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boxchart  (MathWorks Inc)


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    MathWorks Inc boxchart
    Boxchart, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/boxchart/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    boxchart - by Bioz Stars, 2026-04
    90/100 stars

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    Generalization of the learned motor memory to different reaching angles. A . Force compensation expressed on reaching movements to targets at different angles (−60° to +60°, where 0° indicates the training direction). All measurements are made on channel trials. Note that participants were never exposed to the curl force field on the −60°, −40°, −20°, 20°, 40° or 60° directions. B . The best-fit parameters for the height of the Gaussian function over training sessions. Model parameters were obtained with the leave-two-out cross validation method, which provided 45 estimates of each parameter. Parameters estimates were plotted with <t>boxchart</t> in Matlab where the line indicates the mean, the shaded notch indicates the 95% confidence intervals, the upper and lower edges of the box contain the upper and lower quartiles, the whiskers contain the nonoutlier maximum and minimum, and any outliers are indicated with small circles. If the shaded notch regions do not overlap, then the parameters have different medians at the 5% significance level. C . The best-fit model parameters of the width of the Gaussian function.
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    Generalization of the learned motor memory to different reaching angles. A . Force compensation expressed on reaching movements to targets at different angles (−60° to +60°, where 0° indicates the training direction). All measurements are made on channel trials. Note that participants were never exposed to the curl force field on the −60°, −40°, −20°, 20°, 40° or 60° directions. B . The best-fit parameters for the height of the Gaussian function over training sessions. Model parameters were obtained with the leave-two-out cross validation method, which provided 45 estimates of each parameter. Parameters estimates were plotted with <t>boxchart</t> in Matlab where the line indicates the mean, the shaded notch indicates the 95% confidence intervals, the upper and lower edges of the box contain the upper and lower quartiles, the whiskers contain the nonoutlier maximum and minimum, and any outliers are indicated with small circles. If the shaded notch regions do not overlap, then the parameters have different medians at the 5% significance level. C . The best-fit model parameters of the width of the Gaussian function.
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    Generalization of the learned motor memory to different reaching angles. A . Force compensation expressed on reaching movements to targets at different angles (−60° to +60°, where 0° indicates the training direction). All measurements are made on channel trials. Note that participants were never exposed to the curl force field on the −60°, −40°, −20°, 20°, 40° or 60° directions. B . The best-fit parameters for the height of the Gaussian function over training sessions. Model parameters were obtained with the leave-two-out cross validation method, which provided 45 estimates of each parameter. Parameters estimates were plotted with boxchart in Matlab where the line indicates the mean, the shaded notch indicates the 95% confidence intervals, the upper and lower edges of the box contain the upper and lower quartiles, the whiskers contain the nonoutlier maximum and minimum, and any outliers are indicated with small circles. If the shaded notch regions do not overlap, then the parameters have different medians at the 5% significance level. C . The best-fit model parameters of the width of the Gaussian function.

    Journal: bioRxiv

    Article Title: Long-term development of a motor memory

    doi: 10.1101/2025.02.22.639647

    Figure Lengend Snippet: Generalization of the learned motor memory to different reaching angles. A . Force compensation expressed on reaching movements to targets at different angles (−60° to +60°, where 0° indicates the training direction). All measurements are made on channel trials. Note that participants were never exposed to the curl force field on the −60°, −40°, −20°, 20°, 40° or 60° directions. B . The best-fit parameters for the height of the Gaussian function over training sessions. Model parameters were obtained with the leave-two-out cross validation method, which provided 45 estimates of each parameter. Parameters estimates were plotted with boxchart in Matlab where the line indicates the mean, the shaded notch indicates the 95% confidence intervals, the upper and lower edges of the box contain the upper and lower quartiles, the whiskers contain the nonoutlier maximum and minimum, and any outliers are indicated with small circles. If the shaded notch regions do not overlap, then the parameters have different medians at the 5% significance level. C . The best-fit model parameters of the width of the Gaussian function.

    Article Snippet: Results were plotted using the boxchart function in Matlab allowing us to compare how the parameters changed over the weeks of training using the 95% confidence intervals of the parameter estimates.

    Techniques: Biomarker Discovery

    Trial-dependent decay of the motor memory in the absence of error information. A . The decay of force compensation in consecutive channel trials in which the lateral error is constrained to zero. Each trace illustrates the mean force compensation across the ten participants in the channel trials. Colors denote the specific sessions. B . Best-fit exponential functions to the mean force compensation across the different sessions. C . Initial force compensation level at the beginning of the repeated channel trials. Values are determined from last five channel trials in the prior force field phase. Parameters estimates were plotted with boxchart in Matlab where the line indicates the mean, the shaded notch indicates the 95% confidence intervals, the upper and lower edges of the box contain the upper and lower quartiles, the whiskers contain the nonoutlier maximum and minimum, and any outliers are indicated with small circles. If the shaded notch regions do not overlap, then the parameters have different medians at the 5% significance level. D . Magnitude of the decay as estimated from a best-fit exponential function of the force compensation. Model parameters were obtained with the leave-two-out cross validation method, which provided 45 estimates of each parameter. E . Time constant of the decay as estimated from a best-fit exponential function of the force compensation during the channel trials.

    Journal: bioRxiv

    Article Title: Long-term development of a motor memory

    doi: 10.1101/2025.02.22.639647

    Figure Lengend Snippet: Trial-dependent decay of the motor memory in the absence of error information. A . The decay of force compensation in consecutive channel trials in which the lateral error is constrained to zero. Each trace illustrates the mean force compensation across the ten participants in the channel trials. Colors denote the specific sessions. B . Best-fit exponential functions to the mean force compensation across the different sessions. C . Initial force compensation level at the beginning of the repeated channel trials. Values are determined from last five channel trials in the prior force field phase. Parameters estimates were plotted with boxchart in Matlab where the line indicates the mean, the shaded notch indicates the 95% confidence intervals, the upper and lower edges of the box contain the upper and lower quartiles, the whiskers contain the nonoutlier maximum and minimum, and any outliers are indicated with small circles. If the shaded notch regions do not overlap, then the parameters have different medians at the 5% significance level. D . Magnitude of the decay as estimated from a best-fit exponential function of the force compensation. Model parameters were obtained with the leave-two-out cross validation method, which provided 45 estimates of each parameter. E . Time constant of the decay as estimated from a best-fit exponential function of the force compensation during the channel trials.

    Article Snippet: Results were plotted using the boxchart function in Matlab allowing us to compare how the parameters changed over the weeks of training using the 95% confidence intervals of the parameter estimates.

    Techniques: Biomarker Discovery