Aging Reduces EEG Markers of Recognition Despite Intact Performance: Implications for Forensic Memory Detection

Abstract

ERP-based forensic memory detection is based on the logic that guilty suspects will hold incriminating knowledge about crimes they have committed, and therefore should show parietal ERP positivities related to recognition when presented with reminders of their crimes. We predicted that such forensic memory detection might however be inaccurate in older adults, because of changes to recognition-related brain activity that occurs with aging. We measured both ERPs and EEG oscillations associated with episodic old/new recognition and forensic memory detection in 30 younger (age < 30) and 30 older (age > 65) adults. EEG oscillations were included as a complementary measure which is less sensitive to temporal variability and component overlap than ERPs. In line with predictions, recognition-related parietal ERP positivities were significantly reduced in the older compared to younger group in both tasks, despite highly similar behavioural performance. We also observed ageing-related reductions in oscillatory markers of recognition in the forensic memory detection test, while the oscillatory effects associated with episodic recognition were similar across age groups. This pattern of results suggests that while both forensic memory detection and episodic recognition are accompanied by ageing-induced reductions in parietal ERP positivities, these reductions may be caused by non-overlapping mechanisms across the two tasks. Our findings suggest that EEG-based forensic memory detection tests are invalid in older populations, limiting their practical applications.

EEG evidence that morally relevant autobiographical memories can be suppressed

“…The distribution of significant clusters in time, space and frequency dimensions was then used to determine when, where and in which EEG frequencies the conditions differed. However, it is important to note that the precise edges and peaks of effects in the spatiotemporal and frequency dimensions cannot be determined with this method (see Maris & Oostenveld, 2007; Hellerstedt et al, 2021; Sassenhagen & Draschkow, 2019) for a detailed explanation of the cluster-based permutation test analysis.…”

“…This Think > No-Think positive difference for morally right memories was most reliable around 500-600ms post-stimulus onset and peaked across central and frontal regions. Although ERP modulations indexing the suppression of recollection often peak over the left parietal scalp (e.g., Bergström et al, 2009aBergström et al, , 2009b, recall-related ERPs can be spread across the whole scalp (e.g., Hellerstedt et al, 2021) and are known to emerge from around 500ms after a reminder is encountered (Staresina & Wimber, 2019). Therefore, the ERP positivity for retrieving compared to suppressing morally right memories likely indexes differences in recollection-related activity for these memories, whereas control over recollection may not have been as successful for morally wrong memories during these initial attempts at suppression.…”

Can neural correlates of encoding explain the context dependence of reward-enhanced memory?

“…The data was recorded during the covid-19 pandemic and the participants were wearing face masks throughout the experiment which necessitated excluding the 15 electrodes covering the face. The EEG data was pre-processed offline using the EEGLAB toolbox in MATLAB with a standard pipeline (see Hellerstedt et al 2021 for a study using a similar pipeline) and the steps and settings were pre-registered. The data was recorded with Cz as reference and was re-referenced offline to the average of the left and the right mastoid electrodes.…”

“…The decomposition was performed between 4 and 30Hz in frequency steps of 1Hz, time steps of 50ms and with a time-window length of five cycles. Oscillatory power in each frequency band was normalised to dB scale using a pre-stimulus baseline period between –700 to –200ms to avoid that the estimation of baseline data points were affected by data in the post-stimulus time window in the stimulation frequency (as in Allen et al, 2020; Hellerstedt et al, 2021).…”