Decision criteria do not shift: Commentary on Mueller and Weidemann (2008)

Balakrishnan, J. D.; MacDonald, Jacqueline

doi:10.3758/pbr.15.5.1022

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…We have shown that decision noise does indeed seem to have a large influence on confidence rating and forced choice responses (Mueller & Weidemann, 2008) and have argued that the RT results presented by Balakrishnan and MacDonald (2008) are consistent with a high degree of noise in the mapping between internal states and RT for a twoalternative forced choice response.…”

Section: Discussionmentioning

confidence: 74%

“…In particular, as Balakrishnan and MacDonald (2008) pointed out, these data seem at odds with a simple model that adjusts a decision criterion up or down on every trial depending on the previous classification response. We do not have an account for these results, but we note that more complex effects of the previous response on a decision criterion (possibly contingent on whether or not the previous response was correct) may be able to explain the small fluctuations in hit and false alarm rates that give rise to these results.…”

Section: Constraints In the Likelihood Ratio Functionmentioning

confidence: 79%

“…The likelihood ratio at any point is equivalent to the slope of the ROC function at that point (see Zhang & Mueller, 2005), and therefore any peak in the ROC function where the slope changes from below 1.0 to above 1.0 (see Mueller & Weidemann, 2008, Figures 9 and 12) corresponds to the point where the likelihood ratio crosses 1.0. Balakrishnan and MacDonald (2008) argue that this point is rarely, if ever, found provide important guidance and constraints for more adequate models of choice under uncertainty.…”

Section: Constraints In the Likelihood Ratio Functionmentioning

confidence: 99%

“…As we demonstrated earlier (Mueller & Weidemann, 2008), this discrepancy between classification noise and confidence noise can mask shifts in decision criteria. Balakrishnan and MacDonald (2008) criticize our work on the basis of three major points. Specifically, they argue that:…”

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Decision noise may mask criterion shifts: Reply to Balakrishnan and MacDonald (2008)

Weidemann

2008

Psychonomic Bulletin & Review

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(2008) argue that RTbased measures of signal detection processes provide evidence against signal detection theory's notion of a flexible decision criterion. They argue that this evidence is immune to the alternative explanation proposed by S. T. Mueller and C. T. Weidemann (2008), that decision noise may mask criterion shifts. We show that noise in response times can produce the same effects as are produced by noise in confidence ratings. Given these results, the evidence is not sufficient to categorically reject the notion of a flexible response policy implemented through shifts in a decision criterion.Theories of signal detection attempt to distinguish perceptual factors from decision effects (such as response biases). To that effect, signal detection theory (SDT) incorporates the notion of a flexible decision criterion that adapts to contingencies in the environment (such as stimulus base rates or pay-off schemes). Balakrishnan (1998aBalakrishnan ( , 1998bBalakrishnan ( , 1999) identified serious problems with SDT that render derived measures of sensitivity and bias (such as d and ) suspect. On the basis of novel measures of bias, Balakrishnan (1998aBalakrishnan ( , 1998bBalakrishnan ( , 1999 argued that the notion of such a flexible decision criterion that shifts in response to stimulus contingencies is fundamentally flawed.We have shown that Balakrishnan's (1998a) results, as well as those from other experiments, can in fact be accounted for by a simple model incorporating a flexible decision criterion (Mueller & Weidemann, 2008). This model is an extension of SDT, but in addition to perceptual noise, it includes noise in the mapping from percepts to responses (decision noise). In particular, we distinguish between two types of decision noise: classification noise and confidence noise. Classification noise refers to noise in the mapping between the percept and the classification response (e.g., yes-no, present-absent, A-B), whereas confidence noise refers to the noise in the mapping between an internal state and a dependent measure indexing response confidence (such as confidence ratings). We view the decision noise model (DNM) as constituting a simple existence proof (rather than a full-fledged alternative to existing theories) that the results from signal detection tasks 1 are compatible with flexible decision criteria that adapt to task contingencies. In the DNM, confidence noise can be larger than classification noise, which is consistent with the results of our receiver operating characteristic (ROC) analyses comparing response-related distalstimulus ROC functions and confidence ROC functions (see Mueller & Weidemann, 2008, for details). As we demonstrated earlier (Mueller & Weidemann, 2008), this discrepancy between classification noise and confidence noise can mask shifts in decision criteria.Balakrishnan and MacDonald (2008) criticize our work on the basis of three major points. Specifically, they argue that:1. Our account of how decision noise may mask criterion shifts should not apply to cases w...

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Section: Discussionmentioning

confidence: 74%

Section: Constraints In the Likelihood Ratio Functionmentioning

confidence: 79%

Section: Constraints In the Likelihood Ratio Functionmentioning

confidence: 99%

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confidence: 99%

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Decision noise may mask criterion shifts: Reply to Balakrishnan and MacDonald (2008)

Weidemann

2008

Psychonomic Bulletin & Review

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“…Confidence ratings turn out to be a convenient example of a strongly predictive measure, enough so that both the suboptimality and bias conditions are satisfied. However, they are satisfied in the situations predicted by sequential detection theory rather than by the static signal detection models (Balakrishnan & MacDonald, 2008).…”

Section: Sufficient Conditions For Suboptimal and Biased Decision Rulesmentioning

confidence: 95%

Performance measures for dynamic signal detection

Balakrishnan

MacDonald

2011

Journal of Mathematical Psychology

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Stimulus expectation alters decision criterion but not sensory signal in perceptual decision making

Bang

Rahnev

2017

Sci Rep

102

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Humans are more likely to report perceiving an expected than an unexpected stimulus. Influential theories have proposed that this bias arises from expectation altering the sensory signal. However, the effects of expectation can also be due to decisional criterion shifts independent of any sensory changes. In order to adjudicate between these two possibilities, we compared the behavioral effects of pre-stimulus cues (pre cues; can influence both sensory signal and decision processes) and post-stimulus cues (post cues; can only influence decision processes). Subjects judged the average orientation of a series of Gabor patches. Surprisingly, we found that post cues had a larger effect on response bias (criterion c) than pre cues. Further, pre and post cues did not differ in their effects on stimulus sensitivity (d’) or the pattern of temporal or feature processing. Indeed, reverse correlation analyses showed no difference in the temporal or feature-based use of information between pre and post cues. Overall, post cues produced all of the behavioral modulations observed as a result of pre cues. These findings show that pre and post cues affect the decision through the same mechanisms and suggest that stimulus expectation alters the decision criterion but not the sensory signal itself.

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Decision criteria do not shift: Commentary on Mueller and Weidemann (2008)

Cited by 6 publications

References 16 publications

Decision noise may mask criterion shifts: Reply to Balakrishnan and MacDonald (2008)

Decision noise may mask criterion shifts: Reply to Balakrishnan and MacDonald (2008)

Performance measures for dynamic signal detection

Stimulus expectation alters decision criterion but not sensory signal in perceptual decision making

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