以社群網路分析探討雙系統大腦決策機制

Abstract

我們日常生活中的行為、決策都深受著我們思考模式的影響，近年來在資管領域中，也越來越多的學者開始研究軟體專案經理在不同情況下進行專案決策時背後的決策思考機制。過去在研究人類決策與思考機制的學者，提出一了套雙系統理論(Dual Proccess Theory)，將人類的決策機制分成兩種不一樣的系統。系統一的決策機制屬於較自動化、依靠情感與直覺；系統二的決策屬於非自動化、依靠邏輯與證據分析。近年來有許多大腦思考模式的相關研究，透過功能性磁振造影(Functional Magnetic Reasoning Imaging, fMRI)來更深入了解不同決策機制下的腦區反應機制，但由於實驗的情境不同與大腦本身的複雜性，使得各個研究的腦區反應結果不盡相同，讓後續學者更難以解讀實驗結果腦區與決策機制的關聯性。\r\n本研究整理近年來與雙系統思考模式相關文獻的實驗結果，並透過Social Network Analysis(SAS,社群網路分析)來分析實驗結果，探討雙系統不同思考模式下，腦區資料的差異以及兩個系統的相關腦區在決策時的關聯性。

Human behavior and decisions are deeply influenced by our thinking mechanisms. In recent years, scholars have investigated the decision making mechanism under different circumstances. A few theories have been proposed. One of which is the dual systems theory of the brain that divides human decision process into two subsystems: one is faster and intuitive while the other is slower and reasoning. System 1 is more automatic and heuristics-based, while system 2 is more deliberate and logical. A number of prior studies have revealed that these two systems coexist and are employed in different decision tasks. Cognitive theories called the dual process theories are also developed based on the dual systems model of the brain.\r\nA large volume of papers in business and decision sciences have been published based on the dual process theories. However, most of them are behavioral in nature that derives interpretations from questionnaire survey or experimental data.\r\nRecent development in cognitive neural science has allowed us to further examine how different brain areas are activated through the use of special instruments such as functional magnetic reasoning Imaging (fMRI) to better understand this dual systems model. Many research results under different contexts have been reported, but different experimental settings and the complexity of the human brains often result in inconsistent observations that are hard to see the complete picture.\r\nThe purpose of this study is to conduct a meta-analysis on existing studies that adopted the dual systems theory to develop a better understanding of how these two subsystem works. We collected experimental results of published literatures and aggregated their findings with the social network analysis, which is a data mining technique used for finding relationships among objects. The circuits of both subsystems are derived and evaluated. The result allows us to better understand the collaboration of brain areas in these two systems.