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Selection and switching in rule-based category learning.

机译：在基于规则的类别学习中进行选择和切换。

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摘要

This work focused on testing the role of selective attention in rule-based category learning tasks as instantiated in the explicit system of the COVIS (Competition between separate Verbal and Implicit Systems) model of category learning (Ashby, Alfonso-Reese, Turken, & Waldron, 1998). The first two experiments manipulated the nature of shifts in selective attention (e.g., switching attention away from a rule or selecting a new rule to attend to) required when learning two consecutive categorization tasks that could differ in the relevant and/or irrelevant dimensions. Experiment 3 introduced a novel paradigm designed to allow the decision strategy to be observable on a trial-by-trial basis in order to track how reinforcement history influences shifts in selective attention. The results of the empirical work suggested that: (1) both weakening of the irrelevant dimension and strengthening of the relevant dimension are important for category learning; and (2) people may not naturally be predisposed to persevere in categorization tasks. These data were used to test a novel computational implementation of the COVIS explicit system. In addition to supporting the empirical results, the computational work: (1) reinforced the idea that differences in extradimensional and intradimensional shifts of attention should be reflected in models of category learning; (2) suggested that COVIS should be modified to maintain a representation of the rule history, as well as the current set of vi active rules, in working memory; and (3) suggested that the rule selection and salience updating mechanisms in COVIS are critical, but that the rule switching mechanism may not be necessary under typical task conditions.
机译：这项工作的重点是测试类别学习（Ashby，Alfonso-Reese，Turken和Waldron）的COVIS（单独的言语和内隐系统之间的竞争）模型的显式系统中实例化的选择性注意在基于规则的类别学习任务中的作用。 （1998年）。当学习两个在相关和/或不相关维度上可能有所不同的连续分类任务时，前两个实验操纵了选择性注意力转移的性质（例如，将注意力从规则转移开或选择要遵循的新规则）。实验3引入了一种新颖的范例，旨在允许在逐个试验的基础上观察决策策略，以便追踪强化历史如何影响选择性注意力的变化。实证研究的结果表明：（1）弱化无关维度和加强相关维度对于范畴学习都很重要; （2）人们可能不会自然而然地倾向于坚持进行分类任务。这些数据用于测试COVIS显式系统的新颖计算实现。除了支持实验结果外，计算工作还包括：（1）加强了这样的观念，即注意力的维度外和维度内转移的差异应反映在类别学习模型中; （2）建议应修改COVIS，以在工作记忆中保持规则历史以及当前的活动规则集的表示； （3）建议COVIS中的规则选择和显着性更新机制至关重要，但是在典型的任务条件下可能不需要规则切换机制。

著录项

  • 作者

    Ell, Shawn William.;

  • 作者单位

    University of California, Santa Barbara.;

  • 授予单位 University of California, Santa Barbara.;
  • 学科 Psychology Cognitive.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 105 p.
  • 总页数 105
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 心理学;
  • 关键词

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