Causal and Chronological Relationships Predict Memory Organization for Nonlinear Narratives

召回 心理学 认知心理学 事件(粒子物理) 叙述的 突出 因果结构 语义记忆 情景记忆 任务(项目管理) 认知 人工智能 语言学 计算机科学 哲学 物理 管理 量子力学 神经科学 经济
作者
James W. Antony,Angelo Lozano,Pahul Dhoat,Janice Chen,Kelly A. Bennion
出处
期刊:Journal of Cognitive Neuroscience [MIT Press]
卷期号:36 (11): 2368-2385 被引量:3
标识
DOI:10.1162/jocn_a_02216
摘要

Abstract While recounting an experience, one can employ multiple strategies to transition from one part to the next. For instance, if the event was learned out of linear order, one can recall events according to the time they were learned (temporal), similar events (semantic), events occurring nearby in time (chronological), or events produced by the current event (causal). To disentangle the importance of these factors, we had participants watch the nonlinear narrative, Memento, under different task instructions and presentation orders. For each scene of the film, we also separately computed semantic and causal networks. We then contrasted the evidence for temporal, semantic, chronological, or causal strategies during recall. Critically, there was stronger evidence for the causal and chronological strategies than semantic or temporal strategies. Moreover, the causal and chronological strategies outperformed the temporal one even when we asked participants to recall the film in the presented order, underscoring the fundamental nature of causal structure in scaffolding understanding and organizing recall. Nevertheless, time still marginally predicted recall transitions, suggesting it operates as a weak signal in the presence of more salient forms of structure. In addition, semantic and causal network properties predicted scene memorability, including a stronger role for incoming causes to an event than its outgoing effects. In summary, these findings highlight the importance of accounting for complex, causal networks in knowledge building and memory.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
辛勤的无血完成签到,获得积分10
4秒前
5秒前
rookie完成签到,获得积分10
5秒前
5秒前
ni完成签到,获得积分10
6秒前
step_stone给step_stone的求助进行了留言
7秒前
7秒前
荒野星辰发布了新的文献求助10
8秒前
敏感的芷完成签到,获得积分20
8秒前
10秒前
10秒前
11秒前
luoshi应助沐风采纳,获得20
11秒前
安南完成签到,获得积分10
11秒前
香蕉冬云完成签到 ,获得积分10
12秒前
自信安荷发布了新的文献求助200
12秒前
鱼雷发布了新的文献求助10
13秒前
兔子发布了新的文献求助10
13秒前
13秒前
田様应助coffee采纳,获得10
14秒前
14秒前
专注鼠标完成签到,获得积分10
14秒前
LingYing完成签到 ,获得积分10
15秒前
cheche完成签到,获得积分10
16秒前
liushun完成签到,获得积分10
16秒前
caoyy发布了新的文献求助10
16秒前
zzt发布了新的文献求助10
17秒前
19秒前
19秒前
章家炜发布了新的文献求助10
20秒前
脑洞疼应助xfxx采纳,获得10
20秒前
wanci应助茶博士采纳,获得10
20秒前
所所应助YYT采纳,获得10
21秒前
匿名网友完成签到 ,获得积分10
21秒前
雪白雍完成签到,获得积分10
22秒前
maomao完成签到,获得积分10
22秒前
我是笨蛋完成签到 ,获得积分10
24秒前
酷波er应助caoyy采纳,获得10
25秒前
25秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
Luis Lacasa - Sobre esto y aquello 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527990
求助须知:如何正确求助?哪些是违规求助? 3108173
关于积分的说明 9287913
捐赠科研通 2805882
什么是DOI,文献DOI怎么找? 1540119
邀请新用户注册赠送积分活动 716941
科研通“疑难数据库(出版商)”最低求助积分说明 709824