Traditional approaches to human information processing tend to deal with perception and action planning in isolation, so that an adequate account of the perception-action interface is still missing On the perceptual side, the dominant cognitive view largely underestimates, and thus fails to account for, the impact of action-related processes on both the processing of perceptual information and on perceptual learning On the action side, most approaches conceive of action planning as a mere continuation of stimulus processing, thus failing to account for the goal-directedness of even the simplest reaction in an experimental task We propose a new framework for a more adequate theoretical treatment of perception and action planning, in which perceptual contents and action plans are coded in a common representational medium by feature codes with distal reference Perceived events (perceptions) and to-be-produced events (actions) are equally represented by integrated, task-tuned networks of feature codes – cognitive structures we call event codes We give an overview of evidence from a wide variety of empirical domains, such as spatial stimulus-response compatibility, sensorimotor synchronization, and ideomotor action, showing that our main assumptions are well supported by the data

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0140525X01000103

The Theory of Event Coding (TEC): a framework for perception and action planning.

According to Fitts' law, human movement can be modeled by analogy to the transmission of information. Fitts' popular model has been widely adopted in numerous research areas, including kinematics, human factors, and (recently) human-computer interaction (HCI). The present study provides a historical and theoretical context for the model, including an analysis of problems that have emerged through the systematic deviation of observations from predictions. Refinements to the model are described, including a formulation for the index of task difficulty that is claimed to be more theoretically sound than Fitts' original formulation. The model's utility in predicting the time to position a cursor and select a target is explored through a review of six Fitts' law studies employing devices such as the mouse, trackball, joystick, touchpad, helmet-mounted sight, and eye tracker. An analysis of the performance measures reveals tremendous inconsistencies, making across-study comparisons difficult. Sources of experimental variation are identified to reconcile these differences.

Fitts' law as a research and design tool in human-computer interaction

A stochastic optimized-submovement model is proposed for Pitts' law, the classic logarithmic tradeoff between the duration and spatial precision of rapid aimed movements. According to the model, an aimed movement toward a specified target region involves a primary submovement and an optional secondary corrective submovement. The submovements are assumed to be programmed such that they minimize average total movement time while maintaining a high frequency of target hits. The programming process achieves this minimization by optimally adjusting the average magnitudes and durations of noisy neuromotor force pulses used to generate the submovements. Numerous results from the literature on human motor performance may be explained in these terms. Two new experiments on rapid wrist rotations yield additional support for the stochastic optimizedsubmovement model. Experiment 1 revealed that the mean durations of primary submovements and of secondary submovements, not just average total movement times, conform to a square-root approximation of Pitts' law derived from the model. Also, the spatial endpoints of primary submovements have standard deviations that increase linearly with average primary-submovement velocity, and the average primary-submovement velocity influences the relative frequencies of secondary submovements, as predicted by the model. During Experiment 2, these results were replicated and extended under conditions in which subjects made movements without concurrent visual feedback. This replication suggests that submovement optimization may be a pervasive property of movement production. The present conceptual framework provides insights into principles of motor performance, and it links the study of physical action to research on sensation, perception, and cognition, where psychologists have been concerned for some time about the degree to which mental processes incorporate rational and normative rules. An enduring issue in the study of the human mind concerns of mathematical probability theory and statistical decision thethe rationality and optimality of the mental processes that guide ory (e.g., see Edwards, 1961; Edwards, Lindman, & Savage,

/pdf/optimality-in-human-motor-performance-ideal-control-of-rapid-2whgus6jwu.pdf

Optimality in human motor performance: Ideal control of rapid aimed movements.

The authors' goal is to apply robotics and automation technology to assist, enhance, quantify, and document neurorehabilitation. This paper reviews a clinical trial involving 20 stroke patients with a prototype robot-aided rehabilitation facility developed at the Massachusetts Institute of Technology, Cambridge, (MIT) and tested at Burke Rehabilitation Hospital, White Plains, NY. It also presents the authors' approach to analyze kinematic data collected in the robot-aided assessment procedure. In particular, they present evidence (1) that robot-aided therapy does not have adverse effects, (2) that patients tolerate the procedure, and (3) that peripheral manipulation of the impaired limb may influence brain recovery. These results are based on standard clinical assessment procedures. The authors also present one approach using kinematic data in a robot-aided assessment procedure.

/pdf/robot-aided-neurorehabilitation-2u069301n4.pdf

Robot-aided neurorehabilitation

The analysis and design of nonlinear feedback systems has recently undergone an exceptionally rich period of progress and maturation, fueled, to a great extent, by (1) the discovery of certain basic conceptual notions, and (2) the identification of classes of systems for which systematic decomposition approaches can result in effective and easily computable control laws. These two aspects are complementary, since the latter approaches are, typically, based upon the inductive verification of the validity of the former system properties under compositions (in the terminology used in [62], the “activation” of theoretical concepts leads to “constructive” control). This expository presentation addresses the first of these aspects, and in particular the precise formulation of questions of robustness with respect to disturbances, formulated in the paradigm of input to state stability. We provide an intuitive and informal presentation of the main concepts. More precise statements, especially about older results, are given in the cited papers, as well as in several previous surveys such as [103] and [105] (of which the present paper represents an update), but we provide a little more detail about relatively recent work. Regarding applications and extensions of the basic framework, we give some pointers to the literature, but we do not focus on feedback design and specific engineering problems; for the latter we refer the reader to textbooks such as [43], [60], [58], [96], [66], [27], [44].

Input to State Stability: Basic Concepts and Results

A pneumatic muscle (PM) system was studied to determine whether a three-element model could describe its dynamics. As far as the authors are aware, this model has not been used to describe the dynamics of PM. A new phenomenological model consists of a contractile (force-generating) element, spring element, and damping element in parallel. The PM system was investigated using an apparatus that allowed precise and accurate actuation pressure (P) control by a linear servo-valve. Length change of the PM was measured by a linear potentiometer. Spring and damping element functions of P were determined by a static perturbation method at several constant P values. These results indicate that at constant P, PM behaves as a spring and damper in parallel. The contractile element function of P was determined by the response to a step input in P, using values of spring and damping elements from the perturbation study. The study showed that the resulting coefficient functions of the three-element model describe the dynamic response to the step input of P accurately, indicating that the static perturbation results can be applied to the dynamic case. This model is further validated by accurately predicting the contraction response to a triangular P waveform. All three elements have pressure-dependent coefficients for pressure P in the range 207 < or = P < or = 621 kPa (30 < or = P < or = 90 psi). Studies with a step decrease in P (relaxation of the PM) indicate that the damping element coefficient is smaller during relaxation than contraction.

Modeling the dynamic characteristics of pneumatic muscle.

Fitts' law and the microstructure of rapid discrete movements.

In motor task learning by instruction, the instructor's skill and intention, which, initially, are extrinsic constraints on the learner's perceiving and acting, eventually become internalized as intrinsic constraints by the learner. How is this process to be described formally? This process takes place via a forcing function that acts both as an anticipatory (informing) influence and a hereditary (controlling) influence. A mathematical strategy is suggested by which such intentions and skills might be dynamically learned. A hypothetical task is discussed in which a blindfolded learner is motorically instructed to pull a spring to a specific target in a specific manner. The modeling strategy involves generalizing Hooke's law to the coupled instructor-spring-learner system. Specifically, dual Volterra functions express the anticipatory and hereditary influences passed via an instructor-controlled forcing function on the shared spring. Boundary conditions (task goals) on the instructor-spring system...

The Intentional Spring: A Strategy for Modeling Systems That Learn to Perform Intentional Acts

A fuzzy learning control technique is used for position tracking involving the vertical movement of a mass attached to a pneumatic muscle. Because the pneumatic muscle is nonlinear and time varying, conventional fixed controllers are less effective than the fuzzy controller proposed in this paper. The controller is of a PID type, with an adaptive fuzzy PD part and a nonfuzzy integral branch. A novelty of the controller is that the fuzzy inverse model, which dynamically adjusts the PD part of the controller, incorporates the internal PM pressure as an input. Experimental results are presented from the pneumatic muscle test facility in the Human Effectiveness Laboratory at Wright-Patterson Air Force Base.

Fuzzy PD+I learning control for a pneumatic muscle

Apparatus to actively assist neuromotor disabled to have better leg functional control/use This system serves as a strength enhancer, support device, and attenuator of spastic motions The disclosed orthosis is worn on the outside of the leg (or legs) and serves as an active/dynamic brace to prevent leg spasms and other untoward leg motion It is made of lightweight material such as fiberglass or aluminum and is actuated by pneumatic gas sources which are small, portable, and carried with the device The device is totally self contained and has no external connection The system has applicability to patients such as: stroke (cerebral vascular accident), TBI (traumatic brain injury), Muscular Dystrophy, and some spinal cord injured

Daniel W. Repperger

Papers

Modeling the dynamic characteristics of pneumatic muscle.

Fitts' law and the microstructure of rapid discrete movements.

The Intentional Spring: A Strategy for Modeling Systems That Learn to Perform Intentional Acts

Fuzzy PD+I learning control for a pneumatic muscle

Mobility assist for the paralyzed, amputeed and spastic person