Perceptual and Decisional Processes in Categorization

分类中的感知和决策过程

• 批准号: 6621164
• 负责人: W Todd TODD MADDOX
• 金额: $ 14.66万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-01-01 至 2006-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6621164
• 关键词: adult human (21+); attention; behavior prediction; behavioral /social science research tag; clinical research; cognition; cues; decision making; discrimination learning; human subject; learning; mathematical model; model design /development; negative reinforcements; neural information processing; performance; psychological models; psychomotor reaction time; reinforcer; sensory feedback; visual perception

DESCRIPTION (provided by applicant): The long-term objective of the proposed research is to identify and quantify the perceptual and cognitive processes that are involved when an observer is presented with a categorization problem in which the prior probabilities (or base-rates) of the categories, and the costs and benefits (or payoffs) associated with categorization decisions are manipulated. With funding from NIH Research Grant # S R01 MH59196 my students and I made significant progress toward understanding the processes involved in decision criterion learning when base-rates and payoffs are manipulated, and toward understanding the complex interplay between several factors that influence base-rate/payoff learning. This work answered many questions, but also suggested many new lines of research. The purpose of this proposal is to expand our previous work in several new directions. The approach taken in the proposed research is to isolate and quantify the influence of several variables on decision criterion learning by comparing human performance with that of the optimal classifier--a hypothetical device that maximizes long-run reward. The aim is to test quantitative models of trial-by-trial and asymptotic performance by developing an "optimal" and several "sub-optimal" models, which instantiate important theoretical constraint. Four lines of research are proposed. Project 1 examines the effects of category distribution manipulations on base-rate and payoff learning. Theoretical work suggests that category discriminability, d', and category variance manipulations have a large effect on the rate of change in reward (or steepness) of the objective reward function which relates objective reward to the location of the decision criterion. If observers are sensitive to differences in steepness (called the flat-maxima hypothesis) then this should affect the speed and asymptote of learning. Project 2 examines the effects of payoff matrix manipulations on decision criterion learning. Theoretical work from our lab suggests that payoff matrix multiplication affect steepness, whereas matrix addition does not. Project 3 examines different types of feedback that might improve decision criterion learning. Especially promising is feedback based on the optimal classifier. Project 4 extends the studies in Project I - 3 to an explicit decision criterion task where observers adjust an observable decision criterion on each trial. These data are useful for testing learning models.

简介(由申请人提供)：建议的长远目标 研究是识别和量化知觉和认知过程 当观察者遇到分类问题时涉及到的 其中类别的先验概率(或基本概率)，以及 与分类决策相关的成本和收益(或收益)是 被操纵了。在国立卫生研究院研究补助金的资助下#S R01 MH59196我的学生 我在理解涉及到的过程方面取得了重大进展 当基本利率和收益被操纵时，决策标准学习，以及 有助于理解以下几个因素之间的复杂相互作用 影响基本利率/回报学习。这项工作回答了许多问题，但 也提出了许多新的研究方向。这项建议的目的是 在几个新的方向上扩展我们以前的工作。采取的方法是在 建议的研究是分离和量化几个变量的影响 人与人的比较决策准则学习研究 最佳分类器--一种使长期回报最大化的假设装置。这个 目的是检验逐次试验的量化模型和渐近性能 通过开发一个“最优”和几个“次最优”模型，实例化了 重要的理论约束。四个研究方向是 建议。项目1考察了类别分布操纵的影响 关于基本利率和收益的学习。理论研究表明，范畴 区分度、d‘和类别方差操作有很大的影响 关于目标报酬函数的报酬(或陡度)变化率 它将客观报酬与决策标准的位置联系起来。如果 观察者对陡度(称为平坦最大值)的差异很敏感 假设)，那么这应该会影响学习的速度和渐近线。 项目2考察了支付矩阵操作对决策的影响 规范学习。我们实验室的理论工作表明，收益矩阵 相乘会影响陡度，而矩阵相加则不会。项目3 检查可能改进决策标准的不同类型的反馈 学习。其中，基于最优分类器的反馈算法尤其有前景。 项目4将项目I-3中的研究扩展为明确的决定 标准任务，观察者在每个标准任务上调整一个可观测的决策标准 审判。这些数据对测试学习模型很有用。