Is signal detection theory fundamentally flawed? A response to Balakrishnan (1998a, 1998b, 1999)

Treisman, Michel

doi:10.3758/bf03196344

Cited by 13 publications

(22 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In psychological studies, one rule of thumb is that d = 0.2 is a "small" effect size, d = 0.5 is a "medium" effect size and d = 0.8 is a "large" effect size (Cohen, 1988). Note that an analogous statistic (d') is used to index "discriminability" (Green & Swets, 1966;Irwin et al, 1993;Macmillan & Creelman, 1991;Triesman, 2002) and quantify recognition memory (Madden et al, 1999). To reduce the influence of extinction, effect sizes were calculated based on the first 2 minutes of the CS presentation.…”

Section: Continuous and Discontinuous Tonesmentioning

confidence: 99%

“…So-called "50 kHz USVs", which actually range in frequency from 35 to 72 kHz, are emitted in conjunction with positive emotional states (Brudzynski, 2005;Knutson et al, 2002), including sexual behavior, rough and tumble play, "tickling", and the anticipation of play, food, or rewarding drugs Knutson et al, 1998;1999;McGinnis & Vakulenko, 2003;Panksepp & Burgdorf, 2000;Wintink & Brudzynski, 2001). Unlike 22 kHz USVs, 50 kHz USVs are usually associated with increased locomotor activity (Wöhr & Schwarting, 2007).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Asymmetrical stimulus generalization following differential fear conditioning

Bang

Allen

Jones

et al. 2008

Neurobiology of Learning and Memory

View full text Add to dashboard Cite

Rodent ultrasonic vocalizations (USVs) are ethologically critical social signals. Rats emit 22 kHz USVs and 50 kHz USVs, respectively, in conjunction with negative and positive affective states. Little is known about what controls emotional reactivity to these social signals. Using male SpragueDawley rats, we examined unconditional and conditional freezing behavior in response to the following auditory stimuli: three 22 kHz USVs, a discontinuous tone whose frequency and on-off pattern matched one of the USVs, a continuous tone with the same or lower frequencies, a 4 kHz discontinuous tone with an on-off pattern matched to one of the USVs, and a 50 kHz USV. There were no differences among these stimuli in terms of the unconditional elicitation of freezing behavior. Thus, the stimuli were equally neutral before conditioning. During differential fear conditioning, one of these stimuli (the CS + ) always co-terminated with a footshock unconditional stimulus (US) and another stimulus (the CS − ) was explicitly unpaired with the US. There were no significant differences among these cues in CS + -elicited freezing behavior. Thus, the stimuli were equally salient or effective as cues in supporting fear conditioning. When the CS + was a 22 kHz USV or a similar stimulus, rats discriminated based on the principal frequency and/or the temporal pattern of the stimulus. However, when these same stimuli served as the CS − , discrimination failed due to generalization from the CS + . Thus, the stimuli differed markedly in the specificity of conditioning. This strikingly asymmetrical stimulus generalization is a novel bias in discrimination.

show abstract

Section: Continuous and Discontinuous Tonesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Asymmetrical stimulus generalization following differential fear conditioning

Bang

Allen

Jones

et al. 2008

Neurobiology of Learning and Memory

View full text Add to dashboard Cite

show abstract

“…As noted by Balakrishnan and MacDonald, these types of changes in perceptual distributions are incompatible with SDT, but are natural consequences of a class of sequential sampling models. Treisman (2002) noted several objections to these arguments, and Balakrishnan and MacDonald (2002) defended their utility. However, the arguments centered on the different ways in which a set of deterministic response criteria might interact to produce the observed results.…”

Section: Lack Of Evidence For Criterion Changementioning

confidence: 99%

“…This result indicates a suboptimal decision rule, because a movement of the decision criteria to optimal locations would eliminate such suboptimalities. As Treisman (2002) pointed out, the belowthe third and fourth criterion. As noise increases more, the small shift becomes undetectable, whereas the larger shift becomes ambiguous.…”

Section: Predictions Of the Dnmmentioning

confidence: 99%

Decision noise: An explanation for observed violations of signal detection theory

Mueller

Weidemann

2008

Psychonomic Bulletin & Review

142

206

View full text Add to dashboard Cite

Signal detection theory (SDT) has become a prominent and useful tool for analyzing performance across a wide spectrum of psychological tasks, from single-cell recordings and perceptual discrimination to high-level categorization, medical decision making, and memory tasks. The utility of SDT comes from its clear and simple account of how detection or classification performance can be translated into psychological quantities, such as sensitivity and bias. Whether its use is appropriate for a specific application depends on a number of underlying assumptions, and even though these assumptions are rarely tested, SDT has proved useful enough that it is considered one of the great successes of cognitive psychology. Yet, SDT has also undergone criticism, which began to emerge when this theory was relatively young. Criticisms of SDTSDT assumes that percepts are noisy and give rise to overlapping perceptual distributions for signal and noise trials. In order to distinguish between signal and noise trials, the observer uses a decision criterion to classify the percepts. Signal responses are "hits" when they are correct and "false alarms" when they are incorrect; similarly, noise responses can be classified as "correct rejections" and "misses." Many criticisms of SDT have centered on how the observer places a decision criterion during a detection or classification task, and whether a deterministic criterion is used at all (see, e.g., Dorfman & Biderman, 1971;Dorfman, Saslow, & Simpson, 1975;Kac, 1969;Kubovy & Healy, 1977;Larkin, 1971).Clearly, when initially performing a signal detection task, 1 an observer may be unable to estimate stimulus distributions and payoff values accurately; thus, one might expect the placement of a decision criterion to improve with experience, approaching a static optimal criterion. Yet, some results suggest that even with extensive practice, responses can be suboptimal: There are numerous demonstrations of human probability micromatching in signal detection tasks (see, e.g., Dusoir, 1974;Lee, 1963;Thomas, 1973Thomas, , 1975 and other demonstrations that static decision criteria are not typically used (e.g., Healy & Kubovy, 1981;Lee & Janke, 1964;Lee & Zentall, 1966;Treisman & Williams, 1984). Despite the fact that models accounting for these dynamics are based on a fairly reasonable assumption (i.e., that the decision criterion should improve with experience), they have not enjoyed the success of classic SDT-probably because they add layers of complexity to the theory that are not easily accommodated or validated. Given that even the basic assumptions required by SDT are rarely tested, it is perhaps not surprising that tests of these additional factors happen even less frequently. 465Copyright 2008 Psychonomic Society, Inc. THEORETICAL AND REVIEW ARTICLESDecision noise: An explanation for observed violations of signal detection theory SHANE T. MUELLERIndiana University, Bloomington, Indiana AND CHRISTOPH T. WEIDEMANN University of Pennsylvania, Philadelphia, PennsylvaniaIn signal detection the...

show abstract

“…The estimation of these internal parameters, as well as of d′ and c, relies on some critical assumptions regarding the statistics of the internal events and the dynamics of the associated decision mechanism. Thus, not surprisingly, the validity of the assumptions made and their associated consequences for the obtained measure of sensitivity are under extensive research (Balakrishnan, 1998a(Balakrishnan, , 1998b(Balakrishnan, , 1999Balakrishnan & MacDonald, 2002Treisman, 2002;Weidemann & Mueller, 2008).…”

Section: Introductionmentioning

confidence: 99%

Decision criteria in dual discrimination tasks estimated using external-noise methods

Zak

Katkov

Goréa

et al. 2012

Atten Percept Psychophys

View full text Add to dashboard Cite

According to classical signal detection theory (SDT), in simple detection or discrimination tasks, observers use a decision parameter based on their noisy internal response to set a boundary between "yes" and "no" responses. Experimental paradigms where performance is limited by internal noise cannot be used to provide an unambiguous measure of the decision criterion and its variability. Here, unidimensional external noise is used to estimate a criterion and its variability in stimulus space. Within this paradigm, the criterion is defined as the stimulus value separating the two response alternatives. This paradigm allows the assessment of interactions between criteria assigned to different targets in dual tasks. Previous studies suggested that observers' criteria interacted or even collapsed to one (hence, nonoptimal) criterion. An alternative interpretation of those results is that observers equated their false alarm (FA) rates. The externalnoise method enables the confrontation of the two hypotheses. It is shown that the variability of observers' criterion in stimulus space is about 1.6 times their measured sensory threshold, suggesting that the presence of external noise increases decision uncertainty. Observers' stimulus criterion settings are close to SDT predictions in single tasks, but not in dual tasks where the two criteria tend to "attract" each other. Observers maintain distinct FA rates even when SDT predicts equal rates. Observers trained in psychophysics or provided with basic notions of SDT exemplified with the present experimental design manage to better separate their criteria in some conditions.

show abstract

Is signal detection theory fundamentally flawed? A response to Balakrishnan (1998a, 1998b, 1999)

Cited by 13 publications

References 19 publications

Asymmetrical stimulus generalization following differential fear conditioning

Asymmetrical stimulus generalization following differential fear conditioning

Decision noise: An explanation for observed violations of signal detection theory

Decision criteria in dual discrimination tasks estimated using external-noise methods

Contact Info

Product

Resources

About