心理学进展  >> Vol. 4 No. 3 (May 2014)

类别归纳推理的贝叶斯模型
The Bayesian Model of Category-Based Induction

DOI: 10.12677/AP.2014.43060, PDF, HTML, 下载: 2,347  浏览: 9,101  国家自然科学基金支持

作者: 邓志超:北京工业大学国际WIC研究院,北京;梁佩鹏:首都医科大学宣武医院,北京;磁共振成像脑信息学北京市重点实验室,北京;钟 宁:北京工业大学国际WIC研究院,北京;磁共振成像脑信息学北京市重点实验室,北京;日本前桥工业大学生命科学与信息学系,前桥,日本

关键词: 类别归纳推理贝叶斯模型可计算模型Category-Based Induction Bayesian Model Computable Model

摘要: 贝叶斯模型为解释类别归纳推理的实验现象提供了一个统一的可计算框架。在该框架下,用不同的类别结构和随机过程表示不同的先验知识,并基于贝叶斯公式预测不同场景下的归纳力度。与其它模型相比,贝叶斯模型有较强的预测力度和更广的应用范围。文章总结了该模型的发展历史及现状,并首次系统阐述了其建模过程。未来研究可结合功能磁共振实验和计算语言学等方法,进一步拓展该模型的推理能力,提高其实际可用性
Abstract: The Bayesian model (BM) of category-based induction provides a unified computable framework for explaining the experimental phenomena (including the premise-conclusion similarity effect, the premise diversity effect, the premise monotonic effect and the premise-conclusion asymmetric effect, etc.) in category-based induction. Within this framework, the inductive reasoning in different contexts (such as induction about the generic biological properties or the causally transmitted properties) requires the constraint of different kinds of prior knowledge. Different kinds of prior knowledge can be represented by different kinds of category structures (i.e., the relationship between categories) and the corresponding stochastic process (i.e., the distribution of features/ properties in the category structure). Thus, BM can get the prior probability distributions for the Bayesian inference engine, and finally, the strength of an inductive argument can be calculated. As compared to the similarity coverage model (SCM) and feature-based inductive model (FBIM), BM can reflect the interaction of categories and properties, and has a clear mathematical basis, and also shows a better ability of prediction. This paper firstly reviews the research history and state of the art of the BM, and summarizes the process of computational cognitive modeling using BM. Secondly, BM is compared with the other models, and then the advantages and disadvantages of the BM are commented in details. Finally, some potential research directions are proposed: 1) further improving the ability of BM to deal with the common sense knowledge (e.g., the predatory behavior of animal), which may help to expand its application scope; 2) further increasing the power of BM to handle multiple objects and features/properties (if we learn that the animal A has the property X, what’s the possibility of the animal B having the property Y?); 3) that in combination with other methodologies (e.g., functional magnetic resonance imaging (fMRI) and computational linguistics, such as corpora), BM may improve its practical availability and reasoning abilities.

文章引用: 邓志超, 梁佩鹏, 钟宁 (2014). 类别归纳推理的贝叶斯模型. 心理学进展, 4(3), 432-439. http://dx.doi.org/10.12677/AP.2014.43060

参考文献

[1] 陈安涛, 李红(2003). 归纳推理心理效应的研究. 心理科学进展, 6期, 607-615.
[2] 王墨耘, 莫雷(2005). 归类不确定情景下特征推理的综合条件概率模型. 心理学报, 4期, 482-490.
[3] 王墨耘, 莫雷(2006). 特征归纳的关联相似性模型. 心理学报, 3期, 333-341.
[4] 王墨耘(2008). 归纳推理的抽样理论. 心理学报, 7期, 800-808.
[5] 尹静, 王墨耘(2009). 对归纳推理贝叶斯模型的检验. 心理学探新, 4期, 46-50.
[6] 张婷婷, 李红, 龙长权, 冯廷勇, 陈安涛, 李福洪, 王秀芳(2007). 归纳推理中的属性中心效应. 心理学报, 5期, 826-836.
[7] Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2008). Bayesian models of cognition. In: R. Sun (Ed.), Cambridge Handbook of Computational Cognitive Modeling. Cambridge: Cambridge University Press.
[8] Heit, E. (1998). A Bayesian analysis of some forms of inductive reasoning. In: M. Oaksford, & N. Chater (Eds.), Rational Models of Cognition (pp. 248-274). Oxford: Oxford University Press.
[9] Kemp, C., & Tenenbaum, J. B. (2003). Theory-based induction. Proceedings of the 25th Annual Conference of the Cognitive Science Society, Cognitive Science Society, Boston.
[10] Kemp, C., & Tenenbaum, J. B. (2008). The discovery of structural form. Proceedings of the National Academy of Sciences, 105, 10687-10692.
[11] Kemp, C., & Tenenbaum, J. B. (2009). Structured statistical models of inductive reasoning. Psychological Review, 116, 2058.
[12] Kemp, C., Shafto, P., & Tenenbaum, J. B. (2012). An integrated account of generalization across objects. Cognitive Psychology, 64, 35-73.
[13] Lo, Y., Sides, A., Rozelle, J., & Osherson, D. (2002). Evidential diversity and premise probability in young children’s inductive judgment. Cognitive Science, 26, 181-206.
[14] Mcdonald, J., Samuels, M., & Rispoli, J. (1996). A hypothesis assessment model of categorical argument strength. Cognition, 59, 199-217.
[15] Medin, D. L., Coley, J. D., Storms, G., & Hayes, B. K. (2003). A relevance theory of induction. Psychonomic Bulletin & Review, 10, 517-532.
[16] Osherson, D. N., Smith, E. E., Wilkie, O., & Lopez, A. (1990). Category-based induction. Psychological Review, 97, 185200.
[17] Sakamoto, K., Terai, A., & Nakagawa, M. (2007). Computational models of inductive reasoning using a statistical analysis of a Japanese corpus. Cognitive Systems Research, 8, 282-299.
[18] Sanjana, E. N., & Tenenbaum, J. B. (2002). Bayesian models of inductive generalization. In: S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in the Neural Information Processing Systems (pp. 51-58). Cambridge: MIT Press.
[19] Shafto, P., Kemp, C., Baraff, L., Coley, J., & Tenenbaum, J. B. (2005). Context-sensitive induction. Proceedings of the 27 Annual Conference of the Cognitive Science Society.
[20] Shafto, P., Kemp, C., Bonawitz, E. B., & Coley, J. D. (2008). Inductive reasoning about causally transmitted properties. Cognition, 109, 175-192.
[21] Sloman, S. A. (1993). Feature-based induction. Cognitive Psychology, 25, 231-280.
[22] Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10, 309-318.
[23] Tenenbaum, J. B., Kemp, C., & Shafto, P. (2007). Theory-based Bayesian models of inductive reasoning. In: A. Feeney, & E. Heit (Eds.), Inductive Reasoning: Experimental, Developmental and Computational Approaches (pp. 167-204). Cambridge: Cambridge University Press.
[24] Weber, M., Thompson-Schill, S. L., Osherson, D., Haxby, J., & Parsonsd, L. (2009). Predicting judged similarity of natural categories from their neural representations. Neuropsychologia, 47, 859-868.