Information leakage in the Response Time‐Based Concealed Information Test

Lukács, Gáspár; Ansorge, Ulrich

doi:10.1002/acp.3565

Cited by 21 publications

(26 citation statements)

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“…Another set of studies also did not collect control data, but instead used computer simulations to produce control data by randomly drawing values from normally distributed values with a mean of zero [24][25][26]. One approach was based on the simulation of individual responses [26].…”

Section: Plos Onementioning

confidence: 99%

“…Previous simulation procedures had various implicit assumptions (i.e., unstated but necessary for the validity of the given procedure) about the SDs: (a) equal SDs in all experiments and designs when using standardized probe RT, reproducible by a specific randomized procedure [26], (b) control data SD equal to liar data SD [25], or (c) equal control data SDs in similar experimental settings [24]. It is logical that similar experiments lead to similar SDs, but the extent of similarity required would be difficult to estimate or test empirically.…”

Section: Empirical Dispersionmentioning

confidence: 99%

“…We included two moderators that were likely to influence the effect size, although they are not theoretically relevant to the present paper (for details, see S1 Appendix). Firstly, CIT Protocol: single-probe (SP) protocol, multiple-probe (MP) protocol, or single-probe protocol with familiarity-related filler items (SPF) [24,25,35,56]. Secondly, Crowdsourcing: crowdsourced (online) experiment or laboratory experiments [57,58].…”

Section: Plos Onementioning

confidence: 99%

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Dispersion matters: Diagnostics and control data computer simulation in Concealed Information Test studies

Lukács

Specker

2020

PLoS ONE

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Binary classification has numerous applications. For one, lie detection methods typically aim to classify each tested person either as "liar" or as "truthteller" based on the given test results. To infer practical implications, as well as to compare different methods, it is essential to assess the diagnostic efficiency, such as demonstrating the number of correctly classified persons. However, this is not always straightforward. In Concealed Information Tests (CITs), the key predictor value (probe-irrelevant difference) for "truthtellers" is always similar (zero on average), and "liars" are always distinguished by a larger value (i.e., a larger number resulting from the CIT test, as compared to the zero baseline). Thereby, in general, the larger predictor values a given CIT method obtains for "liars" on average, the better this method is assumed to be. This has indeed been assumed in countless studies, and therefore, when comparing the classification efficiencies of two different designs, the mean difference of "liar" predictor values in the two designs were simply compared to each other (hence not collecting "truthteller" data to spare resources). We show, based on the metadata of 12 different experimental designs collected in response time-based CIT studies, that differences in dispersion (i.e., variance in the data, e.g. the extent of random deviations from the zero average in case of "truthtellers") can substantially influence classification efficiency-to the point that, in extreme cases, one design may even be superior in classification despite having a larger mean "liar" predictor value. However, we also introduce a computer simulation procedure to estimate classification efficiency in the absence of "truthteller" data, and validate this procedure via a meta-analysis comparing outcomes based on empirical data versus simulated data.

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Section: Plos Onementioning

confidence: 99%

Section: Empirical Dispersionmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

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Dispersion matters: Diagnostics and control data computer simulation in Concealed Information Test studies

Lukács

Specker

2020

PLoS ONE

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Section: The Response Time-based Concealed Information Testmentioning

confidence: 99%

Dispersion matters: Diagnostics and control data computer simulation in Concealed Information Test studies

Lukács¹,

Specker²

2020

Preprint

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The efficiency of a predictor variable in classifying between two conditions may be inferred from the mean difference between the predictors for one condition (e.g., positive condition) and the other condition (e.g., negative condition). In certain cases, such as when using the Concealed Information Test (CIT), one of the conditions (here: negative) can be assumed to have a known constant mean value (here: zero) in every scenario. In this case, it may be assumed, and it indeed has been assumed in countless studies, that when comparing the classification efficiency of two different designs, it is sufficient to test the mean difference in the condition (here: positive) whose mean value may differ depending on the design, and thereby spare resources by not collecting data, in either design, for the other condition (negative), because the data would in any case represent a known constant (zero). We show, based on the meta-data of 12 different experimental designs collected in response time-based CIT studies, that dispersion (i.e., variance in the data) can substantially influence classification efficiency – to the point that, in extreme cases, one design may even be superior in classification despite having a larger mean value. However, we also introduce a computer simulation procedure to estimate classification efficiency in the absence of control data, and validate this procedure via a meta-analysis comparing outcomes based on real data versus simulated data.

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“…The items are repeatedly shown in a random sequence, and all of them have to be responded to with the same response keys, except one arbitrary target -a randomly selected, originally also irrelevant item that has to be responded to with the other response key. Since guilty examinees recognize the probe as the relevant item in respect of the deception detection scenario, it will become unique among the irrelevants and in this respect more similar to the rarely occurring target (Lukács & Ansorge, 2019a). Due Lukács, G., et al (2020).…”

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Response Time Concealed Information Test on Smartphones

Lukács

Kleinberg

Kunzi

et al. 2020

Collabra: Psychology

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The Response Time-Based Concealed Information Test (RT-CIT) can reveal when a person recognizes a relevant (probe) item among other, irrelevant items, based on comparatively slower responding to the probe item. Thereby, if a person is concealing the knowledge about the relevance of this item (e.g., recognizing it as a murder weapon), this deception can be revealed. So far, the RT-CIT has been used only on desktop computers. In Experiment 1 (n = 72; within-subject), we compare the probe-irrelevant differences when using the conventional desktop-based CIT to using a smartphone-based CIT, demonstrating practical equivalence. In Experiment 2 (n = 116; within-subject), we demonstrate that using thumbs for responses (while holding the smartphone) leads to equally efficient CIT results as using conventional index finger responses. At the same time, this second experiment also demonstrates how smartphone-based studies may be efficiently run in large groups, using the participants' own smartphones. Finally, as an interesting addition, here for the first time we also measured keypress durations (i.e., the time durations of holding down the response keys) in the RT-CIT, which we found to be significantly shorter for probe than for irrelevant items.

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Information leakage in the Response Time‐Based Concealed Information Test

Cited by 21 publications

References 81 publications

Dispersion matters: Diagnostics and control data computer simulation in Concealed Information Test studies

Dispersion matters: Diagnostics and control data computer simulation in Concealed Information Test studies

Dispersion matters: Diagnostics and control data computer simulation in Concealed Information Test studies

Response Time Concealed Information Test on Smartphones

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