& One of the functions of everyday human language is to communicate meaning. Thus, when one hears or reads the sentence, ‘‘John gave a book to Mary,’ ’ some aspect of an event concerning the transfer of possession of a book from John to Mary is (hopefully) transmitted. One theoretical approach to language referred to as construction grammar emphasizes this link between sentence structure and meaning in the form of grammatical constructions. The objective of the current research is to (1) outline a functional description of grammatical construction processing based on principles of psycholinguistics, (2) develop a model of how these functions can be implemented in human neurophysiology, and then (3) demonstrate the feasibility of the resulting model in processing languages of typologically diverse natures, that is, English, French, and Japanese. In this context, particular interest will be directed toward the processing of novel compositional structure of relative phrases. The simulation results are discussed in the context of recent neurophysiological studies of language processing. &

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

The saccadic system is adjusted throughout lifetime. A learning scheme that also works in newborns is unlikely to rely on sophisticated object recognition, memory, and geometrical difference computation. The lack of a geometrical model is also appealing to roboticists as it renders unnecessary any hardware-specific parameterization. We review findings on the feedback signal used for saccade learning, and differentiate two possible mechanisms. The first contains a signed feedback, i.e. whether the saccade was too long or too short, and may also be influenced by magnification of the saccade target at the fovea. The second mechanism contains as feedback only the “goodness” of resolution, which is larger near the fovea; hence, there is no vectorial error signal. We demonstrate and compare both mechanisms in a model in which the first mechanism implements horizontal, the second vertical saccade adaptation. This model takes into account the separate neuroanatomical pathways for horizontal and vertical saccade control. 1.

CONTENTS LIST OF FIGURES..............................................................................................................VII LIST OF TABLES..................................................................................................................X CHAPTER 1: INTRODUCTION.............................................................................................1 THE ISSUES OF MOTOR CONTROL ...........................................................................................1 HISTORY OF THE BASAL GANGLIA .........................................................................................3 HYPOTHESIS ..........................................................................................................................7 ORGANIZATION OF DISSERTATION.................................

■ Appetitive goal-directed behavior can be associated with a cue-triggered expectancy that it will lead to a particular reward, a process thought to depend on the OFC and basolateral amygdala complex. We developed a biologically informed neural network model of this system to investigate the separable and complementary roles of these areas as the main components of a flexible expectancy system. These areas of interest are part of a neural network with additional subcortical areas, including the central nucleus of amygdala, ventral (limbic) and dorsomedial (associative) striatum. Our simulations are consistent with the view that the amygdala maintains Pavlovian associations through incremental updating of synaptic strength and that the OFC supports flexibility by maintaining an activation-based working memory of the recent reward history. Our model provides a mechanistic explanation for electrophysiological evidence that cue-related firing in OFC neurons is nonselectively early after a contingency change and why this nonselective firing is critical for promoting plasticity in the amygdala. This ambiguous activation results from the simultaneous maintenance of recent outcomes and obsolete Pavlovian contingencies in working memory. Furthermore, at the beginning of reversal, the OFC is critical for supporting responses that are no longer inappropriate. This result is inconsistent with an exclusive inhibitory account of OFC function. ■

The classical notion that the cerebellum and the basal ganglia are dedicated to motor control is under dispute given increasing evidence of their involvement in non-motor functions. Is it then impossible to characterize the functions of the cerebellum, the basal ganglia and the cerebral cortex in a simplistic manner? This paper presents a novel view that their computational roles can be characterized not by asking what are the "goals" of their computation, such as motor or sensory, but by asking what are the "methods " of their computation, specifically, their learning algorithms. There is currently enough anatomical, physiological, and theoretical evidence to support the hypotheses that the cerebellum is a specialized organism for supervised learning, the basal ganglia are for reinforcement learning, and the cerebral cortex is for unsupervised learning. This paper investigates how the learning modules specialized for these three kinds of learning can be assembled into goa...

Short-term memory for serial order, like other domains of memory, is subject to constructive effects. In particular, background knowledge concerning regularities in sequential structure can affect serial recall.

We describe two acalculic patients, one with a left subcortical lesion and the other with a right inferior parietal lesion and Gerstmann's syndrome. Both suffered from "pure anarithmetia": they could read arabic numerals and write them to dictation, but experienced a pronounced calculation deficit. On closer analysis, however, distinct deficits were found. The subcortical case suffered from a selective deficit of rote verbal knowledge, including but not limited to arithmetic tables, while her semantic knowledge of numerical quantities was intact. Conversely the inferior parietal case suffered from a category-specific impairment of quantitative numerical knowledge, particularly salient in subtraction and number bissection tasks, with preserved knowledge of rote arithmetic facts. This double dissociation suggests that numerical knowledge is processed in different formats within distinct cerebral pathways. We suggest that a left subcortical network contributes to the storage and retrieval of rote verbal arithmetic facts, while a bilateral inferior parietal network is dedicated to the mental manipulation of numerical quantities.

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predict differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior.

A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-�re neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a suf�ciently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states