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Metamnemonic predictions of lineup identification.

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Title: Metamnemonic predictions of lineup identification.
Authors: McKinley, Geoffrey L. (AUTHOR), Benjamin, Aaron S. (AUTHOR), Gronlund, Scott D. (AUTHOR)
Source: Memory. Sep2023, Vol. 31 Issue 8, p1019-1038. 20p.
Subjects: Memory, Reliability (Personality trait), Judgment (Psychology), Confidence intervals, Calibration, Descriptive statistics
Abstract: After a crime is committed, investigators may query witnesses about whether they believe they will be to identify the perpetrator. However, we know little about how such metacognitive judgments are related to performance on a subsequent lineup identification task. The extant research has found the strength of this relationship to be small or nonexistent, which conflicts with the large body of literature indicating a moderate relationship between predictions and performance on memory tasks. In Studies 1–3, we induce variation in encoding quality by having participants watch a mock crime video with either low, medium, or high exposure quality, and then assess their future lineup performance. Calibration analysis revealed that assessments of future lineup performance were predictive of identification accuracy. This relationship was driven primarily by poor performance following low assessments. Studies 4 and 5 showed that these predictions are not based on a witness's evaluation of their encoding experience, nor on a contemporaneous assessment of memory strength. These results reinforce the argument that variation in memory quality is needed to obtain reliable relationships between predictions and performance. An unexpected finding is that witnesses who made a prediction shortly after encoding evinced superior memory compared to those who made a prediction later. [ABSTRACT FROM AUTHOR]
Copyright of Memory is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: After a crime is committed, investigators may query witnesses about whether they believe they will be to identify the perpetrator. However, we know little about how such metacognitive judgments are related to performance on a subsequent lineup identification task. The extant research has found the strength of this relationship to be small or nonexistent, which conflicts with the large body of literature indicating a moderate relationship between predictions and performance on memory tasks. In Studies 1–3, we induce variation in encoding quality by having participants watch a mock crime video with either low, medium, or high exposure quality, and then assess their future lineup performance. Calibration analysis revealed that assessments of future lineup performance were predictive of identification accuracy. This relationship was driven primarily by poor performance following low assessments. Studies 4 and 5 showed that these predictions are not based on a witness's evaluation of their encoding experience, nor on a contemporaneous assessment of memory strength. These results reinforce the argument that variation in memory quality is needed to obtain reliable relationships between predictions and performance. An unexpected finding is that witnesses who made a prediction shortly after encoding evinced superior memory compared to those who made a prediction later. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Memory is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/09658211.2023.2218123
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        Text: English
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        Type: general
      – SubjectFull: Reliability (Personality trait)
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              Text: Sep2023
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