Perceptual and Decisional Processes in Categorization

分类中的感知和决策过程

• 批准号: 6430716
• 负责人: W Todd TODD MADDOX
• 金额: $ 13.68万
• 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/6430716
• 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.

描述（由申请人提供）：拟议的长期目标 研究的目的是识别和量化感知和认知过程 当观察者面对分类问题时， 其中类别的先验概率（或基本比率），以及 与分类决策相关的成本和收益（或收益）是 被操纵了在美国国立卫生研究院研究资助#SR 01 MH 59196的资助下， 我在理解这一过程方面取得了重大进展， 当基本利率和收益被操纵时的决策标准学习，以及 为了理解几个因素之间复杂的相互作用， 影响基本利率/收益学习。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。所采取的做法 建议的研究是隔离和量化几个变量的影响 通过比较人类的表现与决策标准的学习 最优分类器--一种假设的设备，可以最大化长期回报。的 目的是测试定量模型的试验-试验和渐近性能 通过开发一个“最优”和几个“次优”模型， 重要的理论约束。 四条研究路线是 提出了项目1研究类别分布操纵的影响 基本利率和收益学习。理论研究表明， 可辨别性、d '和类别方差操作具有较大的影响 目标奖励函数的奖励变化率（或陡度） 其将客观奖励与决策标准的位置相关联。如果 观测者对陡度（称为平坦极大值）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研究了收益矩阵操作对决策的影响 标准学习我们实验室的理论研究表明， 乘法影响陡度，而矩阵加法不影响。项目3 检查可能改善决策标准的不同类型的反馈 学习特别有前途的是基于最优分类器的反馈。 项目4将项目I - 3中的研究扩展到一个明确的决策 标准任务，观察者调整每个可观察的决策标准， 审判这些数据对于测试学习模型非常有用。