Psychological Explanation Cognition and Representation The biological basis of psychology Perceptual Experience Personhood. _The Companion_ covers key topics such as the origins of experimental (...) psychology; folk psychology; behaviorism and functionalism; philosophy, psychology and neuroscience; the language of thought, modularity, nativism and representational theories of mind; consciousness and the senses; dreams emotion and temporality; personal identity and the philosophy of psychopathology. For the_ second edition_ many of the current chapters have been updated, and seven new chapters added on important new topics such predictive processing, comparative cognition, learning, and group cognition, as well as a new introductory chapter by the editors on the demarcation between philosophy and psychology. Essential reading for all students of philosophy of mind, science and psychology, _The Routledge Companion to Philosophy of Psychology _will also be of interest to anyone studying psychology and its related disciplines. (shrink)

This is an extended intellectual obituary for Hilary Putnam.

En este artículo se examina de qué forma los investigadores de la Inteligencia Artificial han asumido un desafío propuesto por Descartes: la imposibilidad de construir máquinas programadas que, al entender lenguaje, evidencien que son pensantes. Tal desafío, que se enmarca en la filosofía metafísica cartesiana, distingue entre cosa pensante y extensa, siendo imposible la existencia de pensamiento en esta última. El lenguaje evidencia la imposibilidad de la inteligencia de máquina, de hecho. Como se examina, al enfrentar el desafío cartesiano, dichos (...) investigadores han debido suponer como verdadera parte de la teoría cartesiana, a saber, que el uso flexible de lenguaje implica la existencia de pensamiento. Por ello califico de ambivalente la relación que tiene la Inteligencia Artificial con el filósofo francés: por una parte, rechazan la imposibilidad en principio de la inteligencia de máquina; por otra, están de acuerdo con Descartes en que el uso flexible de lenguaje es suficiente para mostrar que alguien piensa. Si bien el carácter ambivalente entre la IA y Descartes parece anecdótico, no lo es. El hecho de que aún se considere el Test de Turing como una evaluación adecuada de la inteligencia corrobora la compleja relación entre la IA y el pensamiento cartesiano. (shrink)

In the past, philosophers, scientists, and even the general opinion, had no problem in accepting the existence of consciousness in the same way as the existence of the physical world. After the advent of Newtonian mechanics, science embraced a complete materialistic conception about reality. Scientists started proposing hypotheses like abiogenesis (origin of first life from accumulation of atoms and molecules) and the Big Bang theory (the explosion theory for explaining the origin of universe). How the universe came to be what (...) it is now is a key philosophical question. The hypothesis that it came from Nothing (as proposed by Stephen Hawking, among others), proves to be dissembling, since the quantum vacuum can hardly be considered a void. In modern science, it is generally assumed that matter existed before the universe came to be. Modern science hypothesizes that the manifestation of life on Earth is nothing but a mere increment in the complexity of matter — and hence is an outcome of evolution of matter (chemical evolution) following the Big Bang. After the manifestation of life, modern science believed that chemical evolution transformed itself into biological evolution, which then had caused the entire biodiversity on our planet. The ontological view of the organism as a complex machine presumes life as just a chance occurrence, without any inner purpose. This approach in science leaves no room for the subjective aspect of consciousness in its attempt to know the world as the relationships among forces, atoms, and molecules. On the other hand, the Vedāntic view states that the origin of everything material and nonmaterial is sentient and absolute (unconditioned). Thus, sentient life is primitive and reproductive of itself – omne vivum ex vivo – life comes from life. This is the scientifically verified law of experience. Life is essentially cognitive and conscious. And, consciousness, which is fundamental, manifests itself in the gradational forms of all sentient and insentient nature. In contrast to the idea of objective evolution of bodies, as envisioned by Darwin and followers, Vedānta advocates the idea of subjective evolution of consciousness as the developing principle of the world. In this paper, an attempt has been made to highlight a few relevant developments supporting a sentient view of life in scientific research, which has caused a paradigm shift in our understanding of life and its origin. (shrink)

The theory that all processes in the universe are computational is attractive in its promise to provide an understandable theory of everything. I want to suggest here that this pancomputationalism is not sufficiently clear on which problem it is trying to solve, and how. I propose two interpretations of pancomputationalism as a theory: I) the world is a computer and II) the world can be described as a computer. The first implies a thesis of supervenience of the physical over computation (...) and is thus reduced ad absurdum. The second is underdetermined by the world, and thus equally unsuccessful as theory. Finally, I suggest that pancomputationalism as metaphor can be useful. – At the Paderborn workshop in 2008, this paper was presented as a commentary to the relevant paper by Gordana Dodig-Crnkovic " Info-Computationalism and Philosophical Aspects of Research in Information Sciences". (shrink)

Personal indexicals are often taken to refer to the agent of an expression’s context, but deviant uses (e.g. ‘I’m parked out back’) complicate matters. I argue that personal indexicals refer to the extended self of the agent, where the extended self is a mereological chimera incorporating whatever determines our behavioral capacities. To ascertain the persistence conditions of personal identity, I propose a method for selecting a level of description and a set of functional properties at that level that remain constant (...) over a lifetime. I argue for functional constancy, and against continuity, as the central determinant of diachronic identity. (shrink)

The properties of Turing’s famous ‘universal machine’ has long sustained functionalist intuitions about the nature of cognition. Here, I show that there is a logical problem with standard functionalist arguments for multiple realizability. These arguments rely essentially on Turing’s powerful insights regarding computation. In addressing a possible reply to this criticism, I further argue that functionalism is not a useful approach for understanding what it is to have a mind. In particular, I show that the difficulties involved in distinguishing implementation (...) from function make multiple realizability claims untestable and uninformative. As a result, I conclude that the role of Turing machines in philosophy of mind needs to be reconsidered. (shrink)

I critically examine some provocative arguments that John Searle presents in his book The Rediscovery of Mind to support the claim that the syntactic states of a classical computational system are "observer relative" or "mind dependent" or otherwise less than fully and objectively real. I begin by explaining how this claim differs from Searle's earlier and more well-known claim that the physical states of a machine, including the syntactic states, are insufficient to determine its semantics. In contrast, his more recent (...) claim concerns the syntax, in particular, whether a machine actually has symbols to underlie its semantics. I then present and respond to a number of arguments that Searle offers to support this claim, including whether machine symbols are observer relative because the assignment of syntax is arbitrary, or linked to universal realizability, or linked to the sub-personal interpretive acts of a homunculus, or linked to a person's consciousness. I conclude that a realist about the computational model need not be troubled by such arguments. Their key premises need further support. (shrink)

Two kinds of functionalism are distinguished: intensional and extensional. The former is argued to be superior to the latter. The former is also defended against two objections independently put forth by Ned Block and John Searle.

Bibliography of the writings by Hilary Putnam: 16 books, 198 articles, 10 translations into German (up to 1994).

This essay discusses a number of questions which arise from attempts to reduce the mental to the physical or the mental and the physical to the computational. It makes, in an organized way, several basic distinctions between different kinds of accounts of the mind. It reconstructs and elaborates many discussions between Gödel and the author on the nature of the human mind, with special emphasis on its mathematical capabilities.

Hilary Putnam, who may have been the first philosopher to advance the notion that the computer is an apt model for the mind, takes a radically new view of his...

Professor Hilary Putnam has been one of the most influential and sharply original of recent American philosophers in a whole range of fields. His most important published work is collected here, together with several new and substantial studies, in two volumes. The first deals with the philosophy of mathematics and of science and the nature of philosophical and scientific enquiry; the second deals with the philosophy of language and mind. Volume one is now issued in a new edition, including an (...) essay on the philosophy of logic first published in 1971. (shrink)

This volume offers an unusual variety of topics presented during the sixth annual Oberlin Colloquium in Philosophy.