A violation of procedure invariance in preference measurement is that the predominant or prominent attribute looms larger in choice than in a matching task. In Experiment 1, this so-called prominence effect was demonstrated for choices between pairs of options, choices to accept single options, and preference ratings of single options. That is, in all these response modes the prominent attribute loomed larger than in matching. The results were replicated in Experiment 2, in which subjects chose between or rated their preference (...) for pairs of options which were matched to be equally attractive either in the same session or 1 week earlier. On the basis of these and previous results, it is argued that the prominence effect is a reliable phenomenon. However, none of several cognitive explanations which have been offered appears to be completely viable. (shrink)

It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces (...) the phenomenon to be explained, the kind of unification afforded by the Bayesian framework to cognitive science does not necessarily reveal aspects of a mechanism. Bayesian unification, nonetheless, can place fruitful constraints on causal–mechanical explanation. 1 Introduction2 What a Great Many Phenomena Bayesian Decision Theory Can Model3 The Case of Information Integration4 How Do Bayesian Models Unify?5 Bayesian Unification: What Constraints Are There on Mechanistic Explanation?5.1 Unification constrains mechanism discovery5.2 Unification constrains the identification of relevant mechanistic factors5.3 Unification constrains confirmation of competitive mechanistic models6 ConclusionAppendix. (shrink)

These essays draw on work in the history and philosophy of science, the philosophy of mind and language, the development of concepts in children, conceptual..

This paper considers the question of whether the influential distinction between levels of explanation introduced by David Marr can be used as a general framework for contemplating levels of explanation in cognitive sciences. Marr introduced three levels at which we can explain cognitive processes: the computational, algorithmic, and implementational levels. Some argue that Marr’s levels of explanation can only be applied to modular cognitive systems. However, since many psychological processes are non-modular, it seems that (...) Marr’s levels of explanation cannot explain such psychological processes. To show that the latter claim is not convincing, the paper draws upon an influential paradigm from cognitive sciences that is based on the principle of free energy. Based on this paradigm, the paper argues that even non-modular psychological processes can be computationally analyzed and algorithmically implemented. The conclusion of the paper is that, at least under the assumption that the func- tion of the mind is to minimize free energy, Marr’s levels of explanation can be successfully used as a general framework for understanding psychological processes at different levels of description. (shrink)

The traditional approach to explanation in cognitive neuroscience is realist about psychological constructs, and treats them as explanatory. On the “standard framework,” cognitive neuroscientists explain behavior as the result of the instantiation of psychological functions in brain activity. This strategy is questioned by results suggesting the distribution of function in the brain, the multifunctionality of individual parts of the brain, and the overlap in neural realization of purportedly distinct psychological constructs. One response to this in the field (...) has been to employ the tools of databasing and machine learning to attempt to find and quantify specific correlations between psychological kinds such as ‘memory’ or ‘attention’ (or sub-kinds thereof) and patterns of activity in the brain. I assess the status and prospects of these projects. I argue that current proponents of the project are vague about their aims, vis-à-vis the standard framework, sometimes suggesting substantiation of the framework, sometimes suggesting retaining the framework but revising the ontology of mental constructs, and sometimes suggesting abandonment of the framework. I argue that extant results from within the projects fail to substantiate the standard framework, and propose an alternative. On my view, psychological constructs should not be viewed as explanantia, but instead as heuristic concepts that help us uncover ways that behaviors can vary and the ways that the brain implements those distinctions. I then discuss the normative upshot of these views for databasing and brain mapping projects. (shrink)

The aim of this paper is to refer basic philosophical approaches to the problem of musical meaning and, on the other hand, to describe some examples of the research on musical meaning found in the field of cognitive neuroscience. By looking at those two approaches together it can be seen that there is still no agreement on how musical meaning should be understood, often due to several methodological problems of which the most important seem to be the possibility of (...) inter-theoretical reduction and application of an accurate theory of explanation. I am suggesting that the application of some form of the mechanistic model of explanation might be found useful for clarifying reductionism-antireductionism dispute concerning musical meaning, and more importantly, for providing some answers for the debate in music-as-language controversy. (shrink)

This article is an experiment. Consider a minimalist model of cognition (models, means of model-building and history of their evolution). In this model, explanation could be defined as a means allowing to advance: production of models and means of model-building (thus, yielding 1st class understanding), exploration and use of them (2nd class), and/or teaching (3rd class). At minimum, 3rd class understanding is necessary for an explanation to be respected.

Active materials are self-propelled non-living entities which, in some circumstances, exhibit a number of cognitively interesting behaviors such as gradient-following, avoiding obstacles, signaling and group coordination. This has led to scientific and philosophical discussion of whether this may make them useful as minimal models of cognition (Hanczyc, 2014; McGivern, 2019). Batterman and Rice (2014) have argued that what makes a minimal model explanatory is that the model is ultimately in the same universality class as the target system, which underpins why (...) it exhibits the same macrobehavior. We appeal to recent research in basal cognition (Lyon et al., 2021) to establish appropriate target systems and essential features of cognition as a target of modeling. Looking at self-propelled oil droplets, a type of active material, we do not find that organization alone indicates that these systems exhibit the essential features of cognition. We then examine the specific behaviors of oil droplets but also fail to find that these demonstrate the essential features of cognition. Without a universality class, Batterman & Rice’s account of the explanatory power of minimal models simply does not apply to cognition. However, we also want to stress that it is not intended to; cognition is not the same type of behavioral phenomena as those found in physics. We then look to the minimal cognition methodology of Beer (1996, 2020a, b) to show how active materials can be explanatorily valuable regardless of their cognitive status because they engage in specific behaviors that have traditionally been expected to involve internal representational dynamics, revealing misconceptions about the cognitive underpinnings of certain, specific behaviors in target systems where such behaviors are cognitive. Further, Beer’s models can also be genuinely explanatory by providing dynamical explanations. (shrink)

Albert Newen and Petra Vetter argue that neurophysiological considerations and psychophysical studies provide striking evidence for cognitive penetration. This commentary focuses mainly on the neurophysiological considerations, which have thus far remained largely absent in the philosophical debate concerning cognitive penetration, and on the cognitive penetration of perceptual experiences, which is the form of cognitive penetration philosophers have debated about the most. It is argued that Newen and Vetter's evidence for cognitive penetration is unpersuasive because they (...) do not sufficiently scrutinize the details of the empirical studies they make use of-the details of the empirical studies are crucial also when the studies are used in philosophical debates. The previous does not mean that cognitive penetration could not occur. Quite the contrary, details of the feedback connections to the visual perceptual module and one of the candidates presented by Newen and Vetter suggest that cognitive penetration can occur in rare cases. (shrink)

Philosophers of psychology debate, among other things, which psychological models, if any, are (or provide) mechanistic explanations. This should seem a little strange given that there is rough consensus on the following two claims: 1) a mechanism is an organized collection of entities and activities that produces, underlies, or maintains a phenomenon, and 2) a mechanistic explanation describes, represents, or provides information about the mechanism producing, underlying, or maintaining the phenomenon to be explained (i.e. the explanandum phenomenon) (Bechtel and (...) Abrahamsen 2005; Craver 2007). If there is a rough consensus on what mechanisms are and that mechanistic explanations describe, represent, or provide information about them, then how is there no consensus on which psychological models are (or provide) mechanistic explanations? Surely the psychological models that are mechanistic explanations are the models that describe, represent, or provide information about mechanisms. That is true, of course; the trouble arises when determining what exactly that involves. Philosophical disagreement over which psychological models are mechanistic explanations is often disagreement about what it means to describe, represent, or provide information about a mechanism, among other things (Hochstein 2016; Levy 2013). In addition, one's position in this debate depends on a host of other seemingly arcane metaphysical issues, such as the nature of mechanisms, computational and functional properties (Piccinini 2016), and realization (Piccinini and Maley 2014), as well as the relation between models, methodologies, and explanations (Craver 2014; Levy 2013; Zednik 2015). Although I inevitably advocate a position, my primary aim in this chapter is to spell out all these relationships and canvas the positions that have been taken (or could be taken) with respect to mechanistic explanation in psychology, using dynamical systems models and cognitive models (or functional analyses) as examples. (shrink)

Despite their success in describing and predicting cognitive behavior, the plausibility of so-called ‘rational explanations’ is often contested on the grounds of computational intractability. Several cognitive scientists have argued that such intractability is an orthogonal pseudoproblem, however, since rational explanations account for the ‘why’ of cognition but are agnostic about the ‘how’. Their central premise is that humans do not actually perform the rational calculations posited by their models, but only act as if they do. Whether or not (...) the problem of intractability is solved by recourse to ‘as if’ explanations critically depends, inter alia, on the semantics of the ‘as if’ connective. We examine the five most sensible explications in the literature, and conclude that none of them circumvents the problem. As a result, rational ‘as if’ explanations must obey the minimal computational constraint of tractability. (shrink)

This paper examines a parallel between Aristotle’s account of phantasia and contemporary psychological models of working memory, a capacity that enables the temporary maintenance and manipulation of information used in many behaviors. These two capacities, though developed within two distinct scientific paradigms, share a common strategy of psychological explanation, Aristotelian Faculty Psychology. This strategy individuates psychological components by their target-domains and functional roles. Working memory and phantasia result from an attempt to individuate the psychological components responsible for flexible thought (...) and are thus implicated in most of our robust cognitive processes, from reading comprehension to problem solving. We then present two novel objections which suggest that these capacities cannot explain our ability to engage in flexible thought. To escape the resultant impasse, we survey alternatives and argue that most promising strategies depend on identifying the behaviors attributed to intelligent thought and action. (shrink)

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

It is not a particularly hard thing to want or seek explanations. In fact, explanations seem to be a large and natural part of our cognitive lives. Children ask why and how questions very early in development and seem genuinely to want some sort of answer, despite our often being poorly equipped to provide them at the appropriate level of sophistication and detail. We seek and receive explanations in every sphere of our adult lives, whether it be to understand (...) why a friendship has foundered, why a car will not start, or why ice expands when it freezes. Moreover, correctly or incorrectly, most of the time we think we know when we have or have not received a good explanation. There is a sense both that a given, successful explanation satisfies a cognitive need, and that a questionable or dubious explanation does not. There are also compelling intuitions about what make good explanations in terms of their form, that is, a sense of when they are structured correctly. (shrink)

People often answer why-questions with what we call experiential explanations: narratives or stories with temporal structure and concrete details. In contrast, on most theories of the epistemic function of explanation, explanations should be abstractive: structured by general relationships and lacking extraneous details. We suggest that abstractive and experiential explanations differ not only in level of abstraction, but also in structure, and that each form of explanation contributes to the epistemic goals of individual learners and of science. In particular, (...) experiential explanations support mental simulation and survive transitions across background theories; as a result, they support learning and help us translate between competing frameworks. Experiential explanations play an irreducible role in human cognition – and perhaps in science. (shrink)

This dissertation argues that cognition is a kind of natural agency. Natural agency is the capacity that certain systems have to act in accordance with their own norms. Natural agents are systems that bias their repertoires in response to affordances in the pursuit of their goals. -/- Cognition is a special mode of this general phenomenon. Cognitive systems are agents that have the additional capacity to actively take their worlds to be certain ways, regardless of whether the world is (...) really that way. In this way, cognitive systems are desituated. Desituatedness is the root of specifically cognitive capacities for representation and abstraction. -/- There are two main reasons why this view needs defending. First, natural agency is typically viewed as incompatible with natural science because it is committed to a teleological mode of explanation. Second, cognition is typically held to be categorically distinct from natural agency. This dissertation argues against both of these views. It argues against the incompatibility of agency and natural science by demonstrating that systems biology, general systems theory, and sciences that deal with complex systems have typically underappreciated conceptual and theoretical resources for grounding agency in the causal structure of the world. These conceptual resources do not, however, reduce agency to systems theory because the normativity inherent in agency demands descriptive resources beyond those of even the most sophisticated systems theory. It argues against the categorical difference between natural agency and cognition by pointing out that separating cognition from a richer web of situated, ecologically embedded relations between the agent and the world generates the frame problem, which is an insuperable obstacle to making cognition that is sufficiently responsive to the complexity of the world. Rooting cognition in natural agency is a more robust empirical bet for theorizing cognition and artificial intelligence. (shrink)

This paper explores the implications of conceptualising phenomenology as explanatory for the ongoing dialogue between the phenomenological tradition and cognitive science, especially enactive approaches to cognition. The first half of the paper offers three interlinked arguments: Firstly, that differentiating between phenomenology and the natural sciences by designating one as descriptive and the other as explanatory undermines opportunities for the kind of productive friction that is required for genuine ‘mutual enlightenment’. Secondly, that conceiving of phenomenology as descriptive rather than explanatory (...) risks committing us to what Zahavi (2019) identifies as the error of equating the phenomenological with the phenomenal. Finally, that the erroneous reduction to the descriptive occludes the rich resources that the phenomenological tradition can contribute to investigations of non-human cognition. The second half of the paper then turns to focus specifically on the promising relationship between phenomenology and enactive approaches to cognition. It will suggest that phenomenology must be seen as having explanatory capacities if it is to shed light on the structures of “mind in life” (Thompson, 2007), before drawing on the model of explanation put forward by Louis Sass to explore what this might look like. (shrink)

There are good, even if inconclusive, reasons to think that cognitive penetration of perception occurs: that cognitive states like belief causally affect, in a relatively direct way, the contents of perceptual experience. The supposed importance of – indeed as it is suggested here, what is definitive of – this possible phenomenon is that it would result in important epistemic and scientific consequences. One interesting and intuitive consequence entirely unremarked in the extant literature concerns the perception of art. Intuition (...) has it that knowledge about art changes how one aesthetically evaluates artworks. A profound explanation of this intuitive fact is that perceptual experiences vary with artistic expertise. Cognitive penetration provides an explanatory mechanism for this latter effect. What one knows or otherwise thinks about art may affect, in one of two ways sketched below, how one perceives art. Differences in aesthetic evaluation may follow, either because high-level aesthetic properties can be perceptually represented or because they causally depend on low-level perceptible properties. All of this lends credence to the hypothesis that the expert better judges art because she better perceives art. And she better perceives art because she better knows art. (shrink)

Debate about cognitive science explanations has been formulated in terms of identifying the proper level(s) of explanation. Views range from reductionist, favoring only neuroscience explanations, to mechanist, favoring the integration of multiple levels, to pluralist, favoring the preservation of even the most general, high-level explanations, such as those provided by embodied or dynamical approaches. In this paper, we challenge this framing. We suggest that these are not different levels of explanation at all but, rather, different styles of (...) explanation that capture different, cross-cutting patterns in cognitive phenomena. Which pattern is explanatory depends on both the cognitive phenomenon under investigation and the research interests occasioning the explanation. This reframing changes how we should answer the basic questions of which cognitive science approaches explain and how these explanations relate to one another. On this view, we should expect different approaches to offer independent explanations in terms of their different focal patterns and the value of those explanations to partly derive from the broad patterns they feature. (shrink)

This dissertation argues that cognition is a kind of natural agency. Natural agency is the capacity that certain systems have to act in accordance with their own norms. Natural agents are systems that bias their repertoires in response to affordances in the pursuit of their goals. Cognition is a special mode of this general phenomenon. Cognitive systems are agents that have the additional capacity to actively take their worlds to be certain ways, regardless of whether the world is really (...) that way. In this way, cognitive systems are desituated. Desituatedness is the root of specifically cognitive capacities for representation and abstraction. There are two main reasons why this view needs defending. First, natural agency is typically viewed as incompatible with natural science because it is committed to a teleological mode of explanation. Second, cognition is typically held to be categorically distinct from natural agency. This dissertation argues against both of these views. It argues against the incompatibility of agency and natural science by demonstrating that systems biology, general systems theory, and sciences that deal with complex systems have typically underappreciated conceptual and theoretical resources for grounding agency in the causal structure of the world. These conceptual resources do not, however, reduce agency to systems theory because the normativity inherent in agency demands descriptive resources beyond those of even the most sophisticated systems theory. It argues against the categorical difference between natural agency and cognition by pointing out that separating cognition from a richer web of situated, ecologically embedded relations between the agent and the world generates the frame problem, which is an insuperable obstacle to making cognition that is sufficiently responsive to the complexity of the world. Rooting cognition in natural agency is a more robust empirical bet for theorizing cognition and artificial intelligence. (shrink)

The view that folk psychology is primarily mindreading beliefs and desires has come under challenge in recent years. I have argued that we also understand others in terms of individual properties such as personality traits and generalizations from past behavior, and in terms of group properties such as stereotypes and social norms (Andrews 2012). Others have also argued that propositional attitude attribution isn’t necessary for predicting others’ behavior, because this can be done in terms of taking Dennett’s Intentional Stance (Zawidzki (...) 2013), appealing to social structures (Maibom 2007), shared norms (McGeer 2007) or via solution based heuristics for reaching equilibrium between social partners (Morton 2003). But it isn’t only prediction that can be done without thinking about what others think; we can explain and understand people in terms of their personality traits, habitual behaviors, and social practices as well. The decentering of propositional attitude attributions goes hand in hand with a move away from taking folk psychology to be primarily a predictive device. While experiments examining folk psychological abilities in children, infants, and other species still rest on asking subjects to predict behavior, theoretical investigations as to the evolutionary function of folk psychology have stressed the role of explanation (Andrews 2012) and regulative functions (McGeer 2007, Zawidzki 2013, Fenici 2011). In this paper I argue that an explanatory role for folk psychology is also a regulative role, and that language is not required for these regulative functions. I will start by drawing out the relationship between prediction, explanation, and regulation of behavior according to both mindreading approaches to folk psychology and the pluralistic account I defend. I will argue that social cognition does not take the form of causal reasoning so much as it does normative reasoning, and will introduce the folk psychological spiral. Then I will examine the cognitive resources necessary for participating in the folk psychological spiral, and I will argue that these cognitive resources can be had without language. There is preliminary evidence that some other species understand one another through a normative lens that, through looping effects, creates expectations that community members strive to live up to. (shrink)

In this paper we introduce two issues relevantly related to the cognitive phenomenology debate, which, to our minds, have not been yet properly addressed: the relation between access and phenomenal consciousness in cognition and the relation between conscious thought and inner speech. In the first case, we ask for an explanation of how we have access to thought contents, and in the second case, an explanation of why is inner speech so pervasive in our conscious thinking. We (...) discuss the prospects of explanation for both sides of the debate and argue that cognitive phenomenology defenders are in an overall advantageous position. We also propose an account of inner speech that differs from other influential explanations in some interesting respects. (shrink)

This is a forthcoming section for the book "Theism and Atheism: Opposing Arguments in Philosophy", edited by Graham Oppy, Gregory Dawes, Evan Fales, Joseph Koterski, Mashhad Al-Allaf, Robert Fastiggi, and David Shatz. I was asked to write a brief essay on whether naturalism or theism can successfully explain the distribution of suffering in our world. Wheras another section covers the possibility that suffering is evidence against theism, my essay is concerned only with the ability for either naturalism or theism to (...) explain suffering. I argue that, for naturalists, suffering is not to be explained by either philosophers of religion or theologians. Instead, naturalists believe that suffering should be explained by the cognitive and social sciences, perhaps in conjunction with political philosophy and philosophy of mind. Moreover, naturalists may take suffering to be an important reason for action. In a world without a transcendent, supernatural being to watch over us, we can only depend upon each other to ameliorate existing conditions, to the extent that they can be ameliorated. On the other hand, theistic explanations of the distibution of suffering slip very easily into problematic theologies when they try to offer explicit explanations for suffering. For example, the world's most vulnerable people are often those who suffer the most, whereas oppressors are often able to prosper. That is, theistic explanations of our world's suffering easily slip into, e.g., the just world fallacy, racist ideology (i.e., that God favors some race(s) of people over others), or patriarchal ideology. Instead of offering an explicit explanation, theists should instead be skeptical theists -- i.e., they should argue that an explanation for our world's suffering is beyond our ken. While skeptical theism avoids the aforementioned problematic implications, if skeptical theism is true, then our world's suffering cannot be fully explained. (shrink)

In this paper, we argue that several recent ‘wide’ perspectives on cognition (embodied, embedded, extended, enactive, and distributed) are only partially relevant to the study of cognition. While these wide accounts override traditional methodological individualism, the study of cognition has already progressed beyond these proposed perspectives towards building integrated explanations of the mechanisms involved, including not only internal submechanisms but also interactions with others, groups, cognitive artifacts, and their environment. The claim is substantiated with reference to recent developments in (...) the study of “mindreading” and debates on emotions. We claim that the current practice in cognitive (neuro)science has undergone, in effect, a silent mechanistic revolution, and has turned from initial binary oppositions and abstract proposals towards the integration of wide perspectives with the rest of the cognitive (neuro)sciences. (shrink)

Many philosophers insist that the revisionary metaphysician—i.e., the metaphysician who offers a metaphysical theory which conflicts with folk intuitions—bears a special burden to explain why certain folk intuitions are mistaken. I show how evidence from cognitive science can help revisionist discharge this explanatory burden. Focusing on composition and persistence, I argue that empirical evidence indicates that the folk operate with a promiscuous teleomentalist view of composition and persistence. The folk view, I argue, deserves to be debunked. In this way, (...) I take myself to have illustrated one key role cognitive science can play in metaphysics; namely by helping the revisionary metaphysician discharge the explanatory burden of providing a plausible explanation of how the folk have gone wrong. (shrink)

A common kind of explanation in cognitive neuroscience might be called functiontheoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes (in the system’s normal environment) the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to (...) the extent that it reveals the causal structure of the mechanism underlying the capacity. If they are right, then a cognitive model that resists a transparent mapping to known neural mechanisms fails to be explanatory. I argue that a functiontheoretic characterization of a cognitive capacity can be genuinely explanatory even absent an account of how the capacity is realized in neural hardware. (shrink)

Following Marr’s famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage in (...) class='Hi'>cognitive processes that make their problem solving algorithms computationally suboptimal, in contrast with the optimal algorithms studied in the computational approach. Therefore, in order to accurately model the human cognitive tasks involved in mathematical problem solving, we need to expand our methodology to also include aspects relevant to the algorithmic level. This allows us to study algorithms that are cognitively optimal for human problem solvers. Since problem solving methods are not universal, I propose that they should be studied in the framework of enculturation, which can explain the expected cultural variance in the humanly optimal algorithms. While mathematical problem solving is used as the case study, the considerations in this paper concern modeling of cognitive tasks in general. (shrink)

Metaphysical explanations, unlike many other kinds of explanation, are standardly thought to be insensitive to our epistemic situation and so are not evaluable by cognitive values such as salience. I consider a case study that challenges this view. Some properties are distributed over an extension. For example, the property of being polka-dotted red on white, when instantiated, is distributed over a surface. Similar properties have been put to work in a variety of explanatory tasks in recent metaphysics, including: (...) providing an analysis of change, giving to presentists truthmakers for past claims; giving to priority monists an account of basic heterogeneous entities; and giving to friends of extended simples an explanation of how an extended simple can enjoy qualitative variation. I argue that such explanations exhibit salience failure. How ought we represent the semantics of salience? Differences in linguistic stress induce semantic differences similar to the semantic differences induced in explanations by differences in salience, and I will draw an analogy with linguistic theories of focus sensitivity to sketch how one might model the role of salience in these kinds of explanations. I end with a few tentative conclusions about the role of cognitive values in metaphysical explanations. Some theorists view the citation of a ground as a sufficient explanation. If certain explanations appealing to distributed properties exhibit attenuated salience, then arguably the mere citation of a ground does not always provide an adequate explanation. (shrink)

The orthodox view of social cognition maintains that mentalizing is an important and pervasive element of our ordinary social interactions. The orthodoxy has come under scrutiny from various sources recently. Critics from the phenomenological tradition argue that phenomenological reflection on our social interactions tells against the orthodox view. Proponents of pluralistic folk psychology argue that our ordinary social interactions extend far beyond mentalizing. Both sorts of critics argue that emphasis in social cognition research ought to be on other elements of (...) our social practices. In this paper, I consider social explanations specifically and argue that social explanations are implicated in many of the social practices highlighted by critics of the orthodox view. (shrink)

This paper addresses the recent rise of the use of alternative medicine in Western countries. It offers a novel explanation of that phenomenon in terms of cognitive and economic factors related to the free-market and patient-centric approach to medicine that is currently in place in those countries, in contrast to some alternative explanations of this phenomenon. Moreover, the paper addresses this troubling trend in terms of the serious harms associated with the use of alternative medical modalities. The explanatory (...) theory defended here is then predicated on the idea that an extreme patient-centric model of medical practice that treats largely ignorant patients as consumers of medical products and services endowed with an essentially unrestricted power of freedom to choose treatments predictably leads serious and avoidable harms. Some important moral and epistemological consequences of this model are then articulated and corrective measures are suggested. (shrink)

Successful athletic performance requires precision in many respects. A batter stands behind home plate awaiting the arrival of a ball that is less than three inches in diameter and moving close to 100 mph. His goal is to hit it with a ba­­t that is also less than three inches in diameter. This impressive feat requires extraordinary temporal and spatial coordination. The sweet spot of the bat must be at the same place, at the same time, as the ball. A (...) basketball player must keep a ball bouncing as she speeds from one end of the court to another, evading defensive players. She may never break pace as she lifts from the ground, throwing the ball fifteen feet toward a hoop that is eighteen inches in diameter. One task facing a psychologist involves explaining how the body does such things within the sometimes very demanding spatial and temporal constraints that a given task imposes. Part of the goal of this chapter is to sketch the commitments of an embodied approach to such an explanation. We shall see that an embodied account of motor skills draws concepts that depart radically from more traditional cognitivist theories of motor activity. Similarly, because an embodied approach to cognition introduces new ways to understand the human capacity for social interaction, it also promises to shed new light on how athletes coordinate their actions with each other. (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)

In this paper, an account of theoretical integration in cognitive (neuro)science from the mechanistic perspective is defended. It is argued that mechanistic patterns of integration can be better understood in terms of constraints on representations of mechanisms, not just on the space of possible mechanisms, as previous accounts of integration had it. This way, integration can be analyzed in more detail with the help of constraintsatisfaction account of coherence between scientific representations. In particular, the account has resources to talk (...) of idealizations and research heuristics employed by researchers to combine separate results and theoretical frameworks. The account is subsequently applied to an example of successful integration in the research on hippocampus and memory, and to a failure of integration in the research on mirror neurons as purportedly explanatory of sexual orientation. (shrink)

Bayesian cognitive science is a research programme that relies on modelling resources from Bayesian statistics for studying and understanding mind, brain, and behaviour. Conceiving of mental capacities as computing solutions to inductive problems, Bayesian cognitive scientists develop probabilistic models of mental capacities and evaluate their adequacy based on behavioural and neural data generated by humans (or other cognitive agents) performing a pertinent task. The overarching goal is to identify the mathematical principles, algorithmic procedures, and causal mechanisms that (...) enable cognitive agents to take uncertainty into account and weigh it appropriately in producing adaptive behaviour. -/- The appeal of Bayesian cognitive science derives from its transparency, normativity, and unifying power. Resources from Bayesian statistics allow cognitive scientists to characterise transparently, in terms of random variables and probabilistic dependencies between them, the inductive problems that many mental capacities are presumed to solve. Given this characterisation, researchers can define an upper bound on one’s performance on that problem and use it as a benchmark for interpreting experimental results, formulating hypotheses about why and when deviations from optimal performance should be expected, and seeking explanations of mental capacities within one encompassing theoretical framework. -/- Although critics question the testability, explanatory power, and plausibility of Bayesian models of mental capacities, Bayesian cognitive science has proved itself to be an incredibly fruitful research programme. Fuelled by empirical and theoretical results from psychology, neuroscience, philosophy, computer science, artificial intelligence, and evolution, Bayesian cognitive science has: advanced our understanding of why cognitive systems should keep track of uncertainty and use it to facilitate adaptive behaviour; clarified how the brain might encode information about the uncertainty of its stimuli and perform computations with probability distributions; and crystallised the insight that cognitive agents’ management of uncertainty might be grounded in predictive processes aimed at avoiding surprising exchanges with the environment. (shrink)

The aim of this paper is to draw the attention of those conducting research on imagery to the different kinds of visual information deployed by expert drawers compared to non-expert drawers. To demonstrate this difference I draw upon the cognitive science literature on vision and imagery to distinguish between three different ways that visual phenomena can be represented in memory: structural descriptions, denotative descriptions, and configural descriptions. Research suggests that perception and imagery deploy the same mental processes and that (...) the drawing of one's imagery would require the simultaneous deployment of these very processes. I reason that drawing a picture of one's imagery is not possible. I hypothesize that when required to draw a picture from memory, both expert and non-expert drawers access their denotative description of the object rather than their imagery. I then suggest how my hypothesis could be tested and if accurate, how this finding would impact upon the design and interpretation of experiments on imagery when drawing is involved. I also suggest why the constraints that perception places upon the simultaneous deployment of imagery appear not to cause the same constraints in autistic savant drawers and visual agnosia patients. (shrink)

Many cognitive scientists, having discovered that some computational-level characterization f of a cognitive capacity φ is intractable, invoke heuristics as algorithmic-level explanations of how cognizers compute f. We argue that such explanations are actually dysfunctional, and rebut five possible objections. We then propose computational-level theory revision as a principled and workable alternative.

Cognitive neuroscience is an interdisciplinary enterprise aimed at explaining cognition and behavior. It appears to be succeeding. What accounts for this apparent explanatory success? According to one prominent philosophical thesis, cognitive neuroscience explains by discovering and describing mechanisms. This "mechanist thesis" is open to at least two interpretations: a strong metaphysical thesis that Carl Craver and David Kaplan defend, and a weaker methodological thesis that William Bechtel defends. I argue that the metaphysical thesis is false and that the (...) methodological thesis is too weak to account for the explanatory promise of cognitive neuroscience. My argument draws support from a representative example of research in this field, namely, the neuroscience of decision-making. The example shows that cognitive neuroscience explains in a variety of ways and that the discovery of mechanisms functions primarily as a way of marshaling evidence in support of the models of cognition that are its principle unit of explanatory significance. -/- The inadequacy of the mechanist program is symptomatic of an implausible but prominent view of scientific understanding. On this view, scientific understanding consists in an accurate and complete description of certain "objective" explanatory relations, that is, relations that hold independently of facts about human psychology. I trace this view to Carl Hempel's logical empiricist reconceptualization of scientific understanding, which then gets extended in Wesley Salmon's causal-mechanistic approach. I argue that the twin objectivist ideals of accuracy and completeness are neither ends we actually value nor ends we ought to value where scientific understanding is concerned. -/- The case against objectivism motivates psychologism about understanding, the view that understanding depends on human psychology. I propose and defend a normative psychologistic framework for investigating the nature of understanding in the mind sciences along three empirically-informed dimensions: 1) What are the ends of understanding? 2) What is the nature of the cognitive strategy that we deploy to achieve those ends; and 3) Under what conditions is our deployment of this strategy effective toward achieving those ends? To articulate and defend this view, I build on the work of Elliot Sober to develop a taxonomy of psychologisms about understanding. Epistemological psychologism, a species of naturalism, is the view that justifying claims about understanding requires appealing to what scientists actually do when they seek understanding. Metaphysical psychologism is the view that the truth-makers for claims about understanding include facts about human psychology. I defend both views against objections. (shrink)

Many traditional theories of creativity put heavy emphasis on an incubation stage in creative cognitive processes. The basic phenomenon is a familiar one: we are working on a task or problem, we leave it aside for some period of time, and when we return attention to the task we have some new insight that services completion of the task. This feature, combined with other ostensibly mysterious features of creativity, has discouraged naturalists from theorizing creativity. This avoidance is misguided: we (...) can maintain unconscious incubated cognition as (sometimes) part of the creative process and we can explain it in scientifically responsible ways. This paper, focusing on the effects of attention on the functional networking of the brain, attempts just such an explanation. It also serves to assuage the naturalist's scepticism about other features of creative cognition. The broad upshot, one would hope, is that philosophers of mind and cognitive scientists return some attention to the long neglected topic of creativity. (shrink)

Design plays an integral role in the functions of modern society. Yet the abstract process by which designers carry out their work is not obvious. The study of design thinking has grown in recent years into a major area of academic research, yet it presently lacks a clear theoretical basis; and as a discipline, its methodologies are disparate. Here, we outline and clarify the framework of the scholarly study of design thinking, introducing the major ideas and concepts upon which the (...) field is based. We then discuss in detail the various methodological issues of the field, and argue that, in its current state, the field of design thinking cannot sustain itself as an independent area of academic research. We suggest that design thinking may best be studied from a sociological or science, technology, and society (STS) studies perspective. (shrink)

In this paper, we defend a novel, multidimensional account of representational unification, which we distinguish from integration. The dimensions of unity are simplicity, generality and scope, non-monstrosity, and systematization. In our account, unification is a graded property. The account is used to investigate the issue of how research traditions contribute to representational unification, focusing on embodied cognition in cognitive science. Embodied cognition contributes to unification even if it fails to offer a grand unification of cognitive science. The study (...) of this failure shows that unification, contrary to what defenders of mechanistic explanation claim, is an important mechanistic virtue of research traditions. (shrink)

We depend upon explanation in order to “make sense” out of our world. And, making sense is all the more important when dealing with change. But, what happens if our explanations are wrong? This question is examined with respect to two types of explanatory model. Models based on labels and categories we shall refer to as “representations.” More complex models involving stories, multiple algorithms, rules of thumb, questions, ambiguity we shall refer to as “compressions.” Both compressions and representations are (...) reductions. But representations are far more reductive than compressions. Representations can be treated as a set of defined meanings – coherence with regard to a representation is the degree of fidelity between the item in question and the definition of the representation, of the label. By contrast, compressions contain enough degrees of freedom and ambiguity to allow us to make internal predictions so that we may determine our potential actions in the possibility space. Compressions are explanatory via mechanism. Representations are explanatory via category. Managers are often confusing their evocation of a representation as the creation of a context of compression . When this type of explanatory error occurs, more errors follow. In the drive for efficiency such substitutions are all too often proclaimed – at the manager’s peril. (shrink)

Cognitive representations are typically analysed in terms of content, vehicle and format. While current work on formats appeals to intuitions about external representations, such as words and maps, in this paper we develop a computational view of formats that does not rely on intuitions. In our view, formats are individuated by the computational profiles of vehicles, i.e., the set of constraints that fix the computational transformations vehicles can undergo. The resulting picture is strongly pluralistic, it makes space for a (...) variety of different formats, and is intimately tied to the computational approach to cognition in cognitive science and artificial intelligence. (shrink)

In recent years, an active research program has emerged that aims to develop a Humean best-system account (BSA) of laws of nature that improves on Lewis’s canonical articulation of the view. Its guiding idea is that the laws are cognitive tools tailored to the specific needs and limitations of creatures like us. While current versions of this “pragmatic Humean” research program fare much better than Lewis’s account along many dimensions, I will argue that they have trouble making sense of (...) certain key features of the practice of fundamental physics. Indeed, these features seem to go against the very idea that laws are useful for agents like us. In my view, Humeans can address these issues by paying more attention to the explanatory role of laws. Following this idea, I will propose an account on which what makes a systematization the best is a kind of explanatory power, understood along the lines of the unificationist theory of explanation. The resulting view, I will argue, can make sense of those features of laws that other pragmatic accounts of laws have trouble explaining. (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)

The cognitive neurosciences are based on the idea that the level of neurons or neural networks constitutes a privileged level of analysis for the explanation of mental phenomena. This paper brings to mind several arguments to the effect that this presumption is ill-conceived and unwarranted in light of what is currently understood about the physical principles underlying mental achievements. It then scrutinizes the question why such conceptions are nevertheless currently prevailing in many areas of psychology. The paper argues (...) that corresponding conceptions are rooted in four different aspects of our common-sense conception of mental phenomena and their explanation, which are illegitimately transferred to scientific enquiry. These four aspects pertain to the notion of explanation, to conceptions about which mental phenomena are singled out for enquiry, to an inductivist epistemology, and, in the wake of behavioristic conceptions, to a bias favoring investigations of input–output relations at the expense of enquiries into internal principles. To the extent that the cognitive neurosciences methodologically adhere to these tacit assumptions, they are prone to turn into a largely a-theoretical and data - driven endeavor while at the same time enhancing the prospects for receiving widespread public appreciation of their empirical findings. (shrink)

Debates about modularity invariably involve a crucial premise about how visual illusions are experienced. This paper argues that these debates are wrongheaded, and that experience of illusions is orthogonal to the core issue of the modularity hypothesis: informational encapsulation.

We have various strategies available to us for understanding another person’s state of mind. Cognitive empathy may be achieved by mental simulation, i.e. by imagining yourself in another’s situation and figuring out what you would think and feel in that situation. Alternatively, you could consider all the relevant information about the person’s situation and folk psychology and draw a sophisticated inference to the best explanation of that person’s perspective. In this chapter, I examine the conditions under which we (...) are likely to use these two familiar strategies for cognitive empathy and when they are likely to be effective. In addition, I discuss a third underexplored pattern of reasoning in understanding others. Self-serving goals, such as anxiety reduction, self-esteem, and confirmation of one’s worldview, distort cognitive empathy. I consider these different strategies in light of hybrid theories of cognitive empathy. (shrink)

Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative (...) mechanistic perspective and concentrates the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration. (shrink)

Perceptual experience has representational content. My argument for this claim is an inference to the best explanation. The explanandum is cognitive penetration. In cognitive penetration, perceptual experiences are either causally influenced, or else are partially constituted, by mental states that are representational, including: mental imagery, beliefs, concepts and memories. If perceptual experiences have representational content, then there is a background condition for cognitive penetration that renders the phenomenon prima facie intelligible. Naïve realist or purely relational accounts (...) of perception leave cognitive penetration less well-explained, even when formulated with so-called ‘standpoints’ or ‘third relata.’. (shrink)

A long series of studies in social psychology have shown that the explanations people give for their own behaviors are fundamentally different from the explanations they give for the behaviors of others. Still, a great deal of uncertainty remains about precisely what sorts of differences one finds here. We offer a new approach to addressing the problem. Specifically, we distinguish between two levels of representation ─ the level of linguistic structure (which consists of the actual series of words used in (...) the explanation) and the level of conceptual structure (which consists of the concepts these words are used to express). We then formulate and test hypotheses both about self-other differences in conceptual structure and about self-other differences in the mapping from conceptual structure to linguistic structure. (shrink)