In everyday tasks, selecting actions in the proper sequence requires a continuously updated representation of temporal context. Many existing models address this problem by positing a hierarchy of processing units, mirroring the roughly hierarchical structure of naturalistic tasks themselves. Such an approach has led to a number of difficulties, including a reliance on overly rigid sequencing mechanisms, an inability to account for context sensitivity in behavior, and a failure to address learning. We consider here an alternative framework, according to which the representation of temporal context is facilitated by recurrent connections within a network mapping from environmental inputs to actions. Applying this approach to a specific, and in many ways prototypical, everyday task (coffee-making), we examine its ability to account for several central characteristics of normal and impaired human performance. The model we consider learns to deal flexibly with a complex set of sequencing constraints, encoding contextual information at multiple time-scales within a single, distributed internal representation. Mildly degrading this context representation leads

Psycholinguistic research has shown that the influence of abstract syntactic knowledge on performance is shaped by particular sentences that have been experienced. To explore this idea, the authors applied a connectionist model of sentence production to the development and use of abstract syntax. The model makes use of (a) error-based learning to acquire and adapt sequencing mechanisms and (b) meaning–form mappings to derive syntactic representations. The model is able to account for most of what is known about structural priming in adult speakers, as well as key findings in preferential looking and elicited production studies of language acquisition. The model suggests how abstract knowledge and concrete experience are balanced in the development and use of syntax.

INTRODUCTION Psycholinguistic research into language production---the process of translating thoughts into speech---has long been associated with connectionist models. Spreading activation models of lexical access in production represent some of the earliest applications of connectionist ideas to psycholinguistic data (e.g., Dell & Reich, 1977; Harley, 1984; MacKay, 1982; Stemberger, 1985). These models combined representations from linguistics with interactive activation principles and sought to explain speech errors, particularly errors resulting from multiple causes or processing levels. For example, "Lizst's second Hungarian restaurant " instead of "rhapsody " involves mistakenly using a word that is associatively, syntactically, and phonologically related to the intended word. Activation that spreads interactively among processing levels seems to be a natural way to account for these kinds of slips. Direct all correspondence to: Gar

The time course of spoken word recognition depends largely on the frequencies of a word and its competitors, or neighbors (similar-sounding words). However, variability in natural lexicons makes systematic analysis of frequency and neighbor similarity difficult. Artificial lexicons were used to achieve precise control over word frequency and phonological similarity. Eye tracking provided time course measures of lexical activation and competition (during spoken instructions to perform visually guided tasks) both during and after word learning, as a function of word frequency, neighbor type, and neighbor frequency. Apparent shifts from holistic to incremental competitor effects were observed in adults and neural network simulations, suggesting such shifts reflect general properties of learning rather than changes in the nature of lexical representations. Current models of spoken word recognition share a set of core assumptions that correspond to what Marslen-Wilson (1993) called the macrostructure of spoken word recognition: As speech is heard, multiple lexical candidates are activated and compete for recognition with strengths proportional to their similarity with the input and their prior probabilities (frequencies of occurrence).

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The paper reports on an experiment in which a group of autonomous agents self-organises through cultural evolution constraints on the combination of the individual sounds (phonemes) in their repertoires. We use a selectionist approach whereby a repertoire evolves by mutations of patterns, constrained by functional pressures from perception and production and the need to conform to the group.

Stoianov, Nerbonne and Bouma (1998) trained Simple Recurrent Networks (SRNs) on graphotactics of Dutch monosyllabic words, overcoming shortcomings of previous implementations. The current report is a continuation of our earlier research, but using phonetic data representations instead of orthographic, that is, learning phonotactics. In addition, we conducted further analysis of neural network performance with regard to some variables such as word frequency, length, neighborhood density and error location. The results are compared with reported psycholinguistics analyses. This informal comparison of SRNs and human performance suggests that SRNs can be used for modeling natural language processing. 1 Introduction -- studying lexical constraints with SRNs. The present paper reports on a project investigating how well natural language phonotactics may be learned using neural networks (NN) (Stoianov, Nerbonne, and Bouma 1998, hereafter SNB98), which is interesting from different perspectiv...

tening to an unfamiliar language we no longer experience sequences of discrete words, but rather hear a continuous stream of speech with boundaries separating individual sentences or utterances. Examination of the physical form of speech confirms the impression given by listening to foreign languages. Speech does not contain gaps or other unambiguous markers of word boundaries -- there is no auditory analog of the spaces between words in printed text (Lehiste, 1960). Thus the perceptual experience of native speakers reflects language- Matt Davis Lexical segmentation and vocabulary acquisition 3 specific knowledge of ways in which to divide speech into words. An important set of questions, therefore, concern the sources of information that are used for segmentation and how infants learn to segment the speech stream in order to learn their first words. The continuous nature of speech might not be a problem for infants learning language if they were `spoon-fed' with sin

Abstract. Learning and behaviour of mobile robots faces limitations. In reinforcement learning, for example, an agent learns a strategy to get to only one specific target point within a state space. However, we can grasp a visually localized object at any point in space or navigate to any position in a room. We present a neural network model in which an agent learns a model of the state space that allows him to get to an arbitrarily chosen goal via a short route. By randomly exploring the state space, the agent learns associations between two adjoining states and the action that links them. Given arbitrary starting and goal positions, routefinding is done in two steps. First, an activation gradient spreads around the goal position along the associative connections. Second, the agent uses state-action associations to determine the actions leading to ascend the gradient toward the goal. All mechanisms are biologically justifiable. 1

In this chapter, we review computer models of cognition that have focused on the use of neural networks. These architectures were inspired by research into how computation works in the brain and subsequent work has produced models of cognition with a distinctive flavor. Processing is characterized by patterns of