Differential time scales of change to learning frequency structures of isometric force tracking.

Abstract

Multiple processes support the persistent (learning) and transient (adaptive) change in behavior over time. We investigated whether practice and rest influence similarly the learning and adaptation of slow and fast frequency structures in isometric force tracking of pathways that varied in their regularity. Participants practiced 25 trials on each of 5 days in either a constant force target or 1 with the 1/f distributional properties of brown or pink noise. There was a reduction in root mean squared error (RMSE) as well as an increasing positive correlation between force output and the target pathway for all noise conditions over days. The spectral frequency analysis of force output and RMSE revealed task dependent outcomes of learning and adaptation as a function of the relatively slow (0-4 Hz) and fast (8-12 Hz) oscillatory time scales. These contrasting findings show that the persistent and transient properties of learning occur across different timescales and dimensions of behavior (force output and outcome-RMSE).

Selective visual scaling of time-scale processes facilitates broadband learning of isometric force frequency tracking

“…A key question that requires further exploration is how these oscillatory time scales of force control interact with the growth/decay time scales of learning and development . The current findings provide further evidence that the oscillatory time scales of motor output show different characteristics of growth/decay time scales of learning (Studenka et al, 2014).…”

“…The principal questions were whether (a) practice would differentially alter the multiple time scales of force output as well as interact with the visual scaling manipulation (Hu & Newell, 2010); (b) the selective visual scaling manipulation would generalize across the multiples time scale of force output in a manner consistent with fractal models of physiology and behavior (Bassingthwaighte et al, 1994;Ward, 2002); and (c) the selective visual scaling manipulation of the practice environment would induce enhanced transfer to novel task conditions compared to the fixed gain group. Practice and force output structure Previous studies have shown bidirectional changes (Newell & Vaillancourt, 2001) and differential time scales of learning (Studenka et al, 2014) of isometric force dynamics as a function of practice. Here, task outcome (RMSE) showed reductions of error over practice (block and day) as well as an interaction between practice and visual scaling conditions.…”

“…They, also, allow for the examination of properties of learning over multiple dimensions of movement (Studenka et al, 2014) to potentially reveal how the perceptual-motor system learns and retains successful movement patterns with redundant motor tasks. The observed persistence of tracking performance under the visual scaling manipulation represents a novel finding for this isometric force tracking task and suggests that augmenting the shorter time scales of visual feedback information of force output exerts a stronger influence on the learning and retention of force output than that of traditional practice structure manipulations including variability of practice (Schmidt, 1975).…”

“…With respect to motor learning, several investigations have shown a persistent change of isometric force structure as a function of practice (Newell, Broderick, Deutsch, & Slifkin, 2003;Sosnoff & Voudrie, 2009); however, only recently have studies explored the effect of different practice conditions on the learning and transfer of isometric force control (King & Newell, 2013;Studenka et al, 2014). The aim of present study was to examine the effect of different informational constraints on the learning and transfer of isometric force dynamics.…”

“…With practice the learner will identify task-relevant sources of perceptual information that support changes of performance and continue to develop a tight coupling between the mapping of information and movement dynamics in the workspace. In this view, information constraints extend beyond the typical preperformance (e.g., instructions) and postperformance (augmented feedback) variables to include concurrent forms of feedback that operate on multiple time scales to interact with the multiple dimensions of motor learning (Kugler et al, 1980;Newell, 1996;Studenka, King, & Newell, 2014).…”