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Adaptive Experimental Methods for Evaluating Computational Models of Cognition

评估认知计算模型的自适应实验方法

基本信息

批准号：
8789395
负责人：
Mark A Pitt
金额：
$ 33.44万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-04-01 至 2017-07-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): The long-term goal of this program of research is to improve scientific inference in psychological science. The topic is investigated in the context of computational models of cognition, which can be extremely difficult to distinguish experimentally because of their complexity and the extent to which they mimic each other. Statistical methods (goodness-of-fit, Akaike Information Criterion) have been the dominant means of model evaluation and selection, and are applied after data have been collected in an experiment. The current project explores a new approach to improving inference by developing corresponding statistical methods that are applied on the front-end of an experiment, while the experiment is being designed. In this approach, dubbed adaptive design optimization (ADO), an experiment is divided into a series of mini-experiments. The design of each mini-experiment is updated based on performance in the preceding mini-experiment. The choice of design values is dictated by a sophisticated search algorithm that constantly pressures the models of interest to fit more and more challenging data points until one model emerges as superior. The adaptive nature of the methodology ensures the design is optimal throughout the testing session, and thereby maximizes the informativeness of the experimental results. Furthermore, the focus on optimizing the design simultaneously ensures that the experiment is highly efficient (e.g., fewer trials and participants). The three specific aims of the proposal are to (1) develop ADO so that it is applicable to a broad range of problems (e.g., various experimental designs, different modeling goals) in the discipline; (2) improve the ADO algorithm so that it can be used in real-time experiments; (3) develop web-based resources to enable researchers to learn about and take advantage of the methodology. The achievement of these three goals is intended to provide researchers with a new technology that can accelerate scientific discovery.

描述（由申请人提供）：本研究计划的长期目标是提高心理科学中的科学推理。该主题是在认知的计算模型的背景下研究的，由于它们的复杂性和相互模仿的程度，这些模型在实验上很难区分。统计方法（拟合优度，赤池信息准则）一直是模型评价和选择的主要手段，并在实验中收集数据后应用。目前的项目探索了一种新的方法来改善推理，通过开发相应的统计方法，应用于实验的前端，而实验正在设计中。在这种被称为自适应设计优化（ADO）的方法中，实验被分成一系列小型实验。每个小型实验的设计都是基于前一个小型实验中的性能进行更新的。设计值的选择由复杂的搜索算法决定，该算法不断对感兴趣的模型施加压力，以拟合越来越多的具有挑战性的数据点，直到一个模型成为上级模型。该方法的自适应性确保了设计在整个测试过程中是最优的，从而最大限度地提高了实验结果的信息量。此外，对优化设计的关注同时确保了实验是高效的（例如，试验和参与者较少）。该提案的三个具体目标是：（1）发展反兴奋剂组织，使其适用于范围广泛的问题（例如，不同的实验设计，不同的建模目标）在学科;（2）改进ADO算法，使其可以用于实时实验;（3）开发基于Web的资源，使研究人员能够学习和利用的方法。这三个目标的实现旨在为研究人员提供一种可以加速科学发现的新技术。