Abstract:In the analysis of continuous data, researchers are often faced with the problem that statistical methods developed for single-point data (e.g., t test, analysis of variance) are not always appropriate for their purposes. Either methodological adaptations of single-point methods will need to be made, or confidence bands are the method of choice. In this article, we compare three prominent techniques to analyze continuous data (single-point methods, Gaussian confidence bands, and function-based resampling metho… Show more
“… Figure 1 Velocity profiles of the receiver’s reaching velocity. Confidence bands were generated using a point-by-point bootstrap approach by Joch et al 16 . The table includes measured mean and peak velocities for all four velocity variations.…”
Section: Resultsmentioning
confidence: 99%
“…In all figures with confidence bands the point-based-resampling-technique (PBRT 16 ) was used, which leads to 95% confidence bands that make it possible to reliably evaluate statistical significance.…”
From a motor control perspective, human-to-human object handovers can be described as coordinated joint-actions transferring the power over an object from a passer to a receiver. Although, human-to-human handovers are very reliable in terms of success, it is unclear how both actors plan and execute their actions independently while taking into account the partners behaviour. Here, we measured grip-forces of passer and receiver while handing over an object. In order to study mutual interaction in human-to-human handovers, we measured how changes in relevant features (sensory information available to the passer and receiver’s reaching velocity) in one partner affect grip-force profiles not only at the manipulated side but also at the partner’s side. The data reveals strong effects of sensory manipulations on time-related (duration and release delay) and dynamometric measures (force rates). Variation of reaching velocities had the largest impact on the receiver’s force rates. Furthermore, there are first indications that the vertical object movement is used as an implicit cue to signal the start of the handover in situations where vision is restricted.
“… Figure 1 Velocity profiles of the receiver’s reaching velocity. Confidence bands were generated using a point-by-point bootstrap approach by Joch et al 16 . The table includes measured mean and peak velocities for all four velocity variations.…”
Section: Resultsmentioning
confidence: 99%
“…In all figures with confidence bands the point-based-resampling-technique (PBRT 16 ) was used, which leads to 95% confidence bands that make it possible to reliably evaluate statistical significance.…”
From a motor control perspective, human-to-human object handovers can be described as coordinated joint-actions transferring the power over an object from a passer to a receiver. Although, human-to-human handovers are very reliable in terms of success, it is unclear how both actors plan and execute their actions independently while taking into account the partners behaviour. Here, we measured grip-forces of passer and receiver while handing over an object. In order to study mutual interaction in human-to-human handovers, we measured how changes in relevant features (sensory information available to the passer and receiver’s reaching velocity) in one partner affect grip-force profiles not only at the manipulated side but also at the partner’s side. The data reveals strong effects of sensory manipulations on time-related (duration and release delay) and dynamometric measures (force rates). Variation of reaching velocities had the largest impact on the receiver’s force rates. Furthermore, there are first indications that the vertical object movement is used as an implicit cue to signal the start of the handover in situations where vision is restricted.
“…This should enable informed decisions regarding which particular measures to compute when planning a conceptually similar experiment, and it allows for gauging how these measures are affected by design choices. Further, we also provide the raw data for more specialized approaches (e.g., Joch et al, 2019 ; Scherbaum et al, 2010 ), as well as the analysis scripts, so every step of the analysis can be easily reproduced.…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, possible modulations of the Simon effect or its sequential adaptation by the setup should be especially meaningful. More advanced statistical methods have been proposed that are explicitly tailored to specific research questions in the context of continuous data (e.g., Joch, Döhring, Maurer, & Müller, 2019 ; Maldonado, Dunbar, & Chemla, 2019 ; Scherbaum, Dshemuchadse, Fischer, & Goschke, 2010 ), and we provide the raw data online to enable the application of these approaches ( www.osf.io/am6yp ).…”
Section: Experiments 1: Manipulation Of Input Devicementioning
The continuous tracking of mouse or finger movements has become an increasingly popular research method for investigating cognitive and motivational processes such as decision-making, action-planning, and executive functions. In the present paper, we evaluate and discuss how apparently trivial design choices of researchers may impact participants’ behavior and, consequently, a study’s results. We first provide a thorough comparison of mouse- and finger-tracking setups on the basis of a Simon task. We then vary a comprehensive set of design factors, including spatial layout, movement extent, time of stimulus onset, size of the target areas, and hit detection in a finger-tracking variant of this task. We explore the impact of these variations on a broad spectrum of movement parameters that are typically used to describe movement trajectories. Based on our findings, we suggest several recommendations for best practice that avoid some of the pitfalls of the methodology. Keeping these recommendations in mind will allow for informed decisions when planning and conducting future tracking experiments.
“…Other kinematic investigations have used single-point data analysis [ 23 , 24 ], for example, by comparing joint angles at the moment of the toe-off [ 23 ] or by using local Gaussian methods on time-normalized data chunks of one-minute duration [ 25 ]. Statistical methods considering whole function-like data curves are, however, crucial because analyzing gait data with single-point approaches may also result in validity and reliability issues [ 26 ]. The influence of altered conditions on continuous data can barely be expressed in a single data point.…”
Triathletes often experience incoordination at the start of a transition run (TR); this is possibly reflected by altered joint kinematics. In this study, the first 20 steps of a run after a warm-up run (WR) and TR (following a 90 min cycling session) of 16 elite, male, long-distance triathletes (31.3 ± 5.4 years old) were compared. Measurements were executed on the competition course of the Ironman Frankfurt in Germany. Pacing and slipstream were provided by a cyclist in front of the runner. Kinematic data of the trunk and leg joints, step length, and step rate were obtained using the MVN Link inertial motion capture system by Xsens. Statistical parametric mapping was used to compare the active leg (AL) and passive leg (PL) phases of the WR and TR. In the TR, more spinal extension (~0.5–1°; p = 0.001) and rotation (~0.2–0.5°; p = 0.001–0.004), increases in hip flexion (~3°; ~65% AL−~55% PL; p = 0.001–0.004), internal hip rotation (~2.5°; AL + ~0–30% PL; p = 0.001–0.024), more knee adduction (~1°; ~80–95% AL; p = 0.001), and complex altered knee flexion patterns (~2–4°; AL + PL; p = 0.001–0.01) occurred. Complex kinematic differences between a WR and a TR were detected. This contributes to a better understanding of the incoordination in transition running.
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