Despite their importance in the history of philosophy and in particular in the work of Aristotle and Kant, mental capacities have been neglected in recent philosophical work. By contrast, the notion of a capacity is deeply entrenched in psychology and the brain sciences. Driven by the idea that a cognitive system has the capacity it does in virtue of its internal components and their organization, it is standard to appeal to capacities in cognitive psychology. The main benefit of invoking capacities (...) in an account of the mind is that it allows for an elegant counterfactual analysis of mental states: it allows us to analyze mental states on three distinct yet interrelated levels. A first level of analysis pertains to the function of mental capacities. A second level of analysis pertains to the mental capacities employed, irrespective of the context in which they are employed. A third level of analysis pertains to the mental capacities employed, taking into account the context in which they are employed. This paper develops an account of perceptual capacities. This account involves an analysis of their function, their individuation and possession conditions, the relation between perceptual capacities and their employment, as well as their informational and neural base conditions. (shrink)

This paper defines and defends a notion of teleological function which is fit to figure in explanations concerning how organic systems, and the items which compose them, are able to perform certain activities, such as surviving and reproducing or pumping blood. According to this notion, a teleological function of an item (such as the heart) is a typical way in which items of that type contribute to some containing system's ability to do some activity. An account of what it is (...) for an item to contribute to a containing system's ability to perform an activity is provided. I argue that the view acquires its normative status in virtue of the fact that it obeys a function-accident distinction and obeys a function-dysfunction distinction; that the view is ahistorical; and, that its ahistoricity provides it with an advantage over one of its main competitors. (shrink)

Habilitation thesis in psychology. The book consists of a collection of reasoning studies. The experimental investigations will take us from people’s reasoning about probabilities, entailments, pragmatic factors, argumentation, and causality to morality. An overarching theme of the book is norm pluralism and individual differences in rationality research.

Piccinini and Craver (Synthese 183:283–311, 2011) argue for the surprising view that psychological explanation, properly understood, is a species of mechanistic explanation. This contrasts with the ‘received view’ (due, primarily, to Cummins and Fodor) which maintains a sharp distinction between psychological explanation and mechanistic explanation. The former is typically construed as functional analysis, the analysis of some psychological capacity into an organized series of subcapacities without specifying any of the structural features that underlie the explanandum capacity. The latter idea, of (...) course, sees explanation as a matter of describing structures that maintain (or produce) the explanandum capacity. In this paper, I defend the received view by criticizing Piccinini and Craver’s argument for the claim that psychological explanation is not distinct from mechanistic explanation, and by showing how psychological explanations can possess explanatory force even when nothing is known about the underlying neurological details. I conclude with a few brief criticisms about the enterprise of mechanistic explanation in general. (shrink)

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...) also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists. (shrink)

Selectionist evolutionary theory has often been faulted for not making novel predictions that are surprising, risky, and correct. I argue that it in fact exhibits the theoretical virtue of predictive capacity in addition to two other virtues: explanatory unification and model fitting. Two case studies show the predictive capacity of selectionist evolutionary theory: parallel evolutionary change in E. coli, and the origin of eukaryotic cells through endosymbiosis.

This chapter sketches what is considered the best interpretation of physicalism, rehearses the best way of defending it, and shows that the physicalism forthcoming is still going to be less than fully satisfying; it is going to leave us short of the satisfaction that might be expected from a philosophical theory. The chapter is organized into three sections. The first section gives an interpretation of physicalism in the spirit of Frank Jackson's; this involves a rich version under which the way (...) things are phenomenally is derivable in principle from physical premisses. The second sketches a representationalist or intentionalist argument for physicalism about phenomenal experience, one which also appeals to Jackson. The third section argues that even if we accept this case for a rich physicalism, still we should not expect to find the doctrine fully satisfying; the simulatory gap will stand between us and a sort of satisfaction that we might have expected to achieve. (shrink)

This book contains a representationalist theory of self-consciousness and of the phenomenal first-person perspective. It draws on empirical data from the cognitive and neurosciences.

Selection explanations explain some non-accidental generalizations in virtue of a selection process. Such explanations are not particulaizable - they do not transfer as explanations of the instances of such generalizations. This is unlike many explanations in the physical sciences, where the explanation of the general fact also provides an explanation of its instances (i.e. standard D-N explanations). Are selection explanations (e.g. in biology) therefore a different kind of explanation? I argue that to understand this issue, we need to see that (...) a standard D-N explanation of some non-accidental generalization (al Fs are Gs) may also ipso facto explain its contrapositive (all non-Gs are non-Fs), but the explanation is particularizable with respect to the former but not to the latter. This can be seen by noting that the Raven Paradox counterexample to the H-D model of confirmation also generates a counterexample to the D-N model of explanation (all ravens are black does not explain why the non-black shoe is a non-raven). In such cases it is natural to take the generalization with the positive predicates to have a particularizable explanation. However, this need not be the case, and in selection explanations it is the generalization with the positive predicates whose explanation is no particularizable. Thus there is no need to suppose that selection explanations are fundamentally different. (shrink)

Evo-Devo exhibits a plurality of scientific “cultures” of practice and theory. When are the cultures acting—individually or collectively—in ways that actually move research forward, empirically, theoretically, and ethically? When do they become imperialistic, in the sense of excluding and subordinating other cultures? This chapter identifies six cultures – three /styles/ (mathematical modeling, mechanism, and history) and three /paradigms/ (adaptationism, structuralism, and cladism). The key assumptions standing behind, under, or within each of these cultures are explored. Characterizing the internal structure of (...) the cultures is necessary for understanding how they collaborate or compete, and how they are fragmented or integrated, in the rich interdisciplinary /trading zone/ (Galison 1997) of Evo-Devo. Evo-Devo is an important example of how science can progress through a radical plurality of perspectives and cultures. (shrink)

Some sensory experiences are pleasant, some unpleasant. This is a truism. But understanding what makes these experiences pleasant and unpleasant is not an easy job. Various difficulties and puzzles arise as soon as we start theorizing. There are various philosophical theories on offer that seem to give different accounts for the positive or negative affective valences of sensory experiences. In this paper, we will look at the current state of art in the philosophy of mind, present the main contenders, critically (...) compare and contrast them. In particular, we want to examine how they handle the reason-giving power of affective states. We will look into two representationalist proposals (Evaluativism and Imperativism) and a functionalist proposal, and argue that, contrary to their own advertisements, the representationalist proposals don’t have good accounts of why and how sensory affect can motivate, rationalize, and justify subsequent behavior and intentional mental activity. We will show that our own functionalist proposal does a much better job in this regard, and that when the representationalist proposals are modified to do a better job, they fare better not because of their representationalist credentials but due to their functionalist ones. (shrink)

Evolutionary psychology is put forward by its defenders as an extension of evolutionary biology, bringing psychology within the integrated causal chain of the hard sciences. It is extolled as a new paradigm for integrating psychology with the rest of science. We argue that such claims misrepresent the methods and explanations of evolutionary biology, and present a distorted view of the consequences that might be drawn from evolutionary biology for views of human nature. General theses about adaptation in biology are empty (...) schemata, not laws of nature allowing the subsumption of mind under biology. Functional thinking is an indispensable tool for psychology, mostly of value in abstractive unification and as a heuristic, but it gains little from association with evolutionary notions of selection. Thus, we argue, the cherished integrative causal model evaporates, and evolutionary phraseology serves no more than rhetorical purposes. Moreover, the universality of human nature and the evolutionary irrelevance of individual variation are presented as biological truths that psychologists should respect in their approach to mind. On closer inspection, this turns out to be rather dubious biology. Psychology might conceivably be better off as a continuation of biology by different means, but evolutionary psychology does not provide the conceptual integration leading to such a happy union. (shrink)

Misremembering is a systematic and ordinary occurrence in our daily lives. Since it is commonly assumed that the function of memory is to remember the past, misremembering is typically thought to happen because our memory system malfunctions. In this paper I argue that not all cases of misremembering are due to failures in our memory system. In particular, I argue that many ordinary cases of misremembering should not be seen as instances of memory’s malfunction, but rather as the normal result (...) of a larger cognitive system that performs a different function, and for which remembering is just one operation. Building upon extant psychological and neuroscientific evidence, I offer a picture of memory as an integral part of a larger system that supports not only thinking of what was the case and what potentially could be the case, but also what could have been the case. More precisely, I claim that remembering is a particular operation of a cognitive system that permits the flexible recombination of different components of encoded traces into representations of possible past events that might or might not have occurred, in the service of constructing mental simulations of possible future events. So that imagination and memory are but one thing, which for diverse considerations hath diverse names.Thomas Hobbes, Leviathan 1.2. (shrink)

There is widespread belief that connectionist networks are dramatically different from classical or symbolic models. However, connectionists rarely test this belief by interpreting the internal structure of their nets. A new approach to interpreting networks was recently introduced by Berkeley et al. (1995). The current paper examines two implications of applying this method: (1) that the internal structure of a connectionist network can have a very classical appearance, and (2) that this interpretation can provide a cognitive theory that cannot be (...) dismissed as a mere implementation. (shrink)

This paper critically examines the claim that parallel distributed processing (PDP) networks are autonomous learning systems. A PDP model of a simple distributed associative memory is considered. It is shown that the 'generic' PDP architecture cannot implement the computations required by this memory system without the aid of external control. In other words, the model is not autonomous. Two specific problems are highlighted: (i) simultaneous learning and recall are not permitted to occur as would be required of an autonomous system; (...) (ii) connections between processing units cannot simultaneously represent current and previous network activation as would be required if learning is to occur. Similar problems exist for more sophisticated networks constructed from the generic PDP architecture. We argue that this is because these models are not adequately constrained by the properties of the functional architecture assumed by PDP modelers. It is also argued that without such constraints, PDP researchers cannot claim to have developed an architecture radically different from that proposed by the Classical approach in cognitive science. (shrink)

The vast literature on negative treatment of outgroups and favoritism toward ingroups provides many local insights but is largely fragmented, lacking an overarching framework that might provide a unified overview and guide conceptual integration. As a result, it remains unclear where different local perspectives conflict, how they may reinforce one another, and where they leave gaps in our knowledge of the phenomena. Our aim is to start constructing a framework to help remedy this situation. We first identify a few key (...) ideas for creating a theoretical roadmap for this complex territory, namely the principles of etiological functionalism and the dual inheritance theory of human evolution. We show how a “molecular” approach to emotions fits into this picture, and use it to illuminate emotions that shape intergroup relations. Finally, we weave the pieces together into the beginnings of a systematic taxonomy of the emotions involved in social interactions, both hostile and friendly. While it is but a start, we have developed the argument in a way that illustrates how the foundational principles of our proposed framework can be extended to accommodate further cases. (shrink)

I challenge the dominant understanding of what it means to say two thoughts are associated. The two views that dominate the current literature treat association as a kind of mechanism that drives sequences of thought. The first, which I call reductive associationism, treats association as a kind of neural mechanism. The second treats association as a feature of the kind of psychological mechanism associative processing. Both of these views are inadequate. I argue that association should instead be seen as a (...) highly abstract filler term, standing in for causal relations between representational states in a system. Associations, so viewed, could be implemented by many different mechanisms. I outline the role that this view gives associative models as part of a top-down characterization of psychological processes of any kind and of any complexity. (shrink)

Double dissociations between perceivable colors and physical properties of colored objects have led many philosophers to endorse relationalist accounts of color. I argue that there are analogous double dissociations between attitudes of belief—the beliefs that people attribute to each other in everyday life—and intrinsic cognitive states of belief—the beliefs that some cognitive scientists posit as cogs in cognitive systems—pitched at every level of psychological explanation. These dissociations provide good reason to refrain from conflating attitudes of belief with intrinsic cognitive states (...) of belief. I suggest that interpretivism provides an attractive account of the former. Like colors, attitudes of belief evolved to be ecological signifiers, not cogs in cognitive systems. (shrink)

It has been commonplace in epistemology since its inception to idealize away from computational resource constraints, i.e., from the constraints of time and memory. One thought is that a kind of ideal rationality can be specified that ignores the constraints imposed by limited time and memory, and that actual cognitive performance can be seen as an interaction between the norms of ideal rationality and the practicalities of time and memory limitations. But a cornerstone of naturalistic epistemology is that normative assessment (...) is constrained by capacities: you cannot require someone to do something they cannot or, as it is usually put, ought implies can. This much we take to be uncontroversial. We argue that differences in architectures, goals and resources imply substantial differences in capacity, and that some of these differences are ineliminable. It follows that some differences in goals and architectural and computational resources matter at the normative level: they constrain what principles of normative epistemology can be used to describe and prescribe their behavior. As a result, we can expect there to be important epistemic differences between the way brains, individuals, and science work. (shrink)

Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action potential to show what is (...) required of an adequate mechanistic model. Mechanistic models are explanatory. (shrink)

We consider approaches to explanation within the cognitive sciences that begin with Marr's computational level or Marr's implementational level and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level. Even at this level, however, we argue that additional constraints relating to the decomposition of the cognitive system into a set (...) of interacting subfunctions are required. Integrated cognitive architectures that permit abstract specification of the functions of components and that make contact with the neural level provide a powerful bridge for linking the algorithmic and representational level to both the computational level and the implementational level. (shrink)

Research on animal behavior is typically organized according to a combination of two influential frameworks: Ernst Mayr’s distinction between proximate and ultimate causes, and Niko Tinbergen’s “four questions”. My aim is to debunk two common interpretive misconceptions about Mayr’s proximate–ultimate distinction and its relationship to Tinbergen’s four questions, and to offer a new interpretation that avoids both. The first misconception is that the proximate–ultimate distinction maps cleanly onto Tinbergen’s four questions, marking a boundary between Tinbergen’s evolutionary and survival value questions (...) versus developmental and mechanistic questions. The second is that Mayr’s proximate–ultimate distinction is meant to rule out the relevance of proximate causes to evolutionary explanations. I argue that neither is plausible given the text and Mayr’s philosophical aims, namely, to argue that evolutionary biology cannot be reduced to either the physical sciences or to other areas of biology. Through a reconstruction of Mayr’s anti-reductionist argument, I develop an interpretation according to which the proximate–ultimate distinction marks two ways that teleological reasoning can be naturalistically grounded in biology, corresponding to Mayr’s distinction between teleonomic and adapted systems. Mayr distinguishes reduction, which the proximate–ultimate distinction is meant to block, from analysis, through which he allows that proximate causes, causes that are neither proximate nor ultimate, and chance can all contribute to evolutionary explanations. I conclude by suggesting some ways in which the interpretation defended here reframes our understanding of Mayr’s disagreements with some evolutionary-developmental biologists. (shrink)

Research on animal behavior is typically organized according to a combination of two influential frameworks: Ernst Mayr’s distinction between proximate and ultimate causes, and Niko Tinbergen’s “four questions”. My aim is to debunk two common interpretive misconceptions about Mayr’s proximate–ultimate distinction and its relationship to Tinbergen’s four questions, and to offer a new interpretation that avoids both. The first misconception is that the proximate–ultimate distinction maps cleanly onto Tinbergen’s four questions, marking a boundary between Tinbergen’s evolutionary and survival value questions (...) versus developmental and mechanistic questions. The second is that Mayr’s proximate–ultimate distinction is meant to rule out the relevance of proximate causes to evolutionary explanations. I argue that neither is plausible given the text and Mayr’s philosophical aims, namely, to argue that evolutionary biology cannot be reduced to either the physical sciences or to other areas of biology. Through a reconstruction of Mayr’s anti-reductionist argument, I develop an interpretation according to which the proximate–ultimate distinction marks two ways that teleological reasoning can be naturalistically grounded in biology, corresponding to Mayr’s distinction between teleonomic and adapted systems. Mayr distinguishes reduction, which the proximate–ultimate distinction is meant to block, from analysis, through which he allows that proximate causes, causes that are neither proximate nor ultimate, and chance can all contribute to evolutionary explanations. I conclude by suggesting some ways in which the interpretation defended here reframes our understanding of Mayr’s disagreements with some evolutionary-developmental biologists. (shrink)

In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work in (...) computational neuroscience that creates difficulties for the mechanist project. Carandini and Heeger (Nat Rev Neurosci 13:51–62, 2012) propose that many neural response properties can be understood in terms of canonical neural computations. These are “standard computational modules that apply the same fundamental operations in a variety of contexts.” Importantly, these computations can have numerous biophysical realisations, and so straightforward examination of the mechanisms underlying these computations carries little explanatory weight. Through a comparison between this modelling approach and minimal models in other branches of science, I argue that computational neuroscience frequently employs a distinct explanatory style, namely, efficient coding explanation. Such explanations cannot be assimilated into the mechanistic framework but do bear interesting similarities with evolutionary and optimality explanations elsewhere in biology. (shrink)

In this paper I link two hitherto disconnected sets of results in the philosophy of emotions and explore their implications for the computational theory of mind. The argument of the paper is that, for just the same reasons that some computationalists have thought that cognition may be a natural kind, so the same can plausibly be argued of emotion. The core of the argument is that emotions are a representation-governed phenomenon and that the explanation of how they figure in behaviour (...) must as such be undertaken in those terms. I conclude with some interdisciplinary reflections in defence of the hypothesis that emotions might be more fundamental in the organization of behaviour than cognition; that, in effect, we may be emoters before we are cognizers . The aim of the paper is: (1) to introduce a number of promising results in philosophical and empirical emotion theory to a wider audience; and (2) to begin the task of organizing those results into a computational theoretical framework. (shrink)

In this paper we consider the emerging position in metaphysics that artifact functions characterize real kinds of artifacts. We analyze how it can circumvent an objection by David Wiggins (Sameness and substance renewed, 2001, 87) and then argue that this position, in comparison to expert judgments, amounts to an interesting fine-grained metaphysics: taking artifact functions as (part of the) essences of artifacts leads to distinctions between principles of activity of artifacts that experts in technology have not yet made. We show, (...) moreover, that our argument holds not only in the artifactual realm but also in biology: taking biological functions as (part of the) essences of organs leads to distinctions between principles of activity of organs that biological experts have not yet made. We run our argument on the basis of analyses of artifact and biological functions as developed in philosophy of technology and of biology, thus importing results obtained outside of metaphysics into the debate on ontological realism. In return, our argument shows that a position in metaphysics provides experts reason for trying to detect differences between principles of activities of artifacts and organs that have not been detected so far. (shrink)

The paper is an examination of the ways and extent to which neuroscience places constraints on cognitive science. In Part I, I clarify the issue, as well as the notion of levels in cognitive inquiry. I then present and address, in Part II, two arguments designed to show that facts from neuroscience are at a level too low to constrain cognitive theory in any important sense. I argue, to the contrary, that there are several respects in which facts from neurophysiology (...) will constrain cognitive theory. Part III then turns to an examination of Connectionism and Classical Cognitivism to determine which, if either, is in a better position to accomodate neural constraints in the ways suggested in Part II. (shrink)

The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call “algorithmic homuncularism,” individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea. Still, I am (...) skeptical about algorithmic homuncularism, and I argue against it by focusing on recent methods for complex data analysis in systems neuroscience. I claim that analyses such as principle components analysis and linear discriminant analysis prevent individuating vehicles as algorithmic homuncularism recommends. Rather, each individual part contributes to a global state space, trajectories of which vary with important task parameters. I argue that, while homuncularism is false, this view still supports a kind of “vehicle realism,” and I apply this view to debates about the explanatory role of representation. (shrink)

How do individuals guide their activities towards some goal? Harry Frankfurt once identified the task of explaining guidance as the central problem in action theory. An explanation has proved to be elusive, however. In this paper, I show how we can marshal empirical research to make explanatory progress. I contend that human agents have a primitive capacity to guide visual attention, and that this capacity is actually constituted by a sub-individual psychological control-system: the executive system. I thus illustrate how we (...) can explain exercises of individual-level guidance by appeal to its sub-individual constitution. This opens up a new avenue for explicating agency. (shrink)

In paradigm exercises of agency, individuals guide their activities toward some goal. A central challenge for action theory is to explain how individuals guide. This challenge is an instance of the more general problem of how to accommodate individuals and their actions in the natural world, as explained by natural science. Two dominant traditions–primitivism and the causal theory–fail to address the challenge in a satisfying way. Causal theorists appeal to causation by an intention, through a feedback mechanism, in explaining guidance. (...) Primitivists postulate primitive agential capacities in their explanations. The latter neglect to explain how primitive capacities integrate with findings from natural science. The former do not explain why some feedback mechanism’s activity amounts to the agent’s guidance. In this paper I argue that both traditions should acknowledge a capacity to guide, as actually constituted by the executive system. I argue that appeals to this empirically discovered psychological system explain how individuals guide in a way that integrates with explanations from cognitive science. Individuals’ capacity to guide is embedded in the natural world through the activity of its constituent (mechanistic) components. (shrink)

Nowadays, philosophers and scientists tend to agree that, even though human and artificial intelligence work quite differently, they can still illuminate aspects of each other, and knowledge in one domain can inspire progress in the other. For instance, the notion of “artificial” or “synthetic” phenomenology has been gaining some traction in recent AI research. In this paper, we ask the question: what (if anything) is the use of thinking about phenomenology in the context of AI, and in particular machine learning? (...) We will isolate one sense of “phenomenology”, namely the sense in which it is commonly understood within analytic philosophy of perception. Then, we will give examples of projects within sensory substitution and restoration science that rely heavily on machine learning and to which, according to us, phenomenology in the sense specified makes a relevant contribution. Finally, we will shed some light on what this contribution looks like and why it is important. (shrink)

The functionalist approach to kinds has suffered recently due to its association with law-based approaches to induction and explanation. Philosophers of science increasingly view nomological approaches as inappropriate for the special sciences like psychology and biology, which has led to a surge of interest in approaches to natural kinds that are more obviously compatible with mechanistic and model-based methods, especially homeostatic property cluster theory. But can the functionalist approach to kinds be weaned off its dependency on laws? Dan Weiskopf has (...) recently offered a reboot of the functionalist program by replacing its nomological commitments with a model-based approach more closely derived from practice in psychology. Roughly, Weiskopf holds that the natural kinds of psychology will be the functional properties that feature in many empirically successful cognitive models, and that those properties need not be localized to parts of an underlying mechanism. I here skeptically examine the three modeling practices that Weiskopf thinks introduce such non-localizable properties: fictionalization, reification, and functional abstraction. In each case, I argue that recognizing functional properties introduced by these practices as autonomous kinds comes at clear cost to those explanations’ counterfactual explanatory power. At each step, a tempting functionalist response is parochialism: to hold that the false or omitted counterfactuals fall outside the modeler’s explanatory aims, and so should not be counted against functional kinds. I conclude by noting the dangers this attitude poses to scientific disagreement, inviting functionalists to better articulate how the individuation conditions for functional kinds might outstrip the perspective of a single modeler. (shrink)

In this paper we argue that the insistence by Fodor et. al. that the Language of Thought hypothesis must be true rests on mistakes about the kinds of explanations that must be provided of cognitive phenomena. After examining the canonical arguments for the LOT, we identify a weak version of the LOT hypothesis which we think accounts for some of the intuitions that there must be a LOT. We then consider what kinds of explanation cognitive phenomena require, and conclude that (...) three main confusions lead to the invalid inference of the truth of a stronger LOT hypothesis from the weak and trivial version. These confusions concern the relationship between syntax and semantics, the nature of higher-level causation in cognitive science, and differing roles of explanations invoking intrinsic structures of minds on the one hand, and aetiological or evolutionary accounts of their properties on the other. (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)

The variation and selection form of explanationcan be prescinded from the evolutionary biologyhome ground in which it was discovered and forwhich it has been most developed. When this isdone, variation and selection explanations arefound to have potential application to a widerange of phenomena, far beyond the classicalbiological ground and the contemporaryextensions into epistemological domains. Itappears as the form of explanation most suitedto phenomena of fit. It is also found toparticipate in multiple interestingrelationships with other forms of explanation. We proceed with (...) an examination of multiplekinds of phenomena, interrelationships withother members of the family of forms ofexplanation, and some novel applications evenwithin the home ground of evolutionary biology. (shrink)

The survival enhancing propensity (SEP) account has a crucial role to play in the analysis of proper function. However, a central feature of the account, its specification of the proper environment to which functions are relativized, is seriously underdeveloped. In this paper, I argue that existent accounts of proper environment fail because they either allow too many or too few characters to count as proper functions. While SEP accounts retain their promise, they are unworkable because of their inability to specify (...) this important feature. However, I suggest that this problem can be overcome by the application of a new strategy for specifying proper environment that is grounded in the operation of natural selection and I conclude by offering a first approximation of such an account. (shrink)

There is general agreement that from the first few months of life, our apprehension of physical objects accords, in some sense, with certain principles. In one philosopher's locution, we are 'perceptually sensitive' to physical principles describing the behavior of objects. But in what does this accordance or sensitivity consist? Are these principles explicitly represented or merely 'implemented'? And what sort of explanation do we accomplish in claiming that our object perception accords with these principles? My main goal here is to (...) suggest answers to these questions. I argue that the object principles are not explicitly represented, first addressing some confusion in the debate about what that means. On the positive side, I conclude that the principles supply a competence account, at Marr's computational level, and that they function like natural constraints in vision. These are among their considerable explanatory benefits - benefits endowed by rules and principles in other cognitive domains as well. Characterizing the explanatory role of the object principles is my main project here, but in pursuing certain sub-goals I am led to other conclusions of interest in their own right. I address an argument that the object principles are explicitly represented which assumes that object perception is substantially thought-like. This provokes a jaunt off the main path which leads to interesting territory: the boundary between thought and perception. I argue that object apprehension is much closer to perception than to thought on the spectrum between the two. (shrink)

In this paper I critically examine the Generality Problem and argue that it does not succeed as an objection to reliabilism. Although those who urge the Generality Problem are correct in claiming that any process token can be given indefinitely many descriptions that pick out indefinitely many process types, they are mistaken in thinking that reliabilists have no principled way to distinguish between relevant and irrelevant process types.