Gualtiero Piccinini

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: According to the Veridicality Thesis, information requires truth. On this view, smoke carries information about there being a fire only if there is a fire, the proposition that the earth has two moons carries information about the earth having two moons only if the earth has two moons, and so on. We reject this Veridicality Thesis. We argue that the main notions of information used in cognitive science and computer science allow A to have information about the obtaining of p even when p is false.

We situate the debate on intentionality within the rise of cognitive neuroscience and argue that cognitive neuroscience can explain intentionality. We discuss the explanatory significance of ascribing intentionality to representations. At first, we focus on views that attempt to render such ascriptions naturalistic by construing them in a deflationary or merely pragmatic way. We then contrast these views with staunchly realist views that attempt to naturalize intentionality by developing theories of content for representations in terms of information and biological function. We echo several other philosophers by arguing that these theories over-generalize unless they are constrained by a theory of the functional role of representational vehicles. This leads to a discussion of the functional roles of representations, and how representations might be realized in the brain. We argue that there’s work to be done to identify a distinctively mental kind of representation. We close by sketching a way forward for the project of naturalizing intentionality. This will not be achieved simply by ascribing the content of mental states to generic neural representations, but by identifying specific neural representations that explain the puzzling intentional properties of mental states.

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.

In the 1950s, Alan Turing proposed his influential test for machine intelligence, which involved a teletyped dialogue between a human player, a machine, and an interrogator. Two readings of Turing's rules for the test have been given. According to the standard reading of Turing's words, the goal of the interrogator was to discover which was the human being and which was the machine, while the goal of the machine was to be indistinguishable from a human being. According to the literal reading, the goal of the machine was to simulate a man imitating a woman, while the interrogator – unaware of the real purpose of the test – was attempting to determine which of the two contestants was the woman and which was the man. The present work offers a study of Turing's rules for the test in the context of his advocated purpose and his other texts. The conclusion is that there are several independent and mutually reinforcing lines of evidence that support the standard reading, while fitting the literal reading in Turing's work faces severe interpretative difficulties. So, the controversy over Turing's rules should be settled in favor of the standard reading.

I address whether neural networks perform computations in the sense of computability theory and computer science. I explicate and defend
the following theses. (1) Many neural networks compute—they perform computations. (2) Some neural networks compute in a classical way.
Ordinary digital computers, which are very large networks of logic gates, belong in this class of neural networks. (3) Other neural networks
compute in a non-classical way. (4) Yet other neural networks do not perform computations. Brains may well fall into this last class.

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.

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.

The following three theses are inconsistent: (1) (Paradigmatic) connectionist systems perform computations. (2) Performing computations requires executing programs. (3) Connectionist systems do not execute programs. Many authors embrace (2). This leads them to a dilemma: either connectionist systems execute programs or they don't compute. Accordingly, some authors attempt to deny (1), while others attempt to deny (3). But as I will argue, there are compelling reasons to accept both (1) and (3). So, we should replace (2) with a more satisfactory account of computation. Once we do, we can see more clearly what is peculiar to connectionist computation

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.

This paper offers an account of what it is for a physical system to be a computing mechanism—a mechanism that performs computations. A computing mechanism is any mechanism whose functional analysis ascribes it the function of generating outputs strings from input strings in accordance with a general rule that applies to all strings. This account is motivated by reasons that are endogenous to the philosophy of computing, but it may also be seen as an application of recent literature on mechanisms. The account can be used to individuate computing mechanisms and the functions they compute and to taxonomize computing mechanisms based on their computing power. This makes it ideal for grounding the comparison and assessment of computational theories of mind and brain.

We propose a novel account of the distinction between innate and acquired biological traits: biological traits are innate to the degree that they are caused by factors intrinsic to the organism at the time of its origin; they are acquired to the degree that they are caused by factors extrinsic to the organism. This account borrows from recent work on causation in order to make rigorous the notion of quantitative contributions to traits by different factors in development. We avoid the pitfalls of previous accounts and argue that the distinction between innate and acquired traits is scientifically useful. We therefore address not only previous accounts of innateness but also skeptics about any account. The two are linked, in that a better account of innateness also enables us better to address the skeptics.

It’s fashionable to maintain that in the near future we can become immortal by uploading our minds to artificial computers. Mind uploading requires three assumptions: that we can construct realistic computational simulations of human brains; that realistic computational simulations of human brains would have conscious minds like those possessed by the brains being simulated; that the minds of the simulated brains survive through the simulation. I will argue that the first two assumptions are implausible and the third is false. Therefore, we will not upload our mind to computers and, most likely, we will not upload anything resembling our mind to computers.

According to pancomputationalism, all physical systems – atoms, rocks, hurricanes, and toasters – perform computations. Pancomputationalism seems to be increasingly popular among some philosophers and physicists. In this paper, we interpret pancomputationalism in terms of computational descriptions of varying strength—computational interpretations of physical microstates and dynamics that vary in their restrictiveness. We distinguish several types of pancomputationalism and identify essential features of the computational descriptions required to support them. By tying various pancomputationalist theses directly to notions of what counts as computation in a physical system, we clarify the meaning, strength, and plausibility of pancomputationalist claims. We show that the force of these claims is diminished when weaknesses in their supporting computational descriptions are laid bare. Specifically, once computation is meaningfully distinguished from ordinary dynamics, the most sensational pancomputationalist claims are unwarranted, whereas the more modest claims offer little more than recognition of causal similarities between physical processes and the most primitive computing processes.

We provide an explicit taxonomy of legitimate kinds of abstraction within constitutive explanation. We argue that abstraction is an inherent aspect of adequate mechanistic explanation. Mechanistic explanations—even ideally complete ones—typically involve many kinds of abstraction and therefore do not require maximal detail. Some kinds of abstraction play the ontic role of identifying the specific complex components, subsets of causal powers, and organizational relations that produce a suitably general phenomenon. Therefore, abstract constitutive explanations are both legitimate and mechanistic.

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

I argue that wholes are neither identical to nor distinct from their parts. Instead, wholes are invariants under some transformations in their parts. Similarly, higher-level properties are neither identical to nor distinct from their lower-level realizers. Instead, higher-level properties are aspects of their realizers that are invariant under some transformations in their realizers. Nowhere in this picture is there any ontological hierarchy between levels of composition or realization. Neither wholes nor their parts are more fundamental. Neither is prior. Neither reduces to the other. Neither is eliminated. Ditto for higher-level properties and their lower-level realizers. Instead, wholes and their properties as well as parts and their properties are different yet invariant aspects of reality. The result is an egalitarian account of composition and realization.

Knowledge is factually grounded belief. This account uses the same ingredients as the traditional analysis—belief, truth, and justification—but posits a different relation between them. While the traditional analysis begins with true belief and improves it by simply adding justification, this account begins with belief, improves it by grounding it, and then improves it further by grounding it in the facts. In other words, for a belief to be knowledge, it's not enough that it be true and justified; for a belief to be knowledge, it must be justified by the facts. This account solves the Gettier problem. Gettierized beliefs fall short of knowledge because, albeit true and justified, they are not grounded in the facts. This account also elucidates why knowledge attributions are sensitive to epistemic standards. It's because whether we take a belief to be grounded in the facts is sensitive to epistemic standards.

Gualtiero Piccinini articulates and defends a mechanistic account of concrete, or physical, computation. A physical system is a computing system just in case it is a mechanism one of whose functions is to manipulate vehicles based solely on differences between different portions of the vehicles according to a rule defined over the vehicles. Physical Computation discusses previous accounts of computation and argues that the mechanistic account is better. Many kinds of computation are explicated, such as digital vs. analog, serial vs. parallel, neural network computation, program-controlled computation, and more. Piccinini argues that computation does not entail representation or information processing although information processing entails computation. Pancomputationalism, according to which every physical system is computational, is rejected. A modest version of the physical Church-Turing thesis, according to which any function that is physically computable is computable by Turing machines, is defended.

We define mereologically invariant composition as the relation between a whole object and its parts when the object retains the same parts during a time interval. We argue that mereologically invariant composition is identity between a whole and its parts taken collectively. Our reason is that parts and wholes are equivalent measurements of a portion of reality at different scales in the precise sense employed by measurement theory. The purpose of these scales is the numerical representation of primitive relations between quantities of being. To show this, we prove representation and uniqueness theorems for composition. Thus, mereologically invariant composition is trans-scalar identity.

We argue that there are two different kinds of altruistic motivation: classical psychological altruism, which generates ultimate desires to help other organisms at least partly for those organisms’ sake, and nonclassical psychological altruism, which generates ultimate desires to help other organisms for the sake of the organism providing the help. We then argue that classical psychological altruism is adaptive if the desire to help others is intergenerationally reliable and, thus, need not be learned. Nonclassical psychological altruism is adaptive when the desire to help others is adaptively learnable. This theory opens new avenues for the interdisciplinary study of psychological altruism.