Criterion noise in ratings-based recognition: Evidence from the effects of response scale length on recognition accuracy.

Benjamin, Aaron S.; Tullis, Jonathan G.; Lee, Ji Hae

doi:10.1037/a0031849

Cited by 35 publications

(50 citation statements)

References 48 publications

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“…Given the impact of criterion noise on performance, it is predicted that ROC points obtained with simple binary responses are above ROC points obtained with confidence-rating scales. ' Benjamin, Tullis, and Lee (2013) reported interesting findings confirming this prediction, although the observed differences in performance were relatively small. The notion that the effect of scale size in recognitionmemory performance is small is corroborated by recent work (e.g., Mickes, Hwe, Wais, & Wixted, 2011;Mickes, Wixted, & Wais, 2007) where 20-and 99-point confidence-rating scales were used, leading to parameter estimates that are indistinct fi'om the ones usually reported in the literature with 6-point scales (e.g., Ratcliff, McKoon, & Tindall, 1994).…”

Section: Remember-know Rocssupporting

confidence: 69%

“…The notion that the effect of scale size in recognitionmemory performance is small is corroborated by recent work (e.g., Mickes, Hwe, Wais, & Wixted, 2011;Mickes, Wixted, & Wais, 2007) where 20-and 99-point confidence-rating scales were used, leading to parameter estimates that are indistinct fi'om the ones usually reported in the literature with 6-point scales (e.g., Ratcliff, McKoon, & Tindall, 1994). Although Benjamin et al's (2013) results are consistent with criterion noise, they do not suggest that criterion noise as such plays an important role in recognition judgments, and they are thus consistent with our conclusion that the contribution of criterion noise for the modeling of recognition memory is typically very small.…”

Section: Remember-know Rocsmentioning

confidence: 69%

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On the measurement of criterion noise in signal detection theory: Reply to Benjamin (2013).

Kellen¹,

Klauer²,

Singmann³

2013

Psychological Review

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Kellen, Klauer, and Singmann (2012) questioned whether possible criterion noise would contribute significantly to modeling recognition memory. Our arguments were based on a reanalysis of the data by Benjamin, Diaz, and Wee (2009) as well as on new experimental data. In a comment, Benjamin (2013) questioned some of Kellen et al.'s conclusions and raised important issues regarding the new experimental data. In this reply, we revisit our arguments and provide new analyses in response to Benjamin's questions and issues.

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Section: Remember-know Rocssupporting

confidence: 69%

Section: Remember-know Rocsmentioning

confidence: 69%

On the measurement of criterion noise in signal detection theory: Reply to Benjamin (2013).

Kellen¹,

Klauer²,

Singmann³

2013

Psychological Review

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“…We are unaware of any reason why memory sensitivity in children and adults cannot be compared under these circumstances. Research with adult participants using a continuous confidence measure has shown that sensitivity does not vary across reduced compared to full rating scales (see Benjamin, Tullis, & Lee, 2013, Figure 2, 4-versus 8-point rating scale; also see Hadjiiski, Chan, Sahiner, Helvie, & Roubidoux, 2007).…”

Section: Materials and Proceduresmentioning

confidence: 99%

Receiver operating characteristic analysis of age-related changes in lineup performance

Humphries

Flowe

2015

Journal of Experimental Child Psychology

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In the basic face memory literature, support has been found for the late maturation hypothesis, which holds that face recognition ability is not fully developed until at least adolescence. Support for the late maturation hypothesis in the criminal lineup identification literature, however, has been equivocal, because of the analytic approach that has been used to examine age-related changes in identification performance. Recently, Receiver Operator Characteristics (ROC) analysis was applied for the first time in the adult eyewitness memory literature to examine whether memory sensitivity differs across different types of lineup tests (Gronlund et al. 2012;Mickes, Flowe, & Wixted, 2012). ROC analysis allows for the separation of memory sensitivity from response bias in the analysis of recognition data. Here, we make the first ROC-based comparison of adults' and children's (5-6 years and 9-10 years) memory performance on lineups by reanalysing data from Humphries, Holliday, & Flowe 3 (2012). In line with the late maturation hypothesis, memory sensitivity was significantly greater for adults compared to young children. Memory sensitivity for older children was similar to adults. The results indicate that the late maturation hypothesis can be generalized to account for age-related performance differences on an eyewitness memory task. The implications for developmental eyewitness memory research are discussed.

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“…; Benjamin, Tullis, & Lee, 2013) For Experiment 2, all of the responses were assumed to be informed source responses because participants only gave source memory responses for items they had recognized. For Experiment 3, the recognized and unrecognized items are known due to participants having been tested on both.…”

Section: The Model Fitmentioning

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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Criterion noise in ratings-based recognition: Evidence from the effects of response scale length on recognition accuracy.

Cited by 35 publications

References 48 publications

On the measurement of criterion noise in signal detection theory: Reply to Benjamin (2013).

On the measurement of criterion noise in signal detection theory: Reply to Benjamin (2013).

Receiver operating characteristic analysis of age-related changes in lineup performance

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

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