ISSN 0439-755X
CN 11-1911/B

心理学报 ›› 2015, Vol. 47 ›› Issue (12): 1445-1453.doi: 10.3724/SP.J.1041.2015.01445

• 论文 • 上一篇    下一篇



  1. (华南师范大学心理应用研究中心/心理学院, 广州 510631)
  • 收稿日期:2014-10-16 出版日期:2015-12-25 发布日期:2015-12-25
  • 通讯作者: 刘志雅, E-mail:
  • 基金资助:


Information Amount and Obviousness Influence Hypothesis Generation

LIU Zhiya; ZHENG Chen   

  1. (Center for Studies of Psychological Application/School of Psychology, South China Normal University, Guangzhou 510631, China)
  • Received:2014-10-16 Online:2015-12-25 Published:2015-12-25
  • Contact: LIU Zhiya, E-mail:


该研究探索了规则的信息量与明显度对规则可获得性的影响。采用改编后的2-4-6任务, 70名大学生参加了两个实验。实验1发现, 规则可获得性除了受到信息量的影响外, 还受到明显度的显著影响; 实验2增加了规则的探测, 发现信息量大、明显度高的规则更容易用语言陈述出来, 而信息量小、明显度低的规则更不容易陈述出来。实验结果启示:信息量大、明显度高的规则可能是一种外显规则, 而信息量小、明显度低的规则可能是一种内隐规则。初步提出了“计算和感知的双加工”模型。

关键词: 学习, 规则形成, 推理, 内隐学习


 This study focuses on the availability of rule learning. Cherubini, Castelvecchio & Cherubini (2005); Cherubini, Rusconi, Russo, Di Bari, & Sacchi (2010) confirmed that the availability of rule learning was influenced by the information amount of the rule. Information amount was explained by how many examples could be covered by a rule. For a rule, the more number of examples could be converted, the less information amount would have. For example, in 2-4-6 task, the information amount in the rule of “even number increase” is 1/n and in the rule of “the third number is the sum of other two” is 1/n2. The information amount theory suggests that a rule with higher information amount is generated more easily than a lower one. However, Some researches (Barsalou,1982; Rips,1989; Medin, Lynch, Coley, & Atran,1997; Shafto, Coley, & Baldwin,2007; Guhe, Pease, & Smail,2011) showed that rule learning would be impacted by the information background of participants.

In this paper, information background was defined as the obviousness of the rule. Inspired by dual process model of deductive reasoning (Evans, 2003, 2010; Sloman, 1996; Barrouillet, 2011), This study assumed that the cognitive process of rule learning might be impacted by the information amount and obviousness both. Dual process model suggested that there were two independent cognitive systems, system 1 was usually described as unconscious and automatic; the system 2 was inherently conscious and controlled. This paper assumed that there might be two independent cognitive systems that manipulating rule learning process. This hypothesis was tested by experiment 1. Additionally, Ashby (1998) also suggested that there were two kinds of category learning. One was the rule-base category learning, the other was information integration. In the case of rule-based learning, participants could abstract a linguistic and explicit rule from materials, while they cannot discover an explicit rule but still can classify materials when doing information integration tasks, which seems to be implicit. This article assume that rule learning process may also conducted by both explicit and implicit systems and which system would be adopted may related to the information amount and obviousness of rules. Experiment 2 was designed to test this hypothesis. With 70 college students' participated, a revised 2-4-6 task was used to examine our hypothesis. Both experiments were presented by Psychtoolbox 3.0 on MATLAB.

Experiment 1 found that there were two independent factors, the information amount and the obviousness of the rule, significantly influence availability of rule learning. Experiment 2 is the same as experiment 1 except a rule description between every block of learning. The result of experiment 2 indicated that rules with high information amount and obviousness are more easier to be learned and expressed, while rules of low information amount combine with less obvious could be learned either but hardly be expressed clearly. These results consist with the dual process model in deductive reasoning and reveal that the rules with high amount information and obviousness are processing by an explicit rule system, and with lower amount information and less obviousness are processing by an implicit rule system.

Key words: learning, hypothesis generation, reasoning, implicit learning