Memory for spatio-temporal contextual details during the retrieval of naturalistic episodes

The influence of the precuneus on the medial temporal cortex determines the subjective quality of memory during the retrieval of naturalistic episodes

Memory retrieval entails dynamic interactions between the medial temporal lobe and areas in the parietal and frontal cortices. Here, we tested the hypothesis that effective connectivity between the precuneus, in the medial parietal cortex, and the medial temporal cortex contributes to the subjective quality of remembering objects together with information about their rich spatio-temporal encoding context. During a 45 min encoding session, the participants were presented with pictures of objects while they actively explored a virtual town. The following day, under fMRI, participants were presented with images of objects and had to report whether: they recognized the object and could remember the place/time of encoding, the object was familiar only, or the object was new. The hippocampus/parahippocampus, the precuneus and the ventro-medial prefrontal cortex activated when the participants successfully recognized objects they had seen in the virtual town and reported that they could remember the place/time of these events. Analyses of effective connectivity showed that the influence exerted by the precuneus on the medial temporal cortex mediates this effect of episodic recollection. Our findings demonstrate the role of the inter-regional connectivity in mediating the subjective experience of remembering and underline the relevance of studying memory in contextually-rich conditions.

Comparing episodic memory outcomes from walking augmented reality and stationary virtual reality encoding experiences

Episodic Memory (EM) is the neurocognitive capacity to consciously recollect personally experienced events in specific spatio-temporal contexts. Although the relevance of spatial and temporal information is widely acknowledged in the EM literature, it remains unclear whether and how EM performance and organisation is modulated by self-motion, and by motor- and visually- salient environmental features (EFs) of the encoding environment. This study examines whether and how EM is modulated by locomotion and the EFs encountered in a controlled lifelike learning route within a large-scale building. Twenty-eight healthy participants took part in a museum-tour encoding task implemented in walking Augmented Reality (AR) and stationary Virtual Reality (VR) conditions. EM performance and organisation were assessed immediately and 48-hours after trials using a Remember/Familiar recognition paradigm. Results showed a significant positive modulation effect of locomotion on distinctive EM aspects. Findings highlighted a significant performance enhancement effect of stairway-adjacent locations compared to dead-end and mid-route stimuli-presentation locations. The results of this study may serve as design criteria to facilitate neurocognitive rehabilitative interventions of EM. The underlying technological framework developed for this study represents a novel and ecologically sound method for evaluating EM processes in lifelike situations, allowing researchers a naturalistic perspective into the complex nature of EM.

The formation of episodic autobiographical memory is predicted by mental imagery, self-reference, and anticipated details

Introduction

Despite the ecological nature of episodic memory (EM) and the importance of consolidation in its functioning, studies tackling both subjects are still scarce. Therefore, the present study aims at establishing predictions of the future of newly encoded information in EM in an ecological paradigm.

Methods

Participants recorded two personal events per day with a SenseCam portable camera, for 10 days, and characterized the events with different subjective scales (emotional valence and intensity, self-concept and self-relevance, perspective and anticipated details at a month, mental images…). They then performed a surprise free recall at 5 days and 1 month after encoding. Machine learning algorithms were used to predict the future of events (episodic or forgotten) in memory at 1 month.

Results

The best algorithm showed an accuracy of 78%, suggesting that such a prediction is reliably possible. Variables that best differentiated between episodic and forgotten memories at 1 month were mental imagery, self-reference, and prospection (anticipated details) at encoding and the first free recall.

Discussion

These results may establish the basis for the development of episodic autobiographical memory during daily experiences.