Following its introduction by Sapir (1933), the term “psychological reality ” has provoked intense reactions from within linguistics as well its neighboring disciplines. Discussions have been particularly heated since the rise of generative grammar, whose proponents made quite strong claims regarding the relationship of theoretical concepts from linguistics to the internal cognitive mechanisms underlying the acquisition and processing of sound patterns. For example, The Sound Pattern of English is asserted to be “a hypothesis concerning the actual internalized grammar of the speaker-hearer ” where grammar refers to “a system which is used in the production and interpretation of utterances (Chomsky and Halle, 1968: 4). ” Although this perspective is by no means universally adopted by phonologists, its dominance in linguistics since the mid-20 th century reflects a major conceptual shift from previous perspectives on the study of language. As noted by Anderson (1985:6; emphasis in original): “Traditionally, linguists have assumed that their concern was the study of languages, taken as (potentially unlimited) sets of possible sentences (or utterances, etc.) forming unitary and coherent systems. Gradually, however, the emphasis in research has shifted…to the properties of grammars, in the sense of

Implementation of a model of the relation between language and sensorimotor cognition

Fodor and Pylyshyn (1988) argue that connectionist models are not able to display systematicity other than by implementing a classical symbol system. This claim entails that connectionism cannot compete with the classical approach as an alternative architectural framework for human cognition. We present a connectionist model of sentence comprehension that does not implement a symbol system yet behaves systematically. It consists in a recurrent neural network that maps sentences describing situations in a microworld, onto representations of these situations. After being trained on particular sentences-situation pairs, the model can comprehend new sentences, even if these describe new situations. We argue that this systematicity arises robustly and in a psychologically plausible manner because it depends on structure inherent in the world.

This paper presents recent research which provides an over-arching model of exemplar theory capable of explaining phenomena across the phonetic and syntactic strata. The model represents a unique exemplar-based account of constituency interactions encompassing both linguistic domains. It yields simulation and experimental results in keeping with experimental findings in the literature on syllable duration variability and offers an exemplar-theoretic account of local grammaticality. In addition, it provides some insights into the nature of exemplar cloud formation, and demonstrates experimentally the potential gains that can be enjoyed via the use of rich exemplar representations. Exemplar Theory was initially proposed in the domain of psychology (Nosofsky, 1986; Hintzman, 1986). However, recent years have seen a growing body of research into exemplar-based

In this study we use a computational model of language learning (MOSAIC) to investigate the extent to which the Optional Infinitive (OI) phenomenon in Dutch and English can be explained in terms of a resource-limited distributional analysis of Dutch and English child-directed speech. The results show that the same version of MOSAIC is able to simulate changes in the pattern of finiteness marking in two children learning Dutch and two children learning English as the average length of their utterances increases. These results suggest that it is possible to explain the key features of the OI phenomenon in both Dutch and English in terms of the interaction between an utterancefinal bias in learning and the distributional characteristics of child-directed speech in the two languages. They also show how computational modelling techniques can be used to investigate the extent to which cross-linguistic similarities in the developmental data can be explained in terms of common processing constraints as opposed to innate knowledge of Universal Grammar. 2

Abstract: This paper is concerned with the large scale structure of the human brain, how that relates to human behaviour, and how certain types of modularity could have emerged through evolution as a result of a combination of fitness advantages and neurophysiological constraints. It is argued that computational modelling involving evolutionary neural network simulations is the best way to explore such issues, and a series of simulation results are presented and discussed. The incorporation of known neurophysiological constraints into the models is seen to be of crucial importance.

Idioms are an ideal testbed for studying the interplay of lexical (content preparing) and syntactic (structure building) mechanisms in language production. This article contributes to the debate about the nature of these mechanismsandtheirrelationshipfromtheviewpointofcomputationalmodeling. We present a neural network model of sentence generation, which is able to produce continuous and discontinuous idioms within regular compositional sentences. The model is a simple recurrent network extended to include a semantic episode representation as an extra input. Our main contribution consists in a detailed analysis of the representational space of the network’s hidden layer, which shows that (1) an implicit structure-content division can arise as a result of internal space reorganization within a single SRN during learning, (2) idioms can be produced by the same general sequencing mechanism that works for regular sentences, (3) the production of idioms is modulated by content-specific mechanisms.