Exploring how high-level relations function in working memory: Unique mechanism of social grouping compared to nonsocial grouping.

心理学 认知心理学 工作记忆 透视图(图形) 社会关系 联想(心理学) 混乱 认知 社会心理学 沟通 人工智能 计算机科学 神经科学 精神分析 心理治疗师
作者
Shengyuan Wang,Zhuomian Lin,Xiaowei Ding
出处
期刊:Journal of Experimental Psychology: General [American Psychological Association]
卷期号:154 (9): 2619-2650 被引量:1
标识
DOI:10.1037/xge0001794
摘要

Humans efficiently perceive high-level relations and use them to facilitate grouping. For instance, interactive agents can be grouped into coherent social units (social grouping), while nonsocial relations like physically compatible shapes or semantic relatedness form nonsocial grouping. In this study, we examined how grouping based on high-level relations functions in working memory (WM). While previous studies have demonstrated that both social and nonsocial relations enhance WM capacity for the relations themselves, these findings provide only a partial answer to how high-level relations function in WM. A critical yet unresolved issue is whether and how high-level relations organize content orthogonal to these relations. We addressed this question from the perspective of feature binding, investigating the impact of social and nonsocial grouping on WM for bound features. Participants memorized bound colors corresponding to social or nonsocial grouping cues. Their responses were fit to the swap model to isolate distinct representational components (the bias and standard deviation of memory, swap rate, and guess rate). Across 13 experiments, we found that grouping based on high-level relations can organize content orthogonal to these relations, but only when the contents themselves are chunkable. Moreover, social and nonsocial grouping functions differently in WM. While social grouping cues led to both enhanced accuracy (absolute memory bias) and increased confusion (swap rate) during maintenance, nonsocial grouping only resulted in heightened confusion during encoding. Together, our results provide a comprehensive account of how grouping based on high-level relations functions in WM and reveal qualitative differences between social and nonsocial grouping. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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
最好驳回了ding应助
刚刚
三生有幸完成签到,获得积分10
刚刚
Copyright应助昏睡的乌龟采纳,获得20
1秒前
1秒前
万康发布了新的文献求助10
1秒前
2秒前
2秒前
希望天下0贩的0应助LLL采纳,获得10
2秒前
3秒前
科研通AI6.2应助马腾采纳,获得10
3秒前
伏玉完成签到,获得积分10
3秒前
研友_bZzO08完成签到,获得积分10
3秒前
4秒前
4秒前
QJ发布了新的文献求助20
4秒前
万能图书馆应助whale采纳,获得10
5秒前
披萨好吃酱完成签到,获得积分10
5秒前
liaoxinghui完成签到,获得积分20
5秒前
顺利秋灵发布了新的文献求助10
5秒前
多读书发布了新的文献求助10
5秒前
elidan发布了新的文献求助10
5秒前
彭于晏应助温柔丹萱采纳,获得10
6秒前
希望天下0贩的0应助longsay采纳,获得10
6秒前
7秒前
小福发布了新的文献求助30
7秒前
希望天下0贩的0应助cwi采纳,获得10
7秒前
7秒前
7秒前
7秒前
科研通AI6.4应助123采纳,获得10
8秒前
8秒前
CWQ完成签到,获得积分10
8秒前
超级山晴完成签到,获得积分10
9秒前
9秒前
9秒前
科研通AI6.2应助拾七采纳,获得10
9秒前
xzx完成签到 ,获得积分10
9秒前
ZoengPak发布了新的文献求助10
9秒前
dshihb发布了新的文献求助10
9秒前
英吉利25发布了新的文献求助10
9秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
48V Low-voltage Power Distribution Network (PDN) Architecture Industry Report, 2024 800
ズームレンズの光学設計に関する研究 800
Fundamentals of Pharmaceutical and Biologics Regulations: A Global Perspective, Second Edition 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7294801
求助须知:如何正确求助?哪些是违规求助? 8913328
关于积分的说明 18872134
捐赠科研通 6961237
什么是DOI,文献DOI怎么找? 3210127
关于科研通互助平台的介绍 2379484
邀请新用户注册赠送积分活动 2186364