Signal detection with criterion noise: Applications to recognition memory.

Benjamin, Aaron S.; Diaz, Michael; Wee, Serena

doi:10.1037/a0014351

Cited by 138 publications

(246 citation statements)

References 250 publications

(471 reference statements)

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“…Thus, it could be argued that the pattern of bivariate correlations, the use of likelihood ratio bounds, and the addition of a noise distribution are distortions forced upon the signal detection framework to cope with a compression pattern that, in reality, largely reflects scale-usage phenomena. In line with this argument, the potentially distorting role of response processes is currently receiving increased attention in the signal detection literature (Benjamin, Diaz, & Wee, 2009;Mueller & Weidemann, 2008).…”

Section: Resultsmentioning

confidence: 89%

Toward a complete decision model of item and source recognition: A discrete-state approach

Klauer

Kellen

2010

Psychonomic Bulletin & Review

110

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Section: Resultsmentioning

confidence: 89%

Toward a complete decision model of item and source recognition: A discrete-state approach

Klauer

Kellen

2010

Psychonomic Bulletin & Review

110

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“…If there is some degree of trial-to-trial criterion variability (e.g. ; Benjamin et al, 2009), it's possible that some items which initially elicited a "no" response in the item recognition due to a high criterion were more likely to be recognized during the source test, eliciting a source retrieval instead of a guess, and so slightly greater than chance performance.…”

Section: Discussionmentioning

confidence: 99%

The list strength effect in source memory: Data and a global matching model

Osth

Fox

McKague

et al. 2018

Journal of Memory and Language

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A critical constraint on models of item recognition comes from the list strength paradigm, in which a proportion of items are strengthened to observe the effect on the non-strengthened items. In item recognition, it has been widely established that increasing list strength does not impair performance, in that performance of a set of items is unaffected by the strength of the other items on the list. However, to date the effects of list strength manipulations have not been measured in the source memory task. We conducted three source memory experiments where items studied in two sources were presented in a pure weak list, where all items were presented once, and a mixed list, where half of the items in both sources were presented four times. Each experiment varied the nature of the testing format. In Experiment 1, in which each study list was only tested on one task (item recognition or source memory), a list strength effect was found in source memory while a null effect was found for item recognition. Experiments 2 and 3 showed robust null list strength effects when either the test phase (Experiment 2) or the analysis (Experiment 3) was restricted to recognized items. An extension of the Osth and Dennis (2015) model was able to account for the results in both tasks in all experiments by assuming that unrecognized items elicit guess responses in the source memory task and that there was low interference among the studied items. The results were also found to be consistent with a variant of the retrieving effectively from memory model (REM;Shiffrin & Steyvers, 1997) that uses ensemble representations.Keywords: recognition memory; source memory; global matching modelsThe list strength effect in source memory: Data and a global matching model A major distinction in episodic memory research concerns the difference between information about learned content and the context in which it occurred. A common memory failure illustrating this distinction is that people remember a fact or detail but have no memory for where they learned the information. The relationship between memory for content and context is studied in the laboratory using the item recognition and source memory paradigms. In the item recognition paradigm, participants study a list of items and at test are asked to discriminate between studied items (targets) and unstudied items (lures). The source memory paradigm presents participants with a set of items in different sources, such as different font colors, studied locations, or sensory modalities. At test, participants judge which source studied items were presented in.A number of computational models of decision making have been developed to explain the relations between item and source memory (e.g.; Banks, 2000;Batchelder & Riefer, 1990; DeCarlo, 2003;Yonelinas, 1999; Hautus, Macmillan, & Rotello, 2008; Glanzer, Hilford, & Kim, 2004;Klauer & Kellen, 2010;Slotnick & Dodson, 2005). These models fall into several frameworks including multivariate signal detection theory, in which participants make ...

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“…Such dependencies are common in many perceptual tasks and could have arisen for a number of reasons. One possibility is that Benjamin, Diaz, & Wee, 2007). We believe that the time has come to acknowledge the importance of decision noise in signal detection tasks and to begin moving beyond the simple application of SDT.…”

Section: Sequential Dependenciesmentioning

confidence: 99%

“…Recently, Benjamin, Diaz, and Wee (2007) noted that reasonable models of signal detection can be formed using decision noise instead of perceptual noise. Although decision noise models are difficult to distinguish from perceptual noise models, Benjamin et al ultimately concluded that decision-noise-only models were untenable.…”

Section: Notesmentioning

confidence: 99%

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

Mueller

Weidemann

2008

Psychonomic Bulletin & Review

143

206

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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...

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Signal detection with criterion noise: Applications to recognition memory.

Cited by 138 publications

References 250 publications

Toward a complete decision model of item and source recognition: A discrete-state approach

Toward a complete decision model of item and source recognition: A discrete-state approach

The list strength effect in source memory: Data and a global matching model

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

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