Megan D. Neilson

Internal models and intermittency: A theoretical account of human tracking behavior

This paper concerns the use of tracking studies to test a theoretical account of the information processing performed by the human CNS during control of movement. The theory provides a bridge between studies of reaction time and continuous tracking. It is proposed that the human CNS includes neuronal circuitry to compute inverse internal models of the multiple input, multiple output, dynamic, nonlinear relationships between outgoing motor commands and their resulting perceptual consequences. The inverse internal models are employed during movement execution to transform preplanned trajectories of desired perceptual consequences into appropriate outgoing motor commands to achieve them. A finite interval of time is required by the CNS to preplan the desired perceptual consequences of a movement and it does not commence planning a new movement until planning of the old one has been completed. This behavior introduces intermittency into the planning of movements. In this paper we show that the gain and phase frequency response characteristics of the human operator in a visual pursuit tracking task can be derived theoretically from these assumptions. By incorporating the effects of internal model inaccuracy and of speed-accuracy trade-off in performance, it is shown that various aspects of experimentally measured tracking behavior can be accounted for.

Stochastic prediction in pursuit tracking: An experimental test of adaptive model theory

In this paper we test the proposition that in pursuit tracking, subjects compute stochastic (statistical) models of the temporal variations in position of the target and use these models to forecast target position for at least a response time interval into the future. A computer simulation of a human operator employing stochastic model prediction of target position is used to generate a synthetic pursuit tracking response signal. Actual pursuit tracking response signals are measured from 10 normal subjects using the same stimulus signal. Cross correlation and spectral analysis are employed to compute gain and phase frequency response characteristics for both synthetic and actual tracking data. The similarity of the gain and phase curves for synthetic and actual data provides compelling evidence in support of the proposition.

Speech motor control and stuttering: a computational model of adaptive sensory-motor processing

Abstract A theoretical account of stuttering is presented in which an inadequacy of neuronal resources for sensory-motor information processing is seen as the basis of the disorder. It is proposed that stutterers are deficient in the processing resources normally responsible for determining and adaptively maintaining the internal models which subserve speech production. A general description of such computational processes is detailed in the form of circuitry for an adaptive controller which can calibrate itself to control any variable, nonlinear, dynamic, multiple input, multiple output system.

An overview of adaptive model theory: solving the problems of redundancy, resources, and nonlinear interactions in human movement control

Adaptive model theory (AMT) is a computational theory that addresses the difficult control problem posed by the musculoskeletal system in interaction with the environment. It proposes that the nervous system creates motor maps and task-dependent synergies to solve the problems of redundancy and limited central resources. These lead to the adaptive formation of task-dependent feedback/feedforward controllers able to generate stable, noninteractive control and render nonlinear interactions unobservable in sensory-motor relationships. AMT offers a unified account of how the nervous system might achieve these solutions by forming internal models. This is presented as the design of a simulator consisting of neural adaptive filters based on cerebellar circuitry. It incorporates a new network module that adaptively models (in real time) nonlinear relationships between inputs with changing and uncertain spectral and amplitude probability density functions as is the case for sensory and motor signals.

What limits high speed tracking performance

Abstract Adaptive Model Theory (AMT) is a computational theory of the information processing performed by the human nervous system in control of movement. The behaviour of six subjects performing visual pursuit tracking was measured as a function of the bandwidth of the target signal. Gain and phase of the relationship between target and response signals were plotted as a function of target signal bandwidth as the bandwidth increased from 0.1–3.9 Hz. Performance characteristics for human subjects were compared with those of computer simulations of tracking behaviour based on AMT. The computer simulations compensated for a 300 ms reaction time delay by predicting the future position of the target 300 ms ahead. Predictions were achieved by an adaptive filter circuit which automatically tuned its weights to generate the best possible predictions by minimizing the variance of the prediction errors. The influence of prediction on tracking behaviour was assessed by running the computer simulations both with and without target signal prediction. Gain and phase frequency response curves for all subjects varied as a function of target signal bandwidth in a manner similar to the computer simulations with prediction. The data support the view that the human nervous system includes adaptive neural filters able to generate predictions of future values of sensory signals by tuning themselves to minimize the variance of prediction errors, as proposed in the AMT.

Influence of control--display compatibility on tracking behaviour.

In continuous tracking tasks it is generally accepted that the level of compatibility between control and display influences the accuracy of tracking performance. However, the nature of the changes in tracking behaviour have not been specified. In this study two experiments are reported in which the effect of control-display compatibility on pursuit visual tracking performance is assessed by means of a cross correlational and spectral analysis. A reduction in control-display compatibility results in a decrease in gain, an increase in phase lag, and an increase in average amplitude of the remnant component of the operators response. The utility of these measures as descriptors of tracking behaviour is discussed with reference to the results obtained if overall measures of error are used.

A neuroengineering solution to the optimal tracking problem

Abstract A mathematical solution is developed for an optimal sensory-to-motor transformation. This specifies a unique vector of motor command signals for goal-directed upper limb movement, conditional on cost of muscular effort vs. performance accuracy. Derivation is based on a realistic model of visual tracking, incorporating characteristics of external tracking system, target and disturbance as well as multivariable, nonlinear, time-varying characteristics of neuromuscular and biomechanical systems internal to the human operator. The optimal transformation removes redundancy in a 58-dimensional muscle system to give a two-dimensional response, thus solving the degrees-of-freedom problem. While adaptive filter neural networks are required to implement the general solution, an instructive linear matrix approximation reveals computational modules with observable behavioural correlates such as prediction, synergy generation and speed–accuracy compromise. PsycINFO classification: 2330

Chapter 17 Adaptive Model Theory: Application to Disorders of Motor Control

Publisher Summary This chapter describes the applications to disorders of motor control. Adaptive Model Theory (AMT) is a computational theory of the information processing performed by the human central nervous system during control of purposive movement. The chapter begins with an overview of AMT which incorporates sensory analysis, response planning and response execution stages of information processing, the concept of intermittency in movement control, and the notion of a Basic Unit of Motor Production or BUMP. There is a development of the idea that a finite interval of time, typically 100–200 ms, is required to preplan a response as a trajectory of desired reference. This sensory trajectory is transformed in real time into appropriately coordinated motor commands with an adaptive internal model of the inverse dynamic relationships between outgoing motor commands and their resulting sensory consequences. In addition, a detailed description of the principles of adaptive modeling of multivariable nonlinear dynamic systems is provided and the chapter overviews an AMT-based computer simulation of human operator performance of a pursuit tracking task. Lastly, the implications of AMT for disorders of motor control as diverse as cerebral palsy and stuttering are explored in the chapter.

Chapter 5 Adaptive optimal control of human tracking

The motor behaivour of subjects performing visual tracking tasks is quantified by identifying the mathematical relationship between the visual information presented to the eye and the resulting motor response generated at the hand. It has long been known that this relationship is equivocal and that no unique mathematical model exists to describe the behaviour of the human operator. In what follows we develop the hypothesis that tracking behaviour is variable because the central nervous system (CNS) functions as an adaptive optimal controller of muscles, biomechanics and external systems. It automatically tunes its input-output relationship to compensate for the dynamics of the system being controlled and to compensate for inherent time delays by predicting future values of the input signals. We explore the proposal that the CNS plans motor responses to achieve goals using a minimum of input muscular energy and that it can trade tracking accuracy against demand for input energy by altering the speed of the response. Hypotheses about information processing performed by the CNS during visual tracking are presented in the form of a computer simulation. Distributed parallel processing circuitry is employed in the simulator to construct adaptive digital filters which operate independently and in parallel. These digital filters mimic the behaviour of hypothesized neural adaptive filters within the CNS. Indeed in general, descriptions of the simulator can be taken as hypotheses about the structure and function of neural circuitry and about the information processing performed by the CNS during control of movement. As with any scientific theory, the hypotheses are tested experimentally by comparing the behaviour of the simulator with that of human subjects performing the same task. A summary of key findings from a number of studies of human tracking behaviour carried out at our laboratory is presented and many of the findings are compared with the behaviour of the simulator.

Depressive illness: the role of aggression further considered.

Measures of depression and undischarged drive were obtained for 292 psychiatric in-patients. In 200 of these cases inhibition of aggression was also assessed. All patients were classified as being endogenously depressed, reactively depressed, or as suffering from non-depressive primary disorders. The latter group was subdivided into secondarily depressed and non-depressed groups. The 3 depressed groups were then compared with the non-depressed subjects in respect of drive level and inhibition of aggressionmall 3 depressed groups showed significantly higher driver level than did the non-depressed subjects. The endogenous depressives also inhibited significantly more of their aggression than did the non-depressed subjects. The results are consistent with a drive inhibition theory of depression. However, while endogenous depression seems to be associated more specifically with the inhibition of aggression, reactive depression may be associated rather with the inhibition of drive generally.