A High-Density EEG investigation of the Misinformation Effect: Differentiating between True and False Memories John E. Kiat & Robert F. Belli Department.

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A High-Density EEG investigation of the Misinformation Effect: Differentiating between True and False Memories John E. Kiat & Robert F. Belli Department of Psychology, University of Nebraska-Lincoln Introduction Procedure Behavioral Results 18 Subjects (10 female, 4 left-handed, mean age =19.94, SD =2.52) participated for credit. Study procedure shown in Figure 3. This study investigated differences in recollective activity (as indexed by the Late Positive Component) between true and false event memories in the misinformation effect paradigm. Behavioral response performance for both item types is given in Table 1 . Pairwise t-tests showed (1) a higher proportion of perceptual Control Endorsements vs. Misinf. Endorsements, t(17) = 8.716, p < .001 and (2) a greater number of perceptual Misinf. rejections vs. perceptual Misinf. endorsements, t(17) = 6.644, p < .001. Table 1 : Mean Proportions and (Standard Deviations) for Control Items and Misinformed “Seen” “Seen & Read” Perceptual Source Attribution “Read” “Guess” Control Endorsement .30 (.07) .26 (.13) .56 (.11) .08 (.09) .09 (.07) Control Rejection .12 (.09) .06 (.04) .18 (.05) .03 (.05) .07 (.05) Misinf. Endorsement .16 (.08) .13 (.07) .29 (.07) .12 (.10) .06 (.05) Misinf. Rejection .27 (.09) .39 (.11) .05 (.06) .08 (.07) Figure 3 : Overall Procedure Misinformation Event Study 30 Minute Retention Interval Event Misinformation Phase 1-day Retention Interval Test Phase Materials Study Materials: Four events; each depicted in 50 digital color slides (taken from Okado & Stark, 2005). Sample slides shown in Figure 1. Two versions presented, counterbalanced across participants, each with a different version of the critical details Figure 1 : Sample Event Slides Misinformation Materials : Four 50-sentence narratives : each sentence describing one of the previously presented slides. 24 details selected for testing. 12 accurately described (i.e. consistent controls). 12 inaccurately described (i.e. misinformed items) Two variants of each narrative counterbalanced across participants, each with a different type of misinformation. Final Test : Participants tested on the 48 misinformed details and 40 of the consistent control details. Testing procedure shown in Figure 2. Temporal-spatial Factor & ERP Results EEG Acquisition Parameters LPC factor time-course & topography by condition (A), overall (B) and jackknifed (C) dipole solutions and (D) grand average waveforms by condition for electrodes with high (> 0.70) loadings (E) presented in Figure 4. One-way RM ANOVA of LPC scores across three response conditions (Control & Misinf. Endorsements, Misinf. Rejection) Sig. effect of Condition (F(2,17) = 6.71, p = .007). Follow-up tests Control Endorsements LPC > Misinf. Endorsements (t(17) = 3.23 p = .006) & Rejections (t(17) = 2.64, p = .017). No sig. difference between Misinf. Endorsement & Rejections (t(17) = 0.57, p = 0.575). To facilitate comparisons with past work, mean voltage within the 477 – 577 ms window for high loading electrodes analyzed. RM ANOVA sig. for condition effect (F(2,17) = 8.93, p = 0.002). Follow-up comparisons effectively identical (Control Endorsements LPC > Misinf. Endorsements (t(17) = 3.76, p = 0.002), trend towards sig. for Control Endorsements vs. Misinf. Rejections (t(17) = 1.95, p = 0.067), no sig. difference between Misinfor. Endorsements & Rejections (t(17) = 0.19, p = 0.85). Temporal-spatial LPC factor & raw electrode LPC cluster voltages presented in Table 3. 256 AgCl electrode Hydrocel Geodesic Sensor Net High-input impedance NetAmps 300 amplifier. Recorded using a Cz reference, later re-referenced to an average reference Impedances <45 kΩ, appropriate for the system. Recorded with 0.1-100 Hz analog filter at 1000 Hz. Figure 4 : LPC Factor Topography, Time-course, Localization & associated Raw Waveforms EEG Preprocessing Digitally filtered : 0.3 – 30 Hz zero-phase shift finite impulse response bandpass filter. Segmentation to critical word onsets (Slide D, Fig 1) Segment Length : 100 ms pre, 2000 ms post-onset. AAR toolbox (Gomez-Herrero et al., 2006) used to remove ocular and EMG artifacts Channels with poor (r <0.40) inter-neighbor correlations and/or extreme voltage fluctuations (>100 µv min-max) identified and interpolated using whole head spline interpolation. Trials with >10% interpolated channels removed prior to averaging. Figure 2 : EEG Testing Procedure ERP Processing Analysis focused on judgments given perceptual (“Seen” or “Seen or Read) source judgments. Components quantified using temporal-spatial PCA [ERP PCA Toolkit (Dien, 2010) ver. 2.49] 34 temporal and 4 spatial factors extracted based on parallel analysis. The LPC factor, a well-established index of recollective activity, selected based on topography & time-course. Source localization of the LPC factor assessed via single-dipole model jack-knifing. LPC component localized to the PCC, an region known to play an role in episodic recollection. Table 3 : Average voltages for LPC Factor and Raw Cluster in µVs., standard errors in parentheses Consistent Control Misinf. Endorsement Misinf. Rejection LPC Factor Voltage 2.77 µV (0.61) 0.92 µV (0.72) 0.41 µV (0.75) Raw LPC Electrode Cluster Voltage 1.88 µV (0.73) 0.66 µV (0.76) 0.52 µV (0.67) Summary The findings suggest that true & false recognition on the misinformation effect may be differentiable on the basis of recollective strength as indexed by the LPC. This difference may be due to true recognition being associated with a stronger memory trace from its stronger, perceptual base and/or from multiple study exposures. Funding & Acknowledgements This project was generously funded by a UNL Center for Brain, Biology and Behavior seed grant. We are also grateful to Craig Stark for making his materials available to us and would also like to thank our research assistants who contributed time to this project. Contact Author: John E. Kiat : johnkiat@huskers.unl.edu or visit www.johnkiat.com