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利用负证据提高论点强度:对归纳模型的一种限制。

Raising argument strength using negative evidence: a constraint on models of induction.

机构信息

Psychology Department, University of Leuven, Tiensestraat 102, 3000 Leuven, Belgium.

出版信息

Mem Cognit. 2011 Nov;39(8):1496-507. doi: 10.3758/s13421-011-0111-2.

Abstract

Both intuitively, and according to similarity-based theories of induction, relevant evidence raises argument strength when it is positive and lowers it when it is negative. In three experiments, we tested the hypothesis that argument strength can actually increase when negative evidence is introduced. Two kinds of argument were compared through forced choice or sequential evaluation: single positive arguments (e.g., "Shostakovich's music causes alpha waves in the brain; therefore, Bach's music causes alpha waves in the brain") and double mixed arguments (e.g., "Shostakovich's music causes alpha waves in the brain, X's music DOES NOT; therefore, Bach's music causes alpha waves in the brain"). Negative evidence in the second premise lowered credence when it applied to an item X from the same subcategory (e.g., Haydn) and raised it when it applied to a different subcategory (e.g., AC/DC). The results constitute a new constraint on models of induction.

摘要

直观上,并且根据基于相似性的归纳理论,当相关证据为正时,它会提高论点的强度,而当它为负时,它会降低论点的强度。在三个实验中,我们检验了一个假设,即当引入负面证据时,论点的强度实际上可以增加。通过强制选择或顺序评估比较了两种论点:单一的积极论点(例如,“肖斯塔科维奇的音乐引起大脑中的α波;因此,巴赫的音乐引起大脑中的α波”)和双重混合论点(例如,“肖斯塔科维奇的音乐引起大脑中的α波,X 的音乐没有;因此,巴赫的音乐引起大脑中的α波”)。第二个前提中的负面证据在适用于同一子类别的项 X 时会降低可信度(例如,海顿),而在适用于不同子类别的项时会提高可信度(例如,AC/DC)。结果为归纳模型提供了一个新的约束条件。

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