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Research ArticleResearch Article: New Research, Cognition and Behavior

Oscillatory Neural Correlates of Police Firearms Decision-Making in Virtual Reality

Nicholas A. Alexander, Clíona L. Kelly, Hongfang Wang, Robert A. Nash, Shaun Beebe, Matthew J. Brookes and Klaus Kessler
eNeuro 8 July 2024, 11 (7) ENEURO.0112-24.2024; https://doi.org/10.1523/ENEURO.0112-24.2024
Nicholas A. Alexander
1Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom
2Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham B4 7E, United Kingdom
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  • ORCID record for Nicholas A. Alexander
Clíona L. Kelly
2Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham B4 7E, United Kingdom
3Yale Child Study Center, Yale University School of Medicine, New Haven, Connecticut 06520
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Hongfang Wang
2Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham B4 7E, United Kingdom
4School of Psychology, University College Dublin, Dublin D4, Ireland
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Robert A. Nash
2Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham B4 7E, United Kingdom
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Shaun Beebe
5Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2QX, United Kingdom
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Matthew J. Brookes
5Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2QX, United Kingdom
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Klaus Kessler
2Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham B4 7E, United Kingdom
4School of Psychology, University College Dublin, Dublin D4, Ireland
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    Figure 1.

    Examples of stimuli from two scenarios. Between the pretrial baseline period and Phase 1, the virtual human walked from behind the wall, into view. Two examples of threat at Phase 1 can be seen (handgun and knife), as well as the two possible outcomes at Phase 2 (surrender and attack). Note that these examples are snapshots from a continuous stimulus presentation to an HMD, so the precise presentation varied continuously with participant movement, and the display technology presented it as a high-resolution 3D scene.

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    Figure 2.

    Summary of response time analysis at the two decision points. The top panel (A) shows the results from Phase 1 of the task, where participants decided to equip either nothing, a Taser, or a Glock. Participants were faster to respond to threat (knife/handgun) than no threat (drinks can), and expert AFO response times were faster than both novice groups at the preparation decision. The bottom panel (B) shows the results from the SDS decision at Phase 2. Again, participants were faster to respond to threat (attack) versus no threat (surrender), and expert AFOs were faster to respond to threat than both novice groups at the SDS decision stage. Additional descriptions of the effects of age can be seen on the right of each panel, along with the age distributions of each group shown with box-and-whisker plots of the interquartile range. Note that the error bars on bar charts show the standard error of the mean and the black bars highlight significant differences with asterisked references to the level of significance.

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    Figure 3.

    Overview of EEG data preparation and source localization. A, B, The sensor-level time–frequency data for expert AFO and matched novice participants averaged across trials where a weapon was present and the virtual human attacked. For the time–frequency representation figures (top), the average of the central nine electrodes was taken (FC1, FC2, Cz, CP1, CP2, FCz, C1, C2, CPz) and baseline corrected against data from −2 to −1 s using decibel (dB) conversion. In these figures, data are time-locked to the onset of both stimuli, which were always 4 s apart: weapon presence (0 s, Phase 1) and compliance (4 s, Phase 2). On-scalp topographies of frequencies of interest from 250 to 750 ms are shown. C, The source estimation for the theta, alpha, and beta bands using eLORETA. The white crosshairs show the peak activity for each band. D, E, Time–frequency data for virtual electrodes placed at the peaks estimated in C, but for each group separately. Note that all color axes are formed of two linear subscales: from zero to the maximum value and from zero to the minimum value, to highlight the topography of each signal.

  • Figure 4.
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    Figure 4.

    Within-subject comparisons of theta (3–7 Hz) power at a virtual electrode positioned at the positive peak in theta activity averaged across all groups (MNI [0, 0, 40], dorsal anterior cingulate cortex). The right and left panels show that when inhibiting a response (equip nothing, Phase 1, or press safety, Phase 2), both groups of participants exhibit greater dACC theta power versus responding to threat (equip firearm, Phase 1, and shoot firearm, Phase 2). The central panel shows that only expert AFOs demonstrate greater theta power when equipping a Taser versus a Glock at Phase 1. The vertical dashed lines represent stimulus onset (black) and average response times (orange or purple). The black horizontal lines indicate the presence and duration of significant clusters. The shaded areas around the lines show the standard error of the mean.

  • Figure 5.
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    Figure 5.

    Comparisons of theta, alpha, and beta activity between expert AFO and matched novice groups within critical decision points. In each case, power has been calculated from a virtual electrode placed at the positive (theta) or negative (alpha, beta) peak of oscillatory power in the brain. A, Comparisons from the preparation stage (Phase 1) when there was no threat and when there was a threat. The main finding was that expert AFOs showed significantly higher preresponse theta power than the matched novices in both conditions. A shorter beta rebound was also observed for AFOs compared with matched novices after response in both conditions. B, Comparisons from the SDS decision (Phase 2). No significant preresponse differences were found. A shorter beta rebound was again observed for AFOs compared with matched novices after response, but only in the threat–attack condition. The dashed vertical lines represent stimulus onset (black) and mean group response times (red or blue). The black horizontal lines indicate the presence and duration of significant clusters. The shaded areas around the lines show the standard error of the mean.

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Oscillatory Neural Correlates of Police Firearms Decision-Making in Virtual Reality
Nicholas A. Alexander, Clíona L. Kelly, Hongfang Wang, Robert A. Nash, Shaun Beebe, Matthew J. Brookes, Klaus Kessler
eNeuro 8 July 2024, 11 (7) ENEURO.0112-24.2024; DOI: 10.1523/ENEURO.0112-24.2024

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Oscillatory Neural Correlates of Police Firearms Decision-Making in Virtual Reality
Nicholas A. Alexander, Clíona L. Kelly, Hongfang Wang, Robert A. Nash, Shaun Beebe, Matthew J. Brookes, Klaus Kessler
eNeuro 8 July 2024, 11 (7) ENEURO.0112-24.2024; DOI: 10.1523/ENEURO.0112-24.2024
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Keywords

  • decision-making
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  • naturalistic stimuli
  • neural oscillations
  • police
  • virtual reality

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