When "bad" is good: How evaluative judgments eliminate the standard anchoring effect.
When "bad" is good: How evaluative judgments eliminate the standard anchoring effect.
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当“坏”是好的时：评价性判断如何消除标准锚定效应。
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
N. Brown
中科院分区：
文献类型：
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作者：
O. Schweickart;Cory Tam;N. Brown
早期对判断锚定的描述将这种影响归因于刻意但不充分的调整过程；最近的理论指出，自动的、基于启动的过程是潜在的原因。在这篇文章中，我们介绍了一种新的锚定评估操作和对标准锚定效应的分解分析，以确定自动锚定与深思熟虑过程在多大程度上驱动锚定。在提供目标估计之前，参与者指出目标是大于还是小于锚，或者锚是否会做出好的或坏的目标估计。与基于启动的账户的预测相反，锚定效应的分解表明，参与者通常提供的估计与他们之前的评估一致；特别是当参与者认为锚定是一个糟糕的目标估计时，锚定被排除。这些发现挑战了将锚定作为数字判断不可避免的偏差的观点，并表明人们对如何在不确定的情况下处理判断中的数字信息具有显着的控制力。(JuncINFO数据库记录(C)2020 APA，版权所有)。
Early accounts of judgmental anchoring attribute the effect to a deliberate, but insufficient, adjustment process; more recent theories point to automatic, priming-based processes as the underlying cause. In this article we introduce a novel anchor assessment manipulation and a decompositional analysis of the standard anchoring effect to determine the extent to which anchoring is driven by automatic versus deliberate processes. Prior to providing a target estimate, participants indicated whether the target was greater or less than the anchor, or whether the anchor would make a good or bad target estimate. Contrary to predictions of priming-based accounts, the decomposition of the anchoring effect revealed that participants generally provided estimates consistent with their prior assessment; in particular, anchoring was eliminated when participants considered the anchor to be a bad target estimate. These findings challenge the view of anchoring as an inevitable bias of numerical judgment and indicate that people have significant control over how they manage numerical information in judgments under uncertainty. (PsycInfo Database Record (c) 2020 APA, all rights reserved).