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This chapter discusses the connection between computation and consciousness. Three theses are sometimes conflated. Functionalism is the view that the mind is the functional organization of the brain. The Computational Theory of Mind (CTM) is the view that the whole mind—not only cognition but consciousness as well—has a computational explanation. When combined with the empirical discovery that the brain is the organ of the mind, CTM entails that the functional organization of the brain is computational. Computational functionalism is the conjunction of the two: the mind is the computational organization of the brain. Contrary to a common assumption, functionalism entails neither CTM nor computational functionalism. This finding makes room for an underexplored possibility: that consciousness be (at least partly) due to the functional organization of the brain without being computational in nature. This is a noncomputational version of functionalism about consciousness.

There are increasingly good reasons to doubt that the ‘classical’ representational theory of mind (RTM) language of thought (LOT) computational theory of mind (CTM) model is anything like a general account of how the cognitive mind works; and the magnitude of our current bewilderment about certain aspects of cognitive processes is the measure of the failure. This is not, of course, a cause for lamentation. A lot of our best science has been learned from the falsification of prima facie interesting ideas: it's a matter of figuring out where you are by finding out where you aren't. Just as Holmes says: ‘When you've eliminated all the impossibles, what's left must be the truth’. Assuming, of course, that there is anything left, and assuming that there is only one of them. This chapter is about what seems to have gone wrong.

Many arguments for multiple realization begin with the claim that psychological states are computational states, and conclude the abstractness of computational states assures their multiple realizability. However, in many cases where a cognitive scientist offers a computational description of a cognitive capacity, on offer is merely a computational gloss that carries with it no commitment to the existence of an internal psychological organization that actually performs computations. Similarly, that the abstractness of computational descriptions entails the multiple realizability of psychological states runs into difficulty. Abstractness is a property of descriptions, not of the processes described. Hence, the abstract character of computational descriptions does little to support the claim that the processes they describe might be multiply realizable. Furthermore, one may insist that computational descriptions of psychological processes succeed because the processes themselves are computational, but this response neglects decades of research that challenges the computational nature of psychological processing.

This chapter explains the problem faced by the computational theory of mind (CTM) caused by the context-sensitive nature of beliefs and the context-insensitive nature of syntactic properties, which are both elements of cognition. The discussion begins by outlining the notion of syntax that is relevant to Fodor’s argument and explaining why it must be construed as being context-insensitive. Fodor’s argument is explored in great detail and it is explained how his notion of “the simplicity of a belief” helped in formulating this argument — one that is consequently proven to have a false premise using assumptions that Fodor made himself. Finally, it is determined whether there is another more plausible argument that can be considered and the empirical question is asked whether there are properties relevant to the type of cognition that makes trouble for CTM or not.

This chapter posits that language of thought’s (LOT) symbols are algorithmic in nature, and that they are distinguished by the role they play in computation. Three arguments will be presented in this chapter to make this case. The first argument claims that the classicist approach taken by LOT requires that symbols be classified according to this role. The second claims that, without the classification specified in the first argument, a number of cognitive processes will appear that fail to supervene on symbols and rules. The third and final argument states that cognitive science requires a natural kind of symbol classified by its total computational role. If the third condition is not satisfied, cognitive science will either be incomplete or its laws will have counterexamples. The second argument also presents problems for computational theory of mind (CTM), because CTM holds that cognitive processing is merely the processing of mental symbols according to set rules.

This chapter illustrates how philosophers endeavoring to integrate mentality into the world that science investigates naturally turn their gaze to computational approaches in cognitive science, such as the computational theory of mind. Three main problems lead to the suspicion that language of thought (LOT) may be an ill-conceived theory in the first place, even after considering the importance of computational approaches and the symbol-processing approach. First, there is the problem of the computational nature of the central system: LOT needs to move forward and orient itself with the current work on higher cognitive function in cognitive science to maintain a coherent account of the nature of the mind. Second, there is the problem of mental state individuation: as theories of the nature of thought, LOT and computational theory of mind (CTM) must update their notion of a mental state to overcome the criticism it has been receiving for years. Lastly, there is the problem of the relation between meaning and symbolic mental states: LOT’s theory of mental content clashes with its symbolic view of thinking, compromising LOT’s ability to explain thought and behavior.

This chapter provides an account of the nature of language of thought’s (LOT) mental symbols. Through a two-fold dialectical strategy, an argument is presented to make the case that LOT and computational theory of mind (CTM) should type symbols based on the role they play in the algorithms of a completed cognitive science. This chapter asserts the importance of symbols in LOT and explains the philosophical roles they play. The philosophical roles that mental symbols are required to play for LOT are outlined before a discussion of how each of the accounts of symbol types fail in their own way. Although some fail for other reasons, many of the theories do not deliver a notion of a symbol that can perform one or more nonnegotiable functions that symbols are supposed to play for the LOT program.

The notion that human beings are simply sophisticated robots or computing machines has spread widely across society in the last several decades. This claim has a basis in a popular theory of cognitive psychology and artificial intelligence advanced by academics and bestselling authors such as Steven Pinker. Anxiety about a robot takeover of society is increasingly debated in the public square by the likes of Elon Musk and Mark Zuckerberg. But this view has also affected how modern people think of their experiences of depression (leading to the overprescription of antidepressants) and even their sense of political identity. Many people today believe their identities are completely determined by a machine-like coding in their genes. This can lead to both private and public forms of despair over the human ability to substantively change the world.

This chapter considers some of the working assumptions about mental representations in relation to language. It begins by assuming that behaviorism is false root and branch; in the paradigm cases, behavior is the effect of mental causes, and the paradigm of explanation in cognitive psychology is the attribution of a creature's actions to its beliefs, intentions, desires, and its other “propositional attitudes.” The paradigm of belief-desire explanation is what Aristotle called a “practical syllogism.” The second assumption deals with the naturalism of explanations in cognitive science, while the third is concerned with the distinction between type and token. Other assumptions relate to psychological reality, the compositionality of propositions and mental representations, the representational theory of mind, the computational theory of mind, and the priority of thought to language. The chapter concludes by lumping all of the above assumptions together in what it calls “basic cognitive science”.

Interrogates Adams and Aizawa’s attempt to protect the intracraniality of “true cognition” by excluding the transcraniality of natural language from cognitive processing. This attempt is first traced back to Fodor’s (1975) language-of-thought hypothesis (LOTH), with a brief history of the Computational Theory of Mind (CTM) in general and the LOTH as perhaps the most influential instantiation of the CTM; then a countermodel is offered based the actual emergence of thought out of embodied (affective-becoming-cognitive) communication with others. The chapter sticks to the verbal-labels model of language that Andy Clark shares with his internalist critics in order to show that a careful enough examination of even this extremely narrow conception of language uncovers far more transcranial connectivity than Clark argues for.

When I point to an object, you and I can agree on what it is (a red, round cup). How does our brain (matter) represent such notions? And how do we (distinct material bodies) apparently converge so we can talk about the same things? Cognitive scientists and philosophers have long assumed that people share abstract concepts (e.g., a cup); to explain how such abstract concepts can give rise to thinking, they further proposed the computational theory of mind. But theories of “embodied cognition” assert that cognition is all “in people’s bones.” What we know as a cup is not an abstract notion but rather the bodily experiences of our sensory and motor interactions with a cup—its shiny color, how it feels in our hands, the smoothness of its surface, its weight, and shape. I suggest that “Embodiment” is alluring because it promises to resolve the mysteries of Dualism (how can material bodies encode the immaterial notion of a cup?) and the origins of ideas (how do we all converge on an understanding that allows us to talk about the same things?). The solution is strikingly simple—just remove the “mind” from the equation. If there is no (immaterial) knowledge, then we no longer need to worry about how knowledge arises from the body and how knowledge can be learned. As discussed in the previous chapter, people erroneously believe that “if it’s in my body” then “it’s inborn.” Dualism and essentialism thus explain some of the lure of embodied cognition.