Connections among Decision Field Theory models of cognition

被引:7
|
作者
Huang, Kai [1 ]
Sen, Suvrajeet [2 ]
Szidarovszky, Ferenc [3 ]
机构
[1] McMaster Univ, DeGroote Sch Business, Hamilton, ON L8S 4M4, Canada
[2] Ohio State Univ, Data Driven Decis Lab, Columbus, OH 43210 USA
[3] Univ Arizona, Dept Syst & Ind Engn, MORE Inst, Tucson, AZ 85721 USA
基金
美国国家科学基金会; 加拿大自然科学与工程研究理事会;
关键词
Decision Field Theory; Sequential sampling; Parallel sampling; Diffusion process; Markov chain; RESPONSE-TIME; ACTIVATION; PREFERENCE; ACCOUNT; SERIAL;
D O I
10.1016/j.jmp.2012.07.005
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Sequential sampling process models provide promising approaches to characterizing human judgment and decision making. In this paper, we study one class of sequential sampling process models referred to as Decision Field Theory (DFT). These models not only provide qualitative explanations of empirical findings, but also provide quantitative predictions of real time performance. We analyze major DFT models in the literature, and demonstrate that models which are seemingly similar can behave very differently, depending upon the cognitive basis for model parameters. Based on this analysis, we propose a mathematical framework which attempts to unify OFT models. (C) 2012 Elsevier Inc. All rights reserved.
引用
收藏
页码:287 / 296
页数:10
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