Late frontal positivity effects in Self-referential Memory: Unique to the Self?

ABSTRACT

The self-reference effect in memory (SRE), in which stimuli related to self are better remembered than other stimuli, has been studied often in the fMRI literature, but much less with EEG. In two experiments, we investigated how self-referencing modulated event-related potential (ERP) markers of the subsequent memory effect, testing whether the same components that reflect memory success are impacted or whether unique components are modulated by self-referencing. Participants were asked to evaluate whether an adjective accurately described either the self or a given other by making a yes/no key press during EEG recording. Then participants were given a surprise recognition memory test where they judged each adjective as old or new. We observed a main effect of self-relevance on a late positivity at right frontal electrodes. A very similar effect was observed when comparing words subsequently remembered to those that were forgotten. However, no interaction was found between self-relevance and subsequent memory, suggesting the frontal positivity is not exclusive to the SRE, but instead a reflection of deeper encoding that leads to better memory. Thus, this frontal positivity may be a marker of a deeper encoding process that is elicited by self-referencing but not exclusive to the SRE.

KEYWORDS:

• self-referencing
• memory
• erp
• self
• eeg
• subsequent memory

Acknowledgments

We thank the Brandeis University Provost Undergraduate Research Fund for supporting this work. Additionally, we thank Wanchen Zhao, Amerta Bai, Maayan Sofer, and Nishaat Mukadam for help collecting and analyzing data. Eric Fields was supported by NIH T32 NS007292.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Supplemental data

Supplemental data for this article can be accessed here.

Notes

¹ We used the moving window peak-to-peak function (the difference between the most positive and most negative voltages within a window) and the step function (the difference between the mean voltage from the first half and second half of a window) as implemented in ERPLAB (https://github.com/lucklab/erplab/wiki/Artifact-Detection-in-Epoched-Data). A peak-to-peak function was applied to the difference between a forehead channel (Fp1, or Fp2 if Fp1 showed a lot of non-ocular noise) and the electrode under the left eye in 200 ms windows with a threshold around 75 μV to detect blinks. Step functions were applied in both 400 ms (to detect brief deflections) and 1000 ms (to detect slow drift) time windows at all channels with thresholds around 55 μV. Finally, a peak-to-peak function was applied in 400 ms time windows at all channels with a high voltage threshold (~300 μV) to detect particularly large EMG and other artifacts not picked up by the step function. Precise thresholds were sometimes adjusted for individual subjects’ data based on visual inspection, but applied equally to all trials (and thus all conditions) for each subject (Luck, 2014, p. 191).

² Analyses of d’ scores converged with the results from hits minus false alarms. Scores were significantly higher for self (M = 1.20, SD = .54) than other (M = .79, SD = .39), t(29) = 8.05, p < .001.

³ Analyses of d’ scores converged with the results from hits minus false alarms. Scores were significantly higher for self (M = 1.22, SD = .47) than other (M = .80, SD = .37), t(25) = 11.40, p < .001.

Additional information

Funding

This work was supported by the National Institutes of Health [T32 NS007292]; Brandeis University Provost Undergraduate Research Fund.