Gualtiero Piccinini

We outline a framework of multilevel neurocognitive mechanisms that incorporates representation and computation. We argue that paradigmatic explanations in cognitive neuroscience fit this framework and thus that cognitive neuroscience constitutes a revolutionary break from traditional cognitive science. Whereas traditional cognitive scientific explanations were supposed to be distinct and autonomous from mechanistic explanations, neurocognitive explanations aim to be mechanistic through and through. Neurocognitive explanations aim to integrate computational and representational functions and structures across multiple levels of organization in order to explain cognition. To a large extent, practicing cognitive neuroscientists have already accepted this shift, but philosophical theory has not fully acknowledged and appreciated its significance. As a result, the explanatory framework underlying cognitive neuroscience has remained largely implicit. We explicate this framework and demonstrate its contrast with previous approaches

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism and connectionism on the other. We defend the relevance to cognitive science of both computation, in a generic sense that we fully articulate for the first time, and information processing, in three important senses of the term. Our account advances some foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects.

The received view is that computational states are individuated at least in part by their semantic properties. I offer an alternative, according to which computational states are individuated by their functional properties. Functional properties are specified by a mechanistic explanation without appealing to any semantic properties. The primary purpose of this paper is to formulate the alternative view of computational individuation, point out that it supports a robust notion of computational explanation, and defend it on the grounds of how computational states are individuated within computability theory and computer science. A secondary purpose is to show that existing arguments for the semantic view are defective.

Roughly speaking, computationalism says that cognition is computation, or that cognitive phenomena are explained by the agent‘s computations. The cognitive processes and behavior of agents are the explanandum. The computations performed by the agents‘ cognitive systems are the proposed explanans. Since the cognitive systems of biological organisms are their nervous 1 systems (plus or minus a bit), we may say that according to computationalism, the cognitive processes and behavior of organisms are explained by neural computations. Some people might prefer to say that cognitive systems are ―realized‖ by nervous systems, and thus that—according to computationalism—cognitive computations are ―realized‖ by neural processes. In this paper, nothing hinges on the nature of the relation between cognitive systems and nervous systems, or between computations and neural processes. For present purposes, if a neural process realizes a computation, then that neural process is a computation. Thus, I will couch much of my discussion in terms of nervous systems and neural computation.1 Before proceeding, we should dispense with a possible red herring. Contrary to a common assumption, computationalism does not stand in opposition to connectionism. Connectionism, in the most general and common sense of the term, is the claim that cognitive phenomena are explained (at some level and at least in part) by the processes of neural networks. This is a truism, supported by most neuroscientific evidence. Everybody ought to be a connectionist in this general sense. The relevant question is, are neural processes computations? More precisely, are the neural processes to be found in the nervous systems of organisms computations? Computationalists say ―yes‖, anti-computationalists say ―no‖. This paper investigates whether any of the arguments on offer against computationalism have a chance at knocking it off.2 Ever since Warren McCulloch and Walter Pitts (1943) first proposed it, computationalism has been subjected to a wide range of objections..

According to some philosophers, computational explanation is proprietary
to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that are being developed by a number of authors.

I argue that neural activity, strictly speaking, is not computation. This is because computation, strictly speaking, is the processing of strings of symbols, and neuroscience shows that there are no neural strings of symbols. This has two consequences. On the one hand, the following widely held consequences of computationalism must either be abandoned or supported on grounds independent of computationalism: (i) that in principle we can capture what is functionally relevant to neural processes in terms of some formalism taken from computability theory (such as Turing Machines), (ii) that it is possible to design computer programs that are functionally equivalent to neural processes in the same sense in which it is possible to design computer programs that are functionally equivalent to each other, (iii) that the study of neural (or mental) computation is independent of the study of neural implementation, (iv) that the Church-Turing thesis applies to neural activity in the sense in which it applies to digital computers. On the other hand, we need to gradually reinterpret or replace computational theories in psychology in terms of theoretical constructs that can be realized by known neural processes, such as the spike trains of neuronal ensembles.

I argue that metaphysicians of mind have not done justice to the notion of accessibility between possible worlds. Once accessibility is given its due, physicalism must be reformulated and conceivability arguments must be reevaluated. To reach these conclusions, I explore a novel way of assessing the zombie conceivability argument. I accept that zombies are possible and ask whether that possibility is accessible from our world in the sense of ‘accessible’ used in possible world semantics. It turns out that the question whether zombie worlds are accessible from our world is equivalent to the question whether physicalism is true at our world. By assuming that zombie worlds are accessible from our world, proponents of the zombie conceivability argument beg the question against physicalism. In other words, it is a mistake to assume that the metaphysical possibility of zombies entails that physicalism is false at our world. I will then consider what happens if a proponent of the zombie conceivability argument should insist that zombie worlds are accessible from our world. I will argue that the same ingredients used in the zombie conceivability argument—whatever exactly they might be—can be used to construct an argument to the opposite conclusion. At that point, we reach a stalemate between physicalism and property dualism: while the possibility of zombies entails property dualism, the possibility of other creatures entails physicalism. Since these two possibilities are mutually inconsistent, either one of them is not genuine or one of them is inaccessible from the actual world. To resolve this stalemate, we need more than traditional conceivability arguments.

This paper samples the large body of neuroscientific evidence suggesting that each mental function takes place within specific neural structures. For instance, vision appears to occur in the visual cortex, motor control in the motor cortex, spatial memory in the hippocampus, and cognitive control in the prefrontal cortex. Evidence comes from neuroanatomy, neurophysiology, neurochemistry, brain stimulation, neuroimaging, lesion studies, and behavioral genetics. If mental functions take place within neural structures, mental functions cannot survive brain death. Therefore, there is no mental life after brain death. 1. The Neural Localization of Mental Functions - 1.1 Perception and Motor Control - 1.2 Memory - 1.3 Emotion - 1.4 Language - 1.5 Thinking - 1.6 Attention and Consciousness - 1.7 Spirituality -- 2. Objections - 2.1 Linguistic Dualism - 2.2 Mere Correlation - 2.3 Neural Plasticity - 2.4 Intentionality - 2.5 Phenomenal Consciousness - 2.6 Subjectivity - 2.7 Self-Knowledge - 2.8 Free Will - 2.9 Are We Just Indulging in Physicalistic Wishful Thinking? -- 3. Conclusion -- Appendix: Physicalism and the Afterlife

Newell was a founder of artificial intelligence and a pioneer in the use of computer simulations in psychology. In collaboration with J. Cliff Shaw and Herbert A. Simon, Newell developed the first list-processing programming language as well as the earliest computer programs for simulating human problem solving. Over a long and prolific career, he contributed to many techniques, such as protocol analysis and heuristic search, that are now part of psychology and computer science. Colleagues remembered Newell for his deep commitment to science, his care for details, and his inexhaustible energy

First-person data have been both condemned and hailed because of their alleged privacy. Critics argue that science must be based on public evidence: since first-person data are private, they should be banned from science. Apologists reply that first-person data are necessary for understanding the mind: since first-person data are private, scientists must be allowed to use private evidence. I argue that both views rest on a false premise. In psychology and neuroscience, the subjects issuing first-person reports and other sources of first-person data play the epistemic role of a (self-) measuring instrument. Data from measuring instruments are public and can be validated by public methods. Therefore, first-person data are as public as other scientific data: their use in science is legitimate, in accordance with standard scientific methodology.

Defending or attacking either functionalism or computationalism requires clarity on what they amount to and what evidence counts for or against them. My goal here is not to evaluate their plausibility. My goal is to formulate them and their relationship clearly enough that we can determine which type of evidence is relevant to them. I aim to dispel some sources of confusion that surround functionalism and computationalism, recruit recent philosophical work on mechanisms and computation to shed light on them, and clarify how functionalism and computationalism may or may not legitimately come together.

I offer an explication of the notion of computer, grounded in the practices of computability theorists and computer scientists. I begin by explaining what distinguishes computers from calculators. Then, I offer a systematic taxonomy of kinds of computer, including hard-wired versus programmable, general-purpose versus special-purpose, analog versus digital, and serial versus parallel, giving explicit criteria for each kind. My account is mechanistic: which class a system belongs in, and which functions are computable by which system, depends on the system's mechanistic properties. Finally, I briefly illustrate how my account sheds light on some issues in the history and philosophy of computing as well as the philosophy of mind.

As our data will show, negative existential sentences containing socalled empty names evoke the same strong semantic intuitions in ordinary speakers and philosophers alike.Santa Claus does not exist.Superman does not exist.Clark Kent does not exist.Uttering the sentences in (1) seems to say something truth-evaluable, to say something true, and to say something different for each sentence. A semantic theory ought to explain these semantic intuitions.The intuitions elicited by (1) are in apparent conflict with the Millian view of proper names. According to Millianism, the meaning (or 'semantic value') of a proper name is just its referent. But empty names, such as 'Santa Claus' and 'Superman', appear to lack a ..

We sketch a novel and improved version of Boorse’s biostatistical theory of functions. Roughly, our theory maintains that (i) functions are non-negligible contributions to survival or inclusive fitness (when a trait contributes to survival or inclusive fitness); (ii) situations appropriate for the performance of a function are typical situations in which a trait contributes to survival or inclusive fitness; (iii) appropriate rates of functioning are rates that make adequate contributions to survival or inclusive fitness (in situations appropriate for the performance of that function); and (iv) dysfunction is the inability to perform a function at an appropriate rate in appropriate situations. Based on our theory, we sketch solutions to three problems that have afflicted Boorse’s theory of function, namely, Kingma’s ([2010]) problem of the situation-specificity of functions, the problem of multi-functional traits, and the problem of how to distinguish between appropriate and inappropriate rates of functioning.

Introspective reports are used as sources of information about other minds, in both everyday life and science. Many scientists and philosophers consider this practice unjustified, while others have made the untestable assumption that introspection is a truthful method of private observation. I argue that neither skepticism nor faith concerning introspective reports are warranted. As an alternative, I consider our everyday, commonsensical reliance on each other’s introspective reports. When we hear people talk about their minds, we neither refuse to learn from nor blindly accept what they say. Sometimes we accept what we are told, other times we reject it, and still other times we take the report, revise it in light of what we believe, then accept the modified version. Whatever we do, we have (implicit) reasons for it. In developing a sound methodology for the scientific use of introspective reports, we can take our commonsense treatment of introspective reports and make it more explicit and rigorous. We can discover what to infer from introspective reports in a way similar to how we do it every day, but with extra knowledge, methodological care, and precision. Sorting out the use of introspective reports as sources of data is going to be a painstaking, piecemeal task, but it promises to enhance our science of the mind and brain.

ABSTRACT:Determining the biological function of phenomenal consciousness appears necessary to explain its origin: evolution by natural selection operates on organisms’ traits based on the biological functions they fulfill. But identifying the function of phenomenal consciousness has proven difficult. Some have proposed that the function of phenomenal consciousness is to facilitate mental processes such as reasoning or learning. But mental processes such as reasoning and learning seem to be possible in the absence of phenomenal consciousness. It is difficult to pinpoint in what way phenomenal consciousness enhances these processes or others like them. In this paper, we explore a possibility that has been neglected to date. Perhaps phenomenal consciousness has no function of its own because it is either a by-product of other traits or a (functionless) accident. If so, then phenomenal consciousness has an evolutionary explanation even though it fulfills no biological function.

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.

Heterophenomenology is a third-person methodology proposed by Daniel Dennett for using first-person reports as scientific evidence. I argue that heterophenomenology can be improved by making six changes: (i) setting aside consciousness, (ii) including other sources of first-person data besides first-person reports, (iii) abandoning agnosticism as to the truth value of the reports in favor of the most plausible assumptions we can make about what can be learned from the data, (iv) interpreting first-person reports (and other first-person behaviors) directly in terms of target mental states rather than in terms of beliefs about them, (v) dropping any residual commitment to incorrigibility of first-person reports, and (vi) recognizing that thirdperson methodology does have positive effects on scientific practices. When these changes are made, heterophenomenology turns into the self-measurement methodology of firstperson data that I have defended in previous papers.

This article defends a modest version of the Physical Church-Turing thesis (CT). Following an established recent trend, I distinguish between what I call Mathematical CT—the thesis supported by the original arguments for CT—and Physical CT. I then distinguish between bold formulations of Physical CT, according to which any physical process—anything doable by a physical system—is computable by a Turing machine, and modest formulations, according to which any function that is computable by a physical system is computable by a Turing machine. I argue that Bold Physical CT is not relevant to the epistemological concerns that motivate CT and hence not suitable as a physical analog of Mathematical CT. The correct physical analog of Mathematical CT is Modest Physical CT. I propose to explicate the notion of physical computability in terms of a usability constraint, according to which for a process to count as relevant to Physical CT, it must be usable by a finite observer to obtain the desired values of a function. Finally, I suggest that proposed counterexamples to Physical CT are still far from falsifying it because they have not been shown to satisfy the usability constraint.

Computationalism says that brains are computing mechanisms, that is, mechanisms that perform computations. At present, there is no consensus on how to formulate computationalism precisely or adjudicate the dispute between computationalism and its foes, or between different versions of computationalism. An important reason for the current impasse is the lack of a satisfactory philosophical account of computing mechanisms. The main goal of this dissertation is to offer such an account.
I also believe that the history of computationalism sheds light on the current debate. By tracing different versions of computationalism to their common historical origin, we can see how the current divisions originated and understand their motivation. Reconstructing debates over computationalism in the context of their own intellectual history can contribute to philosophical progress on the relation between brains and computing mechanisms and help determine how brains and computing mechanisms are alike, and how they differ. Accordingly, my dissertation is divided into a historical part, which traces the early history of computationalism up to 1946, and a philosophical part, which offers an account of computing mechanisms.
The two main ideas developed in this dissertation are that (1) computational states are to be identified functionally not semantically, and (2) computing mechanisms are to be studied by functional analysis. The resulting account of computing mechanism, which I call the functional account of computing mechanisms, can be used to identify computing mechanisms and the functions they compute. I use the functional account of computing mechanisms to taxonomize computing mechanisms based on their different computing power, and I use this taxonomy of computing mechanisms to taxonomize different versions of computationalism based on the functional properties that they ascribe to brains. By doing so, I begin to tease out empirically testable statements about the functional organization of the brain that different versions of computationalism are committed to. I submit that when computationalism is reformulated in the more explicit and precise way I propose, the disputes about computationalism can be adjudicated on the grounds of empirical evidence from neuroscience.

A common presupposition in the concepts literature is that concepts constitute a sin- gular natural kind. If, on the contrary, concepts split into more than one kind, this literature needs to be recast in terms of other kinds of mental representation. We offer two new arguments that concepts, in fact, divide into different kinds: (a) concepts split because different kinds of mental representation, processed independently, must be posited to explain different sets of relevant phenomena; (b) concepts split because different kinds of mental representation, processed independently, must be posited to explain responses to different kinds of category. Whether these arguments are sound remains an open empirical question, to be resolved by future empirical and theoretical work.