归纳推理
论证(复杂分析)
演绎推理
计算机科学
人工智能
集合(抽象数据类型)
维数之咒
过程(计算)
任务(项目管理)
维数(图论)
推论
机器学习
自然语言处理
数学
操作系统
经济
生物化学
化学
管理
程序设计语言
纯数学
作者
Rachel Stephens,John C. Dunn,Brett K. Hayes
出处
期刊:Psychological Review
[American Psychological Association]
日期:2017-12-21
卷期号:125 (2): 218-244
被引量:51
摘要
Single-process accounts of reasoning propose that the same cognitive mechanisms underlie inductive and deductive inferences. In contrast, dual-process accounts propose that these inferences depend upon 2 qualitatively different mechanisms. To distinguish between these accounts, we derived a set of single-process and dual-process models based on an overarching signal detection framework. We then used signed difference analysis to test each model against data from an argument evaluation task, in which induction and deduction judgments are elicited for sets of valid and invalid arguments. Three data sets were analyzed: data from Singmann and Klauer (2011), a database of argument evaluation studies, and the results of an experiment designed to test model predictions. Of the large set of testable models, we found that almost all could be rejected, including all 2-dimensional models. The only testable model able to account for all 3 data sets was a model with 1 dimension of argument strength and independent decision criteria for induction and deduction judgments. We conclude that despite the popularity of dual-process accounts, current results from the argument evaluation task are best explained by a single-process account that incorporates separate decision thresholds for inductive and deductive inferences. (PsycINFO Database Record
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