Task layout.

<p>A-B-C) A representative sequence of two consecutive trials with different target distance and target width with unspecified location of the subsequent target.</p

Results endpoint variability.

<p>Plot showing the means and standard errors of the endpoint variability at the target (Var) of penalty condition for both groups (healthy children and children with dystonia) for all the Indexes of difficulty (ID). Black bars: No Penalty condition, NP; white bars: Yes Penalty condition, YP. The asterisk mark (*) indicates a statistical difference p < .05.</p

Representative endpoint distribution at the target.

<p>Endpoint distribution of the fingertip on the screen for both penalty conditions (No Penalty, NP; Yes Penalty, YP) in a typical healthy child (on the top panels) and patient P2 (at the bottom panels). The grey shaded areas circumscribe the ellipse defined by the two principal components of the dispersion of the distribution in the x-y plane on the iPad^®’s screen. The black lines represents the eigenvectors of the principal components which were taken as the axes of the ellipse, while the length of the axes were determined by the corresponding eigenvalues.</p

Model of cost and probability of error in the speed-accuracy trade-off.

<p>Theoretical model of predicted movement time (MT) over Index of Difficulty (ID) by taking into account the effects of different magnitude of signal-dependent noise (K_SDN, left vs right panels) and cost of error at the target (C_E, black solid vs grey dashed lines). Panels A and B predict that C_E would not affect the offset of Fitts’ linear relationship (constant η_B), <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139988#pone.0139988.e001" target="_blank">Eq 1</a>; panels C and D predict that C_E would not have effects on MT, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139988#pone.0139988.e002" target="_blank">Eq 2</a>; and in panels E and F, C_E would have effects on both the slope of the relationship between MT and ID and the offset (C_E x η_B), <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139988#pone.0139988.e003" target="_blank">Eq 3</a>.</p

Target locations and penalty conditions.

<p>A) The nine locations of the targets on the screen (black circles) are shown; the dashed lines indicate the movement distances between targets. B) A representative sequence of three consecutive trials with different target distance and target width with score penalty for missed targets (Yes Penalty condition, YP). The score at each trial was based on the speed of the movement and the actual ID; note the values displayed in the figure were chosen only for demonstrative purpose. In case a target was missed, as the last move (c) shows, a 30 bubbles penalty was subtracted from the Total Score (TS, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139988#sec003" target="_blank">methods</a>) at the end of a block of 45 sequential targets. C) A similar representative sequence of three consecutive trials with different target distance and target width with none penalty for missed targets (No Penalty condition, NP). In this case, if a target was missed then no penalty was subtracted from the TS.</p

Characteristics of children with dystonia.

<p>BAD, Barry-Albright Dystonia Scale.</p

Perceived Cost and Intrinsic Motor Variability Modulate the Speed-Accuracy Trade-Off

<div><p>Fitts’ Law describes the speed-accuracy trade-off of human movements, and it is an elegant strategy that compensates for random and uncontrollable noise in the motor system. The control strategy during targeted movements may also take into account the rewards or costs of any outcomes that may occur. The aim of this study was to test the hypothesis that movement time in Fitts’ Law emerges not only from the accuracy constraints of the task, but also depends on the perceived cost of error for missing the targets. Subjects were asked to touch targets on an iPad^® screen with different costs for missed targets. We manipulated the probability of error by comparing children with dystonia (who are characterized by increased intrinsic motor variability) to typically developing children. The results show a strong effect of the cost of error on the Fitts’ Law relationship characterized by an increase in movement time as cost increased. In addition, we observed a greater sensitivity to increased cost for children with dystonia, and this behavior appears to minimize the average cost. The findings support a proposed mathematical model that explains how movement time in a Fitts-like task is related to perceived risk.</p></div

Fitts’ Law relationship.

<p>Mean movement time (MT) for both groups separated by penalty conditions (filled circles—No Penalty, NP; unfilled circles—Yes Penalty, YP) as a function of the Index of Difficulty (ID). The straight lines show the best fits by the least squares method.</p

Movement time and Index of Performance.

<p>Plots showing the means and standard errors of the Movement Time (MT) (Panel A) and Index of Performance (IP) (Panel B) for both groups (healthy children and children with dystonia). Black bars: No Penalty condition, NP; white bars: Yes Penalty condition, YP. Asterisk mark (*) indicates a statistical difference p < .05.</p

Effectiveness and Safety of a Novel Care Model for the Management of Type 2 Diabetes at One Year: an Open Label, Non-Randomized, Controlled Study

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