Honda Hidehito, Matsuka Toshihiko, Ueda Kazuhiro
Graduate School of Arts and Sciences, The University of Tokyo.
Department of Cognitive and Information Science, Chiba University.
Cogn Sci. 2017 May;41 Suppl 5:1093-1118. doi: 10.1111/cogs.12395. Epub 2016 Jul 20.
Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use (Kahneman & Frederick, 2005). In this framework, it is predicted that people will rely on heuristic or knowledge-based inference depending on the subjective difficulty of the inference task. We conducted competitive tests of binary choice inference models representing simple heuristics (fluency and familiarity heuristics) and knowledge-based inference models. We found that a simple heuristic model (especially a familiarity heuristic model) explained inference patterns for subjectively difficult inference tasks, and that a knowledge-based inference model explained subjectively easy inference tasks. These results were consistent with the predictions of the attribute substitution framework. Issues on usage of simple heuristics and psychological processes are discussed.
一些关于二元选择推理的研究者认为，人们基于简单启发式方法进行推理，比如识别、流畅性或熟悉度。另一些人则认为，人们基于现有知识进行推理。为了检验启发式方法和知识运用之间的界限，我们从启发式使用中的属性替换角度（Kahneman & Frederick，2005）来考察二元选择推理过程。在此框架下，可以预测，人们将根据推理任务的主观难度依赖于启发式或基于知识的推理。我们对代表简单启发式方法（流畅性和熟悉度启发式方法）的二元选择推理模型和基于知识的推理模型进行了竞争性测试。我们发现，一个简单启发式模型（尤其是熟悉度启发式模型）解释了主观上困难的推理任务的推理模式，而一个基于知识的推理模型解释了主观上容易的推理任务。这些结果与属性替换框架的预测一致。文中还讨论了简单启发式方法的使用问题和心理过程。
