PERCEPTUAL LEARNING: HUMAN VS. OPTIMAL BAYESIAN

感知学习：人类与机器

• 批准号: 6811542
• 负责人: Miguel Patricio Eckstein
• 金额: $ 24.42万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA BARBARA
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6811542
• 关键词: behavioral /social science research tag; clinical research; computational neuroscience; computer simulation; cues; discrimination learning; evaluation /testing; experience; experimental designs; human subject; learning; mathematical model; method development; model design /development; neural information processing; performance; psychophysics; statistics /biometry; visual perception; visual stimulus

DESCRIPTION (provided by applicant): Human performance in a large range of visual tasks improves with practice. One process by which observers' accuracy increases is due to an improvement in their ability to use task-relevant (signal) information. However, the dynamics of the human neural algorithm mediating this learning process is mostly unknown. We hypothesize that a new experimental paradigm can help elucidate the human learning algorithm as well as identify the human sources of learning inefficiency by allowing comparisons of empirically measured human perceptual learning performance to that of an optimal Bayesian learner and other suboptimal learning models. To achieve this goal we propose four Specific Aims: (1) to develop an experimental paradigm that allows the investigator to compare human perceptual learning to the learning of an optimal Bayesian learner as well as other suboptimal learning models for five specific tasks involving learning about different visual attributes; (2) to develop a battery of diagnostic tests to compare human and model learning; (3) to measure human visual performance psychophysically for the five proposed visual tasks and compare them to model performance using the battery of diagnostic tests; and (4) to use the developed experimental and theoretical framework to evaluate the efficiency of the human use of different modes of feedback on learning. The proposed work will improve our understanding of the human neural algorithms mediating the dynamics of adult perceptual learning and identify different sources of inefficiency in human learning. Finally, the experimental protocols and theoretical framework proposed will provide a novel, powerful and flexible framework that other researchers can use to evaluate normal adult and infant perceptual learning in a variety of tasks and sensory modalities, to assess learning in humans with visual disorders and/or learning disabilities, and could potentially be used in conjunction with cell recording and/or brain imaging.

描述（由申请人提供）：在各种视觉任务中的人类绩效随着实践而改善。观察者的精度提高的一个过程是由于他们使用与任务相关（信号）信息的能力提高了。但是，介导这种学习过程的人类神经算法的动力学大多是未知的。我们假设一个新的实验范式可以帮助阐明人类学习算法，并通过比较凭经验测量的人类感知学习绩效与最佳的贝叶斯学习者和其他次级学习模型的经验测量的人类感知学习绩效来确定人类学习效率低下的来源。为了实现这一目标，我们提出了四个具体目标：（1）开发一个实验范式，该范式使研究人员可以将人类感知学习与学习最佳的贝叶斯学习者以及其他次优的学习模型的学习，以涉及涉及有关不同视觉属性的五个特定任务； （2）开发一系列诊断测试以比较人类和模型学习； （3）为五个提出的视觉任务以心理物理测量人类的视觉表现，并将它们与使用诊断测试的电池进行对比较； （4）使用开发的实验和理论框架来评估人类使用不同反馈模式学习的效率。拟议的工作将提高我们对介导成人知觉学习动态的人类神经算法的理解，并确定人类学习效率低下的不同来源。最后，提出的实验方案和理论框架将提供一个新颖，强大而灵活的框架，其他研究人员可以用来评估正常的成人和婴儿感知学习，以各种任务和感觉方式，以评估具有视觉障碍和学习障碍的人类学习，并可能与细胞记录相结合和/或脑部图像来评估。