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Jia W, Li S, Qian S, Wang M, Bao W, Zhao J, Cheong KH, Xie N. The intuitive decision preference and EEG features based on commonality heuristic. Comput Biol Med 2023; 160:106845. [PMID: 37120985 DOI: 10.1016/j.compbiomed.2023.106845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 03/10/2023] [Accepted: 03/30/2023] [Indexed: 05/02/2023]
Abstract
People tend to make intuitive decisions based on certain heuristics. We have observed that there is an intuitive heuristic that tends to prioritize the most common features as the selection result. In order to study the influence of cognitive limitation and context induction on the intuitive thinking of common items, a questionnaire experiment with multidisciplinary features and similarity associations is designed. The experimental results reveal the existence of three classes of subjects. The behavioral features of Class I subjects show that cognitive limitations and task context fail to induce intuitive decision-making based on common items; instead, they rely heavily on rational analysis. The behavioral features of Class II subjects show a mixture of intuitive decision-making and rational analysis, with priority given to rational analysis. The behavioral features of Class III subjects indicate that the induction of the task context reinforces the reliance on intuitive decision-making. The electroencephalogram (EEG) feature responses (mainly in the β and γ bands) of the three classes of subjects reflect their respective decision-making thinking characteristics. The event-related potential (ERP) results demonstrate that Class III subjects induce a late positive P600 component with a significantly higher average wave amplitude than the other two classes, which may be related to the "oh yes" behavior for the common item intuitive decision method.
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Affiliation(s)
- Wanying Jia
- Department of Management Science and Engineering, Anhui University of Technology, Anhui, Maanshan 243002, China
| | - Songjie Li
- Department of Management Science and Engineering, Anhui University of Technology, Anhui, Maanshan 243002, China
| | - Shao Qian
- Department of Management Science and Engineering, Anhui University of Technology, Anhui, Maanshan 243002, China
| | - Meng Wang
- Department of Business, Anhui University of Technology, Anhui Maanshan 243002, China
| | - Wei Bao
- Department of Management Science and Engineering, Anhui University of Technology, Anhui, Maanshan 243002, China
| | - Jie Zhao
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore 487372, Singapore
| | - Kang Hao Cheong
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore 487372, Singapore.
| | - Nenggang Xie
- Department of Management Science and Engineering, Anhui University of Technology, Anhui, Maanshan 243002, China; Anhui Province Key Laboratory of Multidisciplinary Management and Control of Complex Systems, Anhui, Maanshan 243002, China.
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