A novel cognitive theory of semantics that proposes that the meanings of words can be described in terms of geometric structures.

First published in 1995, The Visual Brain in Action remains a seminal publication in the cognitive sciences. It presents a model for understanding the visual processing underlying perception and action, proposing a broad distinction within the brain between two kinds of vision: conscious perception and unconscious 'online' vision. It argues that each kind of vision can occur quasi-independently of the other, and is separately handled by a quite different processing system. In the 11 years since publication, the book has provoked (...) considerable interest and debate - throughout both cognitive neuroscience and philosophy, while the field has continued to flourish and develop. -/- For this new edition, the text from the original edition has been left untouched, standing as a coherent statement of the authors' position. However, a very substantial epilogue has been added to the book in which Milner and Goodale review some of the key developments that support or challenge the views that were put forward in the first edition. The new chapter summarizes developments in various relevant areas of psychology, neuroscience and behaviour. It notably supplements the main text by updating the reader on the contributions that have emerged from the use of functional neuroimaging, which was in its infancy when the first edition was written. Neuroimaging, and functional MRI in particular, has revolutionized the field over the past 11 years by allowing investigators to plot in detail the patterns of activity within the visual brains of behaving and perceiving humans. The authors show how its use now allows scientists to test and confirm their proposals, based as they then were largely on evidence accrued from primate neuroscience in conjunction with studies of neurological patients. (shrink)

The reconciliation of theories of concepts based on prototypes, exemplars, and theory-like structures is a longstanding problem in cognitive science. In response to this problem, researchers have recently tended to adopt either hybrid theories that combine various kinds of representational structure, or eliminative theories that replace concepts with a more finely grained taxonomy of mental representations. In this paper, we describe an alternative approach involving a single class of mental representations called “semantic pointers.” Semantic pointers are symbol-like representations that result (...) from the compression and recursive binding of perceptual, lexical, and motor representations, effectively integrating traditional connectionist and symbolic approaches. We present a computational model using semantic pointers that replicates experimental data from categorization studies involving each prior paradigm. We argue that a framework involving semantic pointers can provide a unified account of conceptual phenomena, and we compare our framework to existing alternatives in accounting for the scope, content, recursive combination, and neural implementation of concepts. (shrink)

We present an account of semantics that is not construed as a mapping of language to the world but rather as a mapping between individual meaning spaces. The meanings of linguistic entities are established via a “meeting of minds.” The concepts in the minds of communicating individuals are modeled as convex regions in conceptual spaces. We outline a mathematical framework, based on fixpoints in continuous mappings between conceptual spaces, that can be used to model such a semantics. If concepts are (...) convex, it will in general be possible for interactors to agree on joint meaning even if they start out from different representational spaces. Language is discrete, while mental representations tend to be continuous—posing a seeming paradox. We show that the convexity assumption allows us to address this problem. Using examples, we further show that our approach helps explain the semantic processes involved in the composition of expressions. (shrink)

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness (...) of the binding problem, the problem of 2, the problem of variables, and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural “blackboard” architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. Perspectives and potential developments of the architectures are discussed. Key Words: binding; blackboard architectures; combinatorial structure; compositionality; language; dynamic system; neurocognition; sentence complexity; sentence structure; working memory; variables; vision. (shrink)

This paper outlines the functional capacities of a novel scheme for cognitive representation and computation, and it explores the possible implementation of this scheme in the massively parallel organization of the empirical brain. The suggestion is that the brain represents reality by means of positions in suitably constitutes phase spaces; and the brain performs computations on these representations by means of coordinate transformations from one phase space to another. This scheme may be implemented in the brain in two distinct forms: (...) (1) as a phase-space sandwich, which may explain certain laminar structures, such as cerebral cortex and the superior colliculus; and (2) as a neural matrix, which may explain other structures, such as the beautifully orthogonal architecture of the cerebellum. (shrink)