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A contrast effect is the enhancement or diminishment, relative to normal, of perception, cognition or related performance as a result of successive (immediately previous) or simultaneous exposure to a stimulus of lesser or greater value in the same dimension. (Here, normal perception, cognition or performance is that which would be obtained in ...
The Chubb illusion is similar to another visual illusion, the contrast effect.The contrast effect is an illusion in which the perceived brightness or luminance of an identical central visual target form on a larger uniform background varies to the test subject depending on the ratio of the central form's luminance to that of its background. [4]
A contrast is defined as the sum of each group mean multiplied by a coefficient for each group (i.e., a signed number, c j). [10] In equation form, = ¯ + ¯ + + ¯ ¯, where L is the weighted sum of group means, the c j coefficients represent the assigned weights of the means (these must sum to 0 for orthogonal contrasts), and ¯ j represents the group means. [8]
In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. [1] Type I error: an innocent person may be convicted. Type II error: a guilty person may be not convicted.
While being one of the most well-known contrast illusions, there are similar effects which cause two regions of identical color to appear differently depending on context: The Cornsweet illusion creates a boundary between two identically-shaded regions with a discontinuous gradient, resulting in the opposing sides appearing to be different.
It has found use in analyzing human response to contrast-detail phantoms. [18] SSIM has also been used on the gradient of images, making it "G-SSIM". G-SSIM is especially useful on blurred images. [19] The modifications above can be combined. For example, 4-G-r* is a combination of 4-SSIM, G-SSIM, and r*.
In early 1990s, one set of researchers made this statement about their decision to use Rodger's method: "We chose Rodger’s method because it is the most powerful post hoc method available for detecting true differences among groups.
The procedures of Bonferroni and Holm control the FWER under any dependence structure of the p-values (or equivalently the individual test statistics).Essentially, this is achieved by accommodating a `worst-case' dependence structure (which is close to independence for most practical purposes).