Analysis and identification of visual-motor integration in human motor control

Date

2023-9-11

Author

Kılıç, Ayşegül

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This research focuses on the complexities of visual-motor integration in humans using target-tracking tasks, emphasizing the dynamics of human-in-loop systems. Drawing from the foundations of the pursuit and compensatory control models, the study accentuates the profound influence of feedback mechanisms and the predictability of inputs on performance outcomes. The research design included four distinct target-tracking tasks, each examining different aspects of visual-motor integration. These tasks were characterized by variations in trajectory types, including single-frequency sinusoids and sum-of-sine, and differences in experimental scenes, ranging from visibility of the target and operator to only error information representation. We collected 25 participants’ position and velocity data with a haptic device. An accurate response was observed when participants received direct visual feedback even under unpredictable stimuli. Another observation was the overall better tracking performance when the input was predictable compared to unpredictable stimuli. Also, we compared the performance enhancement created by the input and feedback. We saw that increasing the predictability of the input was more effective than increasing the feedback supplied to improve the performance. Moreover, the consistent emergence of a "U" shaped magnitude response across unpredictable input experiments alludes to potential intrinsic properties of the human motor system, possibly influenced by factors such as neural resonance. This research integrates conventional methodologies with more realistic scenarios, offering a layered and more nuanced understanding of human capabilities and limitations in motor control models and opening up opportunities for designing systems that can better interact with or augment human abilities.

Subject Keywords

Human manual target tracking, Visual-motor integration, Input predictability, Feedback predecitability, System identification

URI

https://hdl.handle.net/11511/105396

Collections

Graduate School of Natural and Applied Sciences, Thesis