Choice Outstanding Academic Title, 1996. In hundreds of articles by experts from around the world, and in overviews and "road maps" prepared by the editor, The Handbook of Brain Theory and Neural Networkscharts the immense progress made in recent years in many specific areas related to two great questions: How does the brain work? and How can we build intelligent machines? While many books have appeared on limited aspects of one subfield or another of brain theory and neural networks, the (...) Handbookcovers the entire sweep of topics—from detailed models of single neurons, analyses of a wide variety of biological neural networks, and connectionist studies of psychology and language, to mathematical analyses of a variety of abstract neural networks, and technological applications of adaptive, artificial neural networks. The excitement, and the frustration, of these topics is that they span such a broad range of disciplines including mathematics, statistical physics and chemistry, neurology and neurobiology, and computer science and electrical engineering as well as cognitive psychology, artificial intelligence, and philosophy. Thus, much effort has gone into making the Handbookaccessible to readers with varied backgrounds while still providing a clear view of much of the recent, specialized research in specific topics. The heart of the book, part III, comprises of 267 original articles by leaders in the various fields, arranged alphabetically by title. Parts I and II, written by the editor, are designed to help readers orient themselves to this vast range of material. Part I, Background, introduces several basic neural models, explains how the present study of brain theory and neural networks integrates brain theory, artificial intelligence, and cognitive psychology, and provides a tutorial on the concepts essential for understanding neural networks as dynamic, adaptive systems. Part II, Road Maps, provides entry into the many articles of part III through an introductory "Meta-Map" and twenty-three road maps, each of which tours all the Part III articles on the chosen theme. (shrink)

In this book, Michael Arbib, a researcher in artificial intelligence and brain theory, joins forces with Mary Hesse, a philosopher of science, to present an integrated account of how humans 'construct' reality through interaction with the social and physical world around them. The book is a major expansion of the Gifford Lectures delivered by the authors at the University of Edinburgh in the autumn of 1983. The authors reconcile a theory of the individual's construction of reality as a (...) network of schemas 'in the head' with an account of the social construction of language, science, ideology and religion to provide an integrated schema-theoretic view of human knowledge. The authors still find scope for lively debate, particularly in their discussion of free will and of the reality of God. The book integrates an accessible exposition of background information with a cumulative marshalling of evidence to address fundamental questions concerning human action in the world and the nature of ultimate reality. (shrink)

Choice Outstanding Academic Title, 1996. In hundreds of articles by experts from around the world, and in overviews and "road maps" prepared by the editor, The Handbook of Brain Theory and Neural Networks charts the immense progress made in recent years in many specific areas related to great questions: How does the brain work? How can we build intelligent machines? While many books discuss limited aspects of one subfield or another of brain theory and neural networks, the Handbook covers the (...) entire sweep of topics—from detailed models of single neurons, analyses of a wide variety of biological neural networks, and connectionist studies of psychology and language, to mathematical analyses of a variety of abstract neural networks, and technological applications of adaptive, artificial neural networks. Expository material makes the book accessible to readers with varied backgrounds while still offering a clear view of the recent, specialized research on specific topics. (shrink)

We focus on the evolution of action capabilities which set the stage for language, rather than analyzing how further brain evolution built on these capabilities to yield a language-ready brain. Our framework is given by the Mirror System Hypothesis, which charts a progression from a monkey-like mirror neuron system (MNS) to a chimpanzee-like mirror system that supports simple imitation and thence to a human-like mirror system that supports complex imitation and language. We present the MNS2 model, a new model of (...) action recognition learning by mirror neurons of the macaque brain and augmented competitive queuing, a model of opportunistic scheduling of action sequences as background for analysis of modeling strategies for simple imitation as seen in the great apes and complex/goal-directed imitation as seen in humans. Implications for the study of language are briefly noted. (shrink)

The article analyzes the neural and functional grounding of language skills as well as their emergence in hominid evolution, hypothesizing stages leading from abilities known to exist in monkeys and apes and presumed to exist in our hominid ancestors right through to modern spoken and signed languages. The starting point is the observation that both premotor area F5 in monkeys and Broca's area in humans contain a “mirror system” active for both execution and observation of manual actions, and that F5 (...) and Broca's area are homologous brain regions. This grounded the mirror system hypothesis of Rizzolatti and Arbib (1998) which offers the mirror system for grasping as a key neural “missing link” between the abilities of our nonhuman ancestors of 20 million years ago and modern human language, with manual gestures rather than a system for vocal communication providing the initial seed for this evolutionary process. The present article, however, goes “beyond the mirror” to offer hypotheses on evolutionary changes within and outside the mirror systems which may have occurred to equip Homo sapiens with a language-ready brain. Crucial to the early stages of this progression is the mirror system for grasping and its extension to permit imitation. Imitation is seen as evolving via a so-called simple system such as that found in chimpanzees (which allows imitation of complex “object-oriented” sequences but only as the result of extensive practice) to a so-called complex system found in humans (which allows rapid imitation even of complex sequences, under appropriate conditions) which supports pantomime. This is hypothesized to have provided the substrate for the development of protosign, a combinatorially open repertoire of manual gestures, which then provides the scaffolding for the emergence of protospeech (which thus owes little to nonhuman vocalizations), with protosign and protospeech then developing in an expanding spiral. It is argued that these stages involve biological evolution of both brain and body. By contrast, it is argued that the progression from protosign and protospeech to languages with full-blown syntax and compositional semantics was a historical phenomenon in the development of Homo sapiens, involving few if any further biological changes. Key Words: gestures; hominids; language evolution; mirror system; neurolinguistics; primates; protolanguage; sign language; speech; vocalization. (shrink)

A new, dramatically updated edition of the classic resource on the constantly evolving fields of brain theory and neural networks.

How might a human communication system be bootstrapped in the absence of conventional language? We argue that motivated signs play an important role (i.e., signs that are linked to meaning by structural resemblance or by natural association). An experimental study is then reported in which participants try to communicate a range of pre-specified items to a partner using repeated non-linguistic vocalization, repeated gesture, or repeated non-linguistic vocalization plus gesture (but without using their existing language system). Gesture proved more effective (measured (...) by communication success) and more efficient (measured by the time taken to communicate) than non-linguistic vocalization across a range of item categories (emotion, object, and action). Combining gesture and vocalization did not improve performance beyond gesture alone. We experimentally demonstrate that gesture is a more effective means of bootstrapping a human communication system. We argue that gesture outperforms non-linguistic vocalization because it lends itself more naturally to the production of motivated signs. (shrink)

Thirty years ago, I elaborated on a position that could be seen as a compromise between an "extreme," symbol-based AI, and a "neurochemical reductionism" in AI. The present article recalls aspects of the espoused framework of schema theory that, it suggested, could provide a better bridge from human psychology to brain theory than that offered by the symbol systems of A. Newell and H. A. Simon.

Intermediate constructs are required as bridges between complex behaviors and realistic models of neural circuitry. For cognitive scientists in general, schemas are the appropriate functional units; brain theorists can work with neural layers as units intermediate between structures subserving schemas and small neural circuits.After an account of different levels of analysis, we describe visuomotor coordination in terms of perceptual schemas and motor schemas. The interest of schemas to cognitive science in general is illustrated with the example of perceptual schemas in (...) high-level vision and motor schemas in the control of dextrous hands.Rana computatrix, the computational frog, is introduced to show how one constructs an evolving set of model families to mediate flexible cooperation between theory and experiment. Rana computatrix may be able to do for the study of the organizational principles of neural circuitry what Aplysia has done for the study of subcellular mechanisms of learning. Approach, avoidance, and detour behavior in frogs and toads are analyzed in terms of interacting schemas. Facilitation and prey recognition are implemented as tectal-pretectal interactions, with the tectum modeled by an array of tectal columns. We show how layered neural computation enters into models of stereopsis and how depth schemas may involve the interaction of accommodation and binocular cues in anurans. (shrink)

Computational modeling of the macaque brain grounds hypotheses on the brain of LCA-m. Elaborations thereof provide a brain model for LCA-c. The Mirror System Hypothesis charts further steps via imitation and pantomime to protosign and protolanguage on the path to a "language-ready brain" in Homo sapiens, with the path to speech being indirect. The material poses new challenges for both experimentation and modeling.

We present a new road map for research on “How the Brain Got Language” that adopts an EvoDevoSocio perspective and highlights comparative neuroprimatology – the comparative study of brain, behavior and communication in extant monkeys and great apes – as providing a key grounding for hypotheses on the last common ancestor of humans and monkeys and chimpanzees and the processes which guided the evolution LCA-m → LCA-c → protohumans → H. sapiens. Such research constrains and is constrained by analysis of (...) the subsequent, primarily cultural, evolution of H. sapiens which yielded cultures involving the rich use of language. (shrink)

In this paper, we offer a Piagetian perspective on the construction of the logico-mathematical schemas which embody our knowledge of logic and mathematics. Logico-mathematical entities are tied to the subject's activities, yet are so constructed by reflective abstraction that they result from sensorimotor experience only via the construction of intermediate schemas of increasing abstraction. The axiom set does not exhaust the cognitive structure (schema network) which the mathematician thus acquires. We thus view truth not as something to be defined within (...) the closed world of a formal system but rather in terms of the schema network within which the formal system is embedded. We differ from Piaget in that we see mathematical knowledge as based on social processes of mutual verification which provide an external drive to any necessary dynamic of reflective abstraction within the individual. From this perspective, we argue that axiom schemas tied to a preferred interpretation may provide a necessary intermediate stage of reflective abstraction en route to acquisition of the ability to use formal systems in abstracto. (shrink)

The idea that some day robots may have emotions has captured the imagination of many and has been dramatized by robots and androids in such famous movies as 2001 Space Odyssey's HAL or Star Trek's Data. By contrast, the editors of this book have assembled a panel of experts in neuroscience and artificial intelligence who have dared to tackle the issue of whether robots can have emotions from a purely scientific point of view. The study of the brain now usefully (...) informs study of the social, communicative, adaptive, regulatory, and experimental aspects of emotion and offers support for the idea that we exploit our own psychological responses in order to feel others' emotions. The contributors show the many ways in which the brain can be analyzed to shed light on emotions. Fear, reward, and punishment provide structuring concepts for a number of investigations. Neurochemistry reveals the ways in which different "neuromodulators" such as serotonin, dopamine, and opioids can affect the emotional valence of the brain. And studies of different regions such as the amygdala and orbitofrontal cortex provide a view of the brain as a network of interacting subsystems. Related studies in artificial intelligence and robotics are discussed and new multi-level architectures are proposed that make it possible for emotions to be implemented. It is now an accepted task in robotics to build robots that perceive human expressions of emotion and can "express" simulated emotions to ease interactions with humans. Looking towards future innovations, some scientists posit roles for emotion with our fellow humans. All of these issues are covered in this timely and stimulating book which is written for researchers and graduated students in neuroscience, cognitive science, psychology, robotics, and artificial intelligence. (shrink)

TA builds on the state of mind (SoM) framework to offer the novelty-seeking model (NSM). The model relates curiosity to creativity but this commentary focuses on creativity: (i) It assesses the SoM + NSM model of creativity-in-the-lab, showing that the focus on semantic networks is inadequate. (ii) It discusses architectural design to sketch ideas for a theory of “big C” creativity.

Much of the debate concerning the question “Was Protolanguage Holophrastic?” assumes that protolanguage existed as a single, stable transitional form between communication systems akin to those of modern primates and human languages as we know them today. The present paper argues for a spectrum of protolanguages preceding modern languages emphasizing that protospeech was intertwined with protosign and gesture; grammar emerged from a growing population of constructions; and an increasing protolexicon drove the emergence of phonological structure. This framework weakens arguments for (...) the view that the earliest protolanguages were not holophrastic while advancing the claim that protolanguages became increasingly compositional over time en route to the emergence of true languages. (shrink)

Much of the debate concerning the question “Was Protolanguage Holophrastic?” assumes that protolanguage existed as a single, stable transitional form between communication systems akin to those of modern primates and human languages as we know them today. The present paper argues for a spectrum of protolanguages preceding modern languages emphasizing that protospeech was intertwined with protosign and gesture; grammar emerged from a growing population of constructions; and an increasing protolexicon drove the emergence of phonological structure. This framework weakens arguments for (...) the view that the earliest protolanguages were not holophrastic while advancing the claim that protolanguages became increasingly compositional over time en route to the emergence of true languages. (shrink)

I reject Jackendoff's view of Universal Grammar as something that evolved biologically but applaud his integration of blackboard architectures. I thus recall the HEARSAY speech understanding system—the AI system that introduced the concept of “blackboard”—to provide another perspective on Jackendoff's architecture.