Lenormand Diane, Fauvel Baptiste, Piolino Pascale
Laboratoire Mémoire, Cerveau & Cognition (LMC2 UR 7536), Institut de Psychologie, Université Paris Cité, Paris, France.
Front Psychol. 2024 Feb 27;15:1355343. doi: 10.3389/fpsyg.2024.1355343. eCollection 2024.
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.
尽管情景记忆（EM）具有生态特性，且巩固在其功能中起着重要作用，但同时涉及这两个主题的研究仍然很少。因此，本研究旨在以生态范式建立对情景记忆中新编码信息未来情况的预测。
参与者使用SenseCam便携式相机，连续10天每天记录两件个人事件，并用不同的主观量表（情绪效价和强度、自我概念和自我相关性、视角以及一个月后的预期细节、心理意象……）对这些事件进行描述。然后，他们在编码后的5天和1个月进行了一次意外的自由回忆。使用机器学习算法预测一个月后记忆中事件（情景性或遗忘）的未来情况。
最佳算法的准确率为78%，这表明这种预测是可靠可行的。在编码时以及首次自由回忆时，最能区分一个月后情景记忆和遗忘记忆的变量是心理意象、自我参照和前瞻性（预期细节）。
这些结果可能为日常经历中情景性自传体记忆的发展奠定基础。
