Information integration and the identification of stimulus noise and criterial noise in absolute judgment.

Nosofsky, Robert M.

doi:10.1037/0096-1523.9.2.299

Cited by 35 publications

(90 citation statements)

References 22 publications

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“…Predictions of this averaging rule have been confirmed in several experiments by explicitly manipulating the number of stimulus presentations given to the subject or the time preceding a decision (see, e.g., Nosofsky, 1983;Swets & (1) (2) CTt = 2CT~+CTE Bonnel et al (1987) from a more general framework suggested by Luce and Green (1978). First, we will describe the basic Thurstonian model for the same-different task as it has been used in focused attention experiments (e.g., Dickman & Meyer, 1988;Egeth & Blecker, 1971;Moss, Myers, & Filmore, 1970).…”

Section: The Sample-size Modelmentioning

confidence: 83%

“…Values of ul were estimated for each condition, by using that both (1: and (1~would be influenced by changes in (11 comes from Nosofsky (1983), who developed a modelbased technique for estimating sensory and criterion variance in identification tasks. He applied this technique in several conditions differing in the physical range over which stimuli varied, and he found evidence that both sensory and criterion variance increased with stimulus range.…”

Section: Lomentioning

confidence: 99%

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Attentional effects on concurrent psychophysical discriminations: Investigations of a sample-size model

Bonnel

Miller

1994

Perception & Psychophysics

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In two experiments, a concurrent discrimination paradigm was used to study the effects of visual attention on psychophysical judgments and the consistency of these effects with a sample-size model in which attention influences the variance of the internal representation used to make psychophysical judgments. Two pairs of lines were presented simultaneously-one on each side of fixation-and subjects had to indicate for each pair separately whether or not the lines had the same length. Attention was manipulated by instructing subjects to pay 100%, 75%, 50%, 25%, or 0% of their attention to the discrimination on one side, with the complementary amount of attention to the other side. In the first experiment, the relationship between attention and discrimination accuracy was consistent with the sample-size model both when attentional allocation varied from trial to trial and when it varied between blocks, and the relationship held over more widely varying attentional allocations than had previously been studied. In addition, discriminations were more accurate overall with varied than with blocked attentional allocation, suggesting that the two types of allocation do not merely differ in the degree to which attention is focused. The second experiment examined the effects of attentional allocation and stimulus variance, the latter being manipulated by randomly incrementing or decrementing line lengths. These manipulations had additive effects on total Thurstonian variance, and a version of the samplesize model gave an excellent quantitative fit to the obtained results. Besides supporting the samplesize model, the results of Experiment 2 suggest that criterion variance is at least as large as sensory variance and that criterion but not sensory variance increases with stimulus variance.

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Section: The Sample-size Modelmentioning

confidence: 83%

Section: Lomentioning

confidence: 99%

Attentional effects on concurrent psychophysical discriminations: Investigations of a sample-size model

Bonnel

Miller

1994

Perception & Psychophysics

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“…Recall that during the perceptual matching task, the stimulus exposure duration was unlimited, whereas during identification, the stimulus exposure duration was 100 msec. Thus, the observer could obtain more perceptual information on each trial during the matching task, resulting in less perceptual variability (Green & Swets, 1967;Luce, 1986;Nosofsky, 1983). In addition, the decision problem is different in the two tasks, which will likely lead to differences in criterial noise.…”

Section: Predicting Identification From Perceptual Matchingmentioning

confidence: 99%

Separating perceptual processes from decisional processes in identification and categorization

Maddox

2001

Perception & Psychophysics

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“…Separate estimates of perceptual and criterial noise have been obtained in the past (see, e.g., Nosofsky, 1983), but these have required a comparison of performance across several different experiments.…”

Section: Decision Bound Theorymentioning

confidence: 99%

Comparing decision bound and exemplar models of categorization

Maddox

Ashby

1993

Perception & Psychophysics

349

576

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The performance of a decision bound model of categorization (Ashby, 1992a; Ashby & Maddox, in press) is compared with the performance of two exemplar models. The first is the generalized context model (e.g., Nosofsky, 1986Nosofsky, , 1992 and the second is a recently proposed deterministic exemplar model (Ashby & Maddox, in press), which contains the generalized context model as a special case. When the exemplars from each category were normally distributed and the optimal decision bound was linear, the deterministic exemplar model and the decision bound model provided roughly equivalent accounts of the data. When the optimal decision bound was nonlinear, the decision bound model provided a more accurate account of the data than did either exemplar model. When applied to categorization data collected by Nosofsky (1986Nosofsky ( ,1989, in which the category exemplars are not normally distributed, the decision bound model provided excellent accounts of the data, in many cases significantly outperforming the exemplar models. The decision bound model was found to be especially successful when (1) single subject analyses were performed, (2) each subject was given relatively extensive training, and (3) the subject's performance was characterized by complex suboptimalities. These results support the hypothesis that the decision bound is of fundamental importance in predicting asymptotic categorization performance and that the decision bound models provide a viable alternative to the currently popular exemplar models of categorization.Decision bound models of categorization (Ashby, 1992a; Ashby & Maddox, in press) assume that the subject learns to assign responses to different regions of perceptual space. When categorizing an object, the subject determines in which region the percept has fallen and then emits the associated response. The decision bound is the partition between competing response regions. In contrast, exemplar models assume that the subject computes the sum of the perceived similarities between the object to be categorized and every exemplar of each relevant category (Medin & Schaffer, 1978;Nosofsky, 1986). Categorization judgments are assumed to depend on the relative magnitude of these various sums.This article compares the ability of decision bound and exemplar models to account for categorization response probabilities in seven different experiments. The aim is

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Information integration and the identification of stimulus noise and criterial noise in absolute judgment.

Cited by 35 publications

References 22 publications

Attentional effects on concurrent psychophysical discriminations: Investigations of a sample-size model

Attentional effects on concurrent psychophysical discriminations: Investigations of a sample-size model

Separating perceptual processes from decisional processes in identification and categorization

Comparing decision bound and exemplar models of categorization

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