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cumulative gaussian (normal) distribution function  (MathWorks Inc)


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    MathWorks Inc cumulative gaussian (normal) distribution function
    Cumulative Gaussian (Normal) Distribution Function, 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/product/cumulative+gaussian+function/pm39874754-111-8-21
    Average 90 stars, based on 1 article reviews
    cumulative gaussian (normal) distribution function - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    Standard Deviation:

    Article Title: Effects of Haptic Information on the Perception of Dynamic 3-D Movement
    Article Snippet: To obtain psychometric functions, the data were fitted with cumulative Gaussian function by maximum likelihood estimation methods using Matlab (MathWorks). shows an example fit of the results from one participant.

    Article Title: A neural mechanism for optic flow parsing in macaque visual cortex.
    Article Snippet: Each sample was fitted with a cumulative Gaussian function, and a 95% confidence interval was computed from the bootstrap distribution (Matlab 2020a ‘bootci’ function with default parameters).

    Article Title: Photoreceptor layer elevation due to subretinal fluid: Impact on visual acuity measurements and simulation from biometrics
    Article Snippet: A cumulative Gaussian function with minimum root mean squared error (RMSE) was used to model the psychometric functions for the VA data, using MATLAB (Figure ).

    Article Title: Dissociable Processes for Orientation Discrimination Learning and Contextual Illusion Magnitude
    Article Snippet: We fitted a cumulative Gaussian function to these data in MATLAB (MathWorks, Inc.) by minimizing the least squares of the residuals between fit and observed data.

    Article Title: Aberrant causal inference and presence of a compensatory mechanism in autism spectrum disorder
    Article Snippet: These data were fit with a cumulative Gaussian function using psignifit , a MATLAB package that implements the maximum-likelihood method ( ).

    Control:

    Article Title: Effects of Haptic Information on the Perception of Dynamic 3-D Movement
    Article Snippet: To obtain psychometric functions, the data were fitted with cumulative Gaussian function by maximum likelihood estimation methods using Matlab (MathWorks). shows an example fit of the results from one participant.

    Article Title: A neural mechanism for optic flow parsing in macaque visual cortex.
    Article Snippet: Each sample was fitted with a cumulative Gaussian function, and a 95% confidence interval was computed from the bootstrap distribution (Matlab 2020a ‘bootci’ function with default parameters).

    Article Title: Photoreceptor layer elevation due to subretinal fluid: Impact on visual acuity measurements and simulation from biometrics
    Article Snippet: A cumulative Gaussian function with minimum root mean squared error (RMSE) was used to model the psychometric functions for the VA data, using MATLAB (Figure ).

    Article Title: Dissociable Processes for Orientation Discrimination Learning and Contextual Illusion Magnitude
    Article Snippet: We fitted a cumulative Gaussian function to these data in MATLAB (MathWorks, Inc.) by minimizing the least squares of the residuals between fit and observed data.

    Article Title: Aberrant causal inference and presence of a compensatory mechanism in autism spectrum disorder
    Article Snippet: These data were fit with a cumulative Gaussian function using psignifit , a MATLAB package that implements the maximum-likelihood method ( ).



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    Experiment data. ( A ) Group mean ( thin dotted lines ) of open-loop ( left ) and closed-loop ( right ) pursuit direction error ( D E ) and ±1 SE ( shaded areas ) as a function of the target-relative moving direction of the first previous (i.e., 1-back) trial ( D R 1 ). “CCW” and “CW” on the x-axis indicate that the target motion direction in the previous trial was counterclockwise (CCW) or clockwise (CW) to that in the current trial. “CCW” and “CW” on the y-axis indicate that pursuit direction was CCW or CW to the target motion direction in the current trial. Thick solid lines indicate the fitted DoG curves. The amplitude of the DoG curve ( a ) indicates the size of serial dependence. ( B ) The size of serial dependence ( a ) in the open-loop response for the previous one to five trials (i.e., 1-back to 5-back). Error bars represent bootstrapped 95% confidence intervals. ( C ) Histograms and <t>Gaussian</t> fits of the overall pursuit direction noise in the open-loop response for children ( green bars and lines ) and adults ( gray bars and lines ). * P < 0.05. ** P < 0.01.
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    Experiment data. ( A ) Group mean ( thin dotted lines ) of open-loop ( left ) and closed-loop ( right ) pursuit direction error ( D E ) and ±1 SE ( shaded areas ) as a function of the target-relative moving direction of the first previous (i.e., 1-back) trial ( D R 1 ). “CCW” and “CW” on the x-axis indicate that the target motion direction in the previous trial was counterclockwise (CCW) or clockwise (CW) to that in the current trial. “CCW” and “CW” on the y-axis indicate that pursuit direction was CCW or CW to the target motion direction in the current trial. Thick solid lines indicate the fitted DoG curves. The amplitude of the DoG curve ( a ) indicates the size of serial dependence. ( B ) The size of serial dependence ( a ) in the open-loop response for the previous one to five trials (i.e., 1-back to 5-back). Error bars represent bootstrapped 95% confidence intervals. ( C ) Histograms and Gaussian fits of the overall pursuit direction noise in the open-loop response for children ( green bars and lines ) and adults ( gray bars and lines ). * P < 0.05. ** P < 0.01.

    Journal: Investigative Ophthalmology & Visual Science

    Article Title: Serial Dependence in Smooth Pursuit Eye Movements of Preadolescent Children and Adults

    doi: 10.1167/iovs.65.14.37

    Figure Lengend Snippet: Experiment data. ( A ) Group mean ( thin dotted lines ) of open-loop ( left ) and closed-loop ( right ) pursuit direction error ( D E ) and ±1 SE ( shaded areas ) as a function of the target-relative moving direction of the first previous (i.e., 1-back) trial ( D R 1 ). “CCW” and “CW” on the x-axis indicate that the target motion direction in the previous trial was counterclockwise (CCW) or clockwise (CW) to that in the current trial. “CCW” and “CW” on the y-axis indicate that pursuit direction was CCW or CW to the target motion direction in the current trial. Thick solid lines indicate the fitted DoG curves. The amplitude of the DoG curve ( a ) indicates the size of serial dependence. ( B ) The size of serial dependence ( a ) in the open-loop response for the previous one to five trials (i.e., 1-back to 5-back). Error bars represent bootstrapped 95% confidence intervals. ( C ) Histograms and Gaussian fits of the overall pursuit direction noise in the open-loop response for children ( green bars and lines ) and adults ( gray bars and lines ). * P < 0.05. ** P < 0.01.

    Article Snippet: We then fitted the percentage data with a cumulative Gaussian function using the maximum likelihood method of the Palamedes Toolbox in MATLAB to obtain an oculometric curve.

    Techniques:

    The percentage of counterclockwise binary pursuit responses in each participant group as a function of the deviation of target motion direction from each data set’s canonical direction for the four cardinal (0°, 90°, 180°, and 270°; upper ) and the four oblique direction data sets (45°, 135°, 225°, and 315°; lower ). Data are fitted with cumulative Gaussian functions ( solid and dashed lines ).

    Journal: Investigative Ophthalmology & Visual Science

    Article Title: Serial Dependence in Smooth Pursuit Eye Movements of Preadolescent Children and Adults

    doi: 10.1167/iovs.65.14.37

    Figure Lengend Snippet: The percentage of counterclockwise binary pursuit responses in each participant group as a function of the deviation of target motion direction from each data set’s canonical direction for the four cardinal (0°, 90°, 180°, and 270°; upper ) and the four oblique direction data sets (45°, 135°, 225°, and 315°; lower ). Data are fitted with cumulative Gaussian functions ( solid and dashed lines ).

    Article Snippet: We then fitted the percentage data with a cumulative Gaussian function using the maximum likelihood method of the Palamedes Toolbox in MATLAB to obtain an oculometric curve.

    Techniques:

    Polar plots of ( A ) the precision (standard deviation of the best-fitting Gaussian function) and ( B ) the size of serial dependence ( a ) in pursuit direction for the four cardinal (0°, 90°, 180°, and 270°) and the four oblique (45°, 135°, 225°, and 315°) direction data sets. The dashed lines in ( B ) represent a values across the 360° circular angle space, as plotted in A.

    Journal: Investigative Ophthalmology & Visual Science

    Article Title: Serial Dependence in Smooth Pursuit Eye Movements of Preadolescent Children and Adults

    doi: 10.1167/iovs.65.14.37

    Figure Lengend Snippet: Polar plots of ( A ) the precision (standard deviation of the best-fitting Gaussian function) and ( B ) the size of serial dependence ( a ) in pursuit direction for the four cardinal (0°, 90°, 180°, and 270°) and the four oblique (45°, 135°, 225°, and 315°) direction data sets. The dashed lines in ( B ) represent a values across the 360° circular angle space, as plotted in A.

    Article Snippet: We then fitted the percentage data with a cumulative Gaussian function using the maximum likelihood method of the Palamedes Toolbox in MATLAB to obtain an oculometric curve.

    Techniques: Standard Deviation