Eric Margolis

The notion of domain specificity plays a central role in some of the most important debates in cognitive science. Yet, despite the widespread reliance on domain specificity in recent theorizing in cognitive science, this notion remains elusive. Critics have claimed that the notion of domain specificity can't bear the theoretical weight that has been put on it and that it should be abandoned. Even its most steadfast proponents have highlighted puzzles and tensions that arise once one tries to go beyond an initial intuitive sketch of what domain specificity involves. In this paper, we address these concerns head on by developing an account of what it means for a cognitive mechanism to be domain specific that overcomes the obstacles that have made domain specificity seem so problematic. We then apply this understanding of domain specificity to one of the key debates that it has figured prominently in—the rationalism-empiricism debate concerning the origins of cognitive traits—and introduce several related theoretical notions that work alongside domain specificity in helping to clarify what makes a view more (or less) rationalist. This example illustrates how the notion of domain specificity can, and should, continue to play a central role in ongoing debates in cognitive science.

Fiona Cowie's book What's Within: Nativism Reconsidered offers an important critical assessment of nativist views of the mind. She provides an account of what nativism consists in, and discusses prominent nativist views of concept acquisition and language acquisition. In the latter case, she also offers an empiricist alternative to Chomskyan nativist accounts, and claims that the main arguments for an innate language faculty—one that embodies Universal Grammar—don't work. We provide an overview of her position, focusing mostly on her views about natural language, and argue that she has underestimated the power the poverty of the stimulus argument and consequently has underestimated the advantages of nativist models of language acquisition.

I argue that the human mind includes an innate domain-specific system for representing precise small numerical quantities. This theory contrasts with object-tracking theories and with domain-general theories that only make use of mental models. I argue that there is a good amount of evidence for innate representations of small numerical quantities and that such a domain-specific system has explanatory advantages when infants’ poor working memory is taken into account. I also show that the mental models approach requires previously unnoticed domain-specific structure and consequently that there is no domain-general alternative to an innate domain-specific small number system.

How do people decide what category an artifact belongs to? Previous studies have suggested that adults and, to some degree, children, categorize artifacts in accordance with the design stance, a categorization system which privileges the designer’s original intent in making categorization judgments. However, these studies have all been conducted in Western, technologically advanced societies, where artifacts are mass produced. In this study, we examined intuitions about artifact categorization among the Shuar, a hunter-horticulturalist society in the Amazon region of Ecuador. We used a forced-choice method similar to previous studies, but unlike these studies, our scenarios involved artifacts that would be familiar to the Shuar. We also incorporated a community condition to examine the possible effect of community consensus on how artifacts are categorized. The same scenarios were presented to university student participants in the UK. Across populations and conditions, participants tended to categorize artifacts in terms of a creator’s intent as opposed to a differing current use. This lends support to the view that the design stance may be a universal feature of human cognition. However, we conclude with some thoughts on the limitations of the present method for studying artifact concepts.

Where do human numerical abilities come from? This article is a commentary on Leibovich et al.’s “From 'sense of number' to 'sense of magnitude' —The role of continuous magnitudes in numerical cognition”. Leibovich et al. argue against nativist views of numerical development by noting limitations in newborns’ vision and limitations regarding newborns’ ability to individuate objects. I argue that these considerations do not undermine competing nativist views and that Leibovich et al.'s model itself presupposes that infant learners have numerical representations.

The topic of this thesis is the nature of human concepts understood as mental symbols or representations. ;Many discussions in this area presuppose an inferential model of concepts taken together with what I call the standard model of concept learning. An inferential model of concepts says that a concept's identity depends upon its participating in inferential dispositions linking it to certain other concepts. For example, one might think that part of what makes a mental symbol the concept BIRD is that it participates in an inferential disposition linking it to the concept ANIMAL. The standard model of concept learning says that learning a concept involves assembling it from previously available concepts. For example, one might think that the concept BIRD is learned by putting together the concept ANIMAL with concepts like WING, FLIES, and so on, yielding a more complex structure. The standard model of concept learning presupposes a form of the inferential model of concepts. Thus one of the reasons many people endorse the inferential model is that they are committed to a broadly empiricist picture of the mind; they assume that most concepts are learned, so they must also assume that most concepts have the sort of structure that the model presupposes. ;My thesis challenges both of these points with respect to a class of concept that has been of central interest in philosophy and cognitive science, namely, the lexical concepts . I argue that the independent evidence for the inferential model in this case is no good and consequently that the standard model of concept learning doesn't work. Still, it remains plausible that many lexical concepts are learned. What we need are new learning models, ones that depart in significant ways from the standard model. I end the thesis by outlining a proposal that may work for the natural kind concepts

What is a concept? Philosophers have given many different answers to this question, reflecting a wide variety of approaches to the study of mind and language. Nonetheless, at the most general level, there are two dominant frameworks in contemporary philosophy. One proposes that concepts are mental representations, while the other proposes that they are abstract objects. This paper looks at the differences between these two approaches, the prospects for combining them, and the issues that are involved in the dispute. We argue that powerful motivations have been offered in support of both frameworks. This suggests the possibility of combining the two. Unlike Frege, we hold that the resulting position is perfectly coherent and well worth considering. Nonetheless, we argue that it should be rejected along with the view that concepts are abstract objects.

One of the most important abilities we have as humans is the ability to think about number. In this chapter, we examine the question of whether there is an essential connection between language and number. We provide a careful examination of two prominent theories according to which concepts of the positive integers are dependent on language. The first of these claims that language creates the positive integers on the basis of an innate capacity to represent real numbers. The second claims that language’s function is to integrate contents from modules that humans share with other animals. We argue that neither model is successful.

At least since W. V. O. Quine's famous critique of the analytic/synthetic distinction, philosophers have been deeply divided over whether there are any analytic truths. One line of thought suggests that the simple fact that people have ' intuitions of analyticity' might provide an independent argument for analyticities. If defenders of analyticity can explain these intuitions and opponents cannot, then perhaps there are analyticities after all. We argue that opponents of analyticity have some unexpected resources for explaining these intuitions and that, accordingly, the argument from intuition fails.

Creations of the Mind presents sixteen original essays by theorists from a wide variety of disciplines who have a shared interest in the nature of artifacts and their implications for the human mind. All the papers are written specially for this volume, and they cover a broad range of topics concerned with the metaphysics of artifacts, our concepts of artifacts and the categories that they represent, the emergence of an understanding of artifacts in infants' cognitive development, as well as the evolution of artifacts and the use of tools by non-human animals. This volume will be a fascinating resource for philosophers, cognitive scientists, and psychologists, and the starting point for future research in the study of artifacts and their role in human understanding, development, and behaviour.Contributors: John R. Searle, Richard E. Grandy, Crawford L. Elder, Amie L. Thomasson, Jerrold Levinson, Barbara C. Malt, Steven A. Sloman, Dan Sperber, Hilary Kornblith, Paul Bloom, Bradford Z. Mahon, Alfonso Caramazza, Jean M. Mandler, Deborah Kelemen, Susan Carey, Frank C. Keil, Marissa L. Greif, Rebekkah S. Kerner, James L. Gould, Marc D. Hauser, Laurie R. Santos, Steven Mithen.

Many philosophers reject the Language of Thought Hypothesis (LOT) on the grounds that is leads to an explanatory regress problem. According to this line of argument, LOT is invoked to explain certain features of natural language, but the language of thought has the very same features and consequently no explanatory progress has been made. In an earlier paper (“Regress Arguments against the Language of Thought”, Analysis 57.1), we argued that this regress argument doesn’t work and that even proponents of LOT have given the regress argument too much credit. In this paper, we extend our critique of the regress argument against LOT by responding to J. Knowles’s commentary of our earlier paper (in “The Language of Thought and Natural Language Understanding”, Analysis 58.4).

One of the most important recent developments in the study of concepts has been the resurgence of interest in nativist accounts of the human conceptual system. However, many theorists suppose that a key feature of neural organization—the brain’s plasticity—undermines the nativist approach to concept acquisition. We argue that, on the contrary, not only does the brain’s plasticity fail to undermine concept nativism, but a detailed examination of the neurological evidence actually provides powerful support for concept nativism.

This article is a commentary on Machery (2009) Doing without Concepts. Concepts are mental symbols that have semantic structure and processing structure. This approach (1) allows for different disciplines to converge on a common subject matter; (2) it promotes theoretical unification; and (3) it accommodates the varied processes that preoccupy Machery. It also avoids problems that go with his eliminativism, including the explanation of how fundamentally different types of concepts can be co-referential.

Many psychologists think that concepts should be understood on analogy with the terms of scientific theories, yet the significance of this claim has always been obscure. In this paper, I clarify the psychological content of the theory analogy, focusing on influential pieces by Susan Carey. Once plainly put, the analogy amounts to the view that a mental representation has its semantic properties by virtue of its role in a restricted knowledge structure. One of the commendable things about Carey's work is that, unlike many other psychologists who appeal to the theory analogy, she takes seriously the need to specify how these structures are constrained. At the same time, the constraints she offers are insufficient. Her account also faces challenges from work in the semantics of natural kind terms.

This paper takes a fresh look at the nativism–empiricism debate, presenting and defending a nativist perspective on the mind. Empiricism is often taken to be the default view both in philosophy and in cognitive science. This paper argues, on the contrary, that there should be no presumption in favor of empiricism (or nativism), but that the existing evidence suggests that nativism is the most promising framework for the scientific study of the mind. Our case on behalf of nativism has four parts. (1) We characterize nativism’s core commitments relative to the contemporary debate between empiricists and nativists, (2) we present the positive case for nativism in terms of two central nativist arguments (the poverty of the stimulus argument and the argument from animals), (3) we respond to a number of influential objections to nativist theories, and (4) we explain the nativist approach to the conceptual system

The Language of Thought Hypothesis is often taken to have the fatal flaw that it generates an explanatory regress. The language of thought is invoked to explain certain features of natural language (e.g., that it is learned, understood, and is meaningful), but, according to the regress argument, the language of thought itself has these same features and hence no explanatory progress has been made. We argue that such arguments rely on the tacit assumption that the entire motivation for the language of thought consists in explaining the explanandum that allegedly generates the regress. But this tacit assumption is simply false. The Language of Thought Hypothesis is a cogent view and one with considerable explanatory advantages.

Philosophers have often claimed that general ideas or representations have their origin in abstraction, but it remains unclear exactly what abstraction as a psychological process consists in. We argue that the Lockean aspiration of using abstraction to explain the origins of all general representations cannot work and that at least some general representations have to be innate. We then offer an explicit framework for understanding abstraction, one that treats abstraction as a computational process that operates over an innate quality space of fine-grained general representations. We argue that this framework has important philosophical implications for the nativism-empiricism dispute, for questions about the acquisition of unstructured representations, and for questions about the relation between human and animal minds.

The Conceptual Mind’s twenty-four newly commissioned essays cover the most important recent theoretical developments in the study of concepts, identifying and exploring the big ideas that will guide further research over the next decade. Topics include concepts and animals, concepts and the brain, concepts and evolution, concepts and perception, concepts and language, concepts across cultures, concept acquisition and conceptual change, concepts and normativity, concepts in context, and conceptual individuation.

A standard view within psychology is that there have been two important shifts in the study of concepts and that each has led to some improvements. The first shift was from the classical theory of concepts to probabilistic theories, including the prototype theory. The second shift was from probabilistic theories to theory-based theories. In this article, I critically evaluate the view that the first shift was a major advance and argue that the prototype theory suffers some of the same problems that have been thought to challenge the classical theory.

Strong nativist views about numerical concepts claim that human beings have at least some innate precise numerical representations. Weak nativist views claim only that humans, like other animals, possess an innate system for representing approximate numerical quantity. We present a new strong nativist model of the origins of numerical concepts and defend the strong nativist approach against recent cross-cultural studies that have been interpreted to show that precise numerical concepts are dependent on language and that they are restricted to speakers of languages with the right kind of structure.

In LOT 2: The Language of Thought Revisited, Jerry Fodor argues that concept learning of any kind—even for complex concepts—is simply impossible. In order to avoid the conclusion that all concepts, primitive and complex, are innate, he argues that concept acquisition depends on purely noncognitive biological processes. In this paper, we show (1) that Fodor fails to establish that concept learning is impossible, (2) that his own biological account of concept acquisition is unworkable, and (3) that there are in fact many promising general models for explaining how concepts are learned.

This chapter provides a critical overview of ten central arguments that philosophers have given in support of a distinction between the conceptual and the nonconceptual. We use these arguments to examine the question of whether (and in what sense) perceptual states might be deemed nonconceptual and also whether (and in what sense) animals and infants might be deemed to lack concepts. We argue that philosophers have implicitly relied on a wide variety of different ways to draw the conceptual/nonconceptual distinction and that all ten of the arguments we discuss face considerable difficulties.