Search results
Results From The WOW.Com Content Network
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).
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.
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.
In statistics, DFFIT and DFFITS ("difference in fit(s)") are diagnostics meant to show how influential a point is in a linear regression, first proposed in 1980. [ 1 ] DFFIT is the change in the predicted value for a point, obtained when that point is left out of the regression:
Total variation distance is half the absolute area between the two curves: Half the shaded area above. In probability theory, the total variation distance is a statistical distance between probability distributions, and is sometimes called the statistical distance, statistical difference or variational distance.
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 ...
In the race model, [11] [12] [23] evidence for each alternative is accumulated separately, and a decision made either when one of the accumulators reaches a predetermined threshold, or when a decision is forced and then the decision associated with the accumulator with the highest evidence is chosen. This can be represented formally by: