为什么解贝叶斯问题这么难?从数字理解到关系推理的要求

IF 2.5 3区 心理学 Q2 PSYCHOLOGY, EXPERIMENTAL
E. Tubau
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引用次数: 1

摘要

在过去的几十年里,理解贝叶斯问题求解困难的来源一直是一个重要的研究目标,数字格式和个人计算能力的影响得到了广泛的研究。然而,对概率数理解的关注掩盖了贝叶斯任务的关系推理需求。当统计数据是口头描述的,因为所要求的数量关系(后验比)与所提供的数量关系(先验比和似然比)不一致时,这种情况尤其严重。在这方面,我提出了贝叶斯推理研究可以通过考虑数学问题解决的符号对齐框架来改进的建议。具体来说,这个框架可以帮助理解基于语言描述的贝叶斯推理的主要困难的来源。从本质上讲,本建议支持数学教育中关于需要培养关系理解以避免误导性对齐和提高问题解决能力的一般主张。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Why can it be so hard to solve Bayesian problems? Moving from number comprehension to relational reasoning demands
Abstract Over the last decades, understanding the sources of the difficulty of Bayesian problem solving has been an important research goal, with the effects of numerical format and individual numeracy being widely studied. However, the focus on the comprehension of probability numbers has overshadowed the relational reasoning demand of the Bayesian task. This is particularly the case when the statistical data are verbally described since the requested quantitative relation (posterior ratio) is misaligned with the presented ones (prior and likelihood ratios). In this regard, here I develop the proposal that research on Bayesian reasoning might improve by considering the notational alignment framework of mathematical problem-solving. Specifically, this framework can help to understand the sources of the main difficulties underlying Bayesian inferences based on verbal descriptions. In essence, the present proposal supports the general claim in math education regarding the need to foster relational comprehension to avoid misleading alignments and improve problem solving.
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来源期刊
Thinking & Reasoning
Thinking & Reasoning PSYCHOLOGY, EXPERIMENTAL-
CiteScore
6.50
自引率
11.50%
发文量
25
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