Experimental investigation of mechanisms seems to make use of causal relations that cut across levels of composition. In bottom-up experiments, one intervenes on parts of a mechanism to observe the whole; in top-down experiments, one intervenes on the whole mechanism to observe certain parts of it. It is controversial whether such experiments really make use of interlevel causation, and indeed whether the idea of causation across levels is even conceptually coherent. Craver and Bechtel have suggested that interlevel causal claims can (...) be analysed in a causal and a non-causal component. I accept this idea but argue that their account should be modified so as to account of cases of apparent downward causation. First, constitution must be distinguished from identity; second, the analysis of downward causation requires the concept of a partial constraint. An analysis along these lines shows that the possibility of downward causation is not refuted by Kim's argument according to which it is incompatible with the completeness of physics. (shrink)

Accounts of mechanistic explanation have emphasized the importance of looking down—decomposing a mechanism into its parts and operations. Using research on visual processing as an exemplar, I illustrate how productive such research has been. But once multiple components of a mechanism have been identified, researchers also need to figure out how it is organized—they must look around and determine how to recompose the mechanism. Although researchers often begin by trying to recompose the mechanism in terms of sequential operations, they frequently (...) find that the components of a mechanism interact in complex ways involving positive and negative feedback and that the organization often exhibits highly interactive local networks linked by a few long-range connections (small-worlds organization) and power law distributions of connections. The mechanisms are themselves active systems that are perturbed by inputs but not set in motion by them. Researchers also need to look up —situate a mechanism in its context, which may be a larger mechanism that modulates its behavior. When looking down is combined with looking around and up, mechanistic research results in an integrated, multi-level perspective. (shrink)

Prominent research programs dealing with the nature and mechanisms of interpersonal social coordination have emerged in cognitive science, developmental psychology and evolutionary anthropology. I argue that the mechanistic approach to explanation in contemporary philosophy of science can facilitate interdisciplinary integration and division of labor between these different disciplinary research programs. By distinguishing phenomenal models from mechanistic models and structural decomposition from functional decomposition in the process of mechanism discovery, I argue that behavioral and cognitive scientists can make interlocking contributions to (...) families of mechanism models representing a) what central forms of interpersonal social coordination consist in, b) what types of agents engage in such forms of coordination, c) what types of cognitive mechanisms give rise to such forms of coordination, d) how the operation of such mechanisms is modulated by the natural and social environments that the agents populate. Thus mechanistic philosophy of science can play a heuristic role in guiding the research strategies of scientists in different scientific disciplines in a manner that is complementary to the research strategies of scientists in other disciplines and thereby efficient from the point of view of the division of epistemic labor. (shrink)

This book contains a selection of the papers presented at the Logic, Reasoning and Rationality 2010 conference in Ghent. The conference aimed at stimulating the use of formal frameworks to explicate concrete cases of human reasoning, and conversely, to challenge scholars in formal studies by presenting them with interesting new cases of actual reasoning. According to the members of the Wiener Kreis, there was a strong connection between logic, reasoning, and rationality and that human reasoning is rational in so far (...) as it is based on logic. Later, this belief came under attack and logic was deemed inadequate to explicate actual cases of human reasoning. Today, there is a growing interest in reconnecting logic, reasoning and rationality. A central motor for this change was the development of non-classical logics and non-classical formal frameworks. The book contains contributions in various non-classical formal frameworks, case studies that enhance our apprehension of concrete reasoning patterns, and studies of the philosophical implications for our understanding of the notions of rationality. (shrink)

The sensorimotor theory of vision and visual consciousness is often described as a radical alternative to the computational and connectionist orthodoxy in the study of visual perception. However, it is far from clear whether the theory represents a significant departure from orthodox approaches or whether it is an enrichment of it. In this study, I tackle this issue by focusing on the explanatory structure of the sensorimotor theory. I argue that the standard formulation of the theory subscribes to the same (...) theses of the dynamical hypothesis and that it affords covering-law explanations. This however exposes the theory to the mere description worry and generates a puzzle about the role of representations. I then argue that the sensorimotor theory is compatible with a mechanistic framework, and show how this can overcome the mere description worry and solve the problem of the explanatory role of representations. By doing so, it will be shown that the theory should be understood as an enrichment of the orthodoxy, rather than an alternative. (shrink)

The received view of dynamical explanation is that dynamical cognitive science seeks to provide covering law explanations of cognitive phenomena. By analyzing three prominent examples of dynamicist research, I show that the received view is misleading: some dynamical explanations are mechanistic explanations, and in this way resemble computational and connectionist explanations. Interestingly, these dynamical explanations invoke the mathematical framework of dynamical systems theory to describe mechanisms far more complex and distributed than the ones typically considered by philosophers. Therefore, contemporary dynamicist (...) research reveals the need for a more sophisticated account of mechanistic explanation. (shrink)

Must mental properties figure in psychological causal laws if they are causally efficacious? And do those psychological causal laws give the essence of mental properties? Contrary to the prevailing consensus, I argue that, on the usual conception of laws that is in play in these debates, there are in fact lawless causally efficacious properties both in and out of the philosophy of mind. I argue that this makes a great difference to the philosophical relevance of empirical psychology. 1 begin by (...) making the case that revolutions and hurricanes are lawless phenomena, before arguing for a similar thesis about creativity, love, courage, dreams, daydreams, and musings. Furthermore, the empirical research on these phenomena suggests that the philosophical issues may be independent of what empirical psychology can tell us. (shrink)

This paper is an attempt to further our understanding of mechanisms conceived of as ontologically separable from laws. What opportunities are there for a mechanistic perspective to be independent of, or even more fundamental than, a law perspective? Advocates of the mechanistic view often play with the possibility of internal and external reliability, or with the paralleling possibilities of enforcing, counteracting, redirecting, etc., the mechanisms’ power to produce To further this discussion I adopt a trope ontology. It is independent of (...) the notion of law, and can easily be adapted to account for such characteristics of mechanisms. The idea of tropes as mechanisms is worked out in some detail. According to the resulting picture, there is still an opportunity to link mechanisms and laws. But while the predominant law view conceives of mechanistic approaches as special kinds of law accounts, this study indicates that the converse may be true. Law accounts are special cases of mechanistic accounts, and they work only in those worlds where the mechanisms are of the right kind. (shrink)

This article compares causal and constitutive explanation. While scientific inquiry usually addresses both causal and constitutive questions, making the distinction is crucial for a detailed understanding of scientific questions and their interrelations. These explanations have different kinds of explananda and they track different sorts of dependencies. Constitutive explanations do not address events or behaviors, but causal capacities. While there are some interesting relations between building and causal manipulation, causation and constitution are not to be confused. Constitution is a synchronous and (...) asymmetric relation between relata that cannot be conceived as independent existences. However, despite their metaphysical differences, the same key ideas about explanation largely apply to both. Causal and constitutive explanations face similar challenges (such as the problems of relevance and explanatory regress) and both are in the business of mapping networks of counterfactual dependence—i.e. mechanisms—although the relevant counterfactuals are of a different sort. In the final section the issue of developmental explanation is discussed. It is argued that developmental explanations deserve their own place in taxonomy of explanations, although ultimately developmental dependencies can be analyzed as combinations of causal and constitutive dependencies. Hence, causal and constitutive explanation are distinct, but not always completely separate forms of explanation. (shrink)

A scientific explanatory project, part-whole explanation, and a kind of science, part-whole science are premised on identifying, investigating, and using parts and wholes. In the biological sciences, mechanistic, structuralist, and historical explanations are part-whole explanations. Each expresses different norms, explananda, and aims. Each is associated with a distinct partitioning frame for abstracting kinds of parts. These three explanatory projects can be complemented in order to provide an integrative vision of the whole system, as is shown for a detailed case study: (...) the tetrapod limb. My diagnosis of part-whole explanation in the biological sciences as well as in other domains exploring evolved, complex, and integrated systems (e.g., psychology and cognitive science) cross-cuts standard philosophical categories of explanation: causal explanation and explanation as unification. Part-whole explanation is itself one essential aspect of part-whole science. (shrink)

Although philosophy has been only a minor contributor to cognitive science to date, this paper describes two projects in naturalistic philosophy of mind and one in naturalistic philosophy of science that have been pursued during the past 30 years and that can make theoretical and methodological contributions to cognitive science. First, stances on the mind–body problem (identity theory, functionalism, and heuristic identity theory) are relevant to cognitive science as it negotiates its relation to neuroscience and cognitive neuroscience. Second, analyses of (...) mental representations address both their vehicles and their contents; new approaches to characterizing how representations have content are particularly relevant to understanding the relation of cognitive agents to their environments. Third, the recently formulated accounts of mechanistic explanation in philosophy of science both provide perspective on the explanatory project of cognitive science and may offer normative guidance to cognitive science (e.g., by providing perspective on how multiple disciplinary perspectives can be integrated in understanding a given mechanism). (shrink)

Social norms are commonly understood as rules that dictate which behaviors are appropriate, permissible, or obligatory in different situations for members of a given community. Many researchers have sought to explain the ubiquity of social norms in human life in terms of the psychological mechanisms underlying their acquisition, conformity, and enforcement. Existing theories of the psychology of social norms appeal to a variety of constructs, from prediction-error minimization, to reinforcement learning, to shared intentionality, to domain-specific adaptations for norm acquisition. In (...) this paper, we propose a novel methodological and conceptual framework for the cognitive science of social norms that we call normative pluralism. We begin with an analysis of the explanatory aims of the cognitive science of social norms. From this analysis, we derive a recommendation for a reformed conception of its explanandum: a minimally psychological construct that we call normative regularities. Our central empirical proposal is that the psychological underpinnings of social norms are most likely realized by a heterogeneous set of cognitive, motivational, and ecological mechanisms that vary between norms and between individuals, rather than by a single type of process or distinctive norm system. This pluralistic approach, we suggest, offers a methodologically sound point of departure for a fruitful and rigorous science of social norms. (shrink)

Leech et al. present a connectionist algorithm as a model of (the development) of analogizing, but they do not specify the algorithm's associated computational-level theory, nor its computational complexity. We argue that doing so may be essential for connectionist cognitive models to have full explanatory power and transparency, as well as for assessing their scalability to real-world input domains.

In this paper, I argue for an embodied, embedded approach to predictive processing and thus align the framework with situated cognition. The recent popularity of theories conceiving of the brain as a predictive organ has given rise to two broad camps in the literature that I call free energy enactivism and cognitivist predictive processing. The two approaches vary in scope and methodology. The scope of cognitivist predictive processing is narrow and restricts cognition to brain processes and structures; it does not (...) consider the body-beyond-brain and the environment as constituents of cognitive processes. Free energy enactivism, on the other hand, includes all self-organizing systems that minimize free energy and thus does not offer any unique explanations for more complex cognitive phenomena that are unique to human cognition. Furthermore, because of its strong commitment to the mind-life continuity thesis, it does not provide an explanation of what distinguishes more sophisticated cognitive systems from simple systems. The account that I develop in this paper rejects both of these radical extremes. Instead, I propose a compromise that highlights the necessary components of predictive processing by making use of a mechanistic methodology of explanation. The starting point of the argument in this paper is that despite the interchangeable use of the terms, prediction error minimization and the free energy principle are not identical. But this distinction does not need to disrupt the status quo of the literature if we consider an alternative approach: Embodied, Embedded Predictive Processing. EEPP accommodates the free energy principle, as argued for by free energy enactivism, but it also allows for mental representations in its explanation of cognition. Furthermore, EEPP explains how prediction error minimization is realized but, unlike cognitivist PP, it allocates a constitutive role to the body in cognition. Despite highlighting concerns regarding cognitivist PP, I do not wish to discredit the role of the neural domain or representations as free energy enactivism does. Neural structures and processes undeniably contribute to the minimization of prediction error but the role of the body is equally important. On my account, prediction error minimization and free energy minimization are deeply dependent on the body of an agent, such that the body-beyond-brain plays a constitutive role in cognitive processing. I suggest that the body plays three constitutive roles in prediction error minimization: The body regulates cognitive activity, ensuring that cognition and action are intricately linked. The body acts as distributor in the sense that it carries some of the cognitive load by fulfilling the function of minimizing prediction error. Finally, the body serves to constrain the information that is processed by an agent. In fulfilling these three roles, the agent and environment enter into a bidirectional relation through influencing and modeling the structure of the other. This connects EEPP to the free energy principle because the whole embodied agent minimizes free energy in virtue of being a model of its econiche. This grants the body a constitutive role as part of the collection of mechanisms that minimize prediction error and free energy. The body can only fulfill its role when embedded in an environment, of which it is a model. In this sense, EEPP offers the most promising alternative to cognitivist predictive processing and free energy enactivism. (shrink)

The recognition that human minds/brains are finite systems with limited resources for computation has led some researchers to advance theTractable Cognition thesis: Human cognitive capacities are constrained by computational tractability. This thesis, if true, serves cognitive psychology by constraining the space of computational‐level theories of cognition. To utilize this constraint, a precise and workable definition of “computational tractability” is needed. Following computer science tradition, many cognitive scientists and psychologists define computational tractability as polynomial‐time computability, leading to theP‐Cognition thesis. This article (...) explains how and why the P‐Cognition thesis may be overly restrictive, risking the exclusion of veridical computational‐level theories from scientific investigation. An argument is made to replace the P‐Cognition thesis by theFPT‐Cognition thesisas an alternative formalization of the Tractable Cognition thesis (here, FPT stands for fixed‐parameter tractable). Possible objections to the Tractable Cognition thesis, and its proposed formalization, are discussed, and existing misconceptions are clarified. (shrink)

ABSTRACTIn this paper I offer an interventionist perspective on the explanatory structure and explanatory power of dynamical models in cognitive science: I argue that some “pure” dynamical models – ones that do not refer to mechanisms at all – in cognitive science are “contextualized causal models” and that this explanatory structure gives such models genuine explanatory power. I contrast this view with several other perspectives on the explanatory power of “pure” dynamical models. One of the main results is that dynamical (...) models need not refer to underlying mechanisms in order to be explanatory. I defend and illustrate this position in terms of dynamical models of the A-not-B error in developmental psychology as elaborated by Thelen and colleagues, and dynamical models of unintentional interpersonal coordination developed by Richardson and colleagues. (shrink)

Theory of mind refers to the human capacity for reasoning about others’ mental states based on observations of their actions and unfolding events. This type of reasoning is notorious in the cognitive science literature for its presumed computational intractability. A possible reason could be that it may involve higher-order thinking. To investigate this we formalize theory of mind reasoning as updating of beliefs about beliefs using dynamic epistemic logic, as this formalism allows to parameterize ‘order of thinking.’ We prove that (...) theory of mind reasoning, so formalized, indeed is intractable. Using parameterized complexity we prove, however, that the ‘order parameter’ is not a source of intractability. We furthermore consider a set of alternative parameters and investigate which of them are sources of intractability. We discuss the implications of these results for the understanding of theory of mind. (shrink)

Characterization of a form of explanation involving grounding on the model of mechanistic causal explanation.

Defenders of the phenomenal concept strategy have to explain how both physical and phenomenal concepts provide a substantive grasp on the nature of their referents, whilst referring to the very same experience. This is the ‘new challenge’ to physicalism. In this paper, I argue that if the physicalist adopts the powerful qualities ontology of properties then a new and powerful version of the phenomenal concept strategy can be developed, which answers the new challenge.

Cybernetics promoted machine-supported investigations of adaptive sensorimotor behaviours observed in biological systems. This methodological approach receives renewed attention in contemporary robotics, cognitive ethology, and the cognitive neurosciences. Its distinctive features concern machine experiments, and their role in testing behavioural models and explanations flowing from them. Cybernetic explanations of behavioural events, regularities, and capacities rely on multiply realizable mechanism schemata, and strike a sensible balance between causal and unifying constraints. The multiple realizability of cybernetic mechanism schemata paves the way to principled (...) comparisons between biological systems and machines. Various methodological issues involved in the transition from mechanism schemata to their machine instantiations are addressed here, by reference to a simple sensorimotor coordination task. These concern the proper treatment of ceteris paribus clauses in experimental settings, the significance of running experiments with correct but incomplete machine instantiations of mechanism schemata, and the advantage of operating with real machines ??? as opposed to simulated ones ??? immersed in real environments. (shrink)

Can scientific evidence prompt us to revise philosophical theories or folk theoretical accounts of phenomena of the mind? We will argue that it can—but only under the condition that they make a so-called ‘ontological commitment’ to something that is actually subject to empirical inquiry. In other words, scientific evidence pertaining to neuroanatomical structure or causal processes only has a refuting effect if philosophical theories and folk notions subscribe to either account. We will illustrate the importance of ‘ontological commitment’ with the (...) ‘neuroanatomical approach’ to amusement as proposed in a recent paper by Palencik :419–434, 2007). We will show that the scientific evidence presented in said neuroanatomical approach has no bearing on the conceptual issues, in that the philosophical theories and folk distinction that are criticized do not subscribe to any account of the underlying neuroanatomical structure or causal processes. Our suggestions in this paper are not limited to philosophical accounts of humor but apply to the relationship of philosophy, common sense and science in general. (shrink)

The aim of this article is to analyze those theories that interpret misidentification delusions in terms of mentalization. The hypothesis under examination holds that a mentalization framework is useful for describing misidentification delusions when identification is thought to be partially based on mentalization. The article provides both a characterization and possible interpretations of such delusions, and possible relations between misidentification and mentalization are scrutinized. Whether the mentalization approach may explain or describe such kinds of mental disorders is considered, with the (...) conclusion that while this approach is unsatisfactory, there is some room for future improvement. (shrink)

The core of the language of thought program is the claim that thinking is the manipulation of symbols according to rules. Yet LOT has said little about symbol natures, and existing accounts are highly controversial. This is a major flaw at the heart of the LOT program: LOT requires an account of symbol natures to naturalize intentionality, to determine whether the brain even engages in symbol manipulations, and to understand how symbols relate to lower-level neurocomputational states. This paper provides the (...) much-needed theory of symbols, and in doing so, alters the LOT program in significant respects. (shrink)

In this essay I defend a theory of psychological explanation that is based on the joint commitment to direct reference and computationalism. I offer a new solution to the problem of Frege Cases. Frege Cases involve agents who are unaware that certain expressions corefer (e.g. that 'Cicero' and 'Tully' corefer), where such knowledge is relevant to the success of their behavior, leading to cases in which the agents fail to behave as the intentional laws predict. It is generally agreed that (...) Frege Cases are a major problem, if not the major problem, that this sort of theory faces. In this essay, I hope to show that the theory can surmount the Frege Cases. (shrink)

What role does moral reasoning play in moral judgment? More specifically, what causal role does moral reasoning have in the production of moral judgments? Recently, many philosophers and psychologists have attempted to answer this question by drawing on empirical data. However, these attempts fall short because there has been no sustained attention to the question of what moral reasoning is. This paper addresses this problem, by providing a general account of moral reasoning in terms of a capacity, and suggests how (...) this account of moral reasoning can provide a way forward in current debates. (shrink)

This paper argues for theoretical modeling and model-construction as central types of activities that philosophers of social ontology engage in. This claim is defended through a detailed case study and revisionary interpretation of Raimo Tuomela’s account of the we-perspective. My interpretation is grounded in Ronald Giere’s account of scientific models, and argued to be compatible with, but less demanding than Tuomela’s own description of his account as a philosophical theory of the social world. My approach is also suggested to be (...) applicable to many other methodologically naturalist accounts of collective intentionality and social ontology. (shrink)

This paper considers two ways functions figure into scientific explanations: (i) via laws?events are causally explained by subsuming those events under functional laws; and (ii) via designs?capacities are explained by specifying the functional design of a system. We argue that a proper understanding of how functions figure into design explanations of capacities makes it clear why such functions are ill-suited to figure into functional-cum-causal law explanations of events, as those explanations are typically understood. We further argue that a proper understanding (...) of how functions enter into design explanations of capacities enables us to show why two prominent objections to functionalism in the philosophy mind?the argument from metaphysically necessary effects (Bennett, 2007; Rupert, 2006) and causal exclusion (Kim, 1993, 1998; Malcolm, 1968)?are misguided when interpreted as posing a threat to functional explanation in science across the board. If those arguments pose a threat at all, they pose it to instances of (i); however, a great number of the functional explanations we find in psychology?and the sciences generally?are instances of (ii). (shrink)

This paper focuses on the cognitive/computational and evolutionary levels. It describes three proposals to make cognition computationally tractable, namely: Resource Rationality, the Adaptive Toolbox and Massive Modularity. While each of these proposals appeals to evolutionary considerations to dissolve the intractability of cognition, Rich, Blokpoel, de Haan, and van Rooij argue that, in each case, the intractability challenge is not resolved, but just relocated to the level of evolution.

This paper focuses on the cognitive/computational and evolutionary levels. It describes three proposals to make cognition computationally tractable, namely: Resource Rationality, the Adaptive Toolbox and Massive Modularity. While each of these proposals appeals to evolutionary considerations to dissolve the intractability of cognition, Rich, Blokpoel, de Haan, and van Rooij argue that, in each case, the intractability challenge is not resolved, but just relocated to the level of evolution.

Interventionism is a theory of causal explanation developed by Woodward and Hitchcock. I defend an interventionist perspective on the causal explanations offered within scientific psychology. The basic idea is that psychology causally explains mental and behavioral outcomes by specifying how those outcomes would have been different had an intervention altered various factors, including relevant psychological states. I elaborate this viewpoint with examples drawn from cognitive science practice, especially Bayesian perceptual psychology. I favorably compare my interventionist approach with well-known nomological and (...) mechanistic theories of psychological explanation. (shrink)

I argue that examining the explanatory power of the hypothesis of extended cognition (HEC) offers a fruitful approach to the problem of cognitive system demarcation. Although in the discussions on HEC it has become common to refer to considerations of explanatory power as a means for assessing the plausibility of the extended cognition approach, to date no satisfying account of explanatory power has been presented in the literature. I suggest that the currently most prominent theory of explanation in the special (...) sciences, James Woodward’s contrastive-counterfactual theory, and an account of explanatory virtues building on that theory can be used to develop a systematic picture of cognitive system demarcation in the psychological sciences. A major difference between my differential influence (DI) account and most other theories of cognitive extension is the cognitive systems pluralism implied by my approach. By examining the explanatory power of competing traditions in psychological memory research, I conclude that internalist and externalist classificatory strategies are characterized by different profiles of explanatory virtues and should often be considered as complementary rather than competing approaches. This suggests a deflationary interpretation of HEC. (shrink)

In this article, I develop an account of the use of intentional predicates in cognitive neuroscience explanations. As pointed out by Maxwell Bennett and Peter Hacker, intentional language abounds in neuroscience theories. According to Bennett and Hacker, the subpersonal use of intentional predicates results in conceptual confusion. I argue against this overly strong conclusion by evaluating the contested language use in light of its explanatory function. By employing conceptual resources from the contemporary philosophy of science, I show that although the (...) use of intentional predicates in mechanistic explanations sometimes leads to explanatorily inert claims, intentional predicates can also successfully feature in mechanistic explanations as tools for the functional analysis of the explanandum phenomenon. Despite the similarities between my account and Daniel Dennett's intentional-stance approach, I argue that intentional stance should not be understood as a theory of subpersonal causal explanation, and therefore cannot be used to assess the explanatory role of intentional predicates in neuroscience. Finally, I outline a general strategy for answering the question of what kind of language can be employed in mechanistic explanations. (shrink)

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)

In the article I discuss functionalist interpretations of Husserlian phenomenology. The first one was coined in the discussion between Hubert Dreyfus and Ronald McIntyre. They argue that Husserl’s phenomenology shares similarities with computational functionalism, and the key similarity is between the concept of noema and the concept of mental representation. I show the weaknesses of that reading and argue that there is another available functionalist reading of Husserlian phenomenology. I propose to shift perspective and approach the relation between phenomenology and (...) functionalism from a methodological perspective, specifically taking into account the functionalist explanatory strategy called functional analysis. I discuss the notion of function in Husserl’s works and Husserl’s idea of functional phenomenology. The key argument I develop is that in functional phenomenology we can find an explanatory strategy which is analogous to the strategy of functional decomposition used in functional analysis. I conclude that the proposed functionalist reading of phenomenology opens a new approach to the integration of phenomenology with cognitive sciences. (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)