Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves; the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands; indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context-dependent while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction.

Contents: 46 pages, including 1 appendix, 1 table, and 16 gures.

Real-time control of the endeffector of a humanoid robot in external coordinates requires computationally efficient solutions of the inverse kinematics problem. In this context, this paper investigates inverse kinematics learningfor resolved motion rate control (RMRC) employingan optimization criterion to resolve kinematic redundancies. Our learningapproach is based on the key observations that learningan inverse of a non uniquely invertible function can be accomplished by augmenting the input representation to the inverse model and by usinga spatially localized learningapproach. We apply this strategy to inverse kinematics learningand demonstrate how a recently developed statistical learning algorithm, Locally Weighted Projection Regression, allows efficient learning of inverse kinematic mappings in an incremental fashion even when input spaces become rather high dimensional. The resultingperformance of the inverse kinematics is comparable to Liegeois ’ [9] analytical pseudo-inverse with optimization. Our results are illustrated with a 30 degree of freedom humanoid robot. 1

This article describes a neural network model of speech motor skill acquisition and speech production that explains a wide range of data on variability, motor equivalence, coarticulation, and rate effects. Model parameters are learned during a babbling phase. To explain how infants learn language-specific variability limits, speech sound targets take the form of convex regions, rather than points, in orosensory coordinates. Reducing target size for better accuracy during slower speech leads to differential effects for vowels and consonants, as seen in experiments previously used as evidence for separate control processes for the 2 sound types. Anticipatory coarticulation arises when targets are reduced in size on the basis of context; this generalizes the well-known look-ahead model of coarticulation. Computer simulations verify the model's properties. The primary goal of the modeling work described in this article is to provide a coherent theoretical framework that provides explanations for a wide range of data concerning the articulator movements used by humans to produce speech sounds. This is carried out by formulating a model that transforms strings of phonemes into continuous articulator movements for

By watching each other's lower oscillating leg, 2 seated Ss kept a common tempo and a particular phase relation of either 0 ° (symmetric mode) or 180 ° (alternate mode). This study investigated the differential stability of the 2 phase modes. In Experiment 1, in which Ss were instructed to remain in the initial phase mode, the alternate phase mode was found to be less stable as the frequency of oscillation increased. In addition, analysis of the nonsteady state cycles revealed evidence of a switching to the symmetric phase mode for the initial alternate phase mode trials. In Experiments 2 and 3, Ss were instructed to remain at a noninitial phase angle if it was found to be more comfortable. The transition observed between the 2 phase modes satisfies the criteria of a physical bifurcation—hysteresis, critical fluctuations, and divergence—and is consonant with previous findings on transitions in limb coordination within a person. The coordination of movements between people is an omnipresent aspect of daily life. Such coordinations consist of the very natural and commonplace coordinations exhibited by people walking and talking together and the very practiced and refined coordinations exhibited by people playing sports

Related perceptual, motor, and cognitive performances were examined to reveal the accuracy of the properties of action spontaneously represented when mentally simulating moving one's hand. The kinematic configuration of the body represented and transformed in mental simulations was not fixed or canonical but corresponded to one's current configuration. Mental simulation time mimicked movement time for natural efficient movement from a posture midway between each of the hand's joint limits into many other postures. Equal time was required for simulated and real movements into more common, comfortable postures; shorter but proportional time was required for simulated movement than real movement into less common postures that involved longer trajectories, coordinated activity at more joints, motion near extremes of joint limits, and uncomfortable kinesthetic sensations. The findings suggest that sensorimotor structures support mental simulations of actions. Humans can envision an object, scene, or event and then inspect the mental representation in a manner that mimics or reflects real perceptual-motor performance (e.g., Craik,

Abstract: Real-time control of the endeffector of a humanoid robot in external coordinates requires computationally efficient solutions of the inverse kinematics problem. In this context, this paper investigates methods of resolved motion rate control (RMRC) that employ optimization criteria to resolve kinematic redundancies. In particular we focus on two established techniques, the pseudo inverse with explicit optimization and the extended Jacobian method. We prove that the extended Jacobian method includes pseudo-inverse methods as a special solution. In terms of computational complexity, however, pseudo-inverse and extended Jacobian differ significantly in favor of pseudo-inverse methods. Employing numerical estimation techniques, we introduce a computationally efficient version of the extended Jacobian with performance comparable to the original version. Our results are illustrated in simulation studies with a multiple degree-of-freedom robot, and were tested on a 30 degree-of-freedom humanoid robot. 1

This article describes a theory of the computations underlying the selection of coordinated motion patterns, especially in reaching tasks. The central idea is that when a spatial target is selected as an object to be reached, stored postures are evaluated for the contributions they can make to the task. Weights are assigned to the stored postures, and a single target posture is found by taking a weighted sum of the stored postures. Movement is achieved by reducing the distance between the starting angle and target angle of each joint. The model explains compensation for reduced joint mobility, tool use, practice effects, performance errors, and aspects of movement kinematics. Extensions of the model can account for anticipation and coarticulation effects, movement through via points, and hierarchical control of series of movements. The goal of this research is a unified theory of the planning and control of physical action. Such a theory, as several authors have noted (Jeannerod, in press; Rosenbaum, 1991; Wing, 1993), has been lacking. Instead, specialized models have been designed to account for data from different tasks. The sentiment

The skill of rhythmic juggling a ball on a racket is investigated from the viewpoint of nonlinear dynamics. The difference equations that model the dynamical system are analyzed by means of local and non-local stability analyses. These analyses yield that the task dynamics offer an economical juggling pattern which is stable even for open-loop actuator motion. For this pattern, two types of predictions are extracted: (i) Stable periodic bouncing is sufficiently characterized by a negative acceleration of the racket at the moment of impact with the ball; (ii) A nonlinear scaling relation maps different juggling trajectories onto one topologically equivalent dynamical system. The relevance of these results for the human control of action was evaluated in an experiment where subjects performed a comparable task of juggling a ball on a paddle. Task manipulations involved different juggling heights and gravity conditions of the ball. The predictions were confirmed: (i) For stable rhythmic performance the paddle’s acceleration at impact is negative and fluctuations of the impact acceleration follow predictions from global stability analysis; (ii) For each subject, the realizations of juggling for the different experimental

Trajectory and kinematics of drawing movements are mutually constrained by functional relationships that reduce the degrees of freedom of the hand-arm system. Previous investigations of these relationships are extended here by considering their development in children between 5 and 12 years of age. Performances in a simple motor task—the continuous tracing of elliptic trajectories—demonstrate that both the phenomenon of isochrony (increase of the average movement velocity with the linear extent of the trajectory) and the so-called two-thirds power law (relation between tangential velocity and curvature) are qualitatively present already at the age of 5. The quantitative aspects of these regularities evolve with age, however, and steady-state adult performance is not attained even by the oldest children. The power-law formalism developed in previous reports is generalized to encompass these developmental aspects of the control of movement. Two general frameworks are currently available to conceptualize the motor-control problem. Broadly, the two frameworks differ in the answer that they give to the question "Where do form and structure come from? " According to the