Contribution of the Late Positive Potential to Emotional Memory: A Joint Neuro-Cognitive Approach to Memory Prediction

Emotional events are better remembered than neutral events. The emotional Context Maintenance and Retrieval model (eCMR) explains this effect by simulating preferential encoding of emotional items, implemented by a free parameter that scales the learning rate. Incorporating a neural signal measured during encoding could make the model's account more specific, permitting a prediction not only of average increased recall but also which emotional items are likely to be recalled. We investigated whether the late positive potential (LPP) event-related potential (ERP) effect could provide this item-specific information in a secondary analysis of EEG recorded while 38 participants encoded negative and neutral pictures for an immediate free recall test (Zarubin et al. 2020). First, we established that the LPP effect was modulated by emotion in an early (400-1000 ms) and a late (1000-2000 ms) time window and this modulation was reliable in 73.7% and 50% of the participants respectively. Second, we investigated at what level the LPP was related to subsequent memory. Emotion-enhanced LPP did not covary with emotion-enhanced memory between participants or across lists within participants, but the early LPP specifically predicted memory for emotional items on a single-trial level. Third, we compared eCMR variants to test whether trial-level early LPP should scale learning for both categories or only for emotional items, and whether it could replace or complement a common learning-rate increase for all emotional items. The best-fitting model combined emotion-specific LPP scaling with the common learning-rate increase. The finding that early LPP predicted enhanced recall only for emotional items is difficult to reconcile with accounts on which the LPP reflects attention or stimulus significance more generally, which predict a relationship to memory for neutral items as well. These results pave the way to quantitative prediction of internal recall trajectories by individual participants without requiring behavioural output.