Pinker, S. & Bloom, P. (1990). Natural language and natural selection. Behavioral and Brain Sciences 13

We examine the question of which aspects of language are uniquely human and uniquely linguistic in light of recent arguments by Hauser, Chomsky, and Fitch that the only such aspect is syntactic recursion, the rest of language being either specific to humans but not to language (e.g., words and concepts) or not specific to humans (e.g., speech perception). We find this argument problematic. It ignores the many aspects of grammar that are not recursive, such as phonology, morphology, case, and agreement. It is inconsistent with the anatomy and neural control of the human vocal tract. And it is weakened by experiments showing that speech perception cannot be reduced to primate audition, that word learning cannot be reduced to fact learning, and that at least one gene involved in speech and language was evolutionarily selected in the human lineage but is not specific to recursion. The recursion-only claim, we suggest, is motivated by Chomsky’s recent approach to syntax, the Minimalist Program, which de-emphasizes the same aspects of language. The approach, however, is sufficiently problematic that it cannot be used to support claims about evolution. We contest other arguments from Chomsky that language is not an adaptation, namely that it is “perfect, ” nonredundant, unusable in any partial form, and badly designed for communication. The hypothesis that language is a complex adaptation for communication which evolved piecemeal avoids all these problems.

this article we outline the foundations of such a theory, working in the general framework of Bayesian inference. Much of our proposal for extending Shepard's theory to the cases of multiple examples and arbitrary stimulus structures has already been introduced in other papers (Griffiths & Tenenbaum, 2000; Tenenbaum, 1997, 1999a, 1999b; Tenenbaum & Xu, 2000). Our goal here is to make explicit the link to Shepard's work and to use our framework to make connections between his work and other models of learning (Feldman, 1997; Gluck & Shanks, 1994; Haussler, Kearns & Schapire, 1994; Kruschke, 1992; Mitchell, 1997), generalization (Nosofsky, 1986; Heit, 1998), and similarity (Chater & Hahn, 1997; Medin, Goldstone & Gentner, 1993; Tversky, 1977). In particular, we will have a lot to say about how our generalization of Shepard's theory relates to Tversky's (1977) well-known set-theoretic models of similarity. Tversky's set-theoretic approach and Shepard's metric space approach are often considered the two classic -- and classically opposed -- theories of similarity and generalization. By demonstrating close parallels between Tversky's approach and our Bayesian generalization of Shepard's approach, we hope to go some way towards unifying these two theoretical approaches and advancing the explanatory power of each. The plan of our article is as follows. In Section 2, we recast Shepard's analysis of generalization in a more general Bayesian framework, preserving the basic principles of his approach in a form that allows us to apply the theory to situations with multiple examples and arbitrary (non-spatially represented) stimulus structures. Sections 3 and 4 describe those extensions, and Section 5 concludes by discussing some implications of our theory for the internalization of...

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Selective deficits in aphasics patients ’ grammatical production and comprehension are often cited as evidence that syntactic processing is modular and localizable in discrete areas of the brain (e.g., Y. Grodzinsky, 2000). The authors review a large body of experimental evidence suggesting that morphosyntactic deficits can be observed in a number of aphasic and neurologically intact populations. They present new data showing that receptive agrammatism is found not only over a range of aphasic groups, but is also observed in neurologically intact individuals processing under stressful conditions. The authors suggest that these data are most compatible with a domain-general account of language, one that emphasizes the interaction of linguistic distributions with the properties of an associative processor working under normal or suboptimal conditions. The primary purpose of this article is to provide empirical arguments in support of a new view of language deficits and their neural correlates, particularly in the realm of syntax. Selective syntactic deficits are often cited as evidence that the human brain contains a bounded and well-defined faculty or module dedicated exclusively to the representation and/or processing of syntax (Caplan & Waters, 1999; Grodzinsky, 1995a,

Human concept learning presents a version of the classic problem of induction, which is made particularly difficult by the combination of two requirements: the need to learn from a rich (i.e. nested and overlapping) vocabulary of possible concepts and the need to be able to generalize concepts reasonably from only a few positive examples. I begin this thesis by considering a simple number concept game as a concrete illustration of this ability. On this task, human learners can with reasonable confidence lock in on one out of a billion billion billion logically possible concepts, after seeing only four positive examples of the concept, and can generalize informatively after seeing just a single example. Neither of the two classic approaches to inductive inference -- hypothesis testing in a constrained space of possible rules and computing similarity to the observed examples -- can provide a complete picture of how people generalize concepts in even this simple setting. This thesis prop...

Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge, and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. We present a Bayesian framework that attempts to meet both goals and describe four applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the four models are defined over different kinds of structures that capture different relationships between the categories in a domain. Our framework therefore shows how statistical inference can operate over structured background knowledge, and we argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.

It is widely assumed that human learning and the structure of human languages are intimately related. This relationship is frequently suggested to be rooted in a language-specific biological endowment, which encodes universal, but arbitrary, principles of language structure (a universal grammar or UG). How might such a UG have evolved? We argue that UG could not have arisen either by biological adaptation or non-adaptationist genetic processes. The resulting puzzle concerning the origin of UG we call the logical problem of language evolution. Because the processes of language change are much more rapid than processes of genetic change, language constitutes a “moving target ” both over time and across different human populations, and hence cannot provide a stable environment to which UG genes could have adapted. We conclude that a biologically determined UG is not evolutionarily viable. Instead, the original motivation for UG—the mesh between learners and languages—arises because language has been shaped to fit the human brain, rather than vice versa. Following Darwin, we view language itself as a complex and interdependent “organism, ” which evolves under selectional pressures from human learning and processing mechanisms. That is, languages are themselves undergoing severe selectional pressure from each generation of language users and learners. This suggests that apparently arbitrary aspects of linguistic structure may result from general learning and processing biases, independent of language. We illustrate how this framework can integrate evidence from different literatures and methodologies to explain core linguistic phenomena, including binding constraints, word order universals, and diachronic language change. 1.

This chapter considers a set of questions at the interface of the study of intuitive theories, causal knowledge, and problems of inductive inference. By an intuitive theory, we mean a cognitive structure that in some important ways is analogous to a scientific theory. It is becoming broadly recognized that intuitive theories play essential roles in organizing

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