"塞翁失馬,焉知非福:公平性覺察與負面情緒提升決策表現的模型估計與反饋負波證據"

Abstract

人生中，我們常因一些外在因素影響自身的心情，例如比較的心態、或是面對生活中各種不公不義，進而影響決策的判斷。本篇研究利用兩階段的實驗設計，藉由數理模型、事件關聯電位揭露情緒對於行為決策影響。受試者首先面臨「命運遊戲」，63位受試者隨機分派到「仁慈組」、「中立組」或是「苛刻組」。每組在過程中受試者會被迫接受一系列的資產分配，覺察其中分配的公平性，進而產生對應的情緒。從「命運遊戲」中所獲得的資產，將為第二階段「機會遊戲」的起始籌碼。「機會遊戲」是一連串的二選一選牌遊戲，同回合呈現的兩張牌會有不同的機率被扣資產。受試者的遊戲目的為最大化自己的總資產，實驗長度約莫兩小時。分析方法上，我們以反饋負波來量測與「預測錯誤」有關之情緒反應，並採用貝氏方法之增強學習模型分析行為資料。結果發現「苛刻組」於「機會遊戲」保留的總資產最高，他們的「非預期」反饋負波值較其他兩組為大。另外，在模型參數表現上他們有較高的趨避損失傾向、較低的即時更新速率、以及較高的選擇一致性。

Equality and fairness in social interaction often elicit affective arousal and show a great impact on decision making. The present study aims to un- cover the mechanism behind such daily-life experiences using the behavioral, model-fitting, and electrophysiological approaches. In the first session of the experiment, Sixty-three paid participants were randomly assigned to one of the “Neutral,” “Harsh,” and “Kind” groups to undertake a different level of perceived fairness. Then a probabilistic gambling task with different proba- bilities of negative-reward assignments was applied to each participant to ex- amine the impact of emotional experience on her choice behavior. Altogether the procedures lasted about two hours. Trial-by-trial data were fitted by a hybrid reinforcement learning model using the Bayesian estimation approach. Brain activities were measured via event-related potentials. Our analyses re- vealed that, compared with other two groups, participants in the Harsh group retained more task scores, exhibited distinct parameter values of the reinforce- ment learning model, and demonstrated a larger feedback-related negativity to unexpected outcomes, suggesting a higher sensitivity to prediction error and a tendency to loss aversion.