five

Data from: Temporal integration by multi-level regularities fosters the emergence of dynamic conscious experience

收藏
科学数据银行2023-12-01 更新2026-04-23 收录
下载链接:
https://www.scidb.cn/detail?dataSetId=7c58246cb9554db0adbaf82e85f651b6
下载链接
链接失效反馈
官方服务:
资源简介:
This dataset includes the data and demos of the five experiments in current research.The relationship between integration and awareness is central to contemporary theories and research on consciousness. Here, we investigated whether and how information integration over time by incorporating the underlying regularities contributes to our awareness of the dynamic world. Using binocular rivalry, we demonstrated that structured visual streams, constituted by shape, motion, or idiom sequences containing perceptual- or semantic-level regularities, predominated over their non-structured but otherwise matched counterparts in the competition for visual awareness. Despite the apparent resemblance, a substantial dissociation of the observed rivalry advantages emerged between perceptual- and semantic-level regularities – these effects stem respectively from nonconscious and conscious temporal integration processes, with the former but not the latter being vulnerable to perturbations in the spatiotemporal integration window. These findings corroborate the essential role of structure-guided information integration in visual awareness and highlight a multi-level mechanism where temporal integration by perceptually and semantically defined regularities fosters the emergence of continuous conscious experience.

本数据集包含本研究五项实验的相关数据及演示材料。整合与觉知的关系,是当代意识研究领域的核心理论与研究焦点。本研究旨在探明:通过吸纳潜在规律实现的跨时间信息整合,是否以及如何助力我们对动态世界的觉知。借助双眼竞争(binocular rivalry)范式,本研究证实:由包含知觉或语义层级规律的形状、运动或习语序列构成的结构化视觉流,在视觉觉知的竞争中,显著优于结构匹配但无结构化的对应刺激组。尽管两类刺激看似相似,但知觉层级与语义层级规律所带来的竞争优势却存在显著分离——这两类效应分别源自无意识与有意识的时间整合过程,其中前者(而非后者)易受时空整合窗口内扰动的影响。本研究结果证实了结构引导的信息整合在视觉觉知中的核心作用,并揭示了一种多层次机制:由知觉与语义层级规律驱动的时间整合过程,促成了连续性有意识体验的形成。
提供机构:
Institute of Psychology, Chinese Academy of Sciences; Peijun Yuan; Ruichen Hu
创建时间:
2023-10-29
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作