COLLABORATIVE RESEARCH: Categorization and Expertise in Human Visual Cognition II

视觉对象识别发生在不同的抽象级别上，从类别级别，例如“狗”，到更具体的个体级别，例如，“我的英语猎犬”。此外，我们可以针对特定类别在这些级别中的一个级别开发“专业知识”；例如，观鸟者是物种级别的专家。本研究将继续探讨分类水平和知觉专业知识在选择对象类别(如脸或鸟)的认知和神经机制发展中的作用。由于不同的方法具有不同的优势和劣势，这项研究将涉及融合证据，包括正常人类的行为心理物理学、脑功能成像(FMRI)和事件相关电位(ERPs)，以及将这些技术推广到脑损伤患者。研究计划分为四个部分，解决不同的问题：1)人们如何成为感性专家？第一组实验将操纵受试者是依靠自己的观察，还是需要反馈和监督。第二组研究将考察关于物体的非视觉知识是否有助于学习过程并影响特定类别区域的组织。其他实验将测试支持物体识别的大脑区域的可塑性，调查一个区域的损害是否可以通过重组其他区域来弥补。2)网络中不同的大脑区域对视觉相似物体的专业知识起着怎样的计算作用？使用功能磁共振成像、事件相关电位和行为测量相结合的实验将调查不同类别选择的大脑区域如何在类别、从属和个人层面上支持识别。3)知觉专长的能力是什么？实验将测试一个人是否可以成为许多不同类别的对象(例如，鸟、狗、车、脸、花等)的专家，以及当同时处理来自不同专业领域的对象时是否存在干扰。4)使用某些对象几何形状可以更容易地获得感知专业知识吗？特别是，对准确人脸识别的适应性压力可能会使系统“偏向”倾向于类似人脸的配置。通过操纵刺激对象的视觉结构，行为学和功能磁共振实验将研究获得专业知识的几何约束。总体而言，这些实验应该有助于我们更好地理解视觉对象识别的本质，阐明单个系统如何支持我们能够执行的广泛的识别任务。这些发现的意义从脑损伤个体康复的可能方案到学习障碍儿童(例如自闭症)的更好教育，再到为人脸和物体识别开发更有效和更强大的机器视觉系统。

Visual object recognition occurs at different levels of abstraction ranging from categorical levels, e.g., "dog," to the more specific individual level, e.g., "my English hound." Moreover, we can develop "expertise" at one of these levels for a given category; for instance, bird watchers are experts at the species level. This research will continue to investigate the roles of level of categorization and perceptual expertise in the development of cognitive and neural mechanisms selective for object categories (such as faces or birds). Because different methods offer different strengths and weaknesses, this research will involve converging evidence, including behavioral psychophysics, functional brain imaging (fMRI), and event related potentials (ERPs) in normal humans, as well as extending these techniques to brain-injured individuals. The research program is divided into four sections addressing different questions:1) How do people become perceptual experts? A first set of experiments will manipulate whether subjects rely on their own observations or require feedback and supervision. A second set of studies will examine whether non-visual knowledge about objects contributes to the learning process and affects the organization of category-specific areas. Other experiments will test the plasticity of the brain regions, which support object recognition, investigating whether damage to one area can be compensated for by reorganization of other areas. 2) What are the computational roles of different brain areas within the network that mediates expertise with visually-similar objects? Experiments using a combination of fMRI, ERP, and behavioral measures will investigate how different category-selective brain areas support identification at the categorical, subordinate, and individual levels. 3) What is the capacity of perceptual expertise? Experiments will test whether one can become an expert with many different classes of objects (e.g., birds, dogs, cars, faces, flowers, etc.), as well as whether there is interference when objects from different expertise domains are processed at the same time. 4) Can perceptual expertise be acquired more easily with some object geometries? In particular, adaptive pressures for accurate face recognition may have "biased" the system to prefer face-like configurations. By manipulating the visual structure of stimulus objects, behavioral and fMRI experiments will investigate the geometric constraints on the acquisition of expertise.Overall, these experiments should help us to better understand the nature of visual object recognition, elucidating how a single system can support the wide range of recognition tasks we are able to perform. The implications of these findings vary from possible protocols for the rehabilitation of brain-injured individuals to the better education of learning-impaired children (e.g., as in autism) to the development of more effective and robust machine vision systems for face and object recognition.