Bayesian adaptive estimation of arbitrary points on a psychometric function

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dc.contributor.authorGarcía Pérez, Miguel Ángel
dc.contributor.authorAlcalá Quintana, Rocío
dc.date.accessioned2023-06-20T11:08:11Z
dc.date.available2023-06-20T11:08:11Z
dc.date.issued2007-05
dc.description.abstractBayesian adaptive methods have been extensively used in psychophysics to estimate the point at which performance on a task attains arbitrary percentage levels, although the statistical properties of these estimators have never been assessed. We used simulation techniques to determine the small-sample properties of Bayesian estimators of arbitrary performance points, specifically addressing the issues of bias and precision as a function of the target percentage level. The study covered three major types of psychophysical task (yes-no detection, 2AFC discrimination and 2AFC detection) and explored the entire range of target performance levels allowed for by each task. Other factors included in the study were the form and parameters of the actual psychometric function Psi, the form and parameters of the model function M assumed in the Bayesian method, and the location of Psi within the parameter space. Our results indicate that Bayesian adaptive methods render unbiased estimators of any arbitrary point on psi only when M=Psi, and otherwise they yield bias whose magnitude can be considerable as the target level moves away from the midpoint of the range of Psi. The standard error of the estimator also increases as the target level approaches extreme values whether or not M=Psi. Contrary to widespread belief, neither the performance level at which bias is null nor that at which standard error is minimal can be predicted by the sweat factor. A closed-form expression nevertheless gives a reasonable fit to data describing the dependence of standard error on number of trials and target level, which allows determination of the number of trials that must be administered to obtain estimates with prescribed precision.
dc.description.departmentDepto. de Psicobiología y Metodología en Ciencias del Comportamiento
dc.description.facultyFac. de Psicología
dc.description.refereedTRUE
dc.description.sponsorshipMinisterio de Educación, Cultura y Deporte
dc.description.sponsorshipMinisterio de Ciencia y Tecnología
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/35700
dc.identifier.doi10.1348/000711006X104596
dc.identifier.issn0007-1102
dc.identifier.officialurlhttp://dx.doi.org/10.1348/000711006X104596
dc.identifier.relatedurlhttp://onlinelibrary.wiley.com/journal/10.1111/(ISSN)2044-8317
dc.identifier.urihttps://hdl.handle.net/20.500.14352/51751
dc.issue.number1
dc.journal.titleThe British journal of mathematical and statistical psychology
dc.language.isoeng
dc.page.final174
dc.page.initial147
dc.publisherThe British Psychological Society
dc.relation.projectIDAP2001-0759
dc.relation.projectIDBSO2001-1685
dc.rights.accessRightsrestricted access
dc.subject.cdu159.9.07
dc.subject.keywordBayes estimation
dc.subject.keywordEstimation theory
dc.subject.keywordArbitrary constants
dc.subject.keywordConstants of integration
dc.subject.keywordPsychometrics
dc.subject.keywordMETA-analysis
dc.subject.ucmPsicología experimental
dc.subject.unesco6106 Psicología Experimental
dc.titleBayesian adaptive estimation of arbitrary points on a psychometric function
dc.typejournal article
dc.volume.number60
dspace.entity.typePublication
relation.isAuthorOfPublicatione5c3695e-f861-4397-94d7-7aa543f0a630
relation.isAuthorOfPublication0a7dbcf6-8a0b-4b47-91af-79bb5db7bb52
relation.isAuthorOfPublication.latestForDiscoverye5c3695e-f861-4397-94d7-7aa543f0a630
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