Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153637
Title: A Hessian-based decomposition characterizes how performance in complex motor skills depends on individual strategy and variability
Authors: Tommasino, Paolo
Maselli, Antonella
Campolo, Domenico
Lacquaniti, Francesco
D'Avella, Andrea
Keywords: Engineering::Mechanical engineering
Issue Date: 2021
Source: Tommasino, P., Maselli, A., Campolo, D., Lacquaniti, F. & D'Avella, A. (2021). A Hessian-based decomposition characterizes how performance in complex motor skills depends on individual strategy and variability. PloS ONE, 16(6), 0253626-. https://dx.doi.org/10.1371/journal.pone.0253626
Journal: PloS ONE
Abstract: In complex real-life motor skills such as unconstrained throwing, performance depends on how accurate is on average the outcome of noisy, high-dimensional, and redundant actions. What characteristics of the action distribution relate to performance and how different individuals select specific action distributions are key questions in motor control. Previous computational approaches have highlighted that variability along the directions of first order derivatives of the action-to-outcome mapping affects performance the most, that different mean actions may be associated to regions of the actions space with different sensitivity to noise, and that action covariation in addition to noise magnitude matters. However, a method to relate individual high-dimensional action distribution and performance is still missing. Here we introduce a decomposition of performance into a small set of indicators that compactly and directly characterize the key performance-related features of the distribution of high-dimensional redundant actions. Central to the method is the observation that, if performance is quantified as a mean score, the Hessian (second order derivatives) of the action-to-score function determines how the noise of the action distribution affects performance. We can then approximate the mean score as the sum of the score of the mean action and a tolerance-variability index which depends on both Hessian and action covariance. Such index can be expressed as the product of three terms capturing noise magnitude, noise sensitivity, and alignment of the most variable and most noise sensitive directions. We apply this method to the analysis of unconstrained throwing actions by non-expert participants and show that, consistently across four different throwing targets, each participant shows a specific selection of mean action score and tolerance-variability index as well as specific selection of noise magnitude and alignment indicators. Thus, participants with different strategies may display the same performance because they can trade off suboptimal mean action for better tolerance-variability and higher action variability for better alignment with more tolerant directions in action space.
URI: https://hdl.handle.net/10356/153637
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0253626
Rights: © 2021 Tommasino et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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