Dynamic Analyses of Learning in Behavioral Experiments

行为实验中学习的动态分析

• 批准号: 6922384
• 负责人: EMERY N BROWN
• 金额: $ 31.68万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-15 至 2010-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6922384
• 关键词: Internet; Macaca mulatta; behavioral /social science research tag; brain electrical activity; brain mapping; clinical research; computer data analysis; computer program /software; computer simulation; computer system design /evaluation; functional magnetic resonance imaging; human subject; learning; mathematical model; method development; neural information processing; performance; statistics /biometry

DESCRIPTION (provided by applicant): Learning experiments are among the most common behavioral studies in neuroscience. While these experiments vary widely in their types of subjects, designs and duration, the basic structure of these studies is similar. On each trial in a sequence, a subject is given a finite amount of time to execute a task and his/her performance is recorded. The most common type of performance data from these experiments is the sequence of correct and incorrect trial (binary) responses. A statistical criterion is used to establish learning by showing that the subject can now perform a previously unfamiliar task with a greater reliability than would be expected by chance. Many techniques are used to analyze this data. However, none treats learning as a dynamic process and none uses dynamic estimation methods tailored for studying sequences of binary responses. To address this important analysis question, we respond to the Neurotechnology Research, Development and Enhancement Announcement (PA-04-006) with a project to develop statistical methods to analyze this common class of behavioral data. The first 4 specific aims of this project are to test the hypothesis that: 1) Learning in behavioral experiments can be accurately analyzed by using a state-space model to represent learning as a dynamic process. 2) Specific characteristics of learning such as response bias, changing learning rates, time-constants for forgetting learned behaviors, and the dynamics of an individual subject learning multiple interleaved tasks can be readily assessed. 3) For experiments with more than 1 group each with multiple subjects performing the same task, individual and population learning curves can be simultaneously estimated, and trial-by-trial between group comparisons of learning can be computed. 4) The analysis paradigm developed in Specific Aims 1 to 3 can be applied to characterize learning in rat, monkey and human behavioral studies of learning. Specific Aim 5 is to develop software to implement the algorithms needed to address Specific Aims 1 to 4, and to make this software freely available on the internet to neuroscientists for use in their data analyses. The project will use stochastic state-space models estimated by maximum likelihood. The long-term objectives of the research are to provide a coherent statistical paradigm to characterize learning in the wide range of behavioral studies in neuroscience. These more accurate quantitative assessments should allow neuroscientists to relate behavioral dynamics more reliably to neurophysiological, biochemical and functional neuroimaging measures of brain dynamics during learning in both normal and pathological conditions.

描述（由申请人提供）：学习实验是神经科学中最常见的行为研究之一。尽管这些实验的主题，设计和持续时间差异很大，但这些研究的基本结构相似。在每个审判中，一个主题有有限的时间来执行任务，并记录了他/她的表现。这些实验中最常见的性能数据类型是正确和不正确试验（二进制）响应的序列。统计标准用于通过证明受试者现在可以执行以前不熟悉的任务，其可靠性比偶然的预期更大的可靠性来建立学习。许多技术用于分析这些数据。但是，没有一个人将学习视为动态过程，没有人使用用于研究二元响应序列的动态估计方法。为了解决这个重要的分析问题，我们通过一个项目来回应神经技术研究，开发和增强公告（PA-04-006），以开发统计方法来分析这种共同的行为数据类别。该项目的前4个特定目的是测试以下假设：1）可以通过使用状态空间模型来准确地分析行为实验中的学习，以将学习表示为动态过程。 2）学习的具体特征，例如响应偏见，改变学习率，忘记学习行为的时间纪念体以及个体学科学习的动态，可以很容易地评估多个交错的任务。 3）对于超过1组的实验，可以同时估算多个执行相同任务的受试者，个人和人群学习曲线，并且可以计算学习学习的小组比较之间的试用。 4）在特定目的1到3中开发的分析范式可以应用于在大鼠，猴子和人类学习的学习中的学习表征。特定目标5是开发软件，以实现解决特定目标1至4所需的算法，并将此软件在Internet上免费提供给神经科学家，以便在其数据分析中使用。该项目将使用最大似然估计的随机状态空间模型。该研究的长期目标是提供一个连贯的统计范式，以表征在神经科学中广泛的行为研究中学习的特征。这些更准确的定量评估应使神经科学家能够更可靠地将行为动力学与神经生理学，生化和功能性神经成像测量在正常和病理条件下学习过程中的脑动力学测量。