Category learning performance can be influenced by many contextual factors, but the effects of these factors are not the same for all learners. The present '/> Use of evidence in a categorization task: analytic and holistic processing modes
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Use of evidence in a categorization task: analytic and holistic processing modes

机译:在分类任务中使用证据:分析和整体处理模式

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AbstractCategory learning performance can be influenced by many contextual factors, but the effects of these factors are not the same for all learners. The present study suggests that these differences can be due to the different ways evidence is used, according to two main basic modalities of processing information, analytically or holistically. In order to test the impact of the information provided, an inductive rule-based task was designed, in which feature salience and comparison informativeness between examples of two categories were manipulated during the learning phases, by introducing and progressively reducing some perceptual biases. To gather data on processing modalities, we devised the Active Feature Composition task, a production task that does not require classifying new items but reproducing them by combining features. At the end, an explicit rating task was performed, which entailed assessing the accuracy of a set of possible categorization rules. A combined analysis of the data collected with these two different tests enabled profiling participants in regard to the kind of processing modality, the structure of representations and the quality of categorial judgments. Results showed that despite the fact that the information provided was the same for all participants, those who adopted analytic processing better exploited evidence and performed more accurately, whereas with holistic processing categorization is perfectly possible but inaccurate. Finally, the cognitive implications of the proposed procedure, with regard to involved processes and representations, are discussed.
机译:<标题>抽象 ara id =“par1”>类别学习表现可能受到许多上下文因素的影响,但这些因素的影响对于所有学习者都不一样。本研究表明,这些差异可能是由于不同方式使用的方式,根据处理信息的两个主要基本方式,分析或全面地使用。为了测试所提供的信息的影响,设计了一种基于归纳规则的任务,其中通过引入和逐步减少一些感知偏见,在学习阶段操纵两个类别的特征Parience和比较信息。要收集关于处理模态的数据,我们设计了活动功能组合任务,这是一种不需要通过组合功能来分类新项目但再现它们的生产任务。最后,执行显式评级任务,这需要评估一组可能的分类规则的准确性。对具有这两种不同测试收集的数据的组合分析使能够分析参与者关于处理模型的种类,表示的结构和分类判断的质量。结果表明,尽管所提供的信息对所有参与者提供的信息是相同的,但是那些采用分析处理的人更好地利用证据并更准确地进行,而全面处理分类是完全可能的,但不准确。最后,讨论了所拟议的程序的认知含义,关于参与过程和陈述。

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