Behavioral and physiological correlates of kinetically tracking a chaotic target

Abstract

Humans can innately track a moving target by anticipating its future position from a brief history of observations. While ballistic trajectories can be readily extrapolated, many natural and artificial systems are governed by more general nonlinear dynamics and, therefore, can produce highly irregular motion. Yet, relatively little is known regarding the behavioral and physiological underpinnings of prediction and tracking in the presence of chaos. Here, we investigated in lab settings whether participants could manually follow the orbit of a paradigmatic chaotic system, the Rö ssler equations, on the (x,y) plane under different settings of a control parameter, which determined the prominence of transients in the target position. Tracking accuracy was negatively related to the level of unpredictability and folding. Nevertheless, while participants initially reacted to the transients, they gradually learned to anticipate it. This was accompanied by a decrease in muscular co-contraction, alongside enhanced activity in the theta and beta EEG bands for the highest levels of chaoticity. Furthermore, greater phase synchronization of breathing was observed. Taken together, these findings point to the possible ability of the nervous system to implicitly learn topological regularities even in the context of highly irregular motion, reflecting in multiple observables at the physiological level.

A novel stretching and folding characterization method based on geometrical and physiological traits of chaotic and intermittent tracking signals

“…This result implies that the hand motion dynamics were more complicated during tracking chaotic targets. Similarly, in [14], LLE, as well as CD, was counted as a measure of chaotic behavior in the hand motion during tracking chaotic signals. The obtained values of the LLE and the CD were positive and non-integer, illustrating the hand motion's chaotic behavior.…”

“…SFs are striking traits of chaotic trajectories [44], such as target tracking with undetermined and nonlinear behavior [14]. Chaotic dynamics arise from the combination of SF mechanisms.…”

“…Chaotic behavior is quantified by this method, emphasizing the dynamical (time-dependent) aspect of the hand motion trajectories [44]. The positive value of the largest Lyapunov exponent (LLE) is typically regarded as an indicator of chaotic behavior [14]. Several methods have been employed to calculate LEs, such as [66,67].…”

“…Different studies have investigated the association between chaotic dynamics and human activities, especially in hand movements [11,12] . Some studies have evaluated voluntary control of hand motion and its chaotic behavior during target tracking tasks [13,14]. In contrast, others have considered the human ability to recognize chaotic target motion against randomness and predict the target's behavior over time [15,16].…”

Information Rate in Humans during Visuomotor Tracking

“…Its model-free nature, coupled with state-of-the-art methods for information estimation, grant our approach enough flexibility for generalizing it to more complex tasks, such as to accommodate higher-dimensional target/tracking spaces [ 27 ] or delay-embeddings of random processes [ 28 ].…”