A learning model for a continuum of sensory states

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

“…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).…”

“…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).…”

Examining recognition criterion rigidity during testing using a biased-feedback technique: Evidence for adaptive criterion learning

“…Previous research suggests that subjects can effectively use feedback to guide and monitor performance in nonrecognition memory domains (Bohil & Maddox, 2003;Elliott, Frith, & Dolan, 1997;Maddox & Bohil, 2001;Papo, Baudonnière, Hugueville, & Caverni, 2003;Pashler, Cepeda, Wixted, & Rohrer, 2005). Additionally, psychophysical studies have proposed learning models in which subjects adjust criteria in response to feedback after errors occur (Dorfman & Biderman, 1971;Kubovy & Healy, 1977;Thomas, 1973). However, despite the prevalence of feedback manipulations in nonmemory domains, the use of feedback information to attempt to influence memory decision criteria is surprisingly rare, and in fact we know of only three reports of such attempts (Estes & Maddox, 1995;Rhodes & Jacoby, 2007;Verde & Rotello, 2007).…”

A learning model for signal detection theory-temporal invariance of learning parameters

“…Unfortunately, the learning parameters of the model are not currently estimable because the learning states lack identifiability. In conclusion, the additive learning model of Dorfman and Biderman (1971) gives a very respectable temporal parameter invariance.…”

“…The purpose of this investigation was to evaluate the temporal invariance of the learning parameters on the data of Dorfman and Biderman (1971). Recall that their study was a two-interval forced-choice simple This research was supported in part by a grant from the National Institute of Mental Health, MH-19963. auditory-detection task with noise continuously present during the experimental session.…”