MODELING FACE PERCEPTION

人脸感知建模

• 批准号: 2890940
• 负责人: GARRISON W COTTRELL
• 金额: $ 14.98万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-08-01 至 2001-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2890940
• 关键词: behavior test; bias; brain injury; clinical research; computational neuroscience; emotions; face expression; human data; learning; model design /development; neural information processing; neuropsychology; perception; performance; photostimulus; psychological models

DESCRIPTION (Applicant's abstract): There has been a great deal of progress in understanding how complex objects, in particular, faces, are processed by humans. The aim of this proposal is to extend recent work on computational models of emotion and face processing to account for recent data concerning face processing in brain-damaged patients and normal controls. In all of these studies, the goal is to understand to what extent face processing must be considered "special", through modeling and experiment. Our specific goals are to: I) analyze the relationship between categorical perception of emotion in our models and in human subjects as elucidated by parallel experiments on the models and humans using identical stimuli; 2) understand further how specialized "modules" may arise for face, object and expression recognition through combinations of competitive learning mechanisms and innate biases; 3) understand the effects of various kinds of 'brain damage" on our model, as a route to understanding patient behavior. These goals will be addressed through careful modeling studies guided by the data. Our hypotheses will be tested by constructing neural network models that process the same face images used in human studies.

描述（申请人摘要）：取得了很大进展 理解复杂的物体，特别是面部，是如何被处理的 人类。 该提案的目的是扩展最近在计算方面的工作 情绪和面部处理模型来解释有关的最新数据 脑损伤患者和正常对照组的面部处理。 在所有的 这些研究的目标是了解面部处理必须达到何种程度 通过建模和实验被认为是“特殊的”。 我们的具体 目标是： I）分析类别感知之间的关系 我们的模型和人类受试者中的情感如平行所阐明的 使用相同的刺激对模型和人类进行实验； 2）理解 进一步说明面部、物体和表情如何出现专门的“模块” 通过竞争性学习机制和 天生的偏见； 3）了解各种“脑损伤”的影响 在我们的模型上，作为理解患者行为的途径。 这些目标 将通过数据指导下的仔细建模研究来解决。 我们的 将通过构建处理的神经网络模型来测试假设 与人类研究中使用的相同的面部图像。