Memory pattern identification for feedback tracking control in human-machine systems

Objective: The aim of this paper was to identify the characteristics of memory patterns with respect to a visual input, perceived by the human operator during a manual control task, which consisted in following a moving target on a display with a cursor.Background: Manual control tasks involve nondeclarative memory. The memory encodings of different motor skills have been referred to as procedural memories. The procedural memories have a pattern, which this paper sought to identify for the particular case of a onedimensional tracking task. Specifically, data recorded from human subjects controlling dynamical systems with different fractional order were investigated.Method: A Finite Impulse Response (FIR) controller was fitted to the data, and pattern analysis was performed to the fitted parameters. Then, the FIR model was further reduced to a lower order controller; from the simplified model, the stability analysis of the human-machine system in closedloop was conducted.Results: It is shown that the FIR model can be employed to identify and represent patterns in human procedural memories during manual control tasks. The obtained procedural memory pattern presents a time scale of about 650 ms before decay. Furthermore, the fitted controller is stable for systems with fractional order less or equal to 1.Conclusion: For systems of different fractional order, the proposed control scheme – based on a FIR model – can effectively characterize the linear properties of manual control in humans.Application: This research supports a biofidelic approach to human manual control modeling over feedback visual perceptions. Relevant applications of this research are: the development of shared-control systems, where a virtual human model assists the human during a control task, and human operator state monitoring.</div

Steering is initiated based on error accumulation.

Vehicle control by humans is possible because the central nervous system is capable of using visual information to produce complex sensorimotor actions. Drivers must monitor errors and initiate steering corrections of appropriate magnitude and timing to maintain a safe lane position. The perceptual mechanisms determining how a driver processes visual information and initiates steering corrections remain unclear. Previous research suggests 2 potential alternative mechanisms for responding to errors: (a) perceptual evidence (error) satisficing fixed constant thresholds (Threshold), or (b) the integration of perceptual evidence over time (Accumulator). To distinguish between these mechanisms, an experiment was conducted using a computer-generated steering correction paradigm. Drivers (N = 20) steered toward an intermittently appearing “road-line” that varied in position and orientation with respect to the driver’s position and trajectory. One key prediction from a Threshold framework is a fixed absolute error response across conditions regardless of the rate of error development, whereas the Accumulator framework predicts that drivers would respond to larger absolute errors when the error signal develops at a faster rate. Results were consistent with an Accumulator framework; thus we propose that models of steering should integrate perceived control error over time in order to accurately capture human perceptual performance