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