Skip to main content Accessibility help
×
Hostname: page-component-7c8c6479df-27gpq Total loading time: 0 Render date: 2024-03-19T05:01:11.607Z Has data issue: false hasContentIssue false

7 - Physiology

Published online by Cambridge University Press:  11 June 2021

Adrian Furnham
Affiliation:
University of London
Get access

Summary

Many assessors assume that it is always better to have physiological data to assess people because it is more accurate, subtle and less prone to errors like faking and impression management. The argument is ‘the body does not lie’. This chapter looks at four techniques that measure different aspects of behaviour: the EEG, the fMRI, the lie detector and voice analysis. There is some overlap in what these different techniques measure, but they all assume that physical data can give powerful clues into a person’s ability, personality and motivation. Whilst there has been a slowing down in research on the EEG and the polygraph because of weak and mixed results, there is a great deal of interest in the fMRI. Many claims have been made for what the fMRI measures though the cost of this research has meant it remains limited and, as yet, totally inappropriate for the assessment of people for jobs. There has also been a long-standing interest, mostly by law enforcement and insurance companies, in voice stress analysis to reveal the emotional state of speakers. The chapter is concerned with the validity of this sort of data and whether it could or should be used in job selection and general person assessment.

Type
Chapter
Information
Twenty Ways to Assess Personnel
Different Techniques and their Respective Advantages
, pp. 396 - 433
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References

Atabaki, A., & Sperling, J. (2014). Learning – Neuromyths and Reality. McKinsey Consulting.Google Scholar
Anokhin, A. P. (2016). Genetics, brain, and personality: searching for immediate pheontypes. In Abscher, J. R., & Cloutlier, J. (Eds.), Neuroimaging Personality, Social Cognition, and Character (pp. 7190). Academic Press.CrossRefGoogle Scholar
Dubois, J., Galdi, P., Han, Y., Paul, L. K., & Adolphs, R. (2018). Resting-state functional brain connectivity best predicts personality dimension of openness to experience. Personality Neuroscience, 5;1:e6Google Scholar
Fingelkurts, A., Fingelkurts, A., & Neves, C. (2020). Neuro-assessment of leadership training. Coaching, 13, 107-145.Google Scholar
Furnham, A. (2018). Myths and misconceptions in developmental and neuro-psychology. Psychology, 9, 249259.CrossRefGoogle Scholar
Gale, A. (1983). Electroencephalographic studies of extraversion–introversion: a case study in the psychophysiology of individual differences. Personality and Individual Differences, 4, 371380.Google Scholar
Gao, Q., Qiang, X., Duan, X., Liao, W., Ding, J., Zhang, Z., Li, Y., Lu, G., & Chen, H. (2013). Extraversion and neuroticism relate to topological properties of resting-state brain networks. Frontiers in Human Neuroscience, 7, 257.Google Scholar
Glover, G. H. (2011). Overview of functional magnetic resonance imaging. Neurosurgical Clinics of North America, 22, 133139.CrossRefGoogle ScholarPubMed
Gray, J. A. (1991). Neural systems, emotion and personality. In Madden IV, J. (Ed.), Neurobiology of learning, emotion and affect (pp. 273306.). New York: Raven Press.Google Scholar
Hwang, J.-P., Tsai, S.-T., Hong, C.-H., Yang, C.-H., Lirng, J.-F., & Yang, Y.-M. (2006). The Vall66Met polymorphism of the brain-derived neurotrophic-factor gene is associated with geriatric depression. Neurobiology of Aging, 27(12), 18341837.CrossRefGoogle Scholar
Jarrett, C. (2014). Great myths of the brain. New York: Wiley-Blackwell.Google Scholar
Mameli, F., Sartori, G., Scarpazza, C., Zangrossi, A., Pietrini, P., Fumagalli, M., & Priori, A. (2016). Honesty. In Abscher, J. R., & Cloutlier, J. (Eds.), Neuroimaging personality, social cognition, and character (pp. 305322). London, UK: Academic Press.Google Scholar
Robinson, D. L. (1999). The technical, neurological, and psychological significance of ‘alpha’, ‘theta’, and ‘delta’ waves confounded in EEG evoked potentials: 1. A study of peak latencies. Electroencephalography and Clinical Neurophysiology, 110, 14271434.Google Scholar
Robinson, D. L. (2000). The technical, neurological, and psychological significance of ‘alpha’, ‘delta’ and ‘theta’ waves confounded in EEG evoked potentials: a study of peak amplitudes. Personality and Individual Differences, 28, 673693.Google Scholar
Robinson, D. L. (2001). How brain arousal systems determine different temperament types and the major dimensions of personality Personality and Individual Differences, 31, 12331259.Google Scholar
Samuels, I. (1959). Reticular mechanisms and behaviour. Psychological Bulletin, 56, 122.Google Scholar
Soloff, P. H., Abraham, K., Burgess, A., Ramaseshan, K., Chowdury, A., & Diwadkar, V. A. (2017). Impulsivity and aggression mediate regional brain responses in Borderline Personality Disorder: an fMRI study. Psychiatry Research: Neuroimaging, 268, 7685.Google Scholar
Tang, Y., Jian, W., Liao, J., Wang, W., & Luo, A. (2013). Identifying individuals with antisocial personality disorder using resting-sate fMRI. PLoS ONE, 8(4), e60652.Google Scholar
van Schie, C. C., Chiu, C.-D., Rombouts, S. A. R. B., Heiser, W. J., & Elzinga, B. M. (2020). Stuck in a negative me: fMRI study on the role of disturbed self-views in social feedback processing in borderline personality disorder. Psychological Medicine, 50(4), 625635.Google Scholar

References

Beattie, G. W., Cutler, A., and Pearson, M. (1982). Why is Mrs Thatcher interrupted so often. Nature, 300(5894), 744747.CrossRefGoogle Scholar
Bull, R. (1988). What is the lie-detection test? In Gale, A. (Ed.), The polygraph test: Lies, truth and science (pp. 1019). London, UK: Sage Publications, Inc; British Psychological Society.Google Scholar
Carroll, D. (1988). How accurate is polygraph lie detection? In Gale, A. (Ed.), The polygraph test: Lies, truth and science (pp. 1928). London, UK: Sage Publications, Inc; British Psychological Society.Google Scholar
Cestaro, V. L.(1996). A comparison between decision accuracy rates obtained using the polygraph instrument and the Computer Voice Stress Analyzer (CVSA) in the absence of jeopardy. Polygraph, 25, 117127.Google Scholar
Damphousse, K. (2008). Voice stress analysis. National Institute of Justice. July.Google Scholar
Damphousse, K., Pointon, L., Upchurch, D., & Moore, R. K. (2007). Assessing the Validity of Voice Stress Analysis Tools in a Jail Setting. Department of Justice.Google Scholar
Ekman, P. (2001) Telling lies: Clues to deceit in the marketplace, politics and marriage. New York City, NY: W. W. Norton.Google Scholar
Fuller, B. F. (1984). Reliability and validity of an interval measure of vocal stress. Psychological Medicine, 1984, 159166.Google Scholar
Gale, A. (Ed.). (1988). The polygraph test: Lies, truth and science. London, UK: Sage Publications, Inc; British Psychological Society.Google Scholar
Gannon, T. A., Beech, A. R., & Ward, T. (2008). Does the polygraph lead to better risk prediction for sexual offenders? Aggression and Violent Behavior, 13(1), 2944.Google Scholar
Gonzalez-Billandon, J., Aroyo, A., Pasquali, D., Tonelli, A., Gori, M., Sciutti, A., Sandini, G., & Rea, F. (2019). Can a robot catch you lying? A machine learning system to detect lies during interactions. Frontiers in Robotics and AI, 6(64), 112.CrossRefGoogle ScholarPubMed
Gudjonsson, G. H. (1988). Compliance in an interrogative setting: a new scale. Personality and Individual Differences, 10, 535540.CrossRefGoogle Scholar
Harnsberger, J. D., Hollien, H., Martin, C. A.,& Hollien, K. A. (2009). Stress and deception in speech: evaluating layered voice analysis. Journal of Forensic Science, 54(3), 642650.CrossRefGoogle ScholarPubMed
Hopkins, C. S., Ratley, R., Benincasa, D., & Grieco, J. (2005). Evaluation of Voice Stress Analysis Technology. Proceedings of the 38th Annual Hawaii International Conference on System Sciences. Honalulu, HI.Google Scholar
Horvath, F., McCloughan, J., Weatherman, D., & Slowik, S. (2013). The accuracy of auditors’ and Layered Voice Analysis (LVA) operators’ judgments of truth and deception during police questioning. Journal of Forensic Sciences, 58(2), 385392.CrossRefGoogle ScholarPubMed
Iacono, W. G., & Ben-Shakhar, G. (2019). Current status of forensic lie detection with the comparison question technique: an update of the 2003 National Academy of Sciences report on polygraph testing. Law and Human Behavior, 43(1), 8698.Google Scholar
Iacono, W. G., & Lykken, D. T. (1997). The validity of the lie detector: two surveys of scientific opinion. Journal of Applied Psychology, 82(3), 426433.Google Scholar
Kleinberg, B., van der Toolen, Y., Arntz, A., & Verschuere, B. (2018). Detecting concealed information on a large scale: possibilities and problems. In Rosenfeld, J. P. (Ed.), Detecting concealed information and deception: Recent developments (pp. 377403). New York: Elsevier Academic Press.Google Scholar
Larson, J. (1932). Lying and its detection: A study of deception and deception tests. Chicago, IL: University of Chicago Press.Google Scholar
Laukka, P., Linnman, C., Åhs, F., Pissiota, A., Frans, Ö., Faria, V., Michelgård, Å., Appel, L., Fredrikson, M., & Furmark, T. (2008). In a nervous voice: acoustic analysis and perception of anxiety in social phobics’ speech. Journal of Nonverbal Behavior, 32(4), 195214.Google Scholar
Lykken, D. (1998). A tremor in the blood: Use and abuse of the lie detector. New York City, NY: Plenum Publishing Corporation.Google Scholar
Meijer, E. H., Verschuere, B., Gamer, M., Merckelbach, H., & Ben‐Shakhar, G. (2016). Deception detection with behavioral, autonomic, and neural measures: conceptual and methodological considerations that warrant modesty. Psychophysiology, 53(5), 593604.Google Scholar
Miner, J. B., & Capps, M. H. (1996). How honesty testing works. Westport, CT: Greenwood Publishing Group.Google Scholar
National Research Council. (2003). The polygraph and lie detection. Washington, DC: National Academies Press.Google Scholar
Oswald, M. (2020). Technologies in the twilight zone: early lie detectors, machine learning and reformist legal realism. International Review of Law, Computers & Technology, 34(2), 214231.Google Scholar
Raskin, D.C. (1988). Does science support polygraph testing? In Gale, A. (Ed.), The polygraph test: Lies, truth and science (pp. 96110). London, UK: Sage Publications, Inc; British Psychological Society.Google Scholar
Toscano, Matthew J. (2019). Polygraph examiners: history, modem status, and admissibility in court. Law School Student Scholarship. 1020. https://scholarship.shu.edu/student_scholarship/1020Google Scholar
Vrij, A. (2000). Detecting lies and deceit: The psychology of lying and implications for professional practice. Chichester, UK: John Wiley & Sons.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Physiology
  • Adrian Furnham, University of London
  • Book: Twenty Ways to Assess Personnel
  • Online publication: 11 June 2021
  • Chapter DOI: https://doi.org/10.1017/9781108953276.008
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Physiology
  • Adrian Furnham, University of London
  • Book: Twenty Ways to Assess Personnel
  • Online publication: 11 June 2021
  • Chapter DOI: https://doi.org/10.1017/9781108953276.008
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Physiology
  • Adrian Furnham, University of London
  • Book: Twenty Ways to Assess Personnel
  • Online publication: 11 June 2021
  • Chapter DOI: https://doi.org/10.1017/9781108953276.008
Available formats
×