This research investigates whether police officers can reliably use behavioral cues to determine whether a person is concealing an object. Using a Lens Model framework, we performed a mega-analysis of three experiments. In each study, officers and laypersons judged whether people were concealing an object and reported “articulable behaviors” they used to perform this task. Although participants were able to articulate behaviors that they believed were helpful, results showed that these behaviors were not related to whether the person was actually concealing. Officers and laypersons were equally poor at judging whether someone was concealing or not. Current officer training on the use of nonverbal behaviors to determine who is concealing a dangerous object may be ineffective, and a reconsideration of training is warranted. In light of the findings, requiring officers to provide “articulable behaviors” in Fourth Amendment cases may not provide a sufficient safeguard against unreasonable searches of civilians.
BakemanR.QueraV. (2011). Sequential analysis and observational methods for the behavioral sciences. Cambridge University Press.
2.
BarclayC. D.CuttingJ. E.KozlowskiL. T. (1978). Temporal and spatial factors in gait perception that influence gender recognition. Perception & Psychophysics, 23(2), 145–152. https://doi.org/10.3758/BF03208295
3.
BlechkoA.DarkerI.GaleA. (2008, October). Skills in detecting gun carrying from CCTV. In 2008 42nd annual IEEE international Carnahan conference on security technology (pp. 265–271). IEEE.
BurgoonJ. K.TwitchellD. P.JensenM. L.MeservyT. O.AdkinsM.KruseJ.NunamakerJ. F. (2009). Detecting concealment of intent in transportation screening: A proof of concept. IEEE Transactions on Intelligent Transportation Systems, 10(1), 103–112. https://doi.org/10.1109/TITS.2008.2011700
9.
ChenH. M.LeeS.RaoR. M.SlamaniM. A.VarshneyP. K. (2005). Imaging for concealed weapon detection: A tutorial overview of development in imaging sensors and processing. IEEE Signal Processing Magazine, 22(2), 52–61. https://doi.org/10.1109/MSP.2005.1406480
10.
Commonwealth v. Evelyn, 485 Mass. 691 (2020)
11.
CooperH.PatallE. A. (2009). The relative benefits of meta-analysis conducted with individual participant data versus aggregated data. Psychological Methods, 14(2), 165–176. https://doi.org/10.1037/a0015565
12.
CurranP. J.HussongA. M. (2009). Integrative data analysis: The simultaneous analysis of multiple data sets. Psychological Methods, 14(2), 81–100. https://doi.org/10.1037/a0015914
13.
DeGrootT.GootyJ. (2009). Can nonverbal cues be used to make meaningful personality attributions in employment interviews?Journal of Business and Psychology, 24, 179–192. https://doi.org/10.1007/s10869-009-9098-0
FrankM. G.EkmanP. (1997). The ability to detect deceit generalizes across different types of high-stake lies. Journal of Personality and Social Psychology, 72(6), 1429–1439. https://doi.org/10.1037/0022-3514.72.6.1429
GunnsR. E.JohnstonL.HudsonS. M. (2002). Victim selection and kinematics: A point-light investigation of vulnerability to attack. Journal of Nonverbal Behavior, 26(3), 129–158. https://doi.org/10.1023/A:1020744915533
19.
HammondK. R.HurschC. J.ToddF. J. (1964). Analyzing the components of clinical inference. Psychological Review, 71, 438–456. https://doi.org/10.1037/h0040736
20.
HammondK. R.WilkinsM. M.ToddF. J. (1966). A research paradigm for the study of interpersonal learning. Psychological Bulletin, 65, 221–232. https://doi.org/10.1037/h0023103
21.
HartwigM.BondC. F. (2011). Why do lie-catchers fail? A Lens Model meta-analysis of human lie judgments. Psychological Bulletin, 137, 643–659. https://doi.org/10.1037/a0023589
22.
Illinois v. Wardlow, 528 U.S. 119, 120 S. Ct. 673, 145 L. Ed. 2d 570 (2000).
23.
MantuaJ.BesseyA. F.MickelsonC. A.ChoynowskiJ. J.NobleJ. J.BurkeT. M.McKeonA. B.SowdenW. J. (2021). Sleep and high-risk behavior in military service members: A mega-analysis of four diverse U.S. Army units. Sleep, 44(4), zsaa221. https://doi.org/10.1093/sleep/zsaa221
24.
MeehanN. C.StrangeC. (2015). Behavioral indicators of legal and illegal gun carrying (No. NRL/MR/5508–15-9597). Naval Research Laboratory.
25.
MeehanN. C.StrangeC.GarintherA. (2021). It’s the walk, not the talk: Behavioral indicators of concealed and unholstered firearms carrying. The Police Journal: Theory, Practice and Principles, 94(4), 462–480. https://doi.org/10.1177/0032258X20960777
26.
MeehanN. C.StrangeC.McClaryM. (2015). Behavioral indicators during a police interdiction. Naval Research Laboratory.
27.
MeissnerC. A.KassinS. M. (2002). “He’s guilty!”: Investigator bias in judgments of truth and deception. Law and Human Behavior, 26(5), 469–480. https://doi.org/10.1023/A:1020278620751
28.
MontepareJ. M.Zebrowitz-McArthurL. (1988). Impressions of people created by age-related qualities of their gaits. Journal of Personality and Social Psychology, 55(4), 547. https://doi.org/10.1037/0022-3514.55.4.547
NickersonR. S. (1998). Confirmation bias. A ubiquitous phenomenon in many guises. Review of General Psychology, 2, 175–220. https://doi.org/10.1037/1089-2680.2.2.175
31.
PollickF. E.KayJ. W.HeimK.StringerR. (2005). Gender recognition from point-light walkers. Journal of Experimental Psychology: Human Perception and Performance, 31(6), 1247–1265. https://doi.org/10.1037/0096-1523.31.6.1247
32.
R Core Team. (2013). R: A language and environment for statistical computing. http://www.R-project.org/
33.
RichardsonM. J.JohnstonL. (2005). Person recognition from dynamic events: The kinematic specification of individual identity in walking style. Journal of Nonverbal Behavior, 29(1), 25–44. https://doi.org/10.1007/s10919-004-0888-9
34.
RivaG.MantovaniF.CapidevilleC. S.PreziosaA.MorgantiF.VillaniD.GaggioliA.BotellaC.AlcañizM. (2007). Affective interactions using virtual reality: The link between presence and emotions. Cyberpsychology & Behavior, 10(1), 45–56. http://doi.org/10.1089/cpb.2006.9993
35.
RunesonS.FrykholmG. (1983). Kinematic specification of dynamics as an informational basis for person-and-action perception: Expectation, gender recognition, and deceptive intention. Journal of Experimental Psychology: General, 112(4), 585–615. https://psycnet.apa.org/doi/10.1037/0096-3445.112.4.585
36.
SheenD. M.McMakinM. L.HallT. E. (2001). Three-dimensional millimeter-wave imaging for concealed weapon detection. IEEE Transactions on Microwave Theory and Techniques, 49(9), 1581–1592. https://doi.org/10.1109/22.942570
37.
StanislawH.TodorovN. (1999). Calculation of signal detection theory measures. Behavior Research Methods, Instruments, & Computers, 31(1), 137–149. https://doi.org/10.3758/BF03207704
38.
SweetD. M.MeissnerC. A.AtkinsonD. J. (2017). Assessing law enforcement performance in behavior-based threat detection tasks involving a concealed weapon or device. Law and Human Behavior, 41(5), 411–421. https://doi.org/10.1037/lhb0000243
39.
Terry v. Ohio 329 U. S. 1. (1968).
40.
United States v. Briggs, 720 F.3d 1281 (10th Cir. 2013).
41.
VrijA. (2008). Detecting lies and deceit: Pitfalls and opportunities. John Wiley.
42.
WeberN.BrewerN. (2003). The effect of judgment type and confidence scale on confidence-accuracy calibration in face recognition. Journal of Applied Psychology, 88(3), 490–499. https://doi.org/10.1037/0021-9010.88.3.490
43.
WheelerS.BookA.CostelloK. (2009). Psychopathic traits and perceptions of victim vulnerability. Criminal Justice and Behavior, 36(6), 635–648. https://doi.org/10.1177/00938548093339
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
