Processing before decision is assumed to be independent for each stimulus and may or may not be task-specific Set size effect can be calculated using the decision integration model based on SDT (Shaw) 1)The internal representation of each stimulus is independent of set size 2)The stimulus representation is noisy; both target and distracters --> the more distracters in a display, the greater the chance that the brightness of one will fall in the target range Model based on SDT
Set size effect can be calculated using the decision integration model based on SDT (Shaw) 3) The decision is determined by the stimulus representation that yields the maximum likelihood (max rule) -- stimulus with the maximum value on any given trial 4) Mean value of distracter’s representation is zero, and its variability is 1 The effect of increasing set size is to shift the distribution of the maximum stimulus representation generated by the set of distracters (determined by whichever distracter happens to generate the highest value).
SDT assumes that the vertical distracters generate a smaller response from the filters selective to the tilted target Discriminating target from distractor: both the mean separation between target and distractors and the intrinsic variablity of these representations determine how discriminable the target is from the distractors for a given orientation difference between target and distractor, as distributions variance increases, discriminability decreases
Response strength p (c) depends on the overlap of both distributions response to the 45 target is in the same location (~9); response to the tilted distractor is shifted rightward (~4 to ~7) Max rule Easy search: tilt among verticalHard search: tilt (45) among tilted (22)
Set Size >1 for finding a single target, a decision based on choosing the largest response across the units is close to the best use of the available information, provided that the responses for each of the units is independent noise interval (distracters only) signal interval (n-1 distracters & target) the observer looks for the largest value of the samples in each presentation and then chooses the presentation interval that has the larger of the two maximum values the greater the set size, the higher the probability that the maximum emerges from the noise interval The maximum rule