Models for the Extrapolation of Target Motion for Manual Interception
Identifieur interne : 001189 ( Ncbi/Merge ); précédent : 001188; suivant : 001190Models for the Extrapolation of Target Motion for Manual Interception
Auteurs : John F. Soechting [États-Unis] ; John Z. Juveli [États-Unis] ; Hrishikesh M. Rao [États-Unis]Source :
- Journal of Neurophysiology [ 0022-3077 ] ; 2009.
Abstract
Intercepting a moving target requires a prediction of the target's future motion. This extrapolation could be achieved using sensed parameters of the target motion, e.g., its position and velocity. However, the accuracy of the prediction would be improved if subjects were also able to incorporate the statistical properties of the target's motion, accumulated as they watched the target move. The present experiments were designed to test for this possibility. Subjects intercepted a target moving on the screen of a computer monitor by sliding their extended finger along the monitor's surface. Along any of the six possible target paths, target speed could be governed by one of three possible rules: constant speed, a power law relation between speed and curvature, or the trajectory resulting from a sum of sinusoids. A go signal was given to initiate interception and was always presented when the target had the same speed, irrespective of the law of motion. The dependence of the initial direction of finger motion on the target's law of motion was examined. This direction did not depend on the speed profile of the target, contrary to the hypothesis. However, finger direction could be well predicted by assuming that target location was extrapolated using target velocity and that the amount of extrapolation depended on the distance from the finger to the target. Subsequent analysis showed that the same model of target motion was also used for on-line, visually mediated corrections of finger movement when the motion was initially misdirected.
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DOI: 10.1152/jn.00398.2009
PubMed: 19571194
PubMed Central: 2746781
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Juveli</name><affiliation wicri:level="2"><nlm:aff id="N0x1ea80e0N0x3b49328">Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota</nlm:aff><country xml:lang="fr">États-Unis</country><placeName><region type="state">Minnesota</region></placeName><wicri:cityArea>Department of Neuroscience, University of Minnesota, Minneapolis</wicri:cityArea></affiliation></author><author><name sortKey="Rao, Hrishikesh M" sort="Rao, Hrishikesh M" uniqKey="Rao H" first="Hrishikesh M." last="Rao">Hrishikesh M. Rao</name><affiliation wicri:level="2"><nlm:aff id="N0x1ea80e0N0x3b49328">Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota</nlm:aff><country xml:lang="fr">États-Unis</country><placeName><region type="state">Minnesota</region></placeName><wicri:cityArea>Department of Neuroscience, University of Minnesota, Minneapolis</wicri:cityArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">19571194</idno><idno type="pmc">2746781</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2746781</idno><idno type="RBID">PMC:2746781</idno><idno type="doi">10.1152/jn.00398.2009</idno><date when="2009">2009</date><idno type="wicri:Area/Pmc/Corpus">000A70</idno><idno type="wicri:Area/Pmc/Curation">000A70</idno><idno type="wicri:Area/Pmc/Checkpoint">002110</idno><idno type="wicri:Area/Ncbi/Merge">001189</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Models for the Extrapolation of Target Motion for Manual Interception</title><author><name sortKey="Soechting, John F" sort="Soechting, John F" uniqKey="Soechting J" first="John F." last="Soechting">John F. Soechting</name><affiliation wicri:level="2"><nlm:aff id="N0x1ea80e0N0x3b49328">Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota</nlm:aff><country xml:lang="fr">États-Unis</country><placeName><region type="state">Minnesota</region></placeName><wicri:cityArea>Department of Neuroscience, University of Minnesota, Minneapolis</wicri:cityArea></affiliation></author><author><name sortKey="Juveli, John Z" sort="Juveli, John Z" uniqKey="Juveli J" first="John Z." last="Juveli">John Z. Juveli</name><affiliation wicri:level="2"><nlm:aff id="N0x1ea80e0N0x3b49328">Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota</nlm:aff><country xml:lang="fr">États-Unis</country><placeName><region type="state">Minnesota</region></placeName><wicri:cityArea>Department of Neuroscience, University of Minnesota, Minneapolis</wicri:cityArea></affiliation></author><author><name sortKey="Rao, Hrishikesh M" sort="Rao, Hrishikesh M" uniqKey="Rao H" first="Hrishikesh M." last="Rao">Hrishikesh M. Rao</name><affiliation wicri:level="2"><nlm:aff id="N0x1ea80e0N0x3b49328">Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota</nlm:aff><country xml:lang="fr">États-Unis</country><placeName><region type="state">Minnesota</region></placeName><wicri:cityArea>Department of Neuroscience, University of Minnesota, Minneapolis</wicri:cityArea></affiliation></author></analytic><series><title level="j">Journal of Neurophysiology</title><idno type="ISSN">0022-3077</idno><idno type="eISSN">1522-1598</idno><imprint><date when="2009">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Intercepting a moving target requires a prediction of the target's future motion. This extrapolation could be achieved using sensed parameters of the target motion, e.g., its position and velocity. However, the accuracy of the prediction would be improved if subjects were also able to incorporate the statistical properties of the target's motion, accumulated as they watched the target move. The present experiments were designed to test for this possibility. Subjects intercepted a target moving on the screen of a computer monitor by sliding their extended finger along the monitor's surface. Along any of the six possible target paths, target speed could be governed by one of three possible rules: constant speed, a power law relation between speed and curvature, or the trajectory resulting from a sum of sinusoids. A go signal was given to initiate interception and was always presented when the target had the same speed, irrespective of the law of motion. The dependence of the initial direction of finger motion on the target's law of motion was examined. This direction did not depend on the speed profile of the target, contrary to the hypothesis. However, finger direction could be well predicted by assuming that target location was extrapolated using target velocity and that the amount of extrapolation depended on the distance from the finger to the target. Subsequent analysis showed that the same model of target motion was also used for on-line, visually mediated corrections of finger movement when the motion was initially misdirected.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">J Neurophysiol</journal-id><journal-id journal-id-type="publisher-id">jn</journal-id><journal-title>Journal of Neurophysiology</journal-title><issn pub-type="ppub">0022-3077</issn><issn pub-type="epub">1522-1598</issn><publisher><publisher-name>American Physiological Society</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">19571194</article-id><article-id pub-id-type="pmc">2746781</article-id><article-id pub-id-type="publisher-id">J00398-9</article-id><article-id pub-id-type="doi">10.1152/jn.00398.2009</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group></article-categories><title-group><article-title>Models for the Extrapolation of Target Motion for Manual Interception</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Soechting</surname><given-names>John F.</given-names></name><xref ref-type="aff" rid="N0x1ea80e0N0x3b49328"></xref></contrib><contrib contrib-type="author"><name><surname>Juveli</surname><given-names>John Z.</given-names></name><xref ref-type="aff" rid="N0x1ea80e0N0x3b49328"></xref></contrib><contrib contrib-type="author"><name><surname>Rao</surname><given-names>Hrishikesh M.</given-names></name><xref ref-type="aff" rid="N0x1ea80e0N0x3b49328"></xref></contrib></contrib-group><aff id="N0x1ea80e0N0x3b49328">Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota</aff><author-notes><fn><p>Address for reprint requests and other correspondence: J. F. Soechting, Dept. of Neuroscience, University of Minnesota, 6-45 Jackson Hall, 321 Church St. SE, Minneapolis, MN 55455 (E-mail: <email>soech001@umn.edu</email>)</p></fn></author-notes><pub-date pub-type="ppub"><month>9</month><year>2009</year></pub-date><pub-date pub-type="epub"><day>1</day><month>7</month><year>2009</year></pub-date><pub-date pub-type="pmc-release"><day>1</day><month>9</month><year>2010</year></pub-date><pmc-comment> PMC Release delay is 12 months and 0 days and was based on the copyright element. </pmc-comment>
<volume>102</volume><issue>3</issue><fpage>1491</fpage><lpage>1502</lpage><history><date date-type="received"><day>7</day><month>5</month><year>2009</year></date><date date-type="accepted"><day>29</day><month>6</month><year>2009</year></date></history><permissions><copyright-statement>Copyright © 2009, American Physiological Society</copyright-statement></permissions><self-uri xlink:title="pdf" xlink:href="z9k00909001491.pdf"></self-uri><abstract><p>Intercepting a moving target requires a prediction of the target's future motion. This extrapolation could be achieved using sensed parameters of the target motion, e.g., its position and velocity. However, the accuracy of the prediction would be improved if subjects were also able to incorporate the statistical properties of the target's motion, accumulated as they watched the target move. The present experiments were designed to test for this possibility. Subjects intercepted a target moving on the screen of a computer monitor by sliding their extended finger along the monitor's surface. Along any of the six possible target paths, target speed could be governed by one of three possible rules: constant speed, a power law relation between speed and curvature, or the trajectory resulting from a sum of sinusoids. A go signal was given to initiate interception and was always presented when the target had the same speed, irrespective of the law of motion. The dependence of the initial direction of finger motion on the target's law of motion was examined. This direction did not depend on the speed profile of the target, contrary to the hypothesis. However, finger direction could be well predicted by assuming that target location was extrapolated using target velocity and that the amount of extrapolation depended on the distance from the finger to the target. Subsequent analysis showed that the same model of target motion was also used for on-line, visually mediated corrections of finger movement when the motion was initially misdirected.</p></abstract></article-meta></front><floats-wrap><fig position="float" id="f1"><label>FIG. 1.</label><caption><p>Target movement paths. ···, the initial portion of the path; target motion beginning at the tail of the arrow. The target changed color (•) such that the expected onset of finger movement occurred at the location indicated (○). The target remained visible until the time of interception. For each path, the target velocity could vary according to 1 of 3 different laws of motion.</p></caption><graphic xlink:href="z9k0090996680001"></graphic></fig><fig position="float" id="f2"><label>FIG. 2.</label><caption><p>Speed profiles. The 2 panels depict the speed profiles for the 3 laws of motion (sum of sines, constant speed, or power law) for 2 of the paths (1 and 5). The traces have been aligned on the expected time of finger movement onset (300 ms after the go signal). Note that target speed is the same for all 3 conditions at <italic>time 0</italic>.</p></caption><graphic xlink:href="z9k0090996680002"></graphic></fig><fig position="float" id="f3"><label>FIG. 3.</label><caption><p>Parameters used to define direction of finger movement. An exemplary trial from path 3 is used. The blue portion of the target's trajectory denotes the time between the go signal and the onset of finger motion. The yellow portion of the target trace denotes the target's motion during the interception movement, and the red trace subsequent target motion after interception. (The target was no longer visible after interception.) ψ<sub>0</sub> is the direction from the start location of the finger to the target at motion onset and ψ<sub>int</sub> the direction to the target at the time of interception. ϕ<sub>f</sub> is the direction of finger motion. This direction 100 ms after motion onset (denoted by the open circle) was used in the statistical analysis of the data [ϕ<sub>f</sub>(100), see <xref ref-type="table" rid="t1">Table 1</xref>].</p></caption><graphic xlink:href="z9k0090996680003"></graphic></fig><fig position="float" id="f4"><label>FIG. 4.</label><caption><p>Exemplary finger movements. The panels show the results of 4 trials from <italic>subject 1.</italic> The target path is shown in the <italic>top part</italic> of each panel, the arrow denoting the direction of target motion. The finger's path is indicated with the heavier trace. The lower portion of each panel depicts the finger's speed, <italic>time 0</italic> corresponding to the onset of finger movement and the trace continuing for 100 ms beyond the time of interception. The filled circles on the paths depict the target's location at the onset of finger movement. Note that in <italic>A</italic>, the target was intercepted on the 1st try with a direct movement to the target. In <italic>B</italic>, the initial attempt was unsuccessful, finger movement slowed down and a second movement was initiated. In <italic>C</italic> and <italic>D</italic>, the finger paths show marked curvature, suggesting visually mediated corrections.</p></caption><graphic xlink:href="z9k0090996680004"></graphic></fig><fig position="float" id="f5"><label>FIG. 5.</label><caption><p>Finger speed profiles. Traces show the mean (±SE) of the speed profiles of all finger movements for the 3 target motion conditions and 2 target paths (<italic>A</italic>, path 3; <italic>B</italic>, path 5). The filled circles and error bars denote the average (±SE) time at which the target was intercepted, excluding trials in which the initial attempt at interception was unsuccessful. Data are from <italic>subject 5.</italic></p></caption><graphic xlink:href="z9k0090996680005"></graphic></fig><fig position="float" id="f6"><label>FIG. 6.</label><caption><p>Average finger paths (<italic>top panel</italic>) and direction of finger movement over time (<italic>bottom panel</italic>). Results are for 2 subjects (<italic>A</italic>, 4; <italic>B</italic>, 5) for target path 3. • on the target path denote the target's average location at the time of interception for the 3 motion conditions. <inline-graphic xlink:href="cjs2118.jpg"></inline-graphic>, encompass ±1 SE, and trials were truncated at the time of interception.</p></caption><graphic xlink:href="z9k0090996680006"></graphic></fig><fig position="float" id="f7"><label>FIG. 7.</label><caption><p>Evaluation of models for direction of finger movement 100 ms after onset of interception. In each model, finger movement is directed to a predicted target location (see <xref ref-type="disp-formula" rid="e5"><italic>Eq. 5</italic></xref>). In the simplest model (velocity) target position and velocity at various time delays between the target motion parameters and the direction of finger motion are used to generate the predicted location. In the other 3 models, one additional term (distance, tangential acceleration, or normal acceleration) is added to this basic model. The root mean square (RMS) error for each subject is normalized relative to the RMS error for the velocity model at a time delay of 100 ms. The traces depict the mean normalized RMS error for the 6 subjects and the shaded area encompasses ± SE.</p></caption><graphic xlink:href="z9k0090996680007"></graphic></fig><fig position="float" id="f8"><label>FIG. 8.</label><caption><p>On-line correction of the direction of finger movement. The <italic>2 columns</italic> (<italic>A</italic> and <italic>B</italic>) depict results for 2 representative trials from <italic>subject 5. Top</italic>: same format as <xref rid="f4" ref-type="fig">Fig. 4</xref>, the paths of the target and the finger and the finger's speed. <italic>Bottom</italic>: the direction of finger movement as a function of time (solid trace) and the fit of the model (<xref ref-type="disp-formula" rid="e6"><italic>Eq. 6</italic></xref>) to the direction of finger movement. Model parameters were adjusted to give the best fit over the interval from 100 ms after movement onset until the time of interception.</p></caption><graphic xlink:href="z9k0090996680008"></graphic></fig><fig position="float" id="f9"><label>FIG. 9.</label><caption><p>Pursuit eye movements for target trajectories generated by a sum of sines (<italic>A</italic>) and for target motion at a constant speed. The solid blue traces are the speed (<italic>top</italic>), horizontal eye velocity (<italic>x</italic> direction, <italic>middle</italic>), and vertical eye velocity (<italic>y</italic> direction, <italic>bottom</italic>) averaged over all 6 subjects. The solid black traces are the corresponding results for target motion. In the sum of sines condition, target motion resulted from the sum of the fundamental frequency and the 1st (vertical velocity) or 2nd harmonic (horizontal velocity). The best fit of the sinusoids to the eye velocity is given by the dashed blue lines. <italic>B</italic>: the constant speed condition introduced higher-frequency harmonics into the signal. The black dashed lines denote the contribution up to the 4<sup>th</sup>-harmonic to the target motion and the dashed blue lines the fit of these sinusoids to the eye velocities. Note that the lower-frequency harmonics gave a good fit to the eye velocities and that, as a result of this low-pass filtering, the speed of smooth pursuit was not constant.</p></caption><graphic xlink:href="z9k0090996680009"></graphic></fig><table-wrap position="float" id="t1"><label>TABLE 1.</label><caption><p>Initial direction of finger movement and direction to target at time of interception</p></caption><table frame="hsides" rules="groups"><thead><tr><th colspan="1" rowspan="2" align="center" valign="bottom">Path</th><th colspan="3" rowspan="1" align="center" valign="bottom"><hr></hr>ϕ<sub>f</sub>(100) − ψ<sub>0</sub><hr></hr></th><th colspan="3" rowspan="1" align="center" valign="bottom">ψ<sub>int</sub> − ψ<sub>0</sub><hr></hr></th></tr><tr><th colspan="1" rowspan="1" align="center" valign="bottom">SS</th><th colspan="1" rowspan="1" align="center" valign="bottom">CS</th><th colspan="1" rowspan="1" align="center" valign="bottom">PL</th><th colspan="1" rowspan="1" align="center" valign="bottom">SS</th><th colspan="1" rowspan="1" align="center" valign="bottom">CS</th><th colspan="1" rowspan="1" align="center" valign="bottom">PL</th></tr></thead><tbody><tr><td colspan="1" rowspan="1" align="char" char="." valign="top">1</td><td colspan="1" rowspan="1" align="center" valign="top">14.1 ± 0.9</td><td colspan="1" rowspan="1" align="center" valign="top">16.3 ± 1.7</td><td colspan="1" rowspan="1" align="center" valign="top">13.6 ± 1.8</td><td colspan="1" rowspan="1" align="center" valign="top">11.8 ± 0.8</td><td colspan="1" rowspan="1" align="center" valign="top">9.2 ± 1.0</td><td colspan="1" rowspan="1" align="center" valign="top">16.3 ± 1.3</td></tr><tr><td colspan="1" rowspan="1" align="char" char="." valign="top">2</td><td colspan="1" rowspan="1" align="center" valign="top">15.0 ± 2.9</td><td colspan="1" rowspan="1" align="center" valign="top">12.8 ± 2.7</td><td colspan="1" rowspan="1" align="center" valign="top">15.4 ± 2.3</td><td colspan="1" rowspan="1" align="center" valign="top">20.5 ± 1.8</td><td colspan="1" rowspan="1" align="center" valign="top">14.2 ± 1.0</td><td colspan="1" rowspan="1" align="center" valign="top">54.5 ± 2.4</td></tr><tr><td colspan="1" rowspan="1" align="char" char="." valign="top">3</td><td colspan="1" rowspan="1" align="center" valign="top">17.9 ± 3.1</td><td colspan="1" rowspan="1" align="center" valign="top">17.3 ± 3.2</td><td colspan="1" rowspan="1" align="center" valign="top">18.8 ± 2.7</td><td colspan="1" rowspan="1" align="center" valign="top">21.2 ± 1.3</td><td colspan="1" rowspan="1" align="center" valign="top">17.5 ± 1.0</td><td colspan="1" rowspan="1" align="center" valign="top">18.7 ± 1.3</td></tr><tr><td colspan="1" rowspan="1" align="char" char="." valign="top">4</td><td colspan="1" rowspan="1" align="center" valign="top">10.9 ± 2.9</td><td colspan="1" rowspan="1" align="center" valign="top">10.2 ± 2.4</td><td colspan="1" rowspan="1" align="center" valign="top">10.9 ± 2.3</td><td colspan="1" rowspan="1" align="center" valign="top">24.0 ± 1.6</td><td colspan="1" rowspan="1" align="center" valign="top">15.9 ± 1.1</td><td colspan="1" rowspan="1" align="center" valign="top">22.9 ± 1.1</td></tr><tr><td colspan="1" rowspan="1" align="char" char="." valign="top">5</td><td colspan="1" rowspan="1" align="center" valign="top">19.3 ± 3.1</td><td colspan="1" rowspan="1" align="center" valign="top">13.9 ± 5.1</td><td colspan="1" rowspan="1" align="center" valign="top">21.0 ± 3.4</td><td colspan="1" rowspan="1" align="center" valign="top">26.2 ± 0.8</td><td colspan="1" rowspan="1" align="center" valign="top">19.5 ± 1.5</td><td colspan="1" rowspan="1" align="center" valign="top">30.3 ± 1.8</td></tr><tr><td colspan="1" rowspan="1" align="char" char="." valign="top">6</td><td colspan="1" rowspan="1" align="center" valign="top">3.4 ± 1.4</td><td colspan="1" rowspan="1" align="center" valign="top">0.4 ± 2.9</td><td colspan="1" rowspan="1" align="center" valign="top">2.8 ± 0.8</td><td colspan="1" rowspan="1" align="center" valign="top">5.9 ± 0.6</td><td colspan="1" rowspan="1" align="center" valign="top">4.6 ± 0.8</td><td colspan="1" rowspan="1" align="center" valign="top">3.8 ± 0.5</td></tr></tbody></table><table-wrap-foot><fn><p>Means ± SE of the direction of finger movement 100 ms after movement onset [ϕ<sub>f</sub>(100)], computed relative to the direction from the finger to the target at movement onset (ψ<sub>0</sub>) and the relative direction to the target at the time of interception (ψ<sub>int</sub> − ψ<sub>0</sub>) for each of the six paths and 3 speed conditions (SS, sum of sines; CS, constant speed; PL, power law). The various angles are defined in <xref rid="f3" ref-type="fig">Fig. 3</xref>. For the paths in which the target moved from left to right (2, 3, and 5) the sign of the values has been inverted.</p></fn></table-wrap-foot></table-wrap><table-wrap position="float" id="t2"><label>TABLE 2.</label><caption><p>Predicted target location by extrapolation</p></caption><table frame="hsides" rules="groups"><thead><tr><th colspan="1" rowspan="1" align="center" valign="bottom">Subject</th><th colspan="1" rowspan="1" align="center" valign="bottom">Velocity <italic>a</italic><sub>1</sub>, s</th><th colspan="1" rowspan="1" align="center" valign="bottom">Distance <italic>a</italic><sub>2</sub>, s/cm</th><th colspan="1" rowspan="1" align="center" valign="bottom">Average Extrapolation, s</th><th colspan="1" rowspan="1" align="center" valign="bottom">Bias <italic>a</italic><sub>5</sub>, °</th><th colspan="1" rowspan="1" align="center" valign="bottom">RMS Error, °</th><th colspan="1" rowspan="1" align="center" valign="bottom">Improvement, %</th></tr></thead><tbody><tr><td colspan="1" rowspan="1" align="center" valign="top"><italic>S1</italic></td><td colspan="1" rowspan="1" align="char" char="." valign="top">−.0288</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.0146</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.1580</td><td colspan="1" rowspan="1" align="char" char="." valign="top">2.45</td><td colspan="1" rowspan="1" align="char" char="." valign="top">6.53</td><td colspan="1" rowspan="1" align="char" char="." valign="top">7.3</td></tr><tr><td colspan="1" rowspan="1" align="center" valign="top"><italic>S2</italic></td><td colspan="1" rowspan="1" align="char" char="." valign="top">−.0583</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.0162</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.1561</td><td colspan="1" rowspan="1" align="char" char="." valign="top">1.54</td><td colspan="1" rowspan="1" align="char" char="." valign="top">8.87</td><td colspan="1" rowspan="1" align="char" char="." valign="top">10.5</td></tr><tr><td colspan="1" rowspan="1" align="center" valign="top"><italic>S3</italic></td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.0330</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.0102</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.1664</td><td colspan="1" rowspan="1" align="char" char="." valign="top">2.66</td><td colspan="1" rowspan="1" align="char" char="." valign="top">7.25</td><td colspan="1" rowspan="1" align="char" char="." valign="top">9.4</td></tr><tr><td colspan="1" rowspan="1" align="center" valign="top"><italic>S4</italic></td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.1012</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.0143</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.2957</td><td colspan="1" rowspan="1" align="char" char="." valign="top">1.32</td><td colspan="1" rowspan="1" align="char" char="." valign="top">11.80</td><td colspan="1" rowspan="1" align="char" char="." valign="top">2.7</td></tr><tr><td colspan="1" rowspan="1" align="center" valign="top"><italic>S5</italic></td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.0149</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.0146</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.1999</td><td colspan="1" rowspan="1" align="char" char="." valign="top">−3.27</td><td colspan="1" rowspan="1" align="char" char="." valign="top">10.73</td><td colspan="1" rowspan="1" align="char" char="." valign="top">16.0</td></tr><tr><td colspan="1" rowspan="1" align="center" valign="top"><italic>S6</italic></td><td colspan="1" rowspan="1" align="char" char="." valign="top">−0.0065</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.0120</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.1345</td><td colspan="1" rowspan="1" align="char" char="." valign="top">−0.49</td><td colspan="1" rowspan="1" align="char" char="." valign="top">9.61</td><td colspan="1" rowspan="1" align="char" char="." valign="top">11.7</td></tr><tr><td colspan="1" rowspan="1" align="center" valign="top">Average</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.0092</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.0136</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.1851</td><td colspan="1" rowspan="1" align="char" char="." valign="top">0.70</td><td colspan="1" rowspan="1" align="char" char="." valign="top">9.13</td><td colspan="1" rowspan="1" align="char" char="." valign="top">9.6</td></tr></tbody></table><table-wrap-foot><fn><p>Values of the coefficients from the model in <xref ref-type="disp-formula" rid="e5"><italic>Eq.</italic> 5</xref> (<italic>a</italic><sub>1</sub>, <italic>a</italic><sub>2</sub>, and <italic>a</italic><sub>5</sub>) using data from all trials for each subject. The value for Average Extrapolation represents the average amount of time by which position was extrapolated, using the coefficients <italic>a</italic><sub>1</sub>, <italic>a</italic><sub>2</sub> and the average distance from the finger to the target. Improvement denotes the improvement in root mean square (RMS) error for this model relative to the model in which the distance coefficient <italic>a</italic><sub>2</sub> was set to zero.</p></fn></table-wrap-foot></table-wrap><table-wrap position="float" id="t3"><label>TABLE 3.</label><caption><p>Target prediction for online corrections of the direction of finger movement</p></caption><table frame="hsides" rules="groups"><thead><tr><th colspan="1" rowspan="1" align="center" valign="bottom">Subject</th><th colspan="1" rowspan="1" align="center" valign="bottom"><italic>N</italic></th><th colspan="1" rowspan="1" align="center" valign="bottom">Velocity <italic>a</italic><sub>1</sub>, s</th><th colspan="1" rowspan="1" align="center" valign="bottom">Distance <italic>a</italic><sub>2</sub>, s/cm</th><th colspan="1" rowspan="1" align="center" valign="bottom">Bias <italic>a</italic><sub>5</sub>, °</th><th colspan="1" rowspan="1" align="center" valign="bottom">Time delay τ<sub>p</sub>, s</th><th colspan="1" rowspan="1" align="center" valign="bottom">Weight <italic>a</italic><sub>6</sub>, m/s<sup>2</sup></th><th colspan="1" rowspan="1" align="center" valign="bottom">RMS Error</th></tr></thead><tbody><tr><td colspan="1" rowspan="1" align="center" valign="top"><italic>S1</italic></td><td colspan="1" rowspan="1" align="char" char="." valign="top">24</td><td colspan="1" rowspan="1" align="center" valign="top">0.054 ± 0.003</td><td colspan="1" rowspan="1" align="center" valign="top">0.068 ± 0.004</td><td colspan="1" rowspan="1" align="center" valign="top">1.55 ± 0.10</td><td colspan="1" rowspan="1" align="center" valign="top">0.07 ± 0.01</td><td colspan="1" rowspan="1" align="center" valign="top">15.47 ± 0.64</td><td colspan="1" rowspan="1" align="center" valign="top">5.2 ± 0.6</td></tr><tr><td colspan="1" rowspan="1" align="center" valign="top"><italic>S2</italic></td><td colspan="1" rowspan="1" align="char" char="." valign="top">7</td><td colspan="1" rowspan="1" align="center" valign="top">0.054 ± 0.004</td><td colspan="1" rowspan="1" align="center" valign="top">0.065 ± 0.005</td><td colspan="1" rowspan="1" align="center" valign="top">1.03 ± 0.19</td><td colspan="1" rowspan="1" align="center" valign="top">0.09 ± 0.01</td><td colspan="1" rowspan="1" align="center" valign="top">17.06 ± 0.72</td><td colspan="1" rowspan="1" align="center" valign="top">2.8 ± 0.5</td></tr><tr><td colspan="1" rowspan="1" align="center" valign="top"><italic>S3</italic></td><td colspan="1" rowspan="1" align="char" char="." valign="top">29</td><td colspan="1" rowspan="1" align="center" valign="top">0.057 ± 0.002</td><td colspan="1" rowspan="1" align="center" valign="top">0.060 ± 0.004</td><td colspan="1" rowspan="1" align="center" valign="top">1.52 ± 0.15</td><td colspan="1" rowspan="1" align="center" valign="top">0.08 ± 0.01</td><td colspan="1" rowspan="1" align="center" valign="top">14.74 ± 0.76</td><td colspan="1" rowspan="1" align="center" valign="top">3.7 ± 0.4</td></tr><tr><td colspan="1" rowspan="1" align="center" valign="top"><italic>S4</italic></td><td colspan="1" rowspan="1" align="char" char="." valign="top">11</td><td colspan="1" rowspan="1" align="center" valign="top">0.062 ± 0.007</td><td colspan="1" rowspan="1" align="center" valign="top">0.082 ± 0.007</td><td colspan="1" rowspan="1" align="center" valign="top">0.90 ± 0.25</td><td colspan="1" rowspan="1" align="center" valign="top">0.07 ± 0.01</td><td colspan="1" rowspan="1" align="center" valign="top">14.09 ± 1.21</td><td colspan="1" rowspan="1" align="center" valign="top">4.8 ± 0.9</td></tr><tr><td colspan="1" rowspan="1" align="center" valign="top"><italic>S5</italic></td><td colspan="1" rowspan="1" align="char" char="." valign="top">26</td><td colspan="1" rowspan="1" align="center" valign="top">0.068 ± 0.004</td><td colspan="1" rowspan="1" align="center" valign="top">0.069 ± 0.007</td><td colspan="1" rowspan="1" align="center" valign="top">0.95 ± 0.16</td><td colspan="1" rowspan="1" align="center" valign="top">0.06 ± 0.01</td><td colspan="1" rowspan="1" align="center" valign="top">15.89 ± 0.96</td><td colspan="1" rowspan="1" align="center" valign="top">5.5 ± 0.7</td></tr><tr><td colspan="1" rowspan="1" align="center" valign="top"><italic>S6</italic></td><td colspan="1" rowspan="1" align="char" char="." valign="top">19</td><td colspan="1" rowspan="1" align="center" valign="top">0.062 ± 0.005</td><td colspan="1" rowspan="1" align="center" valign="top">0.064 ± 0.005</td><td colspan="1" rowspan="1" align="center" valign="top">1.38 ± 0.15</td><td colspan="1" rowspan="1" align="center" valign="top">0.06 ± 0.01</td><td colspan="1" rowspan="1" align="center" valign="top">15.71 ± 1.05</td><td colspan="1" rowspan="1" align="center" valign="top">6.5 ± 1.0</td></tr><tr><td colspan="1" rowspan="1" align="center" valign="top">Average</td><td colspan="1" rowspan="1" align="center" valign="top"></td><td colspan="1" rowspan="1" align="center" valign="top">0.059</td><td colspan="1" rowspan="1" align="center" valign="top">0.068</td><td colspan="1" rowspan="1" align="center" valign="top">1.22</td><td colspan="1" rowspan="1" align="center" valign="top">0.07</td><td colspan="1" rowspan="1" align="center" valign="top">15.49</td><td colspan="1" rowspan="1" align="center" valign="top">4.7</td></tr></tbody></table><table-wrap-foot><fn><p>Values are means ± SE and <italic>N</italic> is the number of trials for each subject. The coefficients are defined in <xref ref-type="disp-formula" rid="e5"><italic>Eqs. 5</italic></xref> and <xref ref-type="disp-formula" rid="e6"><italic>6.</italic></xref> A visual time delay (τ<sub>v</sub>) of 100 ms was used in the model.</p></fn></table-wrap-foot></table-wrap></floats-wrap></pmc><affiliations><list><country><li>États-Unis</li></country><region><li>Minnesota</li></region></list><tree><country name="États-Unis"><region name="Minnesota"><name sortKey="Soechting, John F" sort="Soechting, John F" uniqKey="Soechting J" first="John F." last="Soechting">John F. Soechting</name></region><name sortKey="Juveli, John Z" sort="Juveli, John Z" uniqKey="Juveli J" first="John Z." last="Juveli">John Z. Juveli</name><name sortKey="Rao, Hrishikesh M" sort="Rao, Hrishikesh M" uniqKey="Rao H" first="Hrishikesh M." last="Rao">Hrishikesh M. Rao</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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