Daniel Weiskopf

Khalidi identifies cognition with Marrian computation. He further argues that Marrian levels of inquiry should be interpreted ontologically as corresponding to distinct semi‐closed causal domains. But this counterintuitively places the causal domain of representations outside of cognition proper. A closer look at Khalidi's account of concepts shows that these allegedly separate Marrian domains are more tightly integrated than he allows. Theories of concepts converge on algorithmic‐representational models rather than computational ones. This suggests that we should reject the wholesale identification of cognition with computation.

Traditionally, theories of concepts in psychology assume that concepts are a single, uniform kind of mental representation. But no single kind of representation can explain all of the empirical data for which concepts are responsible. I argue that the assumption that concepts are uniformly the same kind of mental structure is responsible for these theories’ shortcomings, and outline a pluralist theory of concepts that rejects this assumption. On pluralism, concepts should be thought of as being constituted by multiple representational kinds, with the particular kind of concept used on an occasion being determined by the context. I argue that endorsing pluralism does not lead to eliminativism about concepts as an object of scientific interest

Neuroscientists have in recent years turned to building models that aim to generate predictions rather than explanations. This “predictive turn” has swept across domains including law, marketing, and neuropsychiatry. Yet the norms of prediction remain undertheorized relative to those of explanation. I examine two styles of predictive modeling and show how they exemplify the normative dynamics at work in prediction. I propose an account of how predictive models, conceived of as technological devices for aiding decision-making, can come to be adequate for purposes that are defined by both their guiding research questions and their larger social context of application.

Indigenous peoples possess enormously rich and articulated knowledge of the natural world. A major goal of research in anthropology and ethnobiology as well as ecology, conservation biology, and development studies is to find ways of integrating this knowledge with that produced by academic and other institutionalized scientific communities. Here I present a challenge to this integration project. I argue, by reference to ethnographic and cross-cultural psychological studies, that the models of the world developed within specialized academic disciplines do not map onto anything existing within traditional beliefs and practices for coping with nature. Traditional ecological knowledge is distributed across a heterogeneous array of overlapping practices within Indigenous cultures, including spiritual and ritual practices that invoke categories, properties, and causal-explanatory models that do not in general converge with those of the academic sciences. In light of this divergence I argue that we should abandon the integration project, and conclude by sketching a notion of knowledge coordination as a possible successor framework.

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience.

This article outlines a program of ethnoontology that brings together empirical research in the ethnosciences with ontological debates in philosophy. First, we survey empirical evidence from heterogeneous cultural contexts and disciplines. Second, we propose a model of cross‐cultural relations between ontologies beyond a simple divide between universalist and relativist models. Third, we argue for an integrative model of ontology building that synthesizes insights from different fields such as biological taxonomy, cognitive science, cultural anthropology, and political ecology. We conclude by arguing that a program of ethnoontology provides philosophers both with insights about traditional issues such as debates about natural kinds and with novel strategies for connecting philosophy with pressing global issues such as the conservation of local environments and the self‐determination of Indigenous communities.

Psychology aims to give us a scientific account of how the mind works. But what does it mean to have a science of the mental, and what sort of picture of the mind emerges from our best psychological theories? This book addresses these philosophical puzzles in a way that is accessible to readers with little or no background in psychology or neuroscience. Using clear and detailed case studies and drawing on up-to-date empirical research, it examines perception and action, the link between attention and consciousness, the modularity of mind, how we understand other minds, and the influence of language on thought, as well as the relationship between mind, brain, body, and world. The result is an integrated and comprehensive overview of much of the architecture of the mind, which will be valuable for both students and specialists in philosophy, psychology, and cognitive science.

Natural kind concepts have the function of tracking categories that exist independently of our beliefs and purposes. But not all ways of tracking categories in the natural world involve conceiving of them as natural kinds. Anthropic concepts represent groups of natural, mind‐independent entities that are apt for serving various human interests, goals, and projects. They represent the natural world under a practical mode of presentation, as a set of material resources that can be transformed to further a host of functions and ends. I argue that cross‐cultural studies of traditional ecological knowledge provide evidence that many chemical, mineral, and biological concepts that are frequently taken to be natural kind concepts turn out on closer inspection to be anthropic concepts. Anthropic concepts are distinguished from artifact concepts, and a form of realism about the anthropic kinds that they refer to is defended.

Having concepts is a distinctive sort of cognitive capacity. One thing that conceptual thought requires is obeying the Generality Constraint: concepts ought to be freely recombinable with other concepts to form novel thoughts, independent of what they are concepts of. Having concepts, then, constrains cognitive architecture in interesting ways. In recent years, spurred on by the rise of evolutionary psychology, massively modular models of the mind have gained prominence. I argue that these architectures are incapable of satisfying the Generality Constraint, and hence incapable of underpinning conceptual thought. I develop this argument with respect to two well-articulated proposals, due to Dan Sperber and Peter Carruthers. Neither proposal gives us a satisfactory explanation of Generality within the confines of a genuinely modular architecture. Massively modular minds may display considerable behavioral and cognitive flexibility, but not humanlike conceptualized thought.

Philosophers have historically been concerned with concepts and their analysis, and in recent decades psychologists have also begun to speculate on what kinds of structures concepts might be. I take concepts to satisfy three core desiderata: they are mental representations, they are the constituents of thoughts, and they are centrally employed in categorization. There are four major theories of concepts currently in play: definitionism, prototype and exemplar theory, the 'theory theory', and conceptual atomism. I survey these theories and argue that none of them adequately satisfies all three of the core properties. The reasons for these failures are principled, and generalize to many other positions that share their assumptions. Concepts do not form a unitary psychological natural kind. I propose taking a pluralist view of concepts. Conceptual representations are adaptively constructed and used by a set of processes that vary with elements of the task, subject, domain, and wider context

In recent decades, attempts to explain autism have been frustrated by the heterogeneous nature of its behavioral symptoms and the underlying genetic, neural, and cognitive mechanisms that produce them. This has led some to propose eliminating the category altogether. The eliminativist inference relies on a conception of psychiatric categories as kinds defined by their underlying mechanistic structure. I review the evidence for eliminativism and propose an alternative model of the family of autisms. On this account, autism is a network category defined by a set of idealized exemplars linked by multiple levels of theoretically significant properties. I argue that this network model better captures the empirical phenomena, the historical growth of the category, and the ways the category has been shaped by social norms and interests. Finally, I defend a realist interpretation of network categories against the challenge from eliminativists.

Certain of our concepts are innate, but many others are learned. Despite the plausibility of this claim, some have argued that the very idea of concept learning is incoherent. I present a conception of learning that sidesteps the arguments against the possibility of concept learning, and sketch several mechanisms that result in the generation of new primitive concepts. Given the rational considerations that motivate their deployment, I argue that these deserve to be called learning mechanisms. I conclude by replying to the objections that these mechanisms cannot produce genuinely new content and cannot be part of genuinely cognitive explanations

Concept empiricists are committed to the claim that the vehicles of thought are re-activated perceptual representations. Evidence for empiricism comes from a range of neuroscientific studies showing that perceptual regions of the brain are employed during cognitive tasks such as categorization and inference. I examine the extant neuroscientific evidence and argue that it falls short of establishing this core empiricist claim. During conceptual tasks, the causal structure of the brain produces widespread activity in both perceptual and non-perceptual systems. I lay out several conditions on what is required for a neural state to be a realizer of the functional role played by concepts, and argue that no subset of this activity can be singled out as the unique neural vehicle of conceptual thought. Finally, I suggest that, while the strongest form of empiricism is probably false, the evidence is consistent with several weaker forms of empiricism

Christopher Gauker proposes that all cognition can be divided into nonconceptual image-based thought and conceptual language-based thought. The division between the two hinges on the representational powers of their respective mediums. I argue that a richer variety of representational states and processes is necessary in order to explain both human and nonhuman cognition. There are aspects of nonhuman cognition that cannot be explained simply by images, and there are aspects of human conceptual thought, particularly those dealing with causal reasoning, that push us towards positing abstract systems of representation that are neither imagistic nor linguistic. Conceptual thought, then, occurs in its own proprietary inner code, not in images or words

Traditionally, the language faculty was supposed to be a device that maps linguistic inputs to semantic or conceptual representations. These representations themselves were supposed to be distinct from the representations manipulated by the hearer’s perceptual and motor systems. Recently this view of language has been challenged by advocates of embodied cognition. Drawing on empirical studies of linguistic comprehension, they have proposed that the language faculty reuses the very representations and processes deployed in perceiving and acting. I review some of the evidence and arguments in favor of the embodied view of language comprehension, and argue that none of it is conclusive. Moreover, the embodied view itself blurs two important distinctions: first, the distinction between linguistic comprehension and its typical consequences; and second, the distinction between representational content and vehicles. Given that these distinctions are well-motivated, we have good reason to reject the embodied view of linguistic understanding.Keywords: Language; Comprehension; Perception; Action; Embodied cognition; Representation.

The "New Synthesis" in cognitive science is committed to the computational theory of mind (CTM), massive modularity, nativism, and adaptationism. In The mind doesn't work that way , Jerry Fodor argues that CTM has problems explaining abductive or global inference, but that the New Synthesis offers no solution, since massive modularity is in fact incompatible with global cognitive processes. I argue that it is not clear how global human mentation is, so whether CTM is imperiled is an open question. Massive modularity also lacks some of the invidious commitments Fodor ascribes to it. Furthermore, Fodor's anti-adaptationist arguments are in tension with his nativism about the contents of modular systems. The New Synthesis thus has points worth preserving.

models of cognition are essentially incomplete because they fail to capture the temporal properties of mental processing. I present two possible interpretations of the dynamicists' argument from time and show that neither one is successful. The disagreement between dynamicists and symbolic theorists rests not on temporal considerations per se, but on differences over the multiple realizability of cognitive states and the proper explanatory goals of psychology. The negative arguments of dynamicists against symbolic models fail, and it is doubtful whether pursuing dynamicists' explanatory goals will lead to a robust psychological theory. Introduction Elements of the symbolic theory Elements of dynamical systems theory The argument from time 4.1 First interpretation of the argument from time 4.2 Second interpretation of the argument from time Limits of dynamical systems theory

Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for abstracting the functional properties of the system, which may not coincide with its mechanistic organization. I describe these techniques and show that despite being non-mechanistic, these cognitive models can satisfy the normative constraints on good explanations

Jean Mandler proposes an original and richly detailed theory of how concepts relate to sensory and motor capacities. I focus on her claims about conceptual representations and the processes that produce them. On her view, concepts are declarative representations of object kind information. First, I argue that since sensorimotor representations may be declarative, there is no bar to percepts being constituents of concepts. Second, I suggest that concepts track kinds and other categories not by representing kind information per se, but rather by being subject to the appropriate sort of inferential dispositions. These dispositions themselves may apply equally to perceptual and non-perceptual representations. Third, I argue that Mandler's proposed redescriptive mechanism for producing conceptual primitives can be viewed as a kind of Fodorian triggering device. Hence there may be less distance between her view and Fodor's than either one has supposed. I suggest that redescription needs to be supplemented with several other kinds of more flexible and open-ended concept learning mechanisms. Finally, I briefly sketch the view of conceptual development that results from adopting these proposals and contrast it with Mandler's

Conceptual atomists argue that most of our concepts are primitive. I take up three arguments that have been thought to support atomism and show that they are inconclusive. The evidence that allegedly backs atomism is equally compatible with a localist position on which concepts are structured representations with complex semantic content. I lay out such a localist position and argue that the appropriate position for a non-atomist to adopt is a pluralist view of conceptual structure. I show several ways in which conceptual pluralism provides an advantage in satisfying the empirical and philosophical demands on a theory of conceptual structure and content

A ‘Radical Simulationist’ account of how folk psychology functions has been developed by Robert Gordon. I argue that Radical Simulationism is false. In its simplest form it is not sufficient to explain our attribution of mental states to subjects whose desires and preferences differ from our own. Modifying the theory to capture these attributions invariably generates innumerable other false attributions. Further, the theory predicts that deficits in mentalizing ought to co-occur with certain deficits in imagining perceptually-based scenarios. I present evidence suggesting that this prediction is false, and outline further possible empirical tests of the theory.

There are good reasons to think natural languages are compositional. But compound nominals (CNs) are largely productive constructions that have proven highly recalcitrant to compositional semantic analysis. I evaluate existing proposals to treat CNs compositionally and argue that they are unsuccessful. I then articulate an alternative proposal according to which CNs contain covert indexicals. Features of the context allow a variety of relations to be expressed using CNs, but this variety is not expressed in the lexicon or the semantic rules of the language. This proposal accounts for the diversity of contents CNs can be used to express while preserving compositionality. Finally, I defend this proposal against some recent anti-contextualist arguments.