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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/pm39874754-111-8-21
    Average 90 stars, based on 1 article reviews
    cumulative gaussian (normal) distribution function - by Bioz Stars, 2026-10
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    Article Title: A stable visual world in primate primary visual cortex
    Article Snippet: To quantify this transition, we fitted a cumulative Gaussian (using Matlab’s “lsqcurvefit” function) to the saccade channel.

    Article Title: Hierarchical computation of 3D motion across macaque areas MT and FST.
    Article Snippet: The ability to discriminate toward/away motion was quantified separately for each cue condition by calculating the proportion of ‘toward’ responses as a function of the directionally signed motion coherence.8 The data were then fit with a cumulative Gaussian using Psignifit 4 in MATLAB (Mathworks) to estimate the response bias (m) and sensitivity (s 1).71

    Article Title: The role of the harmonic vector average in motion integration
    Article Snippet: We fitted a cumulative Gaussian to the observer's data and determined the point of subjective equality (PSE) and 95% confidence intervals around this value in Matlab using a bootstrapping technique (Wichmann and Hill, , ).

    Article Title: Eye-gaze and arrow cues influence elementary sound perception
    Article Snippet: Each psychometric function was a sigmoidal curve, modelled as a cumulative Gaussian in M atlab (The MathWorks Inc., CA) using the maximum-likelihood method (psignifit, http://www.bootstrap-software.org/psignifit/ ) [ 15 ].

    Article Title: Hierarchical computation of 3D motion across macaque areas MT and FST
    Article Snippet: The data were then fit with a cumulative Gaussian using Psignifit 4 in MATLAB (Mathworks) to estimate the response bias ( μ ) and sensitivity ( σ −1 ).

    Article Title: Evidence for multisensory integration in the elicitation of prior entry by bimodal cues
    Article Snippet: The resulting psychometric functions were fitted with a cumulative Gaussian using the Palamedes toolbox for MATLAB (Kingdom and Prins ).

    Article Title: The role of the harmonic vector average in motion integration
    Article Snippet: We fitted a cumulative Gaussian to the observers' data and determined the PSE and 95% confidence intervals around this value in Matlab using a bootstrapping technique (Wichmann and Hill, , ).

    Article Title: Loss of Cntnap2 in the Rat Causes Autism-Related Alterations in Social Interactions, Stereotypic Behavior, and Sensory Processing.
    Article Snippet: Autism spectrum disorder (ASD) is characterized by social interaction and communication impairments, as well as restrictive/repetitive patterns of behavior, interests or activities, which can coexist with intellectual disability and altered sensory processing.. To study the mechanisms underlying these core features of ASD, preclinical research has developed animal models with manipulations in ASD-linked genes, such as CNTNAP2.. In order to fully interpret the findings from mechanistic studies, the extent to which these models display behaviors consistent with ASD must be determined.



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    cumulative gaussian (normal) distribution function  (MathWorks Inc)
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    MathWorks Inc cumulative gaussian (normal) distribution function
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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 <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 <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