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Can You Hear That Peak? Utilization of Auditory and Visual Feedback at Peak Limb Velocity

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Title: Can You Hear That Peak? Utilization of Auditory and Visual Feedback at Peak Limb Velocity
Language: English
Authors: Loria, Tristan, de Grosbois, John, Tremblay, Luc
Source: Research Quarterly for Exercise and Sport. 2016 87(3):254-261.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 8
Publication Date: 2016
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Neurological Organization, Cues, Auditory Stimuli, Visual Stimuli, Feedback (Response), Exercise Physiology, Human Body, Mechanics (Physics), Motion, College Students, Foreign Countries
DOI: 10.1080/02701367.2016.1196810
ISSN: 0270-1367
Abstract: Purpose: At rest, the central nervous system combines and integrates multisensory cues to yield an optimal percept. When engaging in action, the relative weighing of sensory modalities has been shown to be altered. Because the timing of peak velocity is the critical moment in some goal-directed movements (e.g., overarm throwing), the current study sought to test whether visual and auditory cues are optimally integrated at that specific kinematic marker when it is the critical part of the trajectory. Methods: Participants performed an upper-limb movement in which they were required to reach their peak limb velocity when the right index finger intersected a virtual target (i.e., a flinging movement). Brief auditory, visual, or audiovisual feedback (i.e., 20 ms in duration) was provided to participants at peak limb velocity. Performance was assessed primarily through the resultant position of peak limb velocity and the variability of that position. Results: Relative to when no feedback was provided, auditory feedback significantly reduced the resultant endpoint variability of the finger position at peak limb velocity. However, no such reductions were found for the visual or audiovisual feedback conditions. Further, providing both auditory and visual cues concurrently also failed to yield the theoretically predicted improvements in endpoint variability. Conclusions: Overall, the central nervous system can make significant use of an auditory cue but may not optimally integrate a visual and auditory cue at peak limb velocity, when peak velocity is the critical part of the trajectory.
Abstractor: As Provided
Number of References: 25
Entry Date: 2016
Accession Number: EJ1111355
Database: ERIC
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  Value: <anid>AN0117576476;rqe01sep.16;2019Mar18.14:21;v2.2.500</anid> <title id="AN0117576476-1">Can You Hear That Peak? Utilization of Auditory and Visual Feedback at Peak Limb Velocity. </title> <p>Purpose: At rest, the central nervous system combines and integrates multisensory cues to yield an optimal percept. When engaging in action, the relative weighing of sensory modalities has been shown to be altered. Because the timing of peak velocity is the critical moment in some goal-directed movements (e.g., overarm throwing), the current study sought to test whether visual and auditory cues are optimally integrated at that specific kinematic marker when it is the critical part of the trajectory. Methods: Participants performed an upper-limb movement in which they were required to reach their peak limb velocity when the right index finger intersected a virtual target (i.e., a flinging movement). Brief auditory, visual, or audiovisual feedback (i.e., 20 ms in duration) was provided to participants at peak limb velocity. Performance was assessed primarily through the resultant position of peak limb velocity and the variability of that position. Results: Relative to when no feedback was provided, auditory feedback significantly reduced the resultant endpoint variability of the finger position at peak limb velocity. However, no such reductions were found for the visual or audiovisual feedback conditions. Further, providing both auditory and visual cues concurrently also failed to yield the theoretically predicted improvements in endpoint variability. Conclusions: Overall, the central nervous system can make significant use of an auditory cue but may not optimally integrate a visual and auditory cue at peak limb velocity, when peak velocity is the critical part of the trajectory.</p> <p>Keywords: Audition; goal-directed reaching; multisensory integration; vision</p> <p>Our environment is filled with multisensory stimuli. Incoming sensory information from the various modalities creates a cohesive perception of our environment, and as such, it is critically relied on for activities of daily living. Within the context of goal-directed action, studies have primarily focused on skills where movement end was the critical moment of the trajectory (i.e., reach to grasp; e.g., Jeannerod, [<reflink idref="bib13" id="ref1">13</reflink>]). However, there are other goal-directed actions such as throwing a baseball, for which the critical moment is ideally peak limb velocity (PLV). That is, to maximize the velocity of an overarm throw, skilled throwers release the ball within 1 ms of PLV (e.g., Jegede, Watts, Stitt, & Hore, [<reflink idref="bib14" id="ref2">14</reflink>]). In research contexts, these movements include tossing (e.g., Fleischauer & Sherwood, [<reflink idref="bib8" id="ref3">8</reflink>]), flicking/flinging (e.g., Dulberg, Amant, & Zettlemoyer, [<reflink idref="bib4" id="ref4">4</reflink>]), and punching (e.g., Cavanagh & Landa, [<reflink idref="bib2" id="ref5">2</reflink>]). Such movements can provide novel perspectives on the use of sensory feedback during goal-directed action because the predominantly utilized sensory modality (i.e., vision) is not particularly salient at PLV. Indeed, the percept of the limb when it reaches PLV is a blur, at best. As such, the current study sought to determine whether auditory feedback alone, visual feedback alone, or combined audiovisual feedback would minimize the variability of the limb position at PLV. As a result, this study was also designed to test if—at PLV—auditory and visual cues are combined in a statistically optimal fashion (e.g., Ernst & Bülthoff, [<reflink idref="bib6" id="ref6">6</reflink>]). Prior to addressing these questions, selected studies were reviewed to ascertain current knowledge on the use of vision, audition, and audiovisual information, specifically for movements where the limb must stop onto a target (e.g., discrete reaches/reaching).</p> <p>During rapid upper-limb reaches, visual information gathered during the movement can be used to perform online trajectory amendments (e.g., Keele & Posner, [<reflink idref="bib16" id="ref7">16</reflink>]). For example, Proteau, Roujoula, and Messier ([<reflink idref="bib19" id="ref8">19</reflink>]) had participants perform a video-aiming task in which vision of a cursor representing the participant's limb was manipulated. Proteau et al. reported that participants were able to initiate a correction soon after the cursor jumped (i.e., after the cursor traveled at least 48 mm), leading them to suggest that visual information is monitored throughout discrete reaching movements (see also Saunders & Knill, [<reflink idref="bib20" id="ref9">20</reflink>]). Yet, in comparison to the lower-velocity phases of a movement, visual feedback of the moving limb may be considerably less reliable due to the blurring of the limb on the retina during the movement. This would especially be the case at PLV. As a result, audition may be a more reliable sensory modality to control the spatial occurrence of PLV, when the critical moment in the trajectory is PLV (e.g., overarm throwing).</p> <p>Previous work has shown that auditory feedback is useful to control the accuracy of upper-limb movements. In the experimental trials of a study conducted by Levy-Tzedek, Hanassy, Abboud, Maidenbaum, and Amedi ([<reflink idref="bib18" id="ref10">18</reflink>]), sighted participants performed upper-limb reaching movements while blindfolded and received auditory feedback via a sensory substitution device (SSD). When provided with auditory feedback only, participants' movements did not differ significantly in terms of movement time (MT), peak velocity, or amplitude as compared with reaches performed with visual feedback. Although participants were more accurate when visual information was available, reaches performed using the SSD had endpoint errors of less than 0.5 cm on average (Levy-Tzedek et al., [<reflink idref="bib18" id="ref11">18</reflink>]). Although the results of Levy-Tzedek et al. suggest auditory feedback is comparable to visual feedback, additional evidence has been provided for the facilitative effects of combined audiovisual feedback.</p> <p>In an observational learning study conducted by Doody, Bird, and Ross ([<reflink idref="bib3" id="ref12">3</reflink>]), participants were provided with a visual, auditory, or audiovisual model of a task requiring the sequential displacement of seven target objects positioned in an ellipse within 2,100 ms. The visual model consisted of an actor displacing the objects in the correct sequence. The auditory model, in contrast, consisted of the sound made by each displaced object without any visual information. The audiovisual model was a combination of the auditory and visual models. Interestingly, the results showed the greatest reduction in mean absolute and root mean square error for the auditory and audiovisual groups in relation to the visual group. Overall, the results of the studies conducted by Lezy-Tzedek et al. (2012) and Doody et al. suggest that auditory-based feedback (i.e., auditory alone or audiovisual combined) can yield more optimal performance in spatial-temporal tasks than visual information alone.</p> <p>The results reported here support the notion of optimal integration of multisensory information. The maximum likelihood estimation (MLE) model forwarded by Ernst and Bülthoff (2004) has provided a theoretical framework for how the central nervous system accomplishes this task. According to the MLE model, the central nervous system combines incoming sensory information across multiple modalities in a statistically optimal fashion (e.g., Alais & Burr, [<reflink idref="bib1" id="ref13">1</reflink>]). Stimulus reliability affects the "weight" given to each sensory modality during the integration process, with lesser weight assigned to unreliable cues (e.g., Ernst & Banks, [<reflink idref="bib5" id="ref14">5</reflink>]). By taking a weighted average during the sensory integration process, sensory estimates from different modalities are combined such that ambiguity is reduced and a more stable percept of a sensory event is formed. A well-cited example of multisensory integration is spatial ventriloquism. This illusion can be characterized by watching television in which the spatial location of auditory signals is biased toward the spatial location of the visual signal (Alais & Burr, [<reflink idref="bib1" id="ref15">1</reflink>]). In fact, multisensory processing is so robust at the perceptual, behavioral, and neuronal levels that some researchers hypothesize that the brain is largely organized for multisensory integration (e.g., Ghazanfar & Schroeder, [<reflink idref="bib9" id="ref16">9</reflink>]).</p> <p>However, it is important to note here that support for the MLE model and other Bayesian models of sensory utilization has primarily stemmed from tasks requiring little or no movement (see Witten & Knudsen, [<reflink idref="bib25" id="ref17">25</reflink>], for a review; cf. Körding & Wolpert, [<reflink idref="bib17" id="ref18">17</reflink>]). Considering that we hardly remain stationary in daily life (or at least we should not), it is important to consider how sensory information from multiple modalities is combined and integrated during goal-directed action. Interestingly, studies that have examined this topic have revealed considerable differences between multisensory integration at rest and multisensory integration during action.</p> <p>It has been reported that audiovisual processing is modulated during fast and accurate reaching movements. In an experiment conducted by Tremblay and Nguyen ([<reflink idref="bib24" id="ref19">24</reflink>]), participants performed a 30-cm movement to a 0.5-cm target. Beneath the terminal point of this movement, participants were presented with an audiovisual fission/fusion illusion (see Shams, Kamitani, & Shimojo, [<reflink idref="bib22" id="ref20">22</reflink>]). In this audiovisual illusion, the presence of auditory beeps has been reported to bias the number of perceived visual flashes. For example, when two flashes and one beep were presented, these events were fused such that participants often erroneously reported seeing only one flash (i.e., fusion illusion). Tremblay and Nguyen reported that participants became less susceptible to the fusion illusion during goal-directed action. Critically, it was found the degree to which participants fused the two visual stimuli was minimized at the highest limb velocity stage of the movement. When taken as a whole, limb velocity can be a useful proxy to test multisensory combination and integration (see Tremblay & de Grosbois, [<reflink idref="bib23" id="ref21">23</reflink>]), especially because engaging in voluntary action significantly alters the integration of multisensory cues (see also Juravle, Deubel, Tan, & Spence, [<reflink idref="bib15" id="ref22">15</reflink>]).</p> <p>The experiments cited earlier (e.g., Proteau et al., [<reflink idref="bib19" id="ref23">19</reflink>]) employed tasks where the participants' goal was to terminate their movement onto a target (i.e., upper-limb reaches). In such instances, visual feedback gathered early in the trajectory was utilized to bring the limb to a halt on its final position. However, the critical position of voluntary movements can be during the trajectory, which could alter when sensory information is primarily gathered. For example, to properly execute an overarm throw, release of the ball at peak velocity is absolutely critical (e.g., Jegede et al., [<reflink idref="bib14" id="ref24">14</reflink>]). To the best of our knowledge, however, no experiments have been conducted on the use of feedback presented at PLV to control the limb position at PLV (i.e., PLV being the critical portion of the trajectory).</p> <p>The aim of the current study was to test whether the utilization and integration of multisensory cues were optimal when auditory, visual, or audiovisual feedback was presented at PLV—that is, when PLV was the critical part of the trajectory (i.e., a flinging movement). Although providing a visual cue normally facilitates performance, this improvement may be modest with a flinging task because visual information about the limb at PLV is less certain than at rest (i.e., blurred image of the limb). As such, it was predicted that because of audition's excellent temporal sensitivity (e.g., Jaekl & Harris, [<reflink idref="bib12" id="ref25">12</reflink>]), providing auditory cues could also yield an improvement in performance, especially at the fastest stage of a rapid limb movement. Finally, based on the presumption that the central nervous system still attempts to integrate multisensory cues at PLV (Ernst & Bülthoff, 2004), visual and auditory cues presented together should yield better endpoint precision than visual or auditory feedback alone. Alternatively, if the integration of the cues is suboptimal at PLV, performance in one of the unisensory conditions (i.e., audition alone) should yield better, or at least as good, performance than the combined audiovisual condition.</p> <hd id="AN0117576476-2">Methods</hd> <p></p> <hd id="AN0117576476-3">Participants</hd> <p>The University of Toronto Research Ethics Board approved the experimental protocol reported herein. Thirteen individuals (6 men) aged an average of 23.7 years (<emph>SD</emph> = 2.2) provided informed consent prior to participating in the experiment. Participants were recruited from the graduate and undergraduate student populations at the University of Toronto. All participants self-reported to be right-handed with normal or corrected-to-normal vision. Participants were financially compensated $10.</p> <hd id="AN0117576476-4">Apparatus</hd> <p>Participants' heads rested on a chin rest (38 cm tall) fastened to a 61-cm × 75-cm table (see Figure 1 for a depiction of the experimental apparatus). A custom-built wooden frame (48 cm tall) with a reflective surface 12 cm in diameter was positioned 13 cm away from the home position. Attached to the uppermost portion of the chin rest was a yellow light-emitting diode (LED) light (1 cm in diameter), which served as the target for the experiment (see Figure 1). The participant viewed the target via the reflective surface such that the target appeared elevated from the surface of the table (i.e., a virtual target). Each trial began with the participant's finger on a 1.5-cm × 1.5-cm piece of Velcro, which served as the home position for the experiment. The movements of the participant were measured along the y axis (i.e., primary movement axis), as well as the x and z axes (i.e., secondary movement axes). The resultant distance between the home position and observed target location was approximately 40 cm. Along the x, y, and z axes, the target was approximately 15 cm to the right, 27 cm away, and 25 cm above the home position, respectively (see axes in Figure 1).</p> <p>Graph: Figure 1 A depiction of the experimental apparatus. Participants' heads rested comfortably on a chin rest while viewing the target via a reflection.</p> <p>The position of the participant's right index finger was monitored throughout the experiment using an Optotrak Certus (Northern Digital Inc., Waterloo, ON, Canada) motion capture system. An infrared light-emitting diode (IRED) was attached to a banjo pick and placed on the tip of the participant's right index finger. The Optotrak real-time sampling rate was set at 500 Hz and controlled using a custom Matlab script (Mathworks Inc., Natick, MA). Likewise, the Matlab script controlled the real-time feedback through an analog output board (PCI-6024E, National Instruments Corp., Austin, TX). The experiment was conducted in a dark room to prevent the participants from viewing their limbs. Also, participants wore a black arm sleeve and the lights in the room were illuminated every 5 min to minimize the effects of retinal and pupil adaptation to darkness (Fedorov & Mkrticheva, [<reflink idref="bib7" id="ref26">7</reflink>]; Hecht, [<reflink idref="bib10" id="ref27">10</reflink>]).</p> <p>A custom piezo electric buzzer (2,350 Hz, 75 dB) with a green LED light affixed to it was positioned proximal to the IRED on the participant's right index finger. This device (i.e., piezo LED) was used to provide participants with augmented feedback during the movement (i.e., for 20 ms only). Instantaneous velocity was calculated using the position data collected from two subsequent samples gathered by the Optotrak system. Movement start location was defined when the limb first exceeded a velocity of 0.03 m/s. Peak velocity was marked as two samples with a velocity decrease, after the limb reached a minimum velocity of 0.8 m/s. The IRED position at peak velocity was used to calculate the resultant endpoint, resultant endpoint variability, and MT.</p> <hd id="AN0117576476-5">Procedure</hd> <p>Prior to beginning the experimental trials, the target and home position were estimated by the participant and recorded using the Optotrak. The participant rested their index finger on the home position for a single collection period, and the average position was identified as the home position. To prevent participants from receiving augmented tactile feedback that would yield an improvement in performance, participants performed their movement toward a virtual target. To estimate the position of the virtual target, the participant placed their finger where the virtual target was perceived. The position of the IRED was gathered for a single collection period, and that average position was identified as the target position. In the experimental trials, participants were instructed to "fling" their index finger through the target as quickly and accurately as possible (see Figure 2). That is, the participant's task was to reach their PLV (i.e., as measured by the velocity of the index finger) as they passed through the center of the virtual target. Following completion of the flinging movement, participants returned their finger to the home position and awaited the signal for the next trial.</p> <p>Graph: Figure 2 Illustration of the flinging movement performed by participants. They were required to "fling" their limb through the air with the goal of aligning their peak velocity when the right index finger intersected a virtual target. The velocity plots correspond to the resultant limb velocity depicted in each panel, taken from a sample trial.</p> <p>Due to the novel dynamics of the task, participants first completed 20 familiarization trials, followed by 30 baseline trials, where no feedback was given (i.e., no-feedback condition). The no-feedback condition was a baseline and thus always performed prior to the experimental feedback conditions (i.e., auditory, visual, and audiovisual). Participants received auditory (piezo beep), visual (LED flash), or audiovisual feedback for 30 trials each in a blocked and counterbalanced order. Real-time feedback was provided for 20 ms when and where PLV was reached via the custom-built piezo LED apparatus affixed to the finger. Based on the sampling frequency of 500 Hz, Matlab processing delays, and hardware transmission delays, the onset of the flash or beep was no more than 10 ms after PLV was reached, which was deemed to be in real-time. In the audiovisual condition, the auditory and visual cues were presented simultaneously.</p> <hd id="AN0117576476-6">Data analysis</hd> <p>To assess the flinging performance across the feedback conditions, MT, average PLV, resultant endpoint, and resultant endpoint variability were analyzed. Resultant endpoint was defined as distance traveled in millimeters from the home position when PLV occurred. Resultant endpoint variability refers to the square root of the sum of the squared differences between PLV position on a given trial and the mean PLV position across trials divided by the number of trials. MT was calculated as the time difference between movement onset and PLV. To determine the effect of feedback condition on these performance measures, separate one-way repeated-measures analyses of variance (ANOVAs) with four levels (i.e., no feedback, auditory, visual, audiovisual) were performed for MT, average PLV, resultant endpoint, and resultant endpoint variability. Alpha was set at ≤ .05, and effects sizes are reported using partial eta squared. Post-hoc statistical power was assessed using G<sups>*</sups>Power Version 3.17 (University of Düsseldorf, Düsseldorf, North Rhine-Westphalia, Germany). Tukey's honestly significant difference (HSD) comparisons were used for post-hoc analyses, when required. Additionally, the predicted weights of resultant endpoint variability in the audiovisual condition were computed using the equation:</p> <p>(<reflink idref="bib1" id="ref28">1</reflink>)</p> <p>Graph</p> <p>In this equation, the theoretically predicted variability of audiovisual judgments (i.e., MLE model; see Ernst & Bülthoff, 2004) is obtained by calculating the square root of the product of the squared resultant endpoint variability in the auditory (A) and visual (V) conditions, divided by the sum of squared resultant endpoint variability in the same conditions.</p> <hd id="AN0117576476-7">Results</hd> <p>Means and standard deviations for all dependent variables can be found in Table 1. On average, participants completed the movement in 150 ms, with an average peak velocity of 2.92 m/s. The ANOVAs performed for MT and PLV revealed that MT did not vary significantly across feedback conditions, <emph>F</emph>(<reflink idref="bib3" id="ref29">3</reflink>, 36) = 1.54, <emph>p</emph> = .23,  = .03, nor did peak velocity, <emph>F</emph>(<reflink idref="bib3" id="ref30">3</reflink>, 36) = 0.181, <emph>p</emph> = .91,  = .02. The analysis of the resultant endpoint (i.e., when the limb reached PLV) failed to yield a significant main effect of feedback, <emph>F</emph>(<reflink idref="bib3" id="ref31">3</reflink>, 36) = 1.68, <emph>p</emph> = .18,  = .12,[<reflink idref="bib1" id="ref32">1</reflink>] post-hoc power = .98.</p> <p>Table 1 Means and between-subject standard deviations for movement time (MT), average peak limb velocity (PLV), resultant endpoint (RE), and resultant endpoint variability (REV).</p> <p> <ephtml> <table><thead><tr><td /><td>MT (ms)</td><td>PLV (m/s)</td><td>RE (mm)</td><td>REV (mm)</td></tr><tr><td>Feedback condition</td><td><italic>M</italic> (<italic>SD</italic>)</td><td><italic>M</italic> (<italic>SD</italic>)</td><td><italic>M</italic> (<italic>SD</italic>)</td><td><italic>M</italic> (<italic>SD</italic>)</td></tr></thead><tbody><tr><td>No feedback</td><td>152.4 (24.8)</td><td>2.89 (0.7)</td><td>242.1 (68.18)</td><td><bold>61.1</bold><bold>(25.5)</bold></td></tr><tr><td>Auditory</td><td>149.9 (19.2)</td><td>2.92 (0.6)</td><td>254.9 (59.6)</td><td><bold>35.9</bold><bold>(13.8)</bold></td></tr><tr><td>Visual</td><td>159.1 (19.1)</td><td>2.92 (0.5)</td><td>262.1 (46.6)</td><td>47.8 (24.7)</td></tr><tr><td>Audiovisual</td><td>157.9 (24.2)</td><td>2.93 (0.5)</td><td>268.9 (35.1)</td><td>47.1 (16.8)</td></tr></tbody></table> </ephtml> </p> <p>2 <emph>Note</emph>. Values in bold indicate a statistically significant difference between feedback conditions.</p> <p>In contrast, the analysis of resultant endpoint variability yielded a main effect of feedback condition, <emph>F</emph>(<reflink idref="bib3" id="ref33">3</reflink>, 36) = 6.24, <emph>p</emph> = .011,  = .34, post-hoc power = .99. Tukey's HSD contrasts revealed that resultant endpoint variability of the finger's position at PLV was significantly lower in the auditory condition than in the no-feedback condition[<reflink idref="bib2" id="ref34">2</reflink>] (see Figure 3). In addition, these resultant endpoint variability values in the visual and audiovisual conditions were not significantly lower than in the no-feedback condition while being not significantly worse than in the auditory condition (i.e., all <emph>p</emph>s>.09, see Table 1).</p> <p>Graph: Figure 3 Variability of resultant endpoint across all experimental conditions (no feedback [No FB], audiovisual [AV]) as well as maximum likelihood estimation-predicted variability in the audiovisual condition (MLE AV). Note. Error bars denote standard error of the mean.</p> <p>To follow up the resultant endpoint variability findings, Equation 1 was used to predict resultant endpoint variability (i.e., MLE model; Ernst & Bülthoff, [<reflink idref="bib6" id="ref35">6</reflink>]). This analysis revealed that predicted resultant endpoint variability values for the audiovisual condition significantly differed from the observed resultant endpoint variability in the same condition, <emph>t</emph>(<reflink idref="bib12" id="ref36">12</reflink>) = 3.90, <emph>p</emph> = .01 (mean observed = 47.3 mm, <emph>SD</emph> = 16.8, mean predicted = 27.1 mm, <emph>SD</emph> = 11.7; see Figure 3).[<reflink idref="bib3" id="ref37">3</reflink>]</p> <hd id="AN0117576476-8">Discussion</hd> <p>The current study investigated the processing and integration of multisensory stimuli at PLV to control the variability of the limb position at PLV. To this end, participants were tasked with flinging their index finger through a virtual target and aligning the moment their right index finger reached its peak velocity with the position of the virtual target. At PLV, participants received auditory, visual, or audiovisual feedback. In addition, trials where no feedback was given were also completed. Based on the MLE model (Ernst & Bülthoff, [<reflink idref="bib6" id="ref38">6</reflink>]), it was hypothesized that performance would be better in the audiovisual condition relative to at least one of the unisensory conditions. Contrary to this hypothesis, it was found that only auditory feedback yielded a significant reduction in resultant endpoint variability as compared with when no feedback was provided. Critically, the observed resultant endpoint variability values in the audiovisual condition significantly differed from MLE-predicted audiovisual values. Further, all observed differences in endpoint variability took place without differences in MT (i.e., no speed–accuracy tradeoffs; Schmidt, Zelaznik, Hawkins, Frank, & Quinn, [<reflink idref="bib21" id="ref39">21</reflink>]). The results thus indicate that the most reliable sensory cue to detect the spatial position of the limb at peak velocity can be audition and that the integration of audiovisual cues at PLV may be suboptimal.</p> <p>Effective overarm throws involve the spatial-temporal coordination when the limb reaches peak velocity. Previous studies have shown that novice overarm throwers release the ball within 10 ms of PLV occurrence (Hore, Ritchie, & Watts, [<reflink idref="bib11" id="ref40">11</reflink>]), whereas skilled throwers time their release within 1 ms of PLV (Jegede et al., [<reflink idref="bib14" id="ref41">14</reflink>]). This timing difference was also observed in the current study. Specifically, the former temporal value (i.e., 10 ms) is very close to the temporal difference (i.e., limb traveling at 2.92 m/s) associated with the difference in the average distance traveled between the no-feedback and audiovisual conditions (i.e., 9 ms; see Table 1). As such, although the difference in endpoint between the no-feedback and audiovisual conditions may seem considerable, the timing differences fall within the normal range of novice overarm throwers.</p> <p>The effect of auditory feedback on performance reflects the notion that audition is the more appropriate modality than vision when controlling the spatial-temporal occurrence of PLV. As noted in the Methods section, the entire experiment was conducted in darkness. Because the limb was traveling at such a high velocity (i.e., approximately 2.92 m/s) when the visual feedback was presented, the LED created a streak of light instead of a discrete indication of where PLV occurred. Although PLV was associated with the first point along this streak, the participants may have been unable to form a stable representation of where PLV occurred, and thus, augmented visual feedback did not reliably improve their performance. As such, auditory feedback may be particularly useful during the highest-velocity phases of upper-limb movements and specifically to augment release timing in overarm throwing to maximize movement efficiency (e.g., Jegede et al., [<reflink idref="bib14" id="ref42">14</reflink>]).</p> <p>When considering the results of the auditory and visual conditions alone, the MLE model (Ernst & Bülthoff, [<reflink idref="bib6" id="ref43">6</reflink>]) would also predict that the results of the audiovisual condition would be at least as good as those of the auditory condition. However, the results suggest that in the audiovisual condition, participants have heavily weighted the visual cue instead of the most reliable sensory cue (i.e., audition), and thus, performance was not facilitated. Support for this claim can be found in the significant differences found for the predicted and actual performances in the audiovisual condition. Specifically, the predicted values of resultant endpoint variability were significantly smaller than the observed values, suggesting that the weights assigned to the auditory and visual cues at PLV were suboptimal. This finding has implications for the optimal integration of multisensory cues during goal-directed action. Specifically, when PLV is the critical moment in the trajectory, optimal multisensory integration may not necessarily occur. As such, future work should focus on the sensory-processing mechanisms that occur throughout rapid limb movements. Finally, the experimental task employed here (i.e., a flinging task) provides a novel approach to studying sensory processing during an ongoing movement with practical applications for skills such as throwing a baseball.</p> <hd id="AN0117576476-9">What does this article add?</hd> <p>This study adds to the literature in two ways. The flinging task we employed provides a useful and novel approach to studying sensory processing throughout an ongoing movement. Although there is predominant focus in the literature on sensory processing during reaches to terminal targets, it is important to focus on sensory processing at PLV because of practical applications in sports contexts, such as baseball. Also, the main findings add to the growing literature on how the central nervous system integrates sensory cues across various modalities during voluntary movements. Overall, this study supports the position that the central nervous system combines and integrates sensory information in a flexible and task-specific manner, but such integration during voluntary action is not necessarily optimal.</p> <hd id="AN0117576476-10">Funding</hd> <p>This research was supported by the Natural Sciences and Engineering Research Council of Canada, the Canada Foundation for Innovation, the Ontario Research Fund, and a Graduate Student Fellowship from the University of Toronto.</p> <ref id="AN0117576476-11"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref13" type="bt">1</bibl> <bibtext> This pattern of results was replicated when conducting individuals' ANOVAs for movement amplitude along the primary (y) and secondary (x, z) movement axes<emph>, Fs</emph> ≤ 1.76, <emph>p</emph>s ≥.13.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref5" type="bt">2</bibl> <bibtext> This pattern of results was also consistent with individual ANOVAs conducted along the three movement axes, <emph>F</emph>s ≥ 3.16, <emph>p</emph>s ≤.05. In addition, Tukey's post-hoc contrasts performed for the movement axes also revealed a significant difference between the auditory and no-feedback conditions (y-axis HSD = 12.89 mm, <emph>p</emph> =.03; x-axis HSD = 6.83 mm, <emph>p</emph> =.01; z-axis HSD = 13.05 mm, <emph>p</emph> =.03).</bibtext> </blist> <blist> <bibl id="bib3" idref="ref12" type="bt">3</bibl> <bibtext> This pattern of results was replicated when conducting individual contrasts across the primary (y) and secondary (x, z) movement axes (i.e., <emph>t</emph>s ≥ 2.76, <emph>p</emph>s ≤.02).</bibtext> </blist> </ref> <ref id="AN0117576476-12"> <title> References </title> <blist> <bibtext> Alais , D. , & Burr , D. (2004). The ventriloquist effect results from near-optimal bimodal integration. Current Biology , 14 , 257 – 262. doi: 10.1016/j.cub.2004.01.029.</bibtext> </blist> <blist> <bibtext> Cavanagh , P. R. , & Landa , J. (1976). A biomechanical analysis of the karate chop. Research Quarterly , 47 , 610 – 618. doi: 10.1080/10671315.1976.10616718.</bibtext> </blist> <blist> <bibtext> Doody , S. G. , Bird , A. M. , & Ross , D. (1985). The effect of auditory and visual models on the acquisition of timing task. Human Movement Science , 4 , 271 – 281. doi: 10.1016/0167-9457(85)90014-4.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref4" type="bt">4</bibl> <bibtext> Dulberg , M. S. , Amant , R. S. , & Zettlemoyer , L. S. (1999). An imprecise mouse gesture for the fast activation of controls. In A. M.  Sasse & C.  Johnson (Eds.), Proceedings of Human-Computer Interaction – INTERACT 1999 (pp. 375 – 385). Amsterdam, The Netherlands : IOS Press.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref14" type="bt">5</bibl> <bibtext> Ernst , M. O. , & Banks , M. S. (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature , 415 , 429 – 434. doi: 10.1038/415429a.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref6" type="bt">6</bibl> <bibtext> Ernst , M. O. , & Bülthoff , H. H. (2004). Merging the senses into a robust percept. Trends in Cognitive Sciences , 8 , 162 – 169. doi: 10.1016/j.tics.2004.02.002.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref26" type="bt">7</bibl> <bibtext> Fedorov , N. T. , & Mkrticheva , L. (1938). Mechanism of light flicker fusion during the course of dark and light adaptation. Nature , 142 , 750 – 751. doi: 10.1038/142750a0.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref3" type="bt">8</bibl> <bibtext> Fleischauer , M. A. , & Sherwood , D. E. (2008). Development of motor error detection capability in an underhand throwing task. In N. P.  Beaulieu (Ed.), Physical activity and children: New research (pp. 195 – 210). New York, NY : Nova Science.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref16" type="bt">9</bibl> <bibtext> Ghazanfar , A. A. , & Schroeder , C. E. (2006). Is neocortex essentially multisensory?  Trends in Cognitive Sciences , 10 , 278 – 285. doi: 10.1016/j.tics.2006.04.008.</bibtext> </blist> <blist> <bibtext> Hecht , S. (1920). The dark adaptation of the human eye. Journal of General Physiology , 2 , 499 – 517. doi: 10.1085/jgp.2.5.499.</bibtext> </blist> <blist> <bibtext> Hore , J. , Ritchie , R. , & Watts , S. (1999). Finger opening in an overarm throw is not triggered by proprioceptive feedback from elbow extension or wrist flexion. Experimental Brain Research , 125 , 302 – 312.</bibtext> </blist> <blist> <bibtext> Jaekl , P. M. , & Harris , L. R. (2007). Auditory-visual temporal integration measured by shifts in perceived temporal location. Neuroscience Letters , 417 , 219 – 224. doi: 10.1016/j.neulet.2007.02.029.</bibtext> </blist> <blist> <bibtext> Jeannerod , M. (1984). The timing of natural prehension movements. 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Trends in Cognitive Sciences , 10 , 319 – 326. doi: 10.1016/j.tics.2006.05.003.</bibtext> </blist> <blist> <bibtext> Levy-Tzedek , S. , Hanassy , S. , Abboud , S. , Maidenbaum , S. , & Amedi , A. (2012). Fast, accurate reaching movements with a visual-to-auditory sensory substitution device. Restorative Neurology and Neuroscience , 30 , 313 – 323. doi: 10.3233/RNN-2012-110219.</bibtext> </blist> <blist> <bibtext> Proteau , L. , Roujoula , A. , & Messier , J. (2009). Evidence for continuous processing of visual information in a manual video-aiming task. Journal of Motor Behavior , 41 , 219 – 231. doi: 10.3200/JMBR.41.3.219-231.</bibtext> </blist> <blist> <bibtext> Saunders , J. A. , & Knill , D. C. (2003). Humans use continuous visual feedback from the hand to control fast reaching movements. Experimental Brain Research , 152 , 341 – 352. doi: 10.1007/s00221-003-1525-2.</bibtext> </blist> <blist> <bibtext> Schmidt , R. A. , Zelaznik , H. , Hawkins , B. , Frank , J. S. , & Quinn , J. T.  Jr. (1979). Motor-output variability: A theory for the accuracy of rapid motor acts. Psychological Review , 86 , 415 – 451.</bibtext> </blist> <blist> <bibtext> Shams , L. , Kamitani , Y. , & Shimojo , S. (2000). What you see is what you hear. Nature , 408 , 788. doi: 10.1038/35048669.</bibtext> </blist> <blist> <bibtext> Tremblay , L. , & de Grosbois , J. (2015). Why encode limb and body displacements in the velocity domain? Neurophysiological and behavioral evidence. In T.  Heinen (Ed.), Advances in visual perception research (pp. 279 – 292). Hauppauge, NY : Nova Science.</bibtext> </blist> <blist> <bibtext> Tremblay , L. , & Nguyen , T. (2010). Real-time decreased sensitivity to an audio-visual illusion during goal-directed reaching. PLoS One , 5 , e8952. doi: 10.1371/journal.pone.0008952.</bibtext> </blist> <blist> <bibtext> Witten , I. B. , & Knudsen , E. I. (2005). Why seeing is believing: Merging auditory and visual worlds. Neuron , 48 , 489 – 496. doi: 10.1016/j.neuron.2005.10.020.</bibtext> </blist> </ref> <aug> <p>By Tristan Loria; John de Grosbois and Luc Tremblay</p> <p>Reported by Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib13" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib14" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib16" firstref="ref7"></nolink> <nolink nlid="nl4" bibid="bib19" firstref="ref8"></nolink> <nolink nlid="nl5" bibid="bib20" firstref="ref9"></nolink> <nolink nlid="nl6" bibid="bib18" firstref="ref10"></nolink> <nolink nlid="nl7" bibid="bib25" firstref="ref17"></nolink> <nolink nlid="nl8" bibid="bib17" firstref="ref18"></nolink> <nolink nlid="nl9" bibid="bib24" firstref="ref19"></nolink> <nolink nlid="nl10" bibid="bib22" firstref="ref20"></nolink> <nolink nlid="nl11" bibid="bib23" firstref="ref21"></nolink> <nolink nlid="nl12" bibid="bib15" firstref="ref22"></nolink> <nolink nlid="nl13" bibid="bib12" firstref="ref25"></nolink> <nolink nlid="nl14" bibid="bib10" firstref="ref27"></nolink> <nolink nlid="nl15" bibid="bib21" firstref="ref39"></nolink> <nolink nlid="nl16" bibid="bib11" firstref="ref40"></nolink>
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  Data: Can You Hear That Peak? Utilization of Auditory and Visual Feedback at Peak Limb Velocity
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  Data: <searchLink fieldCode="AR" term="%22Loria%2C+Tristan%22">Loria, Tristan</searchLink><br /><searchLink fieldCode="AR" term="%22de+Grosbois%2C+John%22">de Grosbois, John</searchLink><br /><searchLink fieldCode="AR" term="%22Tremblay%2C+Luc%22">Tremblay, Luc</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Research+Quarterly+for+Exercise+and+Sport%22"><i>Research Quarterly for Exercise and Sport</i></searchLink>. 2016 87(3):254-261.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
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  Data: 8
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Neurological+Organization%22">Neurological Organization</searchLink><br /><searchLink fieldCode="DE" term="%22Cues%22">Cues</searchLink><br /><searchLink fieldCode="DE" term="%22Auditory+Stimuli%22">Auditory Stimuli</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+Stimuli%22">Visual Stimuli</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Exercise+Physiology%22">Exercise Physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Human+Body%22">Human Body</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanics+%28Physics%29%22">Mechanics (Physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Motion%22">Motion</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
– Name: DOI
  Label: DOI
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  Data: 10.1080/02701367.2016.1196810
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  Data: 0270-1367
– Name: Abstract
  Label: Abstract
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  Data: Purpose: At rest, the central nervous system combines and integrates multisensory cues to yield an optimal percept. When engaging in action, the relative weighing of sensory modalities has been shown to be altered. Because the timing of peak velocity is the critical moment in some goal-directed movements (e.g., overarm throwing), the current study sought to test whether visual and auditory cues are optimally integrated at that specific kinematic marker when it is the critical part of the trajectory. Methods: Participants performed an upper-limb movement in which they were required to reach their peak limb velocity when the right index finger intersected a virtual target (i.e., a flinging movement). Brief auditory, visual, or audiovisual feedback (i.e., 20 ms in duration) was provided to participants at peak limb velocity. Performance was assessed primarily through the resultant position of peak limb velocity and the variability of that position. Results: Relative to when no feedback was provided, auditory feedback significantly reduced the resultant endpoint variability of the finger position at peak limb velocity. However, no such reductions were found for the visual or audiovisual feedback conditions. Further, providing both auditory and visual cues concurrently also failed to yield the theoretically predicted improvements in endpoint variability. Conclusions: Overall, the central nervous system can make significant use of an auditory cue but may not optimally integrate a visual and auditory cue at peak limb velocity, when peak velocity is the critical part of the trajectory.
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  Data: 2016
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  Data: EJ1111355
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      – TitleFull: Can You Hear That Peak? Utilization of Auditory and Visual Feedback at Peak Limb Velocity
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