SavedAdd your journal

Journals / Güvenlik Bilimleri Dergisi / 2026 / Cilt: 15 Sayı: 1

DETECTING DECEPTION IN FORENSIC PSYCHOLOGY: A TRADITIONAL REVIEW ON THE EVALUATION OF PSYCHOPHYSIOLOGICAL METHODS

ADLİ PSİKOLOJİDE ALDATMA DAVRANIŞI TESPİTİ: PSİKOFİZYOLOJİK YÖNTEMLERİN DEĞERLENDİRİLMESİ

İpek Ergönül
Pages
42–63
Publication Date
2026-05-22
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This review summarizes the psychophysiological methods used in forensic psychology and aims to provide guidance to those working in the field.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Deception detection has long been a focus of interest in psychology, law enforcement, and forensic sciences because of the critical importance of accurately identifying dishonest behavior in both legal and societal contexts.
📊
Key Findings & Data:This has increased the reliance on psychophysiological methods that provide objective and measurable indicators of physiological changes associated with deceptive behavior.
💡
Conclusion & Impact:This review summarizes the psychophysiological methods used in forensic psychology and aims to provide guidance to those working in the field.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Aldatma Tespiti · Psikofizyoloji · Elektroensefalografi

Abstract

Deception detection has long been a focus of interest in psychology, law enforcement, and forensic sciences because of the critical importance of accurately identifying dishonest behavior in both legal and societal contexts. Traditional methods such as behavioral observation and self-reports are based on subjective interpretations, making them less reliable. This has increased the reliance on psychophysiological methods that provide objective and measurable indicators of physiological changes associated with deceptive behavior. Scientists and doctors can use tools like polygraphs, electroencephalography, functional magnetic resonance imaging, functional near-infrared spectroscopy, heart rate variability, galvanic skin conductance, and more to look into the physical connections between mental and emotional processes that happen when someone is lying. In particular, the increased cognitive load and emotional stress that are often associated with dishonest behavior can manifest as changes in heart rate, skin conductance, and brain activity. The application of psychophysiological methods in infidelity detection has significant potential due to their ability to provide consistent and quantitative data. Furthermore, it may contribute to the development of more reliable and valid instruments, along with better elucidation of the underlying mechanisms of deception. In this context, studies in psychophysiology can help researchers and practitioners increase the accuracy of deception detection, reduce errors, and improve outcomes in high-risk scenarios. This review summarizes the psychophysiological methods used in forensic psychology and aims to provide guidance to those working in the field.

Özet

Aldatma tespiti, hem yasal hem de toplumsal bağlamlarda dürüst olmayan davranışların doğru bir şekilde belirlenmesinin kritik öneme sahip olması nedeni ile psikoloji, kolluk kuvvetleri ve adli bilimler alanında uzun süredir ilgi odağı olmuştur. Davranışsal gözlem ve öz-bildirim gibi geleneksel yöntemlerin öznel yorumlara dayanması nedeni ile daha az güvenilir olmasına neden olmaktadır. Bu durum, aldatıcı davranışlarla ilişkili fizyolojik değişikliklerin objektif ve ölçülebilir göstergelerini sağlayan psikofizyolojik yöntemlere olan bağımlılığı artırmıştır. Poligraf testi, elektroensefalografi, fonksiyonel manyetik rezonans görüntüleme, işlevsel yakın kızılaltı spektroskopi, kalp atım hızı, galvanik deri iletkenliği gibi psikofizyolojik teknikler, araştırmacıların ve uygulayıcıların aldatma sırasında ortaya çıkan bilişsel ve duygusal süreçlerin fizyolojik bağlantılarını incelemesine olanak tanır. Özellikle aldatma davranışı ile genellikle ilişkili olarak artan bilişsel yük ve duygusal stres, kalp atış hızında, deri iletkenliğinde ve beyin aktivitesinde değişiklikler olarak kendini gösterebilmektedir. Psikofizyolojik yöntemlerin aldatma tespitinde uygulanması, tutarlı ve nicel veriler sağlayabilme yetenekleri nedeni ile önemli bir potansiyele sahiptir. Ayrıca, aldatma daranışının temel mekanizmalarının daha iyi aydınlatılması ile birlikte daha güvenilir ve geçerli araçların geliştirilmesine katkıda bulunabilir. Bu bağlamda psikofizyoloji alanında yapılan çalışmalar sayesinde araştırmacılar ve uygulayıcılar, aldatma tespitinin doğruluğunu artırabilir, hataları azaltabilir ve yüksek riskli senaryolarda sonuçları iyileştirebilir. Bu derleme ile adli psikoloji alanında kullanılan psikofizyolojik yöntemler özetlenmiş olup alan çalışanlarına yol göstermesi hedeflenmiştir.

🔖

Cite This Article

Ergönül, İ. (2026). DETECTING DECEPTION IN FORENSIC PSYCHOLOGY: A TRADITIONAL REVIEW ON THE EVALUATION OF PSYCHOPHYSIOLOGICAL METHODS. Güvenlik Bilimleri Dergisi, Cilt: 15 Sayı: 1, 42–63. https://doi.org/10.28956/gbd.1625631

📄 Full Text PDF Viewer

↗New Tab

Loading PDF document...

References (67)

  1. KAYNAKÇA Afzali, M. U., Seren‐Grace, A. P., Palmer, R. W., Neumann, E., Makarious, S., Wilson, D., & Jones, R. D. (2022). Detection of concealed knowledge via the ERP ‐based technique Brain Fingerprinting : Real‐life and real‐crime incidents. Psychophysiology, 59(11). https://doi.org/10.1111/psyp.14110
  2. Alsufyani, A., Hajilou, O., Zoumpoulaki, A., Filetti, M., Alsufyani, H., Solomon, C. J., Gibson, S. J., Alroobaea, R., & Bowman, H. (2019). Breakthrough percepts of famous faces. Psychophysiology, 56(1). https://doi.org/10.1111/psyp.13279
  3. Başar, E., Özgören, M., Öniz, A., Schmiedt, C., & Başar-Eroğlu, C. (2007). Brain oscillations differentiate the picture of one’s own grandmother. International Journal of Psychophysiology, 64(1), 81–90. https://doi.org/10.1016/j.ijpsycho.2006.07.002
  4. Ben-Shakhar, G. (2012). Current research and potential applications of the concealed information test: An Overview. Frontiers in Psychology, 3. https://doi.org/10.3389/fpsyg.2012.00342
  5. Bhutta, M. R., Keum-Shik Hong, Naseer, N., & Khan, M. J. (2015). Spontaneous lie detection using functional near-infrared spectroscopy in an interactive game. 2015 10th Asian Control Conference (ASCC), 1–5. https://doi.org/10.1109/ASCC.2015.7244666
  6. Cacioppo, J. T., Tassinary, L. G., & Berntson, G. G. (2007). Psychophysiological science: interdisciplinary approaches to classic questions about the mind. In J. T. Cacioppo, L. G. Tassinary, & G. Berntson (Eds.), Handbook of Psychophysiology (pp. 1–16). Cambridge University Press. https://doi.org/10.1017/CBO9780511546396.001
  7. Constâncio, A. S., Tsunoda, D. F., Silva, H. de F. N., Silveira, J. M. da, & Carvalho, D. R. (2023). Deception detection with machine learning: A systematic review and statistical analysis. PLOS ONE, 18(2), e0281323. https://doi.org/10.1371/journal.pone.0281323
  8. Council, N. R. (2003). The Polygraph and Lie Detection. National Academies Press. https://doi.org/10.17226/10420
  9. Cui, Q., Vanman, E. J., Wei, D., Yang, W., Jia, L., & Zhang, Q. (2014). Detection of deception based on fMRI activation patterns underlying the production of a deceptive response and receiving feedback about the success of the deception after a mock murder crime. Social Cognitive and Affective Neuroscience, 9(10), 1472–1480. https://doi.org/10.1093/scan/nst134
  10. Davatzikos, C., Ruparel, K., Fan, Y., Shen, D. G., Acharyya, M., Loughead, J. W., Gur, R. C., & Langleben, D. D. (2005). Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection. NeuroImage, 28(3), 663–668. https://doi.org/10.1016/j.neuroimage.2005.08.009
  11. Ding, X. P., Gao, X., Fu, G., & Lee, K. (2013). Neural correlates of spontaneous deception: A functional near-infrared spectroscopy (fNIRS)study. Neuropsychologia, 51(4), 704–712. https://doi.org/10.1016/j.neuropsychologia.2012.12.018
  12. Dinges, L., Fiedler, M.-A., Al-Hamadi, A., Hempel, T., Abdelrahman, A., Weimann, J., Bershadskyy, D., & Steiner, J. (2024). Exploring facial cues: automated deception detection using artificial intelligence. Neural Computing and Applications, 36(24), 14857–14883. https://doi.org/10.1007/s00521-024-09811-x
  13. Farwell, L. A. (2012). Brain fingerprinting: a comprehensive tutorial review of detection of concealed information with event-related brain potentials. Cognitive Neurodynamics, 6(2), 115–154. https://doi.org/10.1007/s11571-012-9192-2
  14. Fullam, R. S., McKie, S., & Dolan, M. C. (2009). Psychopathic traits and deception: functional magnetic resonance imaging study. British Journal of Psychiatry, 194(3), 229–235. https://doi.org/10.1192/bjp.bp.108.053199
  15. Gamer, M. (2012). Validity of the concealed information test in realistic mock crime scenarios; comment on Bradley, Malik, and Cullen. Perceptual and Motor Skills, 115(2), 427–431. https://doi.org/10.2466/22.07.27.PMS.115.5.427-431
  16. Gamer, M., & Berti, S. (2010). Task relevance and recognition of concealed information have different influences on electrodermal activity and event-related brain potentials. Psychophysiology, 47(2), 355–364. https://doi.org/10.1111/j.1469-8986.2009.00933.x
  17. Gamer, M., Rill, H.-G., Vossel, G., & Gödert, H. W. (2006). Psychophysiological and vocal measures in the detection of guilty knowledge. International Journal of Psychophysiology, 60(1), 76–87. https://doi.org/10.1016/j.ijpsycho.2005.05.006
  18. Ganis, G. (2014). Deception detection using neuroimaging. In Detecting Deception (pp. 105–121). Wiley. https://doi.org/10.1002/9781118510001.ch5
  19. Ganis, G., Rosenfeld, J. P., Meixner, J., Kievit, R. A., & Schendan, H. E. (2011). Lying in the scanner: covert countermeasures disrupt deception detection by functional magnetic resonance imaging. NeuroImage, 55(1), 312–319. https://doi.org/10.1016/j.neuroimage.2010.11.025
  20. Göçen, H. B., & Özden, A. V. (2024). Autonomic dysfunction in psychiatric disorders. Psikiyatride Güncel Yaklaşımlar, 16(3), 401–409. https://doi.org/10.18863/pgy.1329118
  21. Hira, S., & Furumitsu, I. (2002). Polygraphic examinations in Japan: application of the guilty knowledge test in forensic investigations. International Journal of Police Science & Management, 4(1), 16–27. https://doi.org/10.1177/146135570200400103
  22. Honts, C. R. (1994). Psychophysiological detection of deception. Current Directions in Psychological Science, 3(3), 77–82. https://doi.org/10.1111/1467-8721.ep10770427
  23. Honts, C. R. (2004). The psychophysiological detection of deception. In The Detection of Deception in Forensic Contexts (pp. 103–124). Cambridge University Press. https://doi.org/10.1017/CBO9780511490071.005
  24. Horowitz, S. W., Kircher, J. C., Honts, C. R., & Raskin, D. C. (1997). The role of comparison questions in physiological detection of deception. Psychophysiology, 34(1), 108–115. https://doi.org/10.1111/j.1469-8986.1997.tb02421.x
  25. Hu, X.-S., Hong, K.-S., & Ge, S. S. (2012). fNIRS-based online deception decoding. Journal of Neural Engineering, 9(2), 026012. https://doi.org/10.1088/1741-2560/9/2/026012
  26. Izzetoglu, M., Izzetoglu, K., Bunce, S., Ayaz, H., Devaraj, A., Onaral, B., & Pourrezaei, K. (2005). Functional near-infrared neuroimaging. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(2), 153–159. https://doi.org/10.1109/TNSRE.2005.847377
  27. Jin, B., Strasburger, A., Laken, S. J., Kozel, F. A., Johnson, K. A., George, M. S., & Lu, X. (2009). Feature selection for fMRI-based deception detection. BMC Bioinformatics, 10(S9), S15. https://doi.org/10.1186/1471-2105-10-S9-S15
  28. King, S. L., & Neal, T. (2024). Applications of AI-enabled deception detection using video, audio, and physiological data: A systematic review. IEEE Access, 12, 135207–135240. https://doi.org/10.1109/ACCESS.2024.3462825
  29. Kitt, A.-B. (2014). Detecting guilty knowledge with eeg- based methods using mock crime scenario. University of Tartu.
  30. Kok, A. (2001). On the utility of P3 amplitude as a measure of processing capacity. Psychophysiology, 38(3), 557–577. https://doi.org/10.1017/S0048577201990559
  31. Kozel, F. A., Johnson, K. A., Mu, Q., Grenesko, E. L., Laken, S. J., & George, M. S. (2005). Detecting deception using functional magnetic resonance imaging. Biological Psychiatry, 58(8), 605–613. https://doi.org/10.1016/j.biopsych.2005.07.040
  32. Langleben, D. D., Loughead, J. W., Bilker, W. B., Ruparel, K., Childress, A. R., Busch, S. I., & Gur, R. C. (2005). Telling truth from lie in individual subjects with fast event‐related fMRI. Human Brain Mapping, 26(4), 262–272. https://doi.org/10.1002/hbm.20191
  33. Lefebvre, C. D., Marchand, Y., Smith, S. M., & Connolly, J. F. (2009). Use of event-related brain potentials (ERPs) to assess eyewitness accuracy and deception. International Journal of Psychophysiology, 73(3), 218–225. https://doi.org/10.1016/j.ijpsycho.2009.03.003
  34. Leue, A., Lange, S., & Beauducel, A. (2012). “Have you ever seen this face?” – Individual differences and event-related potentials during deception. Frontiers in Psychology, 3. https://doi.org/10.3389/fpsyg.2012.00570
  35. Li, F., Zhu, H., Gao, Q., Xu, G., Li, X., Hu, Z., & He, S. (2015). Using functional near-infrared spectroscopy (fNIRS) to detect the prefrontal cortical responses to deception under different motivations. Biomedical Optics Express, 6(9), 3503. https://doi.org/10.1364/BOE.6.003503
  36. Li, F., Zhu, H., Xu, J., Gao, Q., Guo, H., Wu, S., Li, X., & He, S. (2018). Lie detection using fnirs monitoring of inhibition-related brain regions discriminates infrequent but not frequent liars. Frontiers in Human Neuroscience, 12. https://doi.org/10.3389/fnhum.2018.00071
  37. Lin, X. A., Wang, C., Zhou, J., Sai, L., & Fu, G. (2021). Neural correlates of spontaneous deception in a non-competitive interpersonal scenario: A functional near-infrared spectroscopy (fNIRS) study. Brain and Cognition, 150, 105704. https://doi.org/10.1016/j.bandc.2021.105704
  38. Lin, X., Sai, L., & Yuan, Z. (2018). Detecting concealed information with fused electroencephalography and functional near-infrared spectroscopy. Neuroscience, 386, 284–294. https://doi.org/10.1016/j.neuroscience.2018.06.049
  39. Lykken, D. T. (1959). The GSR in the detection of guilt. Journal of Applied Psychology, 43(6), 385–388. https://doi.org/10.1037/h0046060
  40. Lykken, D. T. (1960). The validity of the guilty knowledge technique: The effects of faking. Journal of Applied Psychology, 44(4), 258–262. https://doi.org/10.1037/h0044413
  41. Meijer, E. H., Selle, N. K., Elber, L., & Ben‐Shakhar, G. (2014). Memory detection with the Concealed Information Test: A meta analysis of skin conductance, respiration, heart rate, and P300 data. Psychophysiology, 51(9), 879–904. https://doi.org/10.1111/psyp.12239
  42. Meixner, J. B., & Rosenfeld, J. P. (2011). A mock terrorism application of the P300‐based concealed information test. Psychophysiology, 48(2), 149–154. https://doi.org/10.1111/j.1469-8986.2010.01050.x
  43. Meixner, J. B., & Rosenfeld, J. P. (2014). Detecting knowledge of incidentally acquired, real-world memories using a p300-based concealed-information test. Psychological Science, 25(11), 1994–2005. https://doi.org/10.1177/0956797614547278
  44. Mertens, R., & Allen, J. J. B. (2008). The role of psychophysiology in forensic assessments: Deception detection, ERPs, and virtual reality mock crime scenarios. Psychophysiology, 45(2), 286–298. https://doi.org/10.1111/j.1469-8986.2007.00615.x
  45. Monteleone, G. T., Phan, K. L., Nusbaum, H. C., Fitzgerald, D., Irick, J.-S., Fienberg, S. E., & Cacioppo, J. T. (2009). Detection of deception using fMRI: Better than chance, but well below perfection. Social Neuroscience, 4(6), 528–538. https://doi.org/10.1080/17470910801903530
  46. Niioka, K., Uga, M., Nagata, T., Tokuda, T., Dan, I., & Ochi, K. (2018). Cerebral hemodynamic response during concealment of information about a mock crime: application of a general linear model with an adaptive hemodynamic response function. Japanese Psychological Research, 60(4), 311–326. https://doi.org/10.1111/jpr.12194
  47. Norman, D. G., Gunnell, D. A., Mrowiec, A. J., & Watson, D. G. (2020). Seen this scene? Scene recognition in the reaction-time Concealed Information Test. Memory & Cognition, 48(8), 1388–1402. https://doi.org/10.3758/s13421-020-01063-z
  48. Nuñez, J. M., Casey, B. J., Egner, T., Hare, T., & Hirsch, J. (2005). Intentional false responding shares neural substrates with response conflict and cognitive control. NeuroImage, 25(1), 267–277. https://doi.org/10.1016/j.neuroimage.2004.10.041
  49. Ogawa, T., Matsuda, I., Tsuneoka, M., & Verschuere, B. (2015). The concealed information test in the laboratory versus japanese field practice: bridging the scientist-practitioner gap. Archives of Forensic Psychology, 1(2), 16–27. https://doi.org/10.16927/afp.2015.1.2
  50. Peth, J., Vossel, G., & Gamer, M. (2012). Emotional arousal modulates the encoding of crime‐related details and corresponding physiological responses in the Concealed Information Test. Psychophysiology, 49(3), 381–390. https://doi.org/10.1111/j.1469-8986.2011.01313.x
  51. Rahman, M. A., & Ahmad, M. (2016). Lie detection from fNIR signal and NeuroImage. 2016 International Conference on Medical Engineering, Health Informatics and Technology (MediTec), 1–6. https://doi.org/10.1109/MEDITEC.2016.7835382
  52. Rahman, M. A., & Ahmad, M. (2017). Lie detection from single feature of functional near infrared spectroscopic (fNIRS) signals. 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE), 1–4. https://doi.org/10.1109/CEEE.2017.8412900
  53. Rosenfeld, J. P. (2020). P300 in detecting concealed information and deception: A review. Psychophysiology, 57(7). https://doi.org/10.1111/psyp.13362
  54. Rosenfeld, J. P., Hu, X., Labkovsky, E., Meixner, J., & Winograd, M. R. (2013). Review of recent studies and issues regarding the P300-based complex trial protocol for detection of concealed information. International Journal of Psychophysiology, 90(2), 118–134. https://doi.org/10.1016/j.ijpsycho.2013.08.012
  55. Rosenfeld, J. P., Labkovsky, E., Winograd, M., Lui, M. A., Vandenboom, C., & Chedid, E. (2008). The Complex Trial Protocol (CTP): A new, countermeasure‐resistant, accurate, P300‐based method for detection of concealed information. Psychophysiology, 45(6), 906–919. https://doi.org/10.1111/j.1469-8986.2008.00708.x
  56. Sai, L., Zhou, X., Ding, X. P., Fu, G., & Sang, B. (2014). Detecting concealed information using functional near-infrared spectroscopy. Brain Topography, 27(5), 652–662. https://doi.org/10.1007/s10548-014-0352-z
  57. Sanchez-Ante, G. (2013). Sistemas Inteligentes: Reportes finales.
  58. Schacter, D. L., & Addis, D. R. (2007). The cognitive neuroscience of constructive memory: remembering the past and imagining the future. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 362(1481), 773–786. https://doi.org/10.1098/rstb.2007.2087
  59. Schnakenberg, P., Hahn, L., Stickel, S., Stickeler, E., Habel, U., Eickhoff, S. B., Chechko, N., & Dukart, J. (2021). Examining early structural and functional brain alterations in postpartum depression through multimodal neuroimaging. Scientific Reports, 11(1), 13551. https://doi.org/10.1038/s41598-021-92882-w
  60. Sharma, R. K., Balhara, Y. P. S., Sagar, R., Deepak, K. K., & Mehta, M. (2011). Heart rate variability study of childhood anxiety disorders. Journal of Cardiovascular Disease Research, 2(2), 115–122. https://doi.org/10.4103/0975-3583.83040
  61. Suchotzki, K., Verschuere, B., & Gamer, M. (2021). How vulnerable is the reaction time concealed information test to faking? Journal of Applied Research in Memory and Cognition, 10(2), 268–277. https://doi.org/10.1016/j.jarmac.2020.10.003
  62. Suchotzki, K., Verschuere, B., Van Bockstaele, B., Ben-Shakhar, G., & Crombez, G. (2017). Lying takes time: A meta-analysis on reaction time measures of deception. Psychological Bulletin, 143(4), 428–453. https://doi.org/10.1037/bul0000087
  63. Tian, F., Sharma, V., Kozel, F. A., & Liu, H. (2009). Functional near-infrared spectroscopy to investigate hemodynamic responses to deception in the prefrontal cortex. Brain Research, 1303, 120–130. https://doi.org/10.1016/j.brainres.2009.09.085
  64. Vega, R., Hernandez-Reynoso, A. G., Linn, E. K., Fuentes-Aguilar, R. Q., Sanchez-Ante, G., Santos-Garcia, A., & Garcia-Gonzalez, A. (2016). Hemodynamic pattern recognition during deception process using functional near-infrared spectroscopy. Journal of Medical and Biological Engineering, 36(1), 22–31. https://doi.org/10.1007/s40846-016-0103-6
  65. Vendemia, J. M. C. (2014). fMRI as a method of detection of deception: A review of experiences. European Polygraph, 8(1), 5–21. https://doi.org/10.2478/ep-2014-0001
  66. Wang, D., Wang, C., Yi, X., Sai, L., Fu, G., & Lin, X. A. (2022). Detecting concealed information using functional near‐infrared spectroscopy (fNIRS ) combined with skin conductance, heart rate, and behavioral measures. Psychophysiology, 59(8). https://doi.org/10.1111/psyp.14029
  67. Winograd, M. R., & Rosenfeld, J. P. (2014). The impact of prior knowledge from participant instructions in a mock crime P300 Concealed Information Test. International Journal of Psychophysiology, 94(3), 473–481. https://doi.org/10.1016/j.ijpsycho.2014.08.002