The Effect of Suspicion on Deception Detection Accuracy: Optimal Level or Opposing Effects?

Kim, Rachel K.; Levine, Timothy R.

doi:10.1080/08934215.2011.615272

Cited by 39 publications

(35 citation statements)

References 24 publications

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“…However, decoders remained truth-biased in all training conditions, contrasting past findings of training reversing decoder bias (Kim & Levine, 2011).…”

Section: Discussioncontrasting

confidence: 98%

Emotion recognition and deception detection

Zloteanu¹

2019

Preprint

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People hold strong beliefs regarding the role of emotional cues in detecting deception. While research on the diagnostic value of such cues has been mixed, their influence on human veracity judgments should not be ignored. Here, we address the relationship between emotional information and veracity judgments. In Study 1, the role of emotion recognition in the process of detecting naturalistic lies was investigated. Decoders’ accuracy was compared based on differences in trait empathy and their ability to recognize microexpressions and subtle expressions. Accuracy was found to be unrelated to facial cue recognition but negatively related to empathy. In Study 2, we manipulated decoders’ emotion recognition ability and the type of lies they saw: experiential or affective. Decoders either received emotion recognition training, bogus training, or no training. In all scenarios, training was not found to impact on accuracy. Experiential lies were easier to detect than affective lies, but, affective emotional lies were easier to detect than affective unemotional lies. The findings suggest that emotion recognition has a complex relationship with veracity judgments.

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“…However, decoders remained truth-biased in all training conditions, contrasting past findings of training reversing decoder bias (Kim & Levine, 2011).…”

Section: Discussioncontrasting

confidence: 98%

Emotion recognition and deception detection

Zloteanu¹

2019

Preprint

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“…As the base rate shifts so that truths outnumber lies, truth-bias becomes positively associated with accuracy. Kim and Levine (2011) recently demonstrated how truth accuracy and lie accuracy cancel out with a 50-50 base rate just as expected by the veracity effect and PL.…”

Section: Pl Model and Idtmentioning

confidence: 73%

“…The reasons behind truth-bias are reviewed by Levine et al (1999) and include how people make sense of communication, how people process information, and how people mentally represent true and false information. The reason behind near-chance deception detection accuracy is that people tend to rely on sender demeanor but it has only little diagnostic utility (Levine et al, 2011).…”

Section: Prior Base-rate Research Is Unfortunately Sparsementioning

confidence: 99%

The Effects of Truth-Lie Base Rate on Interactive Deception Detection Accuracy

et al. 2014

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Consistent with the Park and Levine's (PL) probability model of deception detection accuracy, previous research has shown that as the proportion of honest messages increases, there is a corresponding linear increase in correct truth–lie discrimination. Three experiments (N = 120, 205, and 243, respectively) varied the truth–lie base rate in an interactive deception detection task. Linear base‐rate effects were observed in all 3 experiments (average effect r#x02009;= .61) regardless of whether the judges were interactive participants or passive observers, previously acquainted or strangers, or previously exposed to truths or lies. The predictive power of the PL probability model appears robust and extends to interactive deception despite PL's logical incompatibility with interpersonal deception theory.

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“…When the situation makes it difficult for speakers to tell the truth, raters are more prepared to infer others are lying (Bond et al, 2013;Levine, Kim & Blair, 2010;Sperber, 2013;Sperber et al, 2010). And those who expect most speakers will lie to them, such as police officers (Moston, Stephenson & Williamson, 1992), show a lie bias (Meissner & Kassin, 2002 Kim & Levine, 2011). This can be seen even from the earliest moments of consideration, suggesting the lie bias can be fast and effortless (Hanks et al, 2011;Richter et al, 2009;Street & Richardson, 2014;van Ravenzwaaij, Mulder, Tuerlinckx & Wagenmakers, 2012).…”

Section: Context: When a Truth Bias Is And Is Not Functionalmentioning

confidence: 97%

ALIED: Humans as adaptive lie detectors.

Street¹

2015

Journal of Applied Research in Memory and Cognition

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People make for poor lie detectors. They have accuracy rates comparable to a coin toss, and come with a set of systematic biases that sway the judgment. This pessimistic view stands in contrast to research showing that people make informed decisions that adapt to the context they operate in. The current article proposes a new theoretical direction for lie detection research. I argue that lie detectors make informed, adaptive judgments in a low-diagnostic world. This Adaptive Lie Detector People have little idea of when they are being lied to, with accuracy rates only marginally above chance (Bond & DePaulo, 2006;Kraut, 1980). They seem to have the wrong beliefs about what cues give away liars (The Global Deception Research Team, 2006), and even with training there is only a modest increase in accuracy (Frank & Feeley, 2003;Hauch, Sporer, Michael & Meissner, 2014). What is more, there is a robust bias to take what others say at face value and believe it is the truth, dubbed the 'truth bias' (Bond & DePaulo, 2006;McCornack & Parks, 1986), which some have taken as evidence that people are gullible (Buller & Burgoon, 1996;O'Sullivan, 2003) and not in control of this bias (Gilbert, 1991). This pessimistic view has been dominant in the field for some time (e.g., Bond & DePaulo, 2006;Kraut, 1980;Mandelbaum, 2014;Vrij, Granhag & Porter, 2010). The current article takes a new position. I draw on recent advances and present a more optimistic view of the lie detector: as one who makes informed judgments in a low diagnostic world. In particular, I take a more Brunswikian point of view and argue that people adapt their judgment strategies based on the nature of the information available (see Brunswik, 1952). This is referred to as the Adaptive Lie Detector account (ALIED). Overview of the AccountThe ALIED account is inspired by advances in the decision-making literature, in particular the adaptive decision making perspective (Gigerenzer & Selten, 2001; Gigerenzer, Todd & The ABC Group, 1999;Platzer & Bröder, 2012;Simon, 1990).In an information-limited world and with a finite cognitive capacity, people can arrive at satisfactorily accurate judgments. By adapting to the context, one can put those limited resources to best use. Simplified strategies such as recognition and heuristics allow for 'satisficing' inferences to be made (Gigerenzer & Selten, 2001;Simon, 1956Simon, , 1990. Notably, people do not default to leaning on their contextual knowledge, 4 but vicariously adapt what information they use in order to make an informed judgment (Brunswik, 1952). These simplified context-general rules help form decisions under uncertainty (Gigerenzer & Gaissmaier, 2011;Simon, 1990).ALIED proposes that people attempt to form a judgment about a specific statement by using information that pertains directly to that specific statement. For instance, a confession that the statement is false is an individuating piece of information that can help us decide whether this particular statement is true or false.Similarly, verbal cues...

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The Effect of Suspicion on Deception Detection Accuracy: Optimal Level or Opposing Effects?

Cited by 39 publications

References 24 publications

Emotion recognition and deception detection

Emotion recognition and deception detection

The Effects of Truth-Lie Base Rate on Interactive Deception Detection Accuracy

ALIED: Humans as adaptive lie detectors.

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