Literature DB >> 6657286

Where is the noise in SDT pain assessment?

R Coppola, R H Gracely.   

Abstract

Many applications of sensory decision theory (SDT) to pain research have used discrimination as a measure of pain or sensory sensitivity. This belief is based on the classical SDT assumption that discrimination and criterion represent separation of sensory and decision processes. This assumed separation stems from a model where all noise or variability is part of the sensory transduction mechanism. We present an alternative formulation that allows for decision variability as well as variability in sensory transduction. This formulation documented by computer simulation shows that decision variability and sensory variability are indistinguishable and that any measure of discriminability is degraded by both. Thus discriminability is influenced by both sensory and non-sensory factors. There is no way of knowing if a drug-induced change in discriminability represents an analgesic effect or a change of the observer's ability to make consistent judgments.

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Year:  1983        PMID: 6657286     DOI: 10.1016/0304-3959(83)90098-2

Source DB:  PubMed          Journal:  Pain        ISSN: 0304-3959            Impact factor:   6.961


  5 in total

1.  Sensational developments in the irritable bowel.

Authors:  B Naliboff; E A Mayer
Journal:  Gut       Date:  1996-11       Impact factor: 23.059

2.  Discriminability of electrocutaneous stimuli after topical anesthesia: detection-theory measurement of sensitivity to painful stimuli.

Authors:  R J Irwin; M J Hautus; N J Dawson; D Welch; M F Bayly
Journal:  Percept Psychophys       Date:  1994-02

3.  During vigilance to painful stimuli: slower response rate is related to high trait anxiety, whereas faster response rate is related to high state anxiety.

Authors:  Timothy J Meeker; Nichole M Emerson; Jui-Hong Chien; Mark I Saffer; Oscar Joseph Bienvenu; Anna Korzeniewska; Joel D Greenspan; Frederick Arthur Lenz
Journal:  J Neurophysiol       Date:  2020-12-16       Impact factor: 2.714

4.  Overcoming pain thresholds with multilevel models-an example using quantitative sensory testing (QST) data.

Authors:  Gerrit Hirschfeld; Markus R Blankenburg; Moritz Süß; Boris Zernikow
Journal:  PeerJ       Date:  2015-11-03       Impact factor: 2.984

5.  Detectability and Bias Indices of Pneumatic Corneal Stimuli Using Signal Detection Theory.

Authors:  Varadharajan Jayakumar; Trefford L Simpson
Journal:  Transl Vis Sci Technol       Date:  2020-11-10       Impact factor: 3.283

  5 in total

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