Hysteresis in visual search.

People perform complex visual tasks. Airplane pilots land planes safely on the ground and baseball players swing bats at speeding fastballs. Drivers weave through traffic and sports fans skillfully track the movements of their favorite team. These are examples of visual search, the process of looking for something. Classic experiments have provided much information about characteristics affecting search efficiency (i.e., efficiency =display size/speed; Treisman & Gelade, 1980), but visual search literature is split on the underlying mechanisms involved in visual search. Visual search may be random (Wolfe, 2007), memory-driven (Zelinsky, 2008), or self-similar over time (Aks, Zelinsky, & Sprott, 2002). These standpoints assign memory at least some role in determining search behavior-the current work explores this possibility by looking for evidence of nonlinearity in visual search response times. Participants performed 250 visual search trials in one of three conditions, ascending-first, descending-first, or random. Ascending-first participants performed 125 searches increasing in difficulty, then 125 searches decreasing in difficulty. Descending-first participants completed 125 searches decreasing in difficulty, then 125 searches increasing in difficulty. Random participants completed 250 searches pseudo-randomly varying in difficulty. We constructed hysteresis plots for each condition and nonlinearity emerged in the data that does not fit traditional concepts of memory, practice, and fatigue. The findings suggest that the term memory may not be a useful concept for describing the visual search process. Hysteresis in visual behavior indicates history-dependence-we suggest the term history as a replacement for memory

Modeling Spatial Asymmetry in Visuomotor Coordination

Coordination is foundational to human movement. One prominent model of coordination is the Haken-Kelso-Bunz which predicts change in relative phase between two oscillators during movement. Use of this model has shown that the body has a natural tendency to prefer certain coordination patterns over others. The generalizability of the model has sparked its use throughout the scientific community to observe movement through the lens of dynamical systems theory. We contend that this model can be advanced further through incorporation of visually perceived spatial asymmetries, a component not currently accounted for by the model. That is, we sought to find if spatial arrangements of coordinated elements impacts preferred coordination patterns. Participants coordinated their arm movements with a visually displayed sinusoidally oscillating stimulus. A user controlled visual stimulus was displayed on a screen that oscillated due to their arm movement. The subjects performed elbow extension moving the user-controlled marker on the screen in a symmetrical or asymmetrical fashion to the computer-generated oscillating marker. To alter the spatial asymmetry, the computer-controlled marker was shifted from the midline of the subject three incremental amounts on both the left and right side. Results suggest that, in the current context, spatial asymmetries can be captured by a modified HKB model

ARWalker: A Virtual Walking Companion Application

Extended Reality (XR) technologies, including Augmented Reality (AR), have attracted significant attention over the past few years and have been utilized in several fields, including education, healthcare, and manufacturing. In this paper, we aim to explore the use of AR in the field of biomechanics and human movement through the development of ARWalker, which is an AR application that features virtual walking companions (avatars). Research participants walk in close synchrony with the virtual companions, whose gait exhibits properties found in the gait of young and healthy adults. As a result, research participants can train their gait to the gait of the avatar, thus regaining the healthy properties of their gait and reducing the risk of falls. ARWalker can especially help older adults and individuals with diseases, who exhibit pathological gait thus being more prone to falls. We implement a prototype of ARWalker and evaluate its systems performance while running on a Microsoft Hololens 2 headset

TIME EVOLUTION IS A SOURCE OF BIAS IN THE WOLF ALGORITHM FOR LARGEST LYAPUNOV EXPONENTS

Human movement is inherently variable by nature. One of the most common analytical tools for assessing movement variability is the largest Lyapunov exponent (LyE) which quantifies the rate of trajectory divergence or convergence in an n-dimensional state space. One popular method for assessing LyE is the Wolf algorithm. Many studies have investigated how Wolf’s calculation of the LyE changes due to sampling frequency, filtering, data normalization, and stride normalization. However, a surprisingly understudied parameter needed for LyE computation is evolution time. The purpose of this study is to investigate how the LyE changes as a function of evolution time in both simulated data and experimental data. The data from 36 healthy subjects were extracted from an ongoing study, investigating whether individuals possess a unique, self-identifying, gait. The subjects performed nine four-minute overground walking trials at their self-selected walking speed. Segment pitch angles from the left and right thigh, shank, and foot were extracted from the first 2-minutes of each trial to calculate LyEs. Simulated data consisted of values from a reconstructed Lorenz attractor. Evolution time was calculated by multiplying a fixed constant by the sampling rate and applying the ceiling function (ceil()) in MATLAB to round up to the nearest whole integer. Multi-level and linear models were used to assess whether the inclusion of fixed effects of evolution time improved prediction of LyE over and above an intercept only model for experimental and simulated data, respectively. Increasing evolution time in Wolf’s algorithm substantially negatively biases LyE values for simulated and experimental data. Overall, careful consideration should be taken when choosing the evolution time. An evolution time of 1.5 seconds produced the closest values to the expected simulated value. Therefore, future research should consider using a similar time for experimental data

A Nonlinear Analysis Software Toolkit for Biomechanical Data

In this paper, we present a nonlinear analysis software toolkit, which can help in biomechanical gait data analysis by implementing various nonlinear statistical analysis algorithms. The toolkit is proposed to tackle the need for an easy-to-use and friendly analyzer for gait data where algorithms seem complex to implement in software and execute. With the availability of our toolkit, people without programming knowledge can run the analysis to receive human gait data analysis results. Our toolkit includes the implementation of several nonlinear analysis algorithms, while it is also possible for users with programming experience to expand its scope by implementing and adding more algorithms to the toolkit. Currently, the toolkit supports MatLab bindings while being developed in Python. The toolkit can seamlessly run as a background process to analyze hundreds of different gait data and produce analysis outcomes and figures that illustrate these results

Stochastic Resonance Reduces Sway and Gait Variability in Individuals With Unilateral Transtibial Amputation: A Pilot Study

Sub-threshold (imperceptible) vibration, applied to parts of the body, impacts how people move and perceive our world. Could this idea help someone who has lost part of their limb? Sub-threshold vibration was applied to the thigh of the affected limb of 20 people with unilateral transtibial amputation. Vibration conditions tested included two noise structures: pink and white. Center of pressure (COP) excursion (range and root-mean-square displacements) during quiet standing, and speed and spatial stride measures (mean and standard deviations of step length and width) during walking were assessed. Pink noise vibration decreased COP displacements in standing, and white noise vibration decreased sound limb step length standard deviation in walking. Sub-threshold vibration positively impacted aspects of both posture and gait; however, different noise structures had different effects. The current study represents foundational work in understanding the potential benefits of incorporating stochastic resonance as an intervention for individuals with amputation

Stochastic Resonance and Heaviness Perception

Heaviness perception is the ability to use haptic feedback from effortful touch to determine the weight of a wielded object. The perception of an object being wielded does not rely solely on the object’s mass, but muscular effort as well. When an object is wielded, torques and moments of inertia are produced. The inertia tensor contains those moments and provides information about how mass is distributed in a rigid body. The corresponding eigenvalues and eigenvectors of the inertia tensor have been related to an object’s perceived magnitudes (e.g., weight) and directions (e.g., orientation with respect to hand), respectively. The inertia tensor can be visualized as an ellipsoid, which is produced in effect from the constraints related to the position of the limb. In this study, we will manipulate the eigenvectors associated with an object in relation to the limb. Recent studies have also provided evidence that adding noise to a weak stimulus can enhance a person’s ability to detect it. Introducing a subthreshold (vibrational) stimulus embedded with noise may, in some cases, improve sensations gained from limb movements. We hypothesize that adding vibrotactile noise of various forms will improve accuracy in perceiving the heaviness of a wielded object

Windowed Multiscale Synchrony: Modeling Time-Varying and Scale-Localized Interpersonal Coordination Dynamics

Social interactions are pervasive in human life with varying forms of interpersonal coordination emerging and spanning different modalities (e.g., behaviors, speech/language, and neurophysiology). However, during social interactions, as in any dynamical system, patterns of coordination form and dissipate at different scales. Historically, researchers have used aggregate measures to capture coordination over time. While those measures (e.g., mean relative phase, cross-correlation, coherence) have provided a wealth of information about coordination in social settings, some evidence suggests that multiscale coordination may change over the time course of a typical empirical observation. To address this gap, we demonstrate an underutilized method, windowed multiscale synchrony, that moves beyond quantifying aggregate measures of coordination by focusing on how the relative strength of coordination changes over time and the scales that comprise social interaction. This method involves using a wavelet transform to decompose time series into component frequencies (i.e., scales), preserving temporal information and then quantifying phase synchronization at each of these scales. We apply this method to both simulated and empirical interpersonal physiological and neuromechanical data. We anticipate that demonstrating this method will stimulate new insights on the mechanisms and functions of synchrony in interpersonal contexts using neurophysiological and behavioral measures