Downward causation exercised by emergent properties of wholes upon their lower-level constituents’ properties has been accused of conceptual and metaphysical incoherence. Only upward causation is usually peacefully accepted. The aim of this paper is to criticize and refuse the traditional hierarchical-vertical way of conceiving both types of causation, although preserving their deepest ontological significance, as well as the widespread acceptance of the traditional atomistic-combinatorial view of the entities and the relations that constitute the so-called ‘emergence base’. Assuming those two perspectives (...) with no reserves, we are condemned to confine our debate to the question of whether reified wholes can have the power to downwardly change or influence their lower-level parts, a question which seems profoundly misleading to me. I therefore propose an alternative relational ontological view, assuming a straightforward horizontal and intra-level way of representing those putative cases of cross-level causation. I finally confront two recent replies to Kim’s well-known objections to DC—Craver and Bechtel and Kistler :595–609, 2009)—, emphasizing their global positive approaches, as well as the reasons why their accounts still seem insufficient to me. I conclude arguing that both Kim’s principle of the causal closure of the physical domain and its allegation of an overdetermination in cases of DC can be surpassed by the new relational ontological perspective presented here. (shrink)

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. (shrink)

We sketch a framework for building a unified science of cognition. This unification is achieved by showing how functional analyses of cognitive capacities can be integrated with the multilevel mechanistic explanations of neural systems. The core idea is that functional analyses are sketches of mechanisms , in which some structural aspects of a mechanistic explanation are omitted. Once the missing aspects are filled in, a functional analysis turns into a full-blown mechanistic explanation. By this process, functional analyses are seamlessly integrated (...) with multilevel mechanistic explanations. (shrink)

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. (shrink)

Many scientists routinely generalize from study samples to larger populations. It is commonly assumed that this cognitive process of scientific induction is a voluntary inference in which researchers assess the generalizability of their data and then draw conclusions accordingly. We challenge this view and argue for a novel account. The account describes scientific induction as involving by default a generalization bias that operates automatically and frequently leads researchers to unintentionally generalize their findings without sufficient evidence. The result is unwarranted, overgeneralized (...) conclusions. We support this account of scientific induction by integrating a range of disparate findings from across the cognitive sciences that have until now not been connected to research on the nature of scientific induction. The view that scientific induction involves by default a generalization bias calls for a revision of the current thinking about scientific induction and highlights an overlooked cause of the replication crisis in the sciences. Commonly proposed interventions to tackle scientific overgeneralizations that may feed into this crisis need to be supplemented with cognitive debiasing strategies against generalization bias to most effectively improve science. (shrink)

In computational complexity theory, decision problems are divided into complexity classes based on the amount of computational resources it takes for algorithms to solve them. In theoretical computer science, it is commonly accepted that only functions for solving problems in the complexity class P, solvable by a deterministic Turing machine in polynomial time, are considered to be tractable. In cognitive science and philosophy, this tractability result has been used to argue that only functions in P can feasibly work as computational (...) models of human cognitive capacities. One interesting area of computational complexity theory is descriptive complexity, which connects the expressive strength of systems of logic with the computational complexity classes. In descriptive complexity theory, it is established that only first-order systems are connected to P, or one of its subclasses. Consequently, second-order systems of logic are considered to be computationally intractable, and may therefore seem to be unfit to model human cognitive capacities. This would be problematic when we think of the role of logic as the foundations of mathematics. In order to express many important mathematical concepts and systematically prove theorems involving them, we need to have a system of logic stronger than classical first-order logic. But if such a system is considered to be intractable, it means that the logical foundation of mathematics can be prohibitively complex for human cognition. In this paper I will argue, however, that this problem is the result of an unjustified direct use of computational complexity classes in cognitive modelling. Placing my account in the recent literature on the topic, I argue that the problem can be solved by considering computational complexity for humanly relevant problem solving algorithms and input sizes. (shrink)

A mechanistic explanation is a desired outcome in many studies of perception. Such explanations require discovering the processes that contribute to the realization of a perceptual phenomenon at different levels of information processing. The present analysis aims at investigating the obstacles to develop such mechanistic explanations and their potential solutions in the context of psychopathology. Geometric-Optical Illusions (GOIs) are among perceptual phenomena that have been studied to understand psychopathology in various clinical populations. In the present analysis, the GOIs will be (...) discussed as a methodological tool in the investigation of the mechanistic approach to understanding perception in such populations. I will argue that the fact that most such studies are mainly focused on the investigation of susceptibility to GOIs can only lead to classifying such phenomena or to reporting some correlations between different aspects of such phenomena with certain brain activities, without formulating a mechanistic explanation. The investigation of the variables that mediate perceptual phenomena at earlier stages of information processing is required for the formulation of such explanations. Thus, the objective of the present analysis is twofold: 1) showing how ignoring such variables have prevented the formulation of mechanistic explanations of responses to GOIs in clinical populations, such as schizophrenia and autism, 2) analyzing the methodological aspects of an effective approach in which a top-down guidance based on phenomenological insights (the first-person data) along with the insights provided by the existing theories help overcome obstacles in developing mechanistic explanations of GOIs. (shrink)

In this paper, I will argue that the NIMH’s new Research Domain of Criteria is a useful test of the philosophical hypothesis of eliminative materialism and demonstrates the superiority of a moderate eliminativism over integrationism, which is a rival philosophical framework for the cognitive sciences. I begin by going over the motivation for RDOC, which rests on the problems with the existing Diagnostic and Statistical Manual of Mental Disorders framework in psychiatry. Then, I introduce the main tenets of RDoC before (...) discussing eliminativism and trying to show what a moderate eliminativism looks like through the example of addiction. I then contrast that approach with intregrationism to the latter’s detriment. I end by wondering whether the broadly political consequences of eliminativism give us non-scientific reasons for resisting it, as it threatens to rob ordinary people of important means of self-description. (shrink)

In this paper, I will argue that the NIMH’s new Research Domain of Criteria is a useful test of the philosophical hypothesis of eliminative materialism and demonstrates the superiority of a moderate eliminativism over integrationism, which is a rival philosophical framework for the cognitive sciences. I begin by going over the motivation for RDOC, which rests on the problems with the existing Diagnostic and Statistical Manual of Mental Disorders framework in psychiatry. Then, I introduce the main tenets of RDoC before (...) discussing eliminativism and trying to show what a moderate eliminativism looks like through the example of addiction. I then contrast that approach with intregrationism to the latter’s detriment. I end by wondering whether the broadly political consequences of eliminativism give us non-scientific reasons for resisting it, as it threatens to rob ordinary people of important means of self-description. (shrink)

Cognitive science is an interdisciplinary conglomerate of various research fields and disciplines, which increases the risk of fragmentation of cognitive theories. However, while most previous work has focused on theoretical integration, some kinds of integration may turn out to be monstrous, or result in superficially lumped and unrelated bodies of knowledge. In this paper, I distinguish theoretical integration from theoretical unification, and propose some analyses of theoretical unification dimensions. Moreover, two research strategies that are supposed to lead to unification are (...) analyzed in terms of the mechanistic account of explanation. Finally, I argue that theoretical unification is not an absolute requirement from the mechanistic perspective, and that strategies aiming at unification may be premature in fields where there are multiple conflicting explanatory models. (shrink)

The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, I (...) argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a “checklist” for a modeler. (shrink)

The debate between the defenders of explanatory unification and explanatory pluralism has been ongoing from the beginning of cognitive science and is one of the central themes of its philosophy. Does cognitive science need a grand unifying theory? Should explanatory pluralism be embraced instead? Or maybe local integrative efforts are needed? What are the advantages of explanatory unification as compared to the benefits of explanatory pluralism? These questions, among others, are addressed in this Synthese’s special issue. In the introductory paper, (...) we discuss the background of the questions, distinguishing integrative theorizing from building unified theories. On the one hand, integrative efforts involve collaboration between various disciplines, fields, approaches, or theories. These efforts could even be quite temporary, without establishing any long-term institutionalized fields or disciplines, but could also contribute to developing new interfield theories. On the other hand, unification can rely on developing complete theories of mechanisms and representations underlying all cognition, as Newell’s “unified theories of cognition”, or may appeal to grand principles, as predictive coding. Here, we also show that unification in contemporary cognitive science goes beyond reductive unity, and may involve various forms of joint efforts and division of explanatory labor. This conclusion is one of the themes present in the content of contributions constituting the special issue. (shrink)

Recently, a number of philosophers of science have claimed that much explanation in the sciences, especially in the biomedical and social sciences, is mechanistic explanation. I argue the account of mechanistic explanation provided in this tradition has not been entirely satisfactory, as it has neglected to describe in complete detail the crucial causal structure of mechanistic explanation. I show how the interventionist approach to causation, especially within a structural equations framework, provides a simple and elegant account of the causal structure (...) of mechanisms. This account explains the many useful insights of traditional accounts of mechanism, such as Carl Craver's account in his book Explaining the Brain , but also helps to correct the omissions of such accounts. One of these omissions is the failure to provide an explicit formulation of a modularity constraint that plays a significant role in mechanistic explanation. One virtue of the interventionist/structural equations framework is that it allows for a simple formulation of a modularity constraint on mechanistic explanation. I illustrate the role of this constraint in the last section of the paper, which describes the form that mechanistic explanation takes in the computational, information-processing paradigm of cognitive psychology. (shrink)

Intentional Mentalism is the view that mental intentionality is primary to non-mental intentionality and that the latter is derived from the former. In this article I examine three views which have been taken to motivate Intentional Mentalism: conventionalism as invoked by Searle, Gricean pragmatism, and the language of thought hypothesis. I argue that none of these views motivates Intentional Mentalism, and that, in fact, the former two imply its rejection.

The experimental research paradigm lies at the core of empirical psychology. New data analytical and computational tools continually enrich its methodological arsenal, while the paradigm’s mission remains the testing of theoretical predictions and causal explanations. Predictions regarding experimental results necessarily point to the future. Once the data are collected, the causal inferences refer to a hypothesis now lying in the past. The experimental paradigm is not designed to permit strong inferences about particular incidents that occurred before predictions were made. In (...) contrast, historical research and scholarship in other humanities focus on this backward direction of inference. The disconnect between forward-looking experimental psychology and backward-looking historical (i.e., narrative) psychology is a challenge in the postmodern era, which can be addressed. To illustrate this possibility, I discuss three historical case studies in light of theory and research in contemporary psychology. (shrink)

The key idea of the interventionist account of causation is that a variable A causes a variable B if and only if B would change if A were manipulated in the appropriate way. This paper raises two problems for Woodward's (2003) version of interventionism. The first is that the conditions it imposes are not sufficient for causation, because these conditions are also satisfied by non-causal relations of nomological dependence expressed in association laws. Such laws ground a relation of mutual manipulability (...) that is incompatible with the asymmetry of causation. Several ways of defending the interventionist account are examined and found unsatisfying. The second problem is that it often seems to be impossible, in a model that contains variables linked by an association law, to satisfy the conditions imposed on interventions on such variables. Various ways to solve this second problem, most importantly the analysis of manipulability in terms of difference making, are examined. Given that none solves the problem, I conclude that the interventionist conditions are neither sufficient nor necessary for causation. It is suggested that they provide an analysis of nomological dependence, which may be supplemented with the notion of a causal process to yield an analysis of causation. (shrink)

We argue that dynamical and mathematical models in systems and cognitive neuro- science explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phe- nomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual receptive fields are (...) explored. (shrink)

Abstract While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turvey's contention that they constitute an alternative to mechanistic explanations. We review several problems dynamical models face as putative explanations when they are not grounded in mechanisms. Further, we argue that the opposition of dynamical models and mechanisms is a false one and that those dynamical models that characterize the operations of mechanisms overcome these problems. By briefly considering examples involving the generation (...) of action potentials and circadian rhythms, we show how decomposing a mechanism and modeling its dynamics are complementary endeavors. (shrink)

This paper addresses the relationship between psychological capacities, as they are understood within cognitive psychology, and neurobiological activities. First, Lycan’s (1987) account of this relationship is examined and certain problems with his account are explained. According to Lycan, psychological capacities occupy a higher level than neurobiological activities in a hierarchy of levels of nature, and psychological entities can be decomposed into neurobiological entities. After discussing some problems with Lycan’s account, a similar, more recent account built around levels of mechanisms is (...) examined (Craver 2007). In the second half of this paper, an alternative is laid out. This new account uses levels of organization and levels of explanation to create a two-dimensional model. Psychological capacities occupy a high level of explanation relative to the cellular and molecular levels of organization. As a result, according to this model, psychological capacities are a particular way of describing the activities that occur at the cellular and molecular levels of organization. (shrink)

Most contemporary philosophers of mind claim to be in search of a 'naturalistic' theory. However, when we look more closely, we find that there are a number of different and even conflicting ideas of what would count as a 'naturalization' of the mind. This article attempts to show what various naturalistic philosophies of mind have in common, and also how they differ from one another. Additionally, it explores the differences between naturalistic philosophies of mind and naturalisms found in ethics, epistemology, (...) and philosophy of science. Section 1 introduces a distinction between two types of project that have been styled 'naturalistic', which I call philosophical naturalism and empirical naturalism . Sections 2 to 6 canvass different strands of philosophical naturalism concerning the mind, followed by a much briefer discussion of attempts to provide empirical naturalizations of the mind in Section 7 . Section 8 concludes the essay with a consideration of the relations between philosophical and empirical naturalism in philosophy of mind, arguing that at least some types of philosophical naturalism are incompatible with empirical naturalism. (shrink)

Cognitive Science, Volume 46, Issue 9, September 2022.

Most philosophical accounts of causation take causal relations to obtain between individuals and events in virtue of nomological relations between properties of these individuals and events. Such views fail to take into account the consequences of the fact that in general the properties of individuals and events will depend upon mechanisms that realize those properties. In this paper I attempt to rectify this failure, and in so doing to provide an account of the causal relevance of higher-level properties. I do (...) this by critiquing one prominent model of higher-level properties—Kim’s functional model of reduction—and contrasting it with a mechanistic approach to higher-level properties and causation. (shrink)

In this paper, I argue that explaining cognitive behavior can be achieved through what I call hybrid explanatory inferences: inferences that posit mechanisms, but also draw on observed regularities. Moreover, these inferences can be used to achieve unification, in the sense developed by Allen Newel in his work on cognitive architectures. Thus, it seems that explanatory pluralism and unification do not rule out each other in cognitive science, but rather that the former represents a way to achieve the latter.

In this paper, I explicate and discuss performance-similarity reasoning as a strategy for mechanism schema evaluation, understood in Lindley Darden’s sense. This strategy involves inferring hypotheses about the mechanism responsible for cognitive capacities from premises describing the performance of those capacities; performance-similarity reasoning is a type of Inference to the Best Explanation, or IBE. Two types of such inferences are distinguished: one in which the performance of two systems is compared, and another when the performance of two systems under intervention (...) is compared. Both types of inferences are illustrated with examples taken from cognitive science. I conclude that performance-similarity reasoning is an important strategy for evaluating mechanistic hypotheses. (shrink)

A single physical process may often be described equally well as computing several different mathematical functions—none of which is explanatorily privileged. How, then, should the computational identity of a physical system be determined? Some computational mechanists hold that computation is individuated only by either narrow physical or functional properties. Even if some individuative role is attributed to environmental factors, it is rather limited. The computational semanticist holds that computation is individuated, at least in part, by semantic properties. She claims that (...) the mechanistic account lacks the resources to individuate the computations performed by some systems, thereby leaving interesting cases of computational indeterminacy unaddressed. This article examines some of these views, and claims that more cases of computational indeterminacy can be addressed, if the system-environment interaction plays a greater role in individuating computations. A new, long-arm functional strategy for individuating computation is advanced. (shrink)

It is commonly held that research efforts in the cognitive and behavioral sciences are mainly directed toward providing explanations and that phenomena figure into scientific practice qua explananda. I contend that these assumptions convey a skewed picture of the research practices in question and of the role played by phenomena. I argue that experimental research often aims at exploring and describing “objects of research” and that phenomena can figure as components of, and as evidence for, such objects. I situate my (...) analysis within the existing literature and illustrate it with examples from memory research. (shrink)

In this comment, I first point out some problems in McCauley's defense of the traditional conception of general analytical levels. Then I present certain reductionist arguments against explanatory pluralism that are not based on the New Wave model of intertheoretic reduction, against which McCauley is arguing. Reductionists that are not committed to this model might not have problems incorporating research on long-term diachronic processes in their analyses. In the last part of the paper, I briefly compare Robert N. McCauley's conception (...) of reduction to some other current accounts, highlighting the differences between them. (shrink)

Philosophers almost universally believe that concepts of supervenience fail to satisfy the standards for physicalism because they offer mere property correlations that are left unexplained. They are thus compatible with non-physicalist accounts of those relations. Moreover, many philosophers not only prefer some kind of functional-role theory as a physically acceptable account of mind-body and other inter-level relations, but they use it as a form of “superdupervenience” to explain supervenience in a physically acceptable way. But I reject a central part of (...) this common narrative. I argue that functional-role theories fail by the same standards for physicalism because they merely state without explaining how a physical property plays or occupies a functional role. They are thus compatible with non-physicalist accounts of that role-occupying relation. I also argue that one cannot redeploy functional-role theory at a deeper level to explain role occupation, specifically by iterating the role-occupant scheme. Instead, one must use part-whole structural and mechanistic explanations that differ from functional-role theory in important ways. These explanations represent a form of “superduperfunctionalism” that stand to functional-role theory as concepts of superdupervenience stand to concepts of supervenience. (shrink)

It is generally accepted that, in the cognitive and neural sciences, there are both computational and mechanistic explanations. We ask how computational explanations can integrate into the mechanistic hierarchy. The problem stems from the fact that implementation and mechanistic relations have different forms. The implementation relation, from the states of an abstract computational system to the physical, implementing states is a homomorphism mapping relation. The mechanistic relation, however, is that of part/whole; the explaining features in a mechanistic explanation are the (...) components of the explanandum phenomenon and their causal organization. Moreover, each component in one level of mechanism is constituted and explained by components of an underlying level of mechanism. Hence, it seems, computational variables and functions cannot be mechanistically explained by the medium-dependent states and properties that implement them. How then, do the computational and the implementational integrate to create the mechanistic hierarchy? After explicating the general problem, we further demonstrate it through a concrete example, of reinforcement learning, in the cognitive and neural sciences. We then examine two possible solutions. On one solution, the mechanistic hierarchy embeds at the same levels computational and implementational properties. This picture fits with the view that computational explanations are mechanistic sketches. On the other solution, there are two separate hierarchies, one computational and another implementational, which are related by the implementation relation. This picture fits with the view that computational explanations are functional and autonomous explanations. It is less clear how these solutions fit with the view that computational explanations are full-fledged mechanistic explanations. Finally, we argue that both pictures are consistent with the reinforcement learning example, but that scientific practice does not align with the view that computational models are merely mechanistic sketches. (shrink)

A popular view presents explanations in the cognitive sciences as causal or mechanistic and argues that an important feature of such explanations is that they allow us to manipulate and control the explanandum phenomena. Nonetheless, whether there can be explanations in the cognitive sciences that are neither causal nor mechanistic is still under debate. Another prominent view suggests that both causal and non-causal relations of counterfactual dependence can be explanatory, but this view is open to the criticism that it is (...) not clear how to distinguish explanatory from non-explanatory relations. In this paper, I draw from both views and suggest that, in the cognitive sciences, relations of counterfactual dependence that allow manipulation and control can be explanatory even when they are neither causal nor mechanistic. Furthermore, the ability to allow manipulation can determine whether non-causal counterfactual dependence relations are explanatory. I present a preliminary framework for manipulation relations that includes some non-causal relations and use two examples from the cognitive sciences to show how this framework distinguishes between explanatory and non-explanatory, non-causal relations. The proposed framework suggests that, in the cognitive sciences, causal and non-causal relations have the same criterion for explanatory value, namely, whether or not they allow manipulation and control. (shrink)

Although a reactive framework has long been dominant in cognitive science and neuroscience, an alternative framework emphasizing dynamics and endogenous activity has recently gained prominence. We review some of the evidence for endogenous activity and consider the implications not only for understanding cognition but also for accounts of explanation offered by philosophers of science. Our recent characterization of dynamic mechanistic explanation emphasizes the coordination of accounts of mechanisms that identify parts and operations with computational models of their activity. These can, (...) and should, be extended to incorporate attention to mechanisms that are not only active, but endogenously active. (shrink)

The creative aspect of language use provides a set of phenomena that a science of language must explain. It is the “central fact to which any signi- ficant linguistic theory must address itself” and thus “a theory of language that neglects this ‘creative’ aspect is of only marginal interest” (Chomsky 1964: 7–8). Therefore, the form and explanatory depth of linguistic science is restricted in accordance with this aspect of language. In this paper, the implications of the creative aspect of language (...) use for a scientific theory of language will be discussed, noting the possible further implications for a science of the mind. It will be argued that a corollary of the creative aspect of language use is that a science of language can study the mechanisms that make language use possible, but that such a science cannot explain how these mechanisms enter into human action in the form of language use. (shrink)

This dissertation explores issues in the philosophy of psychology and metaphysics through the lens of the emotion of disgust, and its corresponding property, disgustingness. The first chapter organizes an extremely large body of data about disgust, imposes two constraints any theory must meet, and offers a cognitive model of the mechanisms underlying the emotion. The second chapter explores the evolution of disgust, and argues for the Entanglement thesis: this uniquely human emotion was formed when two formerly distinct mechanisms, one dedicated (...) to monitoring food intake and protecting against poisons, the other dedicated to protecting against parasitic infection, where driven together until they became functionally integrated. The third chapter explores the sorts of acquisition mechanisms that could give rise to the patterns of individual and cultural level variation we find with disgust elicitors. It argues for the Empathic Acquisition thesis, which holds that one important route for the social acquisition and transmission of disgust elicitors is linked to empathic recognition of facial expressions of the emotion. The fourth chapter builds on the Entanglement thesis, and embeds the emotion of disgust in gene-culture coevolutionary theory and the tribal instincts hypothesis. The Co-opt thesis is defended, which maintains that disgust was co-opted to play an important role in our moral psychology, particularly in our cognition of social norms and ethnic boundary markers. In doing so, however, it brings to bear many features initially linked to poisons and parasites. This explains the puzzling and troublesome character of moral judgments linked to disgust. After shifting gears from psychology to metaphysics, the fifth chapter recasts the Humean tradition of projectivism in the terminology of cognitive science. Using examples such as disgust, I argue that a psychologized projectivism is able to make sense of the idea that some properties are projected onto the world, rather than found there to begin with. The final chapter criticizes three other accounts of the property of disgustingness, two inspired by functionalism in the philosophy of color, one inspired by fittingness accounts in metaethics. I argue that none provide nearly as satisfactory account of the property as the psychologized projectivism articulated previously. (shrink)

According to a classic position in analytic philosophy of mind, we must interpret agents as largely rational in order to be able to attribute intentional mental states to them. However, adopting this position requires clarifying in what way and by which criteria agents can still be irrational. In this paper I will offer one such criterion. More specifically, I argue that the kind of rationality methodologically required by intentional interpretation is to be specified in terms of psychological efficacy. Thereby, this (...) notion can be distinguished from a more commonly used notion of rationality and hence cannot be shown to be undermined by the potential prevalence of a corresponding kind of irrationality. (shrink)

It has been distinguished between unified cognitive architecture and multiple-process architecture. Based on this distinction, we will try to show that if processes coordination criteria are explicated and analyzed, double-process theories for reasoning and decision making have difficulties to consolidate as multiple process architecture.

Models of memory in cognitive science and philosophy have traditionally explained human remembering in terms of storage and retrieval. This tendency has been entrenched by reliance on computationalist explanations over the course of the twentieth century; even research programs that eschew computationalism in name, or attempt the revision of traditional models, demonstrate tacit commitment to computationalist assumptions. It is assumed that memory must be stored by means of an isomorphic trace, that memory processes must divide into conceptually distinct systems and (...) phases, and that human remembering consists in inner, cognitive processes that are implemented by distinct neural processes. This dissertation draws on recent empirical work, and on philosophical arguments from Ludwig Wittgenstein and others, to demonstrate that this latent computationalism in the study of memory is problematic, and that it can and should be eliminated. Cognitive psychologists studying memory have encountered numerous data in recent decades that belie archival models. In cognitive neuroscience, establishing the neural basis of storage and retrieval processes has proven elusive. A number of revised models on offer in memory science, that have taken these issues into account, fail to sufficiently extricate the archival framework. Several impasses in memory science are products of these underlying computationalist assumptions. Wittgenstein and other philosophers offer a number of arguments against the need for, and the efficacy of, the storage and retrieval of traces in human remembering. A study of these arguments clarifies the ways that these computationalist assumptions are presently impeding the science of memory, and provides ways forward in removing them. We can and should characterize and model human memory without invoking the storage and retrieval of traces. A range of work in connectionism, dynamical systems theory, and recent philosophical accounts of memory demonstrate how the science of memory can proceed without these assumptions, toward non-archival models of remembering. (shrink)

According to the sensorimotor account, vision does not imply theconstruction of internally generated representations of the environment, butit isthe skillful exercise of the sensorimotor contingencies obeying sense-specific laws. In this short study, I focus on the notion of “sensorimotor law” and characterize the kind of explanation providedby the sensorimotor theory as a form of covering law model. I then question the nature of such sensorimotor laws and describe them as mechanisms. I show that a mechanistic interpretation provides a better account (...) of the sensorimotor invariances, which fosters us to rebalance the explanatory burden of sensorimotor action and information. Finally, I show that the question of the roleof representations within the sensorimotor theory should be reconsidered. (shrink)

Psychologists and philosophers have not yet resolved what they take implicit attitudes to be; and, some, concerned about limitations in the psychometric evidence, have even challenged the predictive and theoretical value of positing implicit attitudes in explanations for social behavior. In the midst of this debate, prominent stakeholders in science have called for scientific communities to recognize and countenance implicit bias in STEM fields. In this paper, I stake out a stakeholder conception of implicit bias that responds to these challenges (...) in ways that are responsive to the psychometric evidence, while also being resilient to the sorts of disagreements and scientific progress that would not undermine the soundness of this call. Along the way, my account advocates for attributing collective (group-level) implicit attitudes rather than individual-level implicit attitudes. This position raises new puzzles for future research on the relationship (metaphysical, epistemic, and ethical) between collective implicit attitudes and individual-level attitudes. (shrink)

As much as assumptions about mechanisms and mechanistic explanation have deeply affected psychology, they have received disproportionately little analysis in philosophy. After a historical survey of the influences of mechanistic approaches to explanation of psychological phenomena, we specify the nature of mechanisms and mechanistic explanation. Contrary to some treatments of mechanistic explanation, we maintain that explanation is an epistemic activity that involves representing and reasoning about mechanisms. We discuss the manner in which mechanistic approaches serve to bridge levels rather than (...) reduce them, as well as the different ways in which mechanisms are discovered. Finally, we offer a more detailed example of an important psychological phenomenon for which mechanistic explanation has provided the main source of scientific understanding. (shrink)

There is an increasing push by journals to ensure that data and products related to published papers are shared as part of a cultural move to promote transparency, reproducibility, and trust in the scientific literature. Yet few journals commit to evaluating their effectiveness in implementing reporting standards aimed at meeting those goals (1, 2). Similarly, though the vast majority of journals endorse peer review as an approach to ensure trust in the literature, few make their peer review data available to (...) evaluate effectiveness toward achieving concrete measures of quality, including consistency and completeness in meeting reporting standards. Remedying these apparent disconnects is critical for closing the gap between guidance recommendations and actual reporting behavior. We see this as a collective action problem requiring leadership and investment by publishers, who can be incentivized through mechanisms that allow them to manage reputational risk and through continued innovation in journal assessment. (shrink)