An application of signal detection theory with finite mixture distributions to source discrimination.
A mixture extension of signal detection theory is applied to source discrimination. The basic idea of the approach is that only a portion of the sources (say A or B) of items to be discriminated is encoded or attended to during the study period. As a result, in addition to 2 underlying probability distributions associated with the 2 sources, there is a 3rd distribution that represents items for which sources were not attended to. Thus, over trials, the observed response results from a mixture of an attended (A or B) distribution and a nonattended distribution. The situation differs in an interesting way from detection in that, for detection, there is mixing only on signal trials and not on noise trials, whereas for discrimination, there is mixing on both A and B trials. Predictions of the mixture model are examined for data from several recent studies and in a new experiment.
D001245:Association Learning; D001288:Attention; D004193:Discrimination Learning; D006801:Humans; D008570:Memory, Short-Term; D011939:Mental Recall; D009949:Orientation; D011597:Psychomotor Performance; D011932:Reading; D012153:Retention, Psychology; D017603:Signal Detection, Psychological; D014706:Verbal Learning