Contribution of the Late Positive Potential to Emotional Memory

This repository contains the manuscript, data, and analysis code for Contribution of the Late Positive Potential to Emotional Memory: A Joint Neuro-Cognitive Approach to Memory Prediction by Robin Hellerstedt, Jordan Gunn, and Deborah Talmi.

Abstract

Emotional events are better remembered than neutral events. The emotional Context Maintenance and Retrieval model (eCMR) assumes that emotional items are preferentially processed during encoding. In eCMR, preferential processing is implemented by a free parameter ($\phi_{\text{emot}}$) that scales the learning rate equally for all emotional items. Incorporating a neural signal measured during encoding could make the model's account more specific by predicting which emotional items are later recalled. We investigated whether the late positive potential (LPP) 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 early LPP thus improved eCMR's prediction of recall, but only for emotional items. This specificity 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.

Setup

The project requires Python 3.12 and uv. The required JAXCMR code is included in vendor/jaxcmr.

uv sync --locked --extra dev
uv run pytest

The manuscript source is index.qmd; rendered versions are in docs/.