In 1951, Lashley highlighted the importance of serial order for the brain and behavioural sciences. He considered the response chaining account untenable and proposed an alternative employing parallel response activation and "schemata for action". Subsequently, much has been learned about sequential behaviour, particularly in the linguistic domain. We argue that these developments support Lashley's picture, and recent computational models compatible with it are described. The models are developed in a series of steps, beginning with the basic problem of parallel response competition and its possible resolution into serial action. At each stage, important limitations of the previous models are identified and simple additions proposed to overcome them, including the provision of learning mechanisms. Each type of model is compared with relevant data, and the importance of error data is emphasized. Taken together, the models constitute a unified approach to serial order which has achieved considerable explanatory success across disparate domains.

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This book is about the interface between natural language and the sensorimotor system. It is obvious that there is an interface between language and sensorimotor cognition, because we can talk about what we see and do. The main proposal in the book is that the interface is more direct than is commonly assumed. To argue for this proposal I focus on a simple concrete episode—a man grabbing a cup—which can be reported in a simple transitive sentence (e.g. the English sentence The man grabbed a cup). In the first part of the book I present a detailed model of the sensorimotor processes involved in experiencing this episode, both as the agent bringing it about and as an observer watching it happen. The model draws on a large body of research in neuroscience and psychology. I also present a model of the syntactic structure of the associated transitive sentence, developed within the entirely separate discipline of theoretical linguistics. This latter model is a version of Chomsky’s ‘Minimalist ’ syntactic theory, which assumes that a sentence reporting the episode has the same underlying syntactic structure (called ‘logical form’) regardless of which language it is in. My main proposal is that these two independently motivated models are in fact closely

An evaluation of distributed learning as a means to attenuate the category proliferation problem in Fuzzy ARTMAP based neural systems is carried out, from both qualitative and quantitative points of view. The study involves two original winner-take-all (WTA) architectures, Fuzzy ARTMAP and FasArt, and their distributed versions, dARTMAP and dFasArt. A qualitative

Most functional magnetic resonance imaging (fMRI) studies examining working memory (WM) load have focused on the prefrontal cortex (PFC) and have demonstrated increased prefrontal activity with increased load. Here we examined WM load effects in the medial temporal lobe (MTL) using an fMRI Sternberg task with novel complex visual scenes. Trials consisted of 3 sequential events: 1) sample presentation (encoding), 2) delay period (maintenance), and 3) probe period (retrieval). During sample encoding, subjects saw either 2 or 4 pictures consecutively. During retrieval, subjects indicated whether the probe picture matched one of the sample pictures. Results revealed that activity in the left anterior hippocampal formation, bilateral retrosplenial area, and left amygdala was greater at retrieval for trials with larger memory load, whereas activity in the PFC was greater at encoding for trials with larger memory load. There was no load effect during the delay. When encoding, maintenance, and retrieval periods were compared with fixation, activity was present in the hippocampal body/tail and fusiform gyrus bilaterally during encoding and retrieval, but not maintenance. Bilateral dorsolateral prefrontal activity was present during maintenance, but not during encoding or retrieval. The results support models of WM predicting that activity in the MTL should be modulated by WM load.

this paper is part of the author's doctoralresearch, supervisedby Noel Sharkey and Henry Shaffer, supported by an SERC studentship. The functions within the model are bipartite. Firstly, bottom-up processes using ART2 classify patterns of pitch and interval use. Secondly, the identities emerging from this process are used to learn associations of pitch, key and degree. These associations allow the encoding, transposition and recovery of tunes in any key. 2 Inducing Key from Pitch Frequency

This article is concerned with the problem of serial order in short-term memory. More specifically, it addresses the question of how we store and retrieve a novel sequence of items in the correct order. This is the task we face when, for example, attempting to dial a telephone number that we have only recently heard. This task of verbatim or serial recall has a long history of laboratory study, underlying much of the research on the forgetting (e.g., Brown, 1958; Peterson & Peterson, 1959), organization (e.g., Miller, 1956/ 1994) and structure (e.g., Baddeley, 1986) associated with short-term memory. Surprisingly however, the basic psychological processes underlying the task remain little understood

asahtan{at}ntu.edu.sg This paper presents a model of neural network embodiment of intentions and planning mechanisms for autonomous agents. The model bridges the dichotomy of symbolic and non-symbolic representation in developing agents. Some novel techniques are introduced that enables the neural network to process and manipulate sequential and hierarchical structures of information. It is suggested that by incorporating intentional agent model which relies on explicit symbolic description with self-organizing neural networks that are good at learning and recognizing patterns, the best from both sides can be exploited. This paper demonstrates that plans can be represented as weighted connections and reasoning processes can be accommodated through multidirectional activations accross different modalities of patterns. The network seamlessly interleaves planning and learning processes towards achieving the goal. Case studies and experiments shows that the model can be used to execute, plan, and capture plans as recipes through experiences.

Some forms of artificial neural network models develop representations that have a high visual information content. An example of this kind of network is the Kohonen Feature Map (KFM). This paper describes how a KFM can be used in a model that categorises memorised sequential patterns of notes into representations of key and degrees of a musical scale. These patterns are derived from abstractions of musical sounds identified with pitch and interval. Both key and degree are important musical structures in the cognitive organisation of tonality. The acquisition of tonal organisation for music is analogous to the acquisition of a language. The representations developed within the KFM form a map that can be seen to correspond directly with the images used by musicians to represent key relations.

This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration. No part of it has been submitted for any other degree or qualification.