CAREER: Computational and Neural Basis of Social Perception

职业：社会感知的计算和神经基础

• 批准号: 1943862
• 负责人: Stefano Anzellotti
• 金额: $ 60.35万
• 依托单位: Boston College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943862&HistoricalAwards=false
• 关键词: CAREER; Computational; Neural; Basis; Social

In order to interact appropriately with others, people need to recognize identities, facial expressions, and actions. What are the brain mechanisms underlying this recognition? This project proposes that face identity and expressions are processed by common brain regions, and that distinct regions are specialized to process static and dynamic information. This could be a particular case of a more general phenomenon: “complementarity”, in which different tasks like recognizing identity and recognizing expressions are performed by the same brain regions, because solving one of the tasks helps to solve the other. Complementarity might be a general principle of organization of the brain. This research aims to shed new light into social perception and more broadly into the large-scale organization of the human brain, through measurement of neural activity and building artificial neural networks. This aims to improve understanding of social cognition and its impairments, such as autism spectrum disorders. The project will design courses and research opportunities for students to receive training at the intersection between neuroscience and artificial intelligence (AI), preparing them to study neuroscience questions with new AI tools, and to build new AI inspired by state-of-the-art research in neuroscience. The project will also reach out to the broader community, using a combination of in-person and online events. According to a classical theory, face identity and facial expressions are processed by separate neural pathways. The identity of a face is recognized by the ventral temporal lobe (occipital face area: OFA, fusiform face area: FFA), while expressions are recognized by the lateral temporal lobe (posterior superior temporal sulcus: pSTS). By contrast, recent research identified identity information in pSTS, and expression information in OFA and FFA. This project hypothesizes that: 1) representations of orthogonal properties like face identity and expressions operate synergistically during recognition, and arise spontaneously within the same brain regions; 2) this phenomenon occurs not only in the case of face identity and expressions, but also in the case of body identity and actions, constituting a broader principle of organization of social perception; 3) ventral and lateral regions are not distinguished by the content they encode, but by the type of properties they process. The project will test these hypotheses by asking participants to watch controlled and naturalistic videos of expressions and actions while undergoing fMRI, and by modeling their responses with encoding models using features from deep neural networks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

为了与他人进行适当的互动，人们需要识别身份、面部表情和动作。这种认知背后的大脑机制是什么？该项目提出，人脸识别和表情是由共同的大脑区域处理的，而不同的大脑区域专门处理静态和动态信息。这可能是一个更普遍现象的特例：“互补性”，即不同的任务，如识别身份和识别表情，由相同的大脑区域执行，因为解决其中一个任务有助于解决另一个任务。互补性可能是大脑组织的一般原则。这项研究旨在通过测量神经活动和构建人工神经网络，为社会感知提供新的视角，并更广泛地研究人类大脑的大规模组织。这旨在提高对社会认知及其缺陷的理解，如自闭症谱系障碍。该项目将为学生设计课程和研究机会，让他们接受神经科学和人工智能（AI）交叉领域的培训，为他们利用新的人工智能工具研究神经科学问题做好准备，并在神经科学最新研究的启发下构建新的人工智能。该项目还将通过面对面和在线活动相结合的方式，接触到更广泛的社区。根据经典理论，面部识别和面部表情是由不同的神经通路处理的。人脸的身份是由腹侧颞叶（枕部面部区：OFA，梭状脸区：FFA）识别的，而表情是由外侧颞叶（颞后上沟：pSTS）识别的。相比之下，最近的研究发现了pSTS的身份信息，以及OFA和FFA的表达信息。该项目假设：1)在识别过程中，面部身份和表情等正交特性的表征协同运作，并在同一大脑区域内自发产生；2)这种现象不仅发生在面孔认同和表情的情况下，也发生在身体认同和动作的情况下，构成了更广泛的社会知觉组织原则；3)腹侧区和侧区不是通过它们编码的内容来区分的，而是通过它们处理的属性类型来区分的。该项目将通过要求参与者在接受功能磁共振成像时观看控制和自然的表情和动作视频来验证这些假设，并通过使用深度神经网络特征的编码模型来模拟他们的反应。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Spontaneous Learning of Face Identity in Expression-Trained Deep Nets

表情训练深度网络中人脸身份的自发学习

• DOI: 10.32470/ccn.2022.1116-0
• 发表时间: 2022
• 期刊: Conference on Cognitive Computational Neuroscience
• 作者: Schwartz, Emily;O'Nell, Kathryn;Saxe, Rebecca;Anzellotti, Stefano
• 通讯作者: Anzellotti, Stefano

Angular Gyrus Responses Show Joint Statistical Dependence with Brain Regions Selective for Different Categories

• DOI: 10.1523/jneurosci.1283-22.2023
• 发表时间: 2023-04-12
• 期刊: JOURNAL OF NEUROSCIENCE
• 影响因子: 5.3
• 作者: Fang，Mengting;Aglinskas，Aidas;Anzellotti，Stefano
• 通讯作者: Anzellotti，Stefano

Exploring the Representational Structure of Trait Knowledge Using Perceived Similarity Judgments

使用感知相似性判断探索特质知识的表征结构

• DOI: 10.1521/soco.2022.40.6.549
• 发表时间: 2022
• 期刊: Social Cognition
• 影响因子: 1.9
• 作者: Kim, Minjae;Young, Liane;Anzellotti, Stefano
• 通讯作者: Anzellotti, Stefano

Functional coordinates: Modeling interactions between brain regions as points in a function space

功能坐标：将大脑区域之间的相互作用建模为功能空间中的点

• DOI: 10.1162/netn_a_00264
• 发表时间: 2022
• 期刊: Network Neuroscience
• 影响因子: 4.7
• 作者: Poskanzer, Craig;Anzellotti, Stefano
• 通讯作者: Anzellotti, Stefano

Intracranial Electroencephalography and Deep Neural Networks Reveal Shared Substrates for Representations of Face Identity and Expressions

• DOI: 10.1523/jneurosci.1277-22.2023
• 发表时间: 2023-06-07
• 期刊: JOURNAL OF NEUROSCIENCE
• 影响因子: 5.3
• 作者: Schwartz，Emily;Alreja，Arish;Anzellotti，Stefano
• 通讯作者: Anzellotti，Stefano