Response bias: Characteristics of detection theory, threshold theory, and "nonparametric" indexes.

Macmillan, Neil A.; Creelman, C. Douglas

doi:10.1037/0033-2909.107.3.401

Cited by 343 publications

(297 citation statements)

References 26 publications

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“…The details of the decision process depend on the particular task, for example, detection versus identification. These have been modeled elsewhere (Macmillan & Creelman, 1990 …”

Section: Appendix E the Perceptual Template Modelmentioning

confidence: 99%

Characterizing observers using external noise and observer models: Assessing internal representations with external noise.

Lü¹,

Dosher²

2008

Psychological Review

229

347

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External noise methods and observer models have been widely used to characterize the intrinsic perceptual limitations of human observers and changes of the perceptual limitations associated with cognitive, developmental, and disease processes by highlighting the variance of internal representations. The authors conducted a comprehensive review of the 5 most prominent observer models through the development of a common formalism. They derived new predictions of the models for a common set of behavioral tests that were compared with the data in the literature and a new experiment. The comparison between the model predictions and the empirical data resulted in very strong constraints on the observer models. The perceptual template model provided the best account of all the empirical data in the visual domain. The choice of the observer model has significant implications for the interpretation of data from other external noise paradigms, as well as studies using external noise to assay changes of perceptual limitations associated with observer states. The empirical and theoretical development suggests possible parallel developments in other sensory modalities and studies of high-level cognitive processes.Keywords: signal detection theory, internal representation, observer model, perceptual template, internal noise Human decisions are based on internal representations of information. Understanding how stimuli are represented internally is one of the classic problems in psychology. This article examines how modifying an external stimulus with external noise can provide insight into how the stimulus is processed by the human observer. We conducted a systematic and comprehensive review of the external noise paradigms and observer models widely used in characterizing the internal response properties of human observers. The observer approach builds on the broadly applicable framework of signal detection theory (SDT) by elaborating the relationships between external stimuli and the internal response distributions that form the basis for decision. The empirical tests introduce external noise-either masking noise or variation in the relevant stimulus dimension-to provide a reference for characterizing and quantifying the limiting factors in perceptual sensitivity. The current review, analysis, and empirical test focus on visual perception. Some of the model properties, especially the empirical findings, may be modality specific. Still, this framework and the findings could serve as an example for parallel development of the empirical methods and theoretical models in other sensory modalities. The model development and testing also have major implications for applications of the external noise paradigms in understanding the mechanisms underlying changes of perceptual sensitivity in different cognitive, disease, and/or developmental states. Internal Response DistributionsSDT provides a general framework for analyzing human decision making in perceptual and cognitive tasks (Green & Swets, 1966;Macmillan & Creelman, 1991). In a ...

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“…The details of the decision process depend on the particular task, for example, detection versus identification. These have been modeled elsewhere (Macmillan & Creelman, 1990 …”

Section: Appendix E the Perceptual Template Modelmentioning

confidence: 99%

Characterizing observers using external noise and observer models: Assessing internal representations with external noise.

Lü¹,

Dosher²

2008

Psychological Review

229

347

View full text Add to dashboard Cite

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“…In univariate signal detection theory, percent correct in FLEXIBLE BIAS 959 2AFC is a sensitivity index that is unaffected by criterion bias (Macmillan & Creelman, 1990Sekuler & Blake, 1990 Stimuli. The stimuli were identical to those of Experiment 2.…”

Section: Methodsmentioning

confidence: 99%

Signal detection comparisons of phonemic and phonetic priming: The flexible-bias problem

Goldinger

1998

Perception & Psychophysics

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The phonemic priming effect may reflect the hidden dynamics of spoken word perception and has thus been a key topic of recent research. This investigation compared phonemic and phonetic priming (cf. Goldinger, Luce, Pisoni, & Marcario, 1992), using signal detection methods. Although these methods were intended to provide separate indices of sensitivity and bias changes, the results were more complex. Instead, phonemic priming engendered a flexible, trial-specific strategy that affected hits and false alarms (and thereby altered sensitivity) but also created behavioral changes indicative of a bias. Together with previous research, the results suggest that phonemic priming data must be interpreted with caution, and they underscore the limitations of signal detection analyses in priming research (Norris, 1995). However, if a researcher can anticipate the likely form a bias will assume, signal detection methods can reveal priming effects.Relative to the voluminous literature on semantic priming effects in printed and spoken word perception, formbased priming has received little attention. Form-based priming refers to experiments in which primes and targets are related along physical, rather than conceptual, dimensions.

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“…However, many authors now recommend measuring response bias with c (Banks, 1970;Macmillan & Creelman, 1990;Snodgrass & Corwin, 1988). This measure assumes that subjects respond yes when the decision variable exceeds the criterion and no otherwise; responses are based directly on the decision variable, which some researchers regard as more plausible than assuming that responses are based on a likelihood ratio (Richardson, 1994).…”

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confidence: 99%

Calculation of signal detection theory measures

Stanislaw

Todorov

1999

Behavior Research Methods, Instruments, & Computers

2,649

2,203

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Signal detection theory (SDT) may be applied to any area of psychology in which two different types of stimuli must be discriminated. Wedescribe several of these areas and the advantages that can be realized through the application of SDT. Three of the most popular tasks used to study discriminability are then discussed, together with the measures that SDT prescribes for quantifying performance in these tasks. Mathematical formulae for the measures are presented, as are methods for calculating the measures with lookup tables, computer software specifically developed for SDTapplications, and general purpose computer software (including spreadsheets and statistical analysis software).Signal detection theory (SOT) is widely accepted by psychologists; the Social Sciences Citation Index cites over 2,000 references to an influential book by Green and Swets (1966) that describes SOT and its application to psychology. Even so, fewer than half ofthe studies to which SOT is applicable actually make use ofthe theory (Stanislaw & Todorov, 1992). One possible reason for this apparent underutilization of SOT is that relevant textbooks rarely describe the methods needed to implement the theory. A typical example is Goldstein's (1996) popular perception textbook, which concludes a nine-page description of SOT with the statement that measures prescribed by SOT "can be calculated ... by means of a mathematical procedure we will not discuss here" (p. 594).The failure of many authors to describe SOT's methods may have been acceptable when lengthy, specialized tables were required to implement the theory. Today, however, readily available computer software makes an SOT analysis no more difficult than a t test. The present paper attempts to demonstrate this and to render SOT available to a larger audience than currently seems to be the case.We begin with a brief overview of SOT, including a description of its performance measures. We then present the formulae needed to calculate these measures. Next, we describe different methods for calculating SOT measures. Finally, we provide sample calculations so that readers can verify their understanding and implementation of the techniques.Weare indebted to

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Response bias: Characteristics of detection theory, threshold theory, and "nonparametric" indexes.

Cited by 343 publications

References 26 publications

Characterizing observers using external noise and observer models: Assessing internal representations with external noise.

Characterizing observers using external noise and observer models: Assessing internal representations with external noise.

Signal detection comparisons of phonemic and phonetic priming: The flexible-bias problem

Calculation of signal detection theory measures

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