A Common Representational Code for Event and Object Concepts in the Brain

对象(语法) 心理学 集合(抽象数据类型) 名词 事件(粒子物理) 认知心理学 认知科学 编码(集合论) 特征(语言学) 意义(存在) 计算机科学 自然语言处理 人工智能 语言学 哲学 物理 量子力学 程序设计语言 心理治疗师
作者
Jia‐Qing Tong,Jeffrey R. Binder,Lisa L. Conant,Stephen Mazurchuk,Andrew J. Anderson,Leonardo Fernandino
出处
期刊:The Journal of Neuroscience [Society for Neuroscience]
卷期号:45 (41): e2166242025-e2166242025
标识
DOI:10.1523/jneurosci.2166-24.2025
摘要

Events and objects are two fundamental ways in which humans conceptualize their experience of the world. Despite the significance of this distinction for human cognition, it remains unclear whether the neural representations of object and event concepts are categorically distinct or, instead, can be explained in terms of a shared representational code. We investigated this question by analyzing fMRI data acquired from human participants (males and females) while they rated their familiarity with the meanings of individual words (all nouns) denoting object and event concepts. Multivoxel pattern analyses indicated that both categories of lexical concepts are represented in overlapping fashion throughout the association cortex, even in the areas that showed the strongest selectivity for one or the other type in univariate contrasts. Crucially, in these areas, a feature-based model trained on neural responses to individual event concepts successfully decoded object concepts from their corresponding activation patterns (and vice versa), showing that these two categories share a common representational code. This code was effectively modeled by a set of experiential feature ratings, which also accounted for the mean activation differences between these two categories. These results indicate that neuroanatomical dissociations between events and objects emerge from quantitative differences in the cortical distribution of more fundamental features of experience. Characterizing this representational code is an important step in the development of theory-driven brain–computer interface technologies capable of decoding conceptual content directly from brain activity.
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