Advances in Cognitive Psychology 13(2), pp. 121-127.
ISSN/ISBN: Not available at this time. DOI: 10.5709/acp-0212-x
Abstract: The first significant (leftmost nonzero) digit of seemingly random numbers often appears to conform to a logarithmic distribution, with more 1s than 2s, more 2s than 3s, and so forth, a phenomenon known as Benford’s law. When humans try to produce random numbers, they often fail to conform to this distribution. This feature grounds the so-called Benford analysis, aiming at detecting fabricated data. A generalized Benford’s law (gBl), extending the classical Benford’s law, has been defined recently. In two studies, we provide some empirical support for the generalized Benford analysis, broadening the classical Benford analysis. We also conclude that familiarity with the numerical domain involved as well as cognitive effort only have a mild effect on the method’s accuracy and can hardly explain the positive results provided here.
Bibtex:
@article {,
AUTHOR = {Gauvrit, Nicolas Gauvrit and Houillon, Jean-Charles and Delahaye, Jean-Paul },
TITLE = {Generalized Benford’s Law as a Lie Detector },
JOURNAL = {Advances in Cognitive Psychology },
YEAR = {2017},
VOLUME = {13},
NUMBER = {2},
PAGES = {121--127},
DOI = {10.5709/acp-0212-x},
URL = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5504535/ },
}
Reference Type: Journal Article
Subject Area(s): General Interest, Psychology