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The JND is a statistical, rather than an exact quantity: from trial to trial, the difference that a given person notices will vary somewhat, and it is therefore necessary to conduct many trials in order to determine the threshold. The JND usually reported is the difference that a person notices on 50% of trials.
The JND does not. The JND is the subjective experience of a difference. 1 and 2 coins are separated by 1 coin (the difference threshold) and 1 JND (I can just tell the difference); 100 and 200 coins are separated by 100 coins (the difference threshold) but just 1 JND (if I can't tell 100 from 199 but can just tell the difference at 100 vs. 200).
If the distance measure is perceptually uniform, then the answer is simply "the set of points whose distance to the reference is less than the just-noticeable-difference (JND) threshold". This requires a perceptually uniform metric in order for the threshold to be constant throughout the gamut (range of colors). Otherwise, the threshold will be ...
Weber found that the just noticeable difference (JND) between two weights was approximately proportional to the weights. Thus, if the weight of 105 g can (only just) be distinguished from that of 100 g, the JND (or differential threshold) is 5 g.
However, the difference in sizes between apples A and C is large enough that you can discern that C is larger than A without a sensitive scale. In psychophysical terms, the size difference between A and C is above the just noticeable difference ('jnd') while the size differences between A and B and B and C are below the jnd.
Discovered by Ernst Heinrich Weber, the JND is a fixed proportion of the reference sensory level, and so the ratio of the JND/reference is roughly constant: = where is the original intensity of the particular stimulation, is the addition to it required for the change to be perceived, and k is a constant.
As a statistical parameter, SSMD (denoted as ) is defined as the ratio of mean to standard deviation of the difference of two random values respectively from two groups. Assume that one group with random values has mean μ 1 {\displaystyle \mu _{1}} and variance σ 1 2 {\displaystyle \sigma _{1}^{2}} and another group has mean μ 2 ...
In statistics, truncation results in values that are limited above or below, resulting in a truncated sample. [1] A random variable y {\displaystyle y} is said to be truncated from below if, for some threshold value c {\displaystyle c} , the exact value of y {\displaystyle y} is known for all cases y > c {\displaystyle y>c} , but unknown for ...