Non-linear Amplification of Variability Through Interaction Across Scales Supports Greater Accuracy in Manual Aiming: Evidence From a Multifractal Analysis With Comparisons to Linear Surrogates in the Fitts Task

Abstract

Movement coordination depends on directing our limbs to the right place and in the right time. Movement science can study this central requirement in the Fitts task that asks participants to touch each of two targets in alternation, as accurately and as fast as they can. The Fitts task is an experimental attempt to focus on how the movement system balances its attention to speed and to accuracy. This balance in the Fitts task exhibits a hierarchical organization according to which finer details (e.g., kinematics of single sweeps from one target to the other) change with relatively broader constraints of task parameters (e.g., distance between targets and width of targets). The present work seeks to test the hypothesis that this hierarchical organization of movement coordination reflects a multifractal tensegrity in which non-linear interactions across scale support stability. We collected movement series data during a easy variant of the Fitts task to apply just such a multifractal analysis with surrogate comparison to allow clearer test of non-linear interactions across scale. Furthermore, we test the role of visual feedback both in potential and in fact, i.e., by manipulating both whether experimenters instructed participants that they might potentially have to close their eyes during the task and whether participants actually closed their eyes halfway through the task. We predict that (1) non-linear interactions across scales in hand movement series will produce variability that will actually stabilize aiming in the Fitts task, reducing standard deviation of target contacts; (2) non-linear interactions across scales in head sway will stabilize aiming following the actual closing eyes; and (3) non-linear interactions across scales in head sway and in hand movements will interact to support stabilizing effects of expectation about closing eyes. In sum, this work attempts to make the case that the multifractal-tensegrity hypothesis supports more accurate aiming behavior in the Fitts task.

Multifractal Mice: Measuring Task Engagement and Readiness-to-hand via Hand Movement

“…We conduct a replication study (N=28), and two new experiments (N=44, N=30), using MFA to analyse recordings of hand movement on a mouse during a game, in order to estimate the multifractality of the hand movement. Multifractality is a measure of complexity which has previously been linked to skill [7], adaptive behaviour [4], and readiness-to-hand during tool breakdown [23]. The results of our three experiments show that multifractality in hand movement correlates with level of engagement, and two other features of readiness-to-hand: the adequacy of the tool, and familiarity with the tool.…”

“…with flexibility and adaptation, executive function, skill, and engagement [4,7,58]. This role in flexible behaviour has been explained both in terms of genesis and function.…”

“…In functional terms, it has been suggested that multifractal variation supports effective, adaptive, behaviour by enriching the movement system's relationship with the task environment, by providing a source of structured variation. [7,[78][79][80].…”

Multifractal signatures of perceptual processing on anatomical sleeves of the human body

“…That is, a single degree of freedom can exhibit different fractal patterns across time or for different-sized events. For that matter, perceptual accuracy may depend sooner on the nonlinearity generating multifractal forms than on nonlinearity generating monofractal forms [24,[32][33][34]. The difference here has to do with the fact that monofractal form is only suggestive of a similar pattern observable at many scales, and it is mute to the reasons for similarity.…”

Multifractal roots of suprapostural dexterity

“…Visually fixating brings a prestressed quiet stance into one with informational coupling with the visual stimulus. Past work has repeatedly implicated fractal fluctuations in the head and upper torso for using visual information to organize action [28,29,[65][66][67][68]. Visual inspection of IRF plots indicated rare instances of effects from or responses from CoM fractality on or to SD (1 trial in the 50-, 135-, and 305-cm conditions), but the regression modeling indicated no stable relationship.…”