The role of estimator variables in eyewitness identification.

Abstract

Estimator variables are factors that can affect the accuracy of eyewitness identifications but that are outside of the control of the criminal justice system. Examples include (1) the duration of exposure to the perpetrator, (2) the passage of time between the crime and the identification (retention interval), (3) the distance between the witness and the perpetrator at the time of the crime. Suboptimal estimator variables (e.g., long distance) have long been thought to reduce the reliability of eyewitness identifications (IDs), but recent evidence suggests that this is not true of IDs made with high confidence and may or may not be true of IDs made with lower confidence. The evidence suggests that though suboptimal estimator variables decrease discriminability (i.e., the ability to distinguish innocent from guilty suspects), they do not decrease the reliability of IDs made with high confidence. Such findings are inconsistent with the longstanding "optimality hypothesis" and therefore require a new theoretical framework. Here, we propose that a signal-detection-based likelihood ratio account-which has long been a mainstay of basic theories of recognition memory-naturally accounts for these findings.

Child Witness Expressions of Certainty are Informative

“…To explain why confidence typically tracks suspect ID accuracy, even in situations where overall memory discrimination accuracy is comparatively poor, we need to consider theoretical models from basic science. In this regard, a constant likelihood ratio signal-detection model from the broader memory literature has recently been applied to account for adult witness memory performance (Colloff et al, 2017;Semmler, Dunn, Wixted, & Mickes, 2018;Stretch & Wixted, 1998).…”

“…It has been proposed that adults learn how to place their confidence criteria optimally through a lifetime of error feedback training about the circumstances in which their memories are and are not accurate (Mickes, Hwe, Wais, & Wixted, 2011;Stretch & Wixted, 1998). The constant likelihood ratio signal-detection model has been applied to account for performance of older adults, showing that they optimally place their criteria to compensate for age-related decline in memory performance (Colloff et al, 2017) and also to show that adults optimally place their criteria to compensate for viewing distance impairments on memory performance (Semmler et al, 2018). As such, theory predicts and data suggest that, at least as adults, eyewitnesses can be reliable; they have metacognitive skills to monitor memory and can usually assign appropriate confidence judgements that reflect their identification accuracy.…”

“…As such, the idea that people behave in a way to maintain constant likelihood ratios on an unequal variance model seems implausible (Stretch & Wixted, 1998). Here, we took the usual approach and fit an equal variance model, so that the predictions of the likelihood ratio model are unambiguous (e.g., Semmler et al, 2018;Stretch & Wixted, 1998). Figure 5 displays the best-fitting model estimated parameters for all age groups.…”

A stab in the Dark - PRE-PRINT

“…An investigation of sub-optimal conditions (i.e., low light and long distance) also makes it possible to investigate the relationship between accuracy, confidence, and response time in sub-optimal conditions. This is relevant because some have argued that if best practice identifcation procedurs are used and no feedback is given to the witness, then high confidence IDs will tend to be accurate, even if witnessing conditions (i.e., estimator variables) are poor (Brewer & Wells, 2006;Sauerland, Sagana, Sporer, & Wixted, 2018;Sauerland & Sporer, 2009;Semmler, Dunn, Mickes, & Wixted, 2018;Sporer, 1993;Weber, Brewer, Wells, Semmler, & Keast, 2004;Wixted & Wells, 2017). It has also been suggested that response times below 10-12 seconds are associated with accuracy (Dunning & Perretta, 2002), but it has also been found that there is a large degree of variation so is not possible to draw a clear boundary that discriminates between accurate or inaccurate decisions (Sauer, Brewer, & Wells, 2008;Weber et al, 2004).…”

“…Previous research indicates that there is an association between high confidence and identification accuracy (for an overview see, Wixted & Wells, 2017), and although some results suggest that the relationship will be negatively impacted by sub-optimal conditions (Deffenbacher, 2008), other suggest this not to be the case (Clark et al, 2015;Palmer, Brewer, Weber, & Nagesh, 2013;Semmler et al, 2018;Sporer et al, 1995;Wixted & Wells, 2017). The latter viewpoint is based on the notion that the relationship between confidence and accuracy will not deteriorate depending on external factors, assuming that the investigation is conducted under pristine conditions, but that sub-optimal conditions will naturally entail a lower average of confidence.…”