Gualtiero Piccinini

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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..

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.

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.

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

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

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.

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.

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.

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.