this paper, I consider a range of English expressions and show that their context-dependency can be characterized in terms of two properties: 1. They specify entities in an evolving model of the discourse that the listener is constructing; 2. The particular entity specified depends on another entity in that part of the evolving "discourse model" that the listener is currently attending to

Many theorists assume that the cognitive system is composed of a collection of encapsulated processing components or modules, each dedicated to performing a particular cognitive function. On this view, selective impairments of cognitive tasks following brain damage, as evidenced by double dissociations, are naturally interpreted in terms of the loss of particular processing components. By contrast, the current investigation examines in detail a double dissociation between concrete and abstract word reading after damage to a connectionist network that pronounces words via meaning and yet has no separable components (Plaut & Shallice, 1993). The functional specialization in the network that gives rise to the double dissociation is not transparently related to the network's structure, as modular theories assume. Furthermore, a consideration of the distribution of effects across quantitatively equivalent individual lesions in the network raises specific concerns about the interpretation of...

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A growing body of work demonstrates that syntactic structure can evolve in populations of genetically identical agents. Traditional explanations for the emergence of syntactic structure employ an argument based on genetic evolution: syntactic structure is specified by an innate Language Acquisition Device (LAD). Knowledge of language is complex, yet the data available to the language learner is sparse. This incongruous situation, termed the "poverty of the stimulus", is accounted for by placing much of the specification of language in the LAD. The assumption is that the characteristic structure of language is somehow coded genetically. The effect of language evolution on the cultural substrate, in the absence of genetic change, is not addressed by this explanation. We show that the poverty of the stimulus introduces a pressure for compositional language structure when we consider language evolution resulting from iterated observational learning. We use a mathematical model to map the space of parameters that result in compositional syntax. Our hypothesis is that compositional syntax cannot be explained by understanding the LAD alone: compositionality is an emergent property of the dynamics resulting from sparse language exposure.

We present a detailed process theory of the moment-by-moment working-memory retrievals and associated control structure that subserve sentence comprehension. The theory is derived from the application of independently motivated principles of memory and cognitive skill to the specialized task of sentence parsing. The resulting theory construes sentence processing as a series of skilled associative memory retrievals modulated by similarity-based interference and fluctuating activation. The cognitive principles are formalized in computational form in the Adaptive Control of Thought–Rational (ACT–R) architecture, and our process model is realized in ACT–R. We present the results of 6 sets of simulations: 5 simulation sets provide quantitative accounts of the effects of length and structural interference on both unambiguous and garden-path structures. A final simulation set provides a graded taxonomy of double center embeddings ranging from relatively easy to extremely difficult. The explanation of center-embedding difficulty is a novel one that derives from the model’s complete reliance on discriminating retrieval cues in the absence of an explicit representation of serial order information. All fits were obtained with only 1 free scaling parameter fixed across the simulations; all other parameters were ACT–R defaults. The modeling results support the hypothesis that fluctuating activation and similarity-based interference are the key factors shaping working memory in sentence processing. We contrast the theory and empirical predictions with several related accounts of sentence-processing complexity.

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.

The authors present a computational model that builds a holographic lexicon representing both word meaning and word order from unsupervised experience with natural language. The model uses simple convolution and superposition mechanisms (cf. B. B. Murdock, 1982) to learn distributed holographic representations for words. The structure of the resulting lexicon can account for empirical data from classic experiments studying semantic typicality, categorization, priming, and semantic constraint in sentence completions. Furthermore, order information can be retrieved from the holographic representations, allowing the model to account for limited word transitions without the need for built-in transition rules. The model demonstrates that a broad range of psychological data can be accounted for directly from the structure of lexical representations learned in this way, without the need for complexity to be built into either the processing mechanisms or the representations. The holographic representations are an appropriate knowledge representation to be used by higher order models of language comprehension, relieving the complexity required at the higher level.

The paper studies how a group of distributed agents may spontaneously and autonomously develop a language to refer to other agents in their environment by engaging in a series of language games. The language is adaptive in the sense that it expands or adjusts to the entry of new agents and new meanings. The paper describes the language formation mechanisms and details the results of computational simulations. Keywords: origins of language, self-organization, distributed agents, open systems. 1 Introduction The paper proposes a set of mechanisms by which a group of distributed agents may develop autonomously a language for identifying other agents in their environment. The set of agents and the set of features used for making distinctions are open-ended. The language autonomously adapts by the individual actions of agents with only local interactions. Concretely, three mechanisms are proposed: (1) Agents adopt wordmeaning associations from others and thus words propagate in the popu...

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We explore how evolutionary game dynamics have to be modified to accomodate a mathematical framework for the evolution of language. In particular, we are interested in the evolution of vocabulary, that is associations between signals and objects. We assume that successful communication contributes to biological fitness: individuals who communicate well leave more offspring. Children inherit from their parents a strategy for language learning (a language acquisition device). We consider three mechanisms whereby language is passed from one generation to the next: (i) parental learning: children learn the language of their parents; (ii) role model learning: children learn the language of individuals with a high payoff; and (iii) random learning: children learn the language of randomly chosen individuals. We show that parental and role model learning outperform random learning. Then we introduce mistakes in language learning and study how this process changes language over time. Mistakes increase the overall efficacy of parental and role model learning: in a world with errors evolutionary adaptation is more e$cient. Our model also provides a simple explanation why homonomy is common while synonymy is rare.