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Michael A Arbib

Publications and source records attributed to Michael A Arbib.

11 recordsLinked to original sources
PubMed2006

A sentence is to speech as what is to action?

This article offers a conceptual framework for integrated analysis of subprocesses in action and language, based on goal-directed action. Anatomical substrates are discussed in the companion paper (Arbib and Bota, 2003) which approaches "Integrative Models of Broca's Area and the Ventral Premotor Cortex" within the context of explaining why the evolution of the human brain yielded mechanisms which support language in a multi-modal vocal-manual-facial system rather than privileging the vocal mode. Arbib and Bota (2003) examine homologies between different cortical areas in macaque and human to revisit the Mirror System Hypothesis (MSH) of Rizzolatti and Arbib (1998)--the notion that the mirror system for grasping (which has its frontal outpost in premotor area F5 of the macaque) provides the substrate for the evolution of the language-ready brain which supports parity of communication. They also offer a critique and extension based on the work of Aboitiz and García (1997; Aboitiz et al., 2006). Arbib and Bota (2003) also discussed the utility of neuroinformatics in relating information across diverse cortical atlases and evaluating degrees of homology for brain regions of interest in different species (for discussion, see Deacon, 2004; Arbib and Bota, 2004).

Animals↗
PubMed2005

Schema-based learning of adaptable and flexible prey- catching in anurans II. Learning after lesioning.

The previous companion paper describes the initial (seed) schema architecture that gives rise to the observed prey-catching behavior. In this second paper in the series we describe the fundamental adaptive processes required during learning after lesioning. Following bilateral transections of the hypoglossal nerve, anurans lunge toward mealworms with no accompanying tongue or jaw movement. Nevertheless anurans with permanent hypoglossal transections eventually learn to catch their prey by first learning to open their mouth again and then lunging their body further and increasing their head angle. In this paper we present a new learning framework, called schema-based learning (SBL). SBL emphasizes the importance of the current existent structure (schemas), that defines a functioning system, for the incremental and autonomous construction of ever more complex structure to achieve ever more complex levels of functioning. We may rephrase this statement into the language of Schema Theory (Arbib 1992, for a comprehensive review) as the learning of new schemas based on the stock of current schemas. SBL emphasizes a fundamental principle of organization called coherence maximization, that deals with the maximization of congruence between the results of an interaction (external or internal) and the expectations generated for that interaction. A central hypothesis consists of the existence of a hierarchy of predictive internal models (predictive schemas) all over the control center-brain-of the agent. Hence, we will include predictive models in the perceptual, sensorimotor, and motor components of the autonomous agent architecture. We will then show that predictive models are fundamental for structural learning. In particular we will show how a system can learn a new structural component (augment the overall network topology) after being lesioned in order to recover (or even improve) its original functionality. Learning after lesioning is a special case of structural learning but clearly shows that solutions cannot be known/hardwired a priori since it cannot be known, in advance, which substructure is going to break down.

Animals↗
PubMed2005

Schema-based learning of adaptable and flexible prey-catching in anurans I. The basic architecture.

A motor action often involves the coordination of several motor synergies and requires flexible adjustment of the ongoing execution based on feedback signals. To elucidate the neural mechanisms underlying the construction and selection of motor synergies, we study prey-capture in anurans. Experimental data demonstrate the intricate interaction between different motor synergies, including the interplay of their afferent feedback signals (Weerasuriya 1991; Anderson and Nishikawa 1996). Such data provide insights for the general issues concerning two-way information flow between sensory centers, motor circuits and periphery in motor coordination. We show how different afferent feedback signals about the status of the different components of the motor apparatus play a critical role in motor control as well as in learning. This paper, along with its companion paper, extend the model by Liaw et al. (1994) by integrating a number of different motor pattern generators, different types of afferent feedback, as well as the corresponding control structure within an adaptive framework we call Schema-Based Learning. We develop a model of the different MPGs involved in prey-catching as a vehicle to investigate the following questions: What are the characteristic features of the activity of a single muscle? How can these features be controlled by the premotor circuit? What are the strategies employed to generate and synchronize motor synergies? What is the role of afferent feedback in shaping the activity of a MPG? How can several MPGs share the same underlying circuitry and yet give rise to different motor patterns under different input conditions? In the companion paper we also extend the model by incorporating learning components that give rise to more flexible, adaptable and robust behaviors. To show these aspects we incorporate studies on experiments on lesions and the learning processes that allow the animal to recover its proper functioning.

Action Potentials↗
PubMed2005

Schizophrenia and the mirror system: an essay.

We analyze how data on the mirror system for grasping in macaque and human ground the mirror system hypothesis for the evolution of the language-ready human brain, and then focus on this putative relation between hand movements and speech to contribute to the understanding of how it may be that a schizophrenic patient generates an action (whether manual or verbal) but does not attribute the generation of that action to himself. We make a crucial discussion between self-monitoring and attribution of agency. We suggest that verbal hallucinations occur when an utterance progresses through verbal creation pathways and returns as a vocalization observed, only to be dismissed as external since no record of its being created has been kept. Schizophrenic patients on this theory then confabulate the agent.

Animals↗
PubMed2005

From monkey-like action recognition to human language: an evolutionary framework for neurolinguistics.

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.

Animal Communication↗
PubMed2004

Infant grasp learning: a computational model.

This paper presents ILGM (the Infant Learning to Grasp Model), the first computational model of infant grasp learning that is constrained by the infant motor development literature. By grasp learning we mean learning how to make motor plans in response to sensory stimuli such that open-loop execution of the plan leads to a successful grasp. The open-loop assumption is justified by the behavioral evidence that early grasping is based on open-loop control rather than on-line visual feedback. Key elements of the infancy period, namely elementary motor schemas, the exploratory nature of infant motor interaction, and inherent motor variability are captured in the model. In particular we show, through computational modeling, how an existing behavior (reaching) yields a more complex behavior (grasping) through interactive goal-directed trial and error learning. Our study focuses on how the infant learns to generate grasps that match the affordances presented by objects in the environment. ILGM was designed to learn execution parameters for controlling the hand movement as well as for modulating the reach to provide a successful grasp matching the target object affordance. Moreover, ILGM produces testable predictions regarding infant motor learning processes and poses new questions to experimentalists.

Computational Biology↗
PubMed2004

Emotions: from brain to robot.

Some robots have been given emotional expressions in an attempt to improve human-computer interaction. In this article we analyze what it would mean for a robot to have emotion, distinguishing emotional expression for communication from emotion as a mechanism for the organization of behavior. Research on the neurobiology of emotion yields a deepening understanding of interacting brain structures and neural mechanisms rooted in neuromodulation that underlie emotions in humans and other animals. However, the chemical basis of animal function differs greatly from the mechanics and computations of current machines. We therefore abstract from biology a functional characterization of emotion that does not depend on physical substrate or evolutionary history, and is broad enough to encompass the possible emotions of robots.

Affect↗
PubMed2004

Integrating databases and expert systems for the analysis of brain structures: connections, similarities, and homologies.

The NeuroHomology Database system (NHDB) combines databases related to brain structures from different species with different knowledge management systems (KMSs) for systematization, evaluation and processing neurobiological data. Special attention is assessment of similarity of data from different species as a basis for exploring neural homologies. NHDB includes modules that handle brain structure and connectivity data, as well as inference engines for evaluation of the stored neurobiological information. The spatial inference engine evaluates the possible topological relations between cortical structures in different neuroanatomical atlases. The connectivity inference engine evaluates the reliability of information pertaining to fiber tracts as those are reflected in the literature. The inference engine for translation of neuroanatomical connections in different atlases evaluates the probability of existence of connections of interest in different parcellation schemes. Finally, the similarity inference engine calculates the overall degree of similarity of pairs of brain structures from different species by taking into account a set of eight criteria. We present examples of search for information in NHDB system, inferences of relations between cortical structures from equivalent neuroanatomical atlases, reconstruction of functional networks of brain structures from data collated from the literature, translation of connectivity matrices in equivalent parcellation schemes, and evaluations of similarities of brain structures from humans, macaques and rats.

Algorithms↗
PubMed2003

Rana computatrix to human language: towards a computational neuroethology of language evolution.

Walter's Machina speculatrix inspired the name Rana computatrix for a family of models of visuomotor coordination in the frog, which contributed to the development of computational neuroethology. We offer here an 'evolutionary' perspective on models in the same tradition for rat, monkey and human. For rat, we show how the frog-like taxon affordance model provides a basis for the spatial navigation mechanisms that involve the hippocampus and other brain regions. For monkey, we recall two models of neural mechanisms for visuomotor coordination. The first, for saccades, shows how interactions between the parietal and frontal cortex augment superior colliculus seen as the homologue of frog tectum. The second, for grasping, continues the theme of parieto-frontal interactions, linking parietal affordances to motor schemas in premotor cortex. It further emphasizes the mirror system for grasping, in which neurons are active both when the monkey executes a specific grasp and when it observes a similar grasp executed by others. The model of human-brain mechanisms is based on the mirror-system hypothesis of the evolution of the language-ready brain, which sees the human Broca's area as an evolved extension of the mirror system for grasping.

Adaptation, Physiological↗
PubMed2002

Schema design and implementation of the grasp-related mirror neuron system.

Mirror neurons within a monkey's premotor area F5 fire not only when the monkey performs a certain class of actions but also when the monkey observes another monkey (or the experimenter) perform a similar action. It has thus been argued that these neurons are crucial for understanding of actions by others. We offer the hand-state hypothesis as a new explanation of the evolution of this capability: the basic functionality of the F5 mirror system is to elaborate the appropriate feedback - what we call the hand state - for opposition-space based control of manual grasping of an object. Given this functionality, the social role of the F5 mirror system in understanding the actions of others may be seen as an exaptation gained by generalizing from one's own hand to an other's hand. In other words, mirror neurons first evolved to augment the "canonical" F5 neurons (active during self-movement based on observation of an object) by providing visual feedback on "hand state," relating the shape of the hand to the shape of the object. We then introduce the MNS1 (mirror neuron system 1) model of F5 and related brain regions. The existing Fagg-Arbib-Rizzolatti-Sakata model represents circuitry for visually guided grasping of objects, linking the anterior intraparietal area (AIP) with F5 canonical neurons. The MNS1 model extends the AIP visual pathway by also modeling pathways, directed toward F5 mirror neurons, which match arm-hand trajectories to the affordances and location of a potential target object. We present the basic schemas for the MNS1 model, then aggregate them into three "grand schemas" - visual analysis of hand state, reach and grasp, and the core mirror circuit - for each of which we present a useful implementation (a non-neural visual processing system, a multijoint 3-D kinematics simulator, and a learning neural network, respectively). With this implementation we show how the mirror system may learn to recognize actions already in the repertoire of the F5 canonical neurons. We show that the connectivity pattern of mirror neuron circuitry can be established through training, and that the resultant network can exhibit a range of novel, physiologically interesting behaviors during the process of action recognition. We train the system on the basis of final grasp but then observe the whole time course of mirror neuron activity, yielding predictions for neurophysiological experiments under conditions of spatial perturbation, altered kinematics, and ambiguous grasp execution which highlight the importance of the timing of mirror neuron activity.

Animals↗