Journal: Attention, perception & psychophysics

Article Title: The Attentional Repulsion Effect and Relative Size Judgments

doi: 10.3758/s13414-018-1612-x

Figure Lengend Snippet: Plots showing the mean proportion of time participants responded that the comparison rectangle was (a) wider or (b) taller than the standard rectangle for the seven comparison variants tested as function of which side the comparison was on (left/right) and the side that the pre-cue was tested on. Curves show the best fitting cumulative Gaussian function for the group means. Error bars show ±1 S.E.M.

Article Snippet: Cumulative Gaussian functions were then fit to the data (GraphPad Prism; GraphPad Software, Inc) to determine the PSE.

Techniques: Comparison

Journal: Attention, perception & psychophysics

Article Title: The Attentional Repulsion Effect and Relative Size Judgments

doi: 10.3758/s13414-018-1612-x

Figure Lengend Snippet: (a) Plots show the mean proportion of time participants responded that the comparison rectangle was farther from fixation than the standard rectangle for the seven comparison offsets tested as a function of which side the comparison rectangle was on (left/right) and the location of the cue (foveal/center/peripheral). Curves show the best fitting cumulative Gaussian function for the group means. (b) Bar graph showing the mean PSE for the comparison rectangle as a function of cue location and the relative side of the comparison rectangle. The average PSE collapsed across comparison side is also shown. The solid horizontal line show the expected PSE if no distortion were present given that the standard rectangle was always offset from fixation by 5.0°. All error bars show ±1 S.E.M.

Article Snippet: Cumulative Gaussian functions were then fit to the data (GraphPad Prism; GraphPad Software, Inc) to determine the PSE.

Techniques: Comparison

Journal: Attention, perception & psychophysics

Article Title: The Attentional Repulsion Effect and Relative Size Judgments

doi: 10.3758/s13414-018-1612-x

Figure Lengend Snippet: (a) Plot shows the mean proportion of time participants responded that the comparison rectangle was wider than the standard rectangle at the seven comparison widths. Width means were tested as a function of which side the comparison rectangle was located (left/right) and the trial stimulus array offset position (foveal/peripheral). Curves show the best fitting cumulative Gaussian function for the group means. (b) Bar graph showing the mean PSE for the comparison rectangle width as a function of comparison rectangle location and the stimulus array offset position (i.e., position of the standard rectangle at fixation or periphery). The solid horizontal line show the expected PSE if no distortion were present given that the standard rectangle always had a width of 3.0°. All error bars show ± 1 S.E.M.

Article Snippet: Cumulative Gaussian functions were then fit to the data (GraphPad Prism; GraphPad Software, Inc) to determine the PSE.

Techniques: Comparison

Journal: Frontiers in Computational Neuroscience

Article Title: Detecting global form: separate processes required for Glass and radial frequency patterns

doi: 10.3389/fncom.2013.00053

Figure Lengend Snippet: Example stimuli are depicted as described in the text. Judgments were made regarding the center of rotation of the Glass pattern relative to the black outer Gaussian markers.

Article Snippet: The data was fit with a cumulative Gaussian (Prism 4.0, Graphpad Software Inc., 2005) using the following variant of Equation 3: (4) Y = 0.5 { 1 + e r f [ x − P S A σ 2 ] } where Y = proportion of responses to the right, erf = error function, x = amount of displacement, PSA = point of subjective alignment, and σ = the standard deviation of the fitted cumulative Gaussian.

Techniques:

Journal: BMC Veterinary Research

Article Title: Wild-type cutoff for Apramycin against Escherichia coli

doi: 10.1186/s12917-020-02522-0

Figure Lengend Snippet: Definitions of the terminology used in this study

Article Snippet: To confirm the presence of more than one MIC distribution, frequency distributions of MIC data were analyzed by nonlinear least squares regression analysis based on the following Cumulative Gaussian Counts equation: Z = ((X-Mean))/SD, Y=N*zdist(z) according to the previous study [ ], in which the Mean is the average of the original distribution, from which the frequency distribution was created; SD is the standard deviation of the original distribution (calculated by Graphpad prism 6.0 software, San Diego, CA).

Techniques: In Vitro