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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).
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.
The DDM assumes that in a 2AFC task, the subject is accumulating evidence for one or other of the alternatives at each time step, and integrating that evidence until a decision threshold is reached. As the sensory input which constitutes the evidence is noisy, the accumulation to the threshold is stochastic rather than deterministic – this ...
A difference threshold (or just-noticeable difference, JND) is the magnitude of the smallest difference between two stimuli of differing intensities that a participant can detect a certain proportion of the time, with the specific percentage depending on the task. Several methods are employed to test this threshold.
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 ...
Discrimination testing is a technique employed in sensory analysis to determine whether there is a detectable difference among two or more products. The test uses a group of assessors (panellists) with a degree of training appropriate to the complexity of the test to discriminate from one product to another through one of a variety of experimental designs.
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: