Validity of the Reaction Time Concealed Information Test in a Prison Sample

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

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 meta-data 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.

Distinguishing true from false confessions using physiological patterns of concealed information recognition - A proof of concept study

Wrongful conviction cases indicate that not all confessors are guilty. However, there is currently no validated method to assess the veracity of confessions. In this preregistered study, we evaluate whether a new application of the Concealed Information Test (CIT) is a potentially valid method to make a distinction between true and false admissions of guilt. Eighty-three participants completed problem-solving tasks, individually and in pairs. Unbeknownst to the participants, their team-member was a confederate, tempting the participant to break the experimental rules by assisting during an individual assignment. Irrespective of actual rule-breaking behavior, all participants were accused of cheating and interrogated. True confessors but not false confessors showed recognition of answers obtained by cheating in the individual task, as evidenced by larger physiological responses to the correct than to plausible but incorrect answers. These findings encourage further investigation on the use of memory detection to discriminate true from false confessions.