经低通滤波的目标类别动力学:ERP 记录的解码方法。

Dynamics of low-pass-filtered object categories: A decoding approach to ERP recordings.

机构信息

Univ. Lille, CNRS, UMR 9193 - SCALab - Sciences Cognitives et Sciences Affectives, F-59000 Lille, France.

Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France.

出版信息

Vision Res. 2023 Mar;204:108165. doi: 10.1016/j.visres.2022.108165. Epub 2022 Dec 28.

Abstract

Rapid analysis of low spatial frequencies (LSFs) in the brain conveys the global shape of the object and allows for rapid expectations about the visual input. Evidence has suggested that LSF processing differs as a function of the semantic category to identify. The present study sought to specify the neural dynamics of the LSF contribution to the rapid object representation of living versus non-living objects. In this EEG experiment, participants had to categorize an object displayed at different spatial frequencies (LSF or non-filtered). Behavioral results showed an advantage for living versus non-living objects and a decrease in performance with LSF pictures of pieces of furniture only. Moreover, despite a difference in classification performance between LSF and non-filtered pictures for living items, the behavioral performance was maintained, which suggests that classification under our specific condition can be based on LSF information, in particular for living items.

摘要

快速分析大脑中的低空间频率(LSF)可以传达物体的全局形状,并允许对视觉输入进行快速预测。有证据表明,LSF 处理的方式因要识别的语义类别而异。本研究旨在确定 LSF 对快速物体表示的贡献的神经动力学,以区分生物与非生物物体。在这个 EEG 实验中,参与者必须根据不同的空间频率(LSF 或未过滤)对物体进行分类。行为结果表明,生物物体比非生物物体具有优势,而且仅当家具的 LSF 图片出现时,表现会下降。此外,尽管对于生物物品,LSF 图片和未过滤图片之间的分类性能存在差异,但行为表现得以维持,这表明在我们特定的条件下,分类可以基于 LSF 信息,特别是对于生物物品。

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