ISSN 0439-755X
CN 11-1911/B
主办:中国心理学会
   中国科学院心理研究所
出版:科学出版社

心理学报, 2018, 50(12): 1449-1459 doi: 10.3724/SP.J.1041.2018.01449

研究报告

一味坚持还是灵活变换:换牌频率的神经结构基础和认知机制

孙雅晨1#, 张汉其2,3#, 李勇辉4, 薛贵,2, 何清华,1,4

1 西南大学心理学部, 教育部认知与人格重点实验室, 重庆市脑科学协同创新中心西南大学分中心, 重庆 400715

2 北京师范大学心理学部, 认知神经科学与学习国家重点实验室, 北京 100875

3 北京大学心理与认知科学学院, 北京 100871

4 中国科学院心理研究所, 中国科学院心理健康重点实验室, 北京 100101

To switch or not to switch?Cognitive and neural mechanisms of card switching behavior

SUN Yachen1#, ZHANG Hanqi2,3#, LI Yonghui4, XUE Gui,2, HE Qinghua,1,4

1 Faculty of Psychology, Key Lab of Cognition and Personality, Chongqing Collaborative Innovation Center for Brain Science, Southwest University, Chongqing 400715, China

2 Faculty of Psychology, National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China

3 School of Psychology and Cognitive Sciences, Peking University, Beijing 100871, China

4 Institute of Psychology, Key Laboratory of Mental Health, Chinese Academy of Sciences, Beijing 100101, China

通讯作者: 薛贵, E-mail:gxue@bnu.edu.cn何清华,E-mail:heqinghua@swu.edu.cn;# 孙雅晨和张汉其为共同第一作者。

收稿日期: 2017-10-24   网络出版日期: 2018-11-30

基金资助: * 国家自然科学基金.  31400959
国家自然科学基金委员会与德国科学基金会合作研究项目.  NSFC61621136008/DFGTRR-169
广东省创新团队珠江团队计划项目.  2016ZT06S220
2017年重庆市留学回国人员创业创新支持计划.  cx2017049
中央高校基本科研业务费专项资金项目.  SWU1809003.  SWU1709106
中国科学院心理健康重点实验室开放课题基金.  KLMH2015G01

Received: 2017-10-24   Online: 2018-11-30

摘要

决策是每个人每天都会进行的活动。二择一时, 有人择一而终, 有人变换不定。这种个体差异可能与坚持性人格和认知灵活性有关。本研究旨在探讨这种行为特征背后的认知神经机制, 以便进一步理解决策变换中存在的个体差异。本研究采用了单变量和多变量体素形态学分析的方法分析了350名大学生(其中女性194人, 平均年龄19.97岁)在随机猜牌任务中的换牌频率与大脑灰质体积的相关情况, 探讨了坚持性人格特质和认知灵活性与换牌频率的关系, 并考察了两者在大脑灰质体积与换牌频率中的中介作用。单变量和多变量体素形态学分析结果都表明, 左侧后扣带回、右侧额中回、右侧额极和右侧脑岛区域的灰质体积可以预测被试的换牌频率; 坚持性人格和认知灵活性在其中起到了中介作用。这些结果阐释了换牌频率个体差异的认知机制和神经基础, 对理解为何有的人一味坚持, 而有的人灵活变换背后的原因提供了理论基础, 同时为开发改善非理性决策行为的方法提供了重要的参考价值。

关键词: 换牌频率 ; 随机猜牌任务 ; 坚持性 ; 认知灵活性 ; 决策 ; 体素形态学分析 ; 重复二元选择

Abstract

Decision making is a common, frequent and important task. It is not uniform though; there are individual differences in decision making processes. One notable differences between decision makers is in repeated binary choice situations. Specifically, when facing repeated binary choices, some people keep choosing the same option while others often switch. Previous research used a random card guessing task to explore the underlying mechanism of such differences in choice strategy. In this task, participants are asked to match a computer-generated “random” choice of a black or red card. The computer does not follow a random choice pattern; it follows a canonical random sequence generated by a Bernoulli process characterized by an equal numbers of black and red choices, switch of color on half of the trials, and streak length following an exponential distribution. In theory, participants should guess cards randomly. Nevertheless, they switch significantly less often than the computer does. In other words, participants present some change resistance and have an increased likelihood to select the same card; this likelihood varies among participants. One notable gap in this research stream pertains to the underlying cognitive and neural mechanism of such card switching behaviors. We partially address this gap in this study.

Three hundred and fifty healthy Chinese college students (194 females, mean age = 19.97 years) were recruited for this study. All of them completed the Card Guessing Task, the Temperament and Character Inventory-Revised (TCI-R), and the Wisconsin Card Sorting Test (WCST). One session of high-resolution magnetic resonance anatomical image was also acquired for each individual using a 3T MRI scanner. First, subjects’ frequency of switching, persistence error on the WCST as an index for cognitive flexibility, and persistence dimension score on TCI-R were calculated. Next, the correlation between gray matter volume (GMV) and frequency of switching was tested with both univariate and multivariate voxel-based morphometry (VBM). In addition, the mediation roles of trait persistence and cognitive flexibility in the GMV and switching frequency were tested.

Results suggested that the mean frequency of card switching in our sample was 43%, which was significantly lower than 50% (p < 0.001). Importantly, the number varied from 0% to 80%, suggesting large between-individual differences. Correlation analysis showed that both trait persistence and cognitive flexibility negatively correlated with card switching frequency. Univariate VBM analysis showed that (1) the GMV in the Frontal Pole (FP), Posterior Cingulate Gyrus (PCC), Putamen and the left Insular Cortex positively correlated with the card switching frequency, and (2) the GMV in the Medial Temporal Lobe and right Insular Cortex negatively correlated with card switching frequency. Multivariate VBM analysis suggested that the GMV of Posterior Cingulate Gyrus (PCC), Middle Frontal Gyrus (MFG), Insular Cortex, and Frontal Pole could significantly predict individuals’ frequency of card switching. Last, mediation analysis revealed that both trait persistence and cognitive flexibility mediate the relationship between GMV of the implicated regions and card switching frequency.

Overall, this study examined individual differences in card switching frequency and the cognitive and neural mechanisms that underlie them. Understanding the reason why some people persist in choosing the same option, while others frequently change their choices is important, and can serve as a basis for understating complex decision making situations that follow a repeated binary choice pattern.

Keywords: card switching frequency ; the card guessing task ; persistence ; cognitive flexibility ; decision making ; voxel-based morphometry ; repeated binary-choice

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本文引用格式

孙雅晨, 张汉其, 李勇辉, 薛贵, 何清华. 一味坚持还是灵活变换:换牌频率的神经结构基础和认知机制 . 心理学报[J], 2018, 50(12): 1449-1459 doi:10.3724/SP.J.1041.2018.01449

SUN Yachen, ZHANG Hanqi, LI Yonghui, XUE Gui, HE Qinghua. To switch or not to switch?Cognitive and neural mechanisms of card switching behavior. Acta Psychologica Sinica[J], 2018, 50(12): 1449-1459 doi:10.3724/SP.J.1041.2018.01449

1 前言

决策是人们根据自己的信念和意愿对生活情境做出判断和选择的行为。从眼下的选择今天穿什么衣服, 到未来的规划以后做什么事情, 都属于决策的范畴。“理性人”假设认为, 面对决策中每个独立的事件, 人们应当不受先前决策及其结果的影响, 而是根据其出现概率来做出选择。而现实是人们经常需要根据之前的决策结果, 不断更新后续选择, 这种行为就被称为适应性决策(Xue, Lu, Levin, & Bechara, 2010)。适应性决策具有很明显的个体差异, 尤其当我们面对二择一的决策时, 不同人表现出完全不同的行为模式:有的人做出择其一而终的决定, 而有的人则不断更换自己的选择。探讨这一个体差异产生的原因和背后机制有助于更好地理解人类决策行为, 并为众多决策障碍患者的治疗提供理论借鉴。

为了测量这一问题, 研究者发明了随机猜牌任务(Xue, He, et al., 2012; Xue, Juan, Chang, Lu, & Dong, 2012)。在该任务中, 被试被告知需要猜测电脑从两张牌(实验中对应的两个选项是红色和黑色的牌)中做出的随机选择结果, 如果猜对了电脑的选择将得到相应的金钱奖赏, 猜错了就一无所得。电脑的随机选择是由伯努利随机过程预先确定下来的, 具有以下三个特点:红牌和黑牌选择的次数相同; 红牌和黑牌之间的转变次数占一半试次; 相同牌出现(连续出现红牌或连续出现黑牌)的持续长度按指数函数分布。对被试而言, 电脑选择红牌和黑牌的概率是相同的, 所以被试猜测并选牌的最优策略应该也是随机的。然而前人研究表明被试的换牌频率(指被试上一次选择与下一次选择不同的选择次数占总选择次数的百分比)显著低于电脑的换牌频率, 这种结果说明被试的换牌频率并不倾向于随机, 而是更倾向固着于某一选项(Xue, Juan, et al., 2012)。研究人员还发现, 换牌频率具有较大的个体差异, 有的被试倾向于一味坚持猜测某个颜色的纸牌, 有的被试则倾向于灵活变换自己的选择(即一会儿猜测电脑选择了黑牌一会儿猜测电脑选择了红牌)。

大量的研究多从心理模型和生理层面对人们这种变换选择的行为现象进行了探讨, 提出了众多理论试图解释这种非理性决策行为, 最突出的三个理论分别是强化学习机制、用于指导探索决策行为的模型机制(Daw, Odoherty, Dayan, Seymour, & Dolan, 2006)以及基于脑损伤病人情感性决策的研究证据提出的躯体标记假说。最初的强化学习机制只能根据外显的行为结果, 通过外在奖惩论阐释人们倾向于某个选项的原因。在此基础上提出的决策模型机制通过计算模型认为理性人受到错误世界指导(对事件随机性存在认知偏差)便会产生次优决策(Green, Benson, Kersten, & Schrater, 2010)。躯体标记假说进一步从生理层面认为躯体标记在决策中起到警醒和反馈的作用, 当决策可能导致负面情绪时, 会自动发出警报信号以免决策者受到损失, 有助于决策者做出适应性决策(Bechara & Damasio, 2005)。这三个经典理论从直接的行为观察、定性的心理描述、定量的生理指标三个不同层次阐释了人们为何做出非理性的换牌决策, 但都没有解释为何人们换牌频率上存在个体差异, 也没有阐明个体差异的内部机制。

基于以往决策领域研究的结果, 坚持性是个体在决策时不确定能否得到即时奖赏下, 依旧保持一种行为策略不变的人格特质(Jung et al., 2010)。这种坚持性行为是由人内在动机引起, 并且受到个人特质和情景交互作用而形成的(Feather, 1962; Pittenger, 2002)。相关研究进一步发现坚持性高的人在决策时有更顽强的毅力, 而且会不断自我强化先前做出的反应以获得其预期的奖励(Bereczkei & Czibor, 2014)。基于此, 我们假设, 在随机猜牌任务中坚定选择一种颜色的牌(如红牌或黑牌)可能也是人格坚持性的一种外在行为体现, 坚持性越强的人, 表现出越坚定选择一个颜色的行为。

另一方面, 在我们进行决策时, 为了应对不断变换的环境, 认知灵活性在其中起到了关键性的作用, 因此我们假设猜牌任务中被试更换选项的行为也与认知灵活性有关。认知灵活性是自身对每一个独立的情境做出适当地变化, 以来应对新情境的能力, 亦或是转变对先前经验形成的反应定势来适应环境的变化的应变能力(李美华, 白学军, 2005)。Dong, Du和Qi (2016)采用经典的爱荷华赌博任务探究不同认知灵活性者的决策能力时认为, 相较于低认知灵活性者, 高认知灵活性的被试能更快地理解任务要求而且他们在测验中赢的钱更多。Xue等人(2012)使用猜牌任务、2-back工作记忆任务和Stroop任务研究赌徒谬误与执行功能的关系时还发现, 执行控制能力与赌徒谬误策略的使用频率呈正相关关系。

虽然使用猜牌任务探求换牌频率的脑机制尚且缺乏实质性的研究, 但一些科研人员已通过功能性磁共振成像技术(fMRI), 开始了对类似非理性决策任务神经基础的探索。例如, 前人对vmPFC (腹内侧前额叶皮层)损伤病人的研究表明, vmPFC与此类决策加工有关(Bechara, Damasio, Tranel, & Damasio, 1997)。De Martino, Kumaran, Seymour和Dolan (2006)进一步采用fMRI考察框架效应的神经机制发现, vmPFC的激活能够帮助被试克服非理性行为, 减小框架效应。Xue等人(2009)也使用fMRI研究了风险决策行为, 认为vmPFC主要负责输赢的体验, 而且赢钱时vmPFC激活程度要高于输钱时的激活程度。其他的研究结果还表明, 在决策任务中, 脑岛通常也会被激活, 脑岛在决策中具有重要作用(Xue et al., 2010)。Clark等人(2008)采用剑桥赌博任务来考察脑岛受损的病人, 发现他们无法根据赢钱概率来调节赌注大小, 最后导致更多地输钱。Xue等人(2010)的实验结果表明与不赌博相比, 赌博会导致被试更强烈的情绪体验, 激活更多的脑岛区域。这些研究无疑确认了腹内侧前额叶皮层和脑岛在非理性决策中的作用。

当然, 采用换牌任务的少数研究也为换牌频率的神经基础提供了一定的实证证据。Xue等人(2012)结合tDCS (经颅直流电刺激)和fMRI两种技术研究赌徒谬误的神经机制, 与将tDCS阳极作用于视觉皮层条件相比, 他们发现将tDCS阳极作用于左侧LPFC (外侧前额叶皮层)提高其兴奋水平能增强被试在猜牌任务中更多地采用赌徒谬误策略(更喜欢在下一次选择中做出与上一次不同选择的换牌行为)。这表明, LPFC的激活与换牌频率有显著的相关性。

总而言之, 在随机猜牌任务中的换牌频率具有极大的个体差异, 该个体差异可能与人格坚持性和认知灵活性有关。前人研究大多关注于类似决策行为的心理模型和生理学层面, 好在也有少量研究尝试揭示相似行为的脑机制, 但对于换牌频率的脑结构基础, 以及人格坚持性和认知灵活性对换牌频率脑机制的中介作用分析尚且缺乏研究证据的支持。鉴于此, 本研究首次尝试结合基于体素的形态测量学(voxel based morphometry, VBM)技术和多变量模式分析(Multi-voxel Pattern Analysis, MVPA)技术来研究换牌频率的神经解剖学基础, 并探索人格坚持性和认知灵活性在其中的中介作用, 以期揭示换牌频率个体差异的心理机制和神经解剖学基础。

2 方法

2.1 实验被试和程序

本研究的被试(350名大学生, 其中女性194人, 平均年龄19.97岁)来自一项大样本基因-脑-行为项目。所有被试均完成了随机猜牌任务、气质性格问卷、威斯康星卡片分类测验和高清大脑结构像扫描。所有被试视力或矫正视力正常, 无精神障碍或过往精神病史。所有被试均被完整地告知实验研究的目的和程序, 并且签订了知情同意书。所有的测量程序得到了国内某师范大学伦理审查委员会的许可。被试按照如下顺序完成实验任务:填写气质性格量表、在电脑上完成威斯康星卡片分类测验任务和随机猜牌任务, 最后在核磁共振仪里进行了结构像脑扫描。

2.2 实验材料

2.2.1 人格坚持性量表

使用Cloninger等人基于三维人格问卷(TPQ)编制的由240个条目组成的TCI-R量表测量坚持性人格特质(Heath, Cloninger, & Martin, 1994)。TCI-R量表主要用于评估影响人格精神生物模型的七个因素(Cloninger, Svrakic, & Przybeck, 1993)。本研究抽取其中坚持性分量表部分用于测量人格坚持性, 该分量表共35个题目, 采用5级李克特评分, 被试根据自己的情况从1(完全不符合)到5(完全符合)评定每个条目与自身描述相符的程度。该量表在本研究中具有较好的信度(Cronbach’s α系数 = 0.833)。

2.2.2 威斯康星卡片分类测验

被试的认知灵活性使用威斯康星卡片分类任务测量, 该实验范式是自Berg在1948年开发出后, 又经Heaton等人加以完善发展出的经典神经心理测验, 目的是为了评定正常成人的抽象思维及思维灵活性(刘哲宁, 1999)。本实验中的威斯康星卡片分类测验任务在计算机上完成, 实验材料由128张反应卡和4张模板卡组成(Heaton, Chelune, Talley, Kay, & Curtiss, 1993)。每张反应卡均存在三个维度:颜色(4种类型:红色、绿色、蓝色、黄色), 形状(4种类型:正方形、圆形、五角形、三角形), 数目(4个类别:1个、2个、3个、4个)。实验时被试不知道明确的测验规则, 只需根据自己认知到的反应卡的某一维度(如根据颜色)点击将其归类到相应的4张模板卡中的一种, 完成选择后电脑会反馈告诉被试正确或错误, 如果错误, 那么被试则需要调整自己的选择。当在一维度(如颜色维度)上连续正确选择10次后, 电脑即会转换维度(如转换到形状维度上)让被试继续做出按键反应。被试需要反应的次数根据其归类的正确性从60次到128次不等, 归类越好则需要选择的次数越少。将被试的持续性错误数(即规则转换后还按照原来规则进行的分类次数)作为评判其认知灵活性高低的指标, 持续性错误数越高, 则其认知灵活性越低。

2.2.3 随机猜牌任务

随机猜牌任务使用Matlab结合Psychotoolbox工具箱编写而成(http://psychtoolbox.org/), 电脑显示器分辨率设置为1024×768。被试需要猜测电脑会选择红色牌还是黑色牌来赢钱, 每猜中一次就赢得1元, 若是猜错一次则输掉1元。实验程序如图1所示:首先被试等待电脑选择红牌或黑牌, 电脑屏幕的左边和右边分别呈现两张牌(一张为红色, 另一张为黑色), 两张牌的位置是随机并且平衡改变的(即两张牌出现在左右不同的位置次数是均等的), 同时明确告知被试电脑选择红牌或黑牌的概率也是随机的(即红牌、黑牌的概率各为50%), 电脑选择时间为1 s; 然后要求被试在2s内猜测电脑选择了红牌还是黑牌, 并做出按键反应; 等待0.5 s后电脑屏幕呈现被试猜测的反馈结果, 反馈结果呈现时间为1 s。实验由两轮任务组成, 每轮任务各有63个试次, 总共126个试次。为降低工作记忆负荷, 电脑最后五次的选择呈现在屏幕的上方。换牌频率被定义为被试上一次选择与下一次选择不同的选择次数占总选择次数的百分比。

图1

图1   随机猜牌任务程序示意图


2.3 核磁共振数据采集与处理

采用3.0 T德国西门子磁共振扫描仪对被试进行脑结构像的采集。T1加权的3-D图像通过快速梯度回波序列获得。扫描参数如下:TR/TE = 2530/3.09 ms, 翻转角 = 10°。扫描按矢状切面取层, FOV = 256 mm × 256 mm, 像素矩阵为 = 256 × 256。最后获得208个厚度为1 mm的矢状切面, 最终空间分辨率为1 mm × 1 mm × 1 mm。

预数据处理使用FSL-VBM分析工具包完成分析。处理步骤如下:1)分割:大脑由灰质、白质、脑脊液等组成, 基于体素的形态测量方法根据组织类型进行结构分割; 2)配准:结构分割后将灰质图像配准到MNI152标准空间的灰质模板上, 再将原始的灰质图像通过线性与非线性的算法再次配准到此模板上以得到好的配准结果; 3)调制:配准后的局部体积的图像再通过非线性方式(除以局部的弯曲区域的雅各比行列式)进一步调整; 4)平滑:使用对称的高斯核(σ = 3 mm)进行平滑, 使体素灰度的概率分布更加符合正态分布, 以满足VBM的基本假设, 提高参数检验的效度, 降低被试间的变异。预处理完成后进行基于体素形态学的统计分析。

研究首先通过基于体素的形态学方法进行单变量分析。单变量VBM是在体素水平上对大脑局部灰质体积与换牌频率进行相关分析, 找出两者有显著相关的脑区。对于每个体素, 使用非参数的置换检验方法(通过FSL randomize v2.1实现)对统计分布进行推断, 零假设概率分布通过对数据进行5000次随机置换得到。全脑采用p < 0.05的错误发现率(FDR)作为多重比较校正的阈限以得到与换牌频率显著正相关和显著负相关的脑区, 结果采用fslview的截图呈现在MNI坐标系中。随后提取出与换牌频率显著相关区域的灰质体积做图(图3图4)。

接下来, 本研究采用MVPA方法进行多变量分析。因为与单体素分析方法相比, 这种方法假设大脑通过较大尺度下分布式的活动来完成某项功能, 而且多体素的活动模式具备更大的信息量, 因此这类方法比单变量分析更加敏感(Jimura & Poldrack, 2012)。这种方法通过灰质体积的分布模式对个体的换牌频率进行预测, 以探究能够预测换牌频率的脑区, 采用线性核的Epsilon-insensitive支持向量回归, 通过PyMVPA (http://www.pymvpa.org/)来实现计算(Hanke et al., 2009)。通过探照灯程序去测量每一个体素集团对换牌频率预测的准确性, 探照半径为3个体素(Kriegeskorte, Goebel, & Bandettini, 2006)。依照前人研究, 我们将SVR的参数设置为0.01 (Jimura & Poldrack, 2012)。研究通过对被试的数据进行10重交叉验证(leave-one-out cross validation)来估计预测的准确性。将350名被试分成10组, 每组35人, 且换牌频率成绩平均值和标准差相近。在每次交叉验证中, 我们选择9个组的(315名被试)的数据训练一个SVR回归模型, 然后利用此回归模型分析剩余一个组被试(35名)成像数据来预测被试换牌频率。通过模型预测的分数与被试实际分数的皮尔逊相关系数来定义模型预测的准确性。假阳性置换检验时采用随机化检验来估计分类准确率在零假设条件下(即脑灰质体积与换牌频率没有关联)的概率分布。每次分析中, 被试的换牌频率被随机打乱, 对数据进行同样的SVR分析。数据打乱是在每一个组内部进行的, 以确保每组的性别比例和换牌频率成绩分布不受影响。程序重复执行1000次用来生成相关系数(分类器预测准确性)的概率分布。因为对全脑进行探照灯内置换检验会产生极大的计算量(> 24000小时的处理时间), 故只对预测准确性大于0.138 (选择的预测准确性阈限0.138是通过多体素模式分析的假阳性置换检验得到的, p < 0.05)的集群进行了置换检验。这些脑区因都高于置换检验所得概率分布中95%的百分位数, 都具有显著的预测能力。为了进一步考察性别和年龄可能对数据结果的影响, 对于两种分析, 本研究均提取出团块的灰质体积数据与换牌频率进行偏相关分析。偏相关采用稳健的Robust Regression (基于Matlab计算)完成, 该分析方法将不受极端数据的影响。偏相关的结果同样采用FDR方法进行多重比较校正。

3 实验结果

3.1 行为结果

运用SPSS 22.0进行统计分析, 计算被试的平均换牌频率及其与电脑换牌频率的差异。分析结果显示被试平均换牌频率为43%, 电脑换牌频率为50%; 进一步进行t检验发现, 被试换牌频率显著小于电脑换牌频率, t(349) = -18.22, p < 0.001。被试换牌频率分布情况如图2所示, 可以发现被试换牌频率具有很大的个体差异。

图2

图2   被试换牌频率的分布直方图


表1列出了所有被试的坚持性人格和认知灵活性(威斯康星卡片分类测验得分)的平均数和标准差, 相关分析发现换牌频率与坚持性呈显著的负相关, 与认知灵活性也呈显著的负相关。

表1   坚持性和认知灵活性的描述统计及其与换牌频率的相关

认知机制平均数 ± 标准差与换牌频率的相关
坚持性116.81 ± 16.61r (249) = -0.23, p <.001
认知灵活性4.99 ± 2.16r (249) = -0.20, p <.001

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3.2 脑成像结果

3.2.1 单变量VBM分析结果

首先, 用单变量全脑分析探讨换牌频率相关的神经结构基础。单变量分析的结果显示, 后扣带回、额中回、额极、脑岛、壳核和颞叶等脑区与换牌频率表现出相关关系。其中, 左侧后扣带回、右侧额中回、左侧额极、左侧脑岛和左侧壳核等脑区灰质体积与换牌频率呈正相关; 右侧脑岛和右侧内侧颞叶的灰质体积与换牌频率呈负相关。

表2图3呈现了所有灰质体积与换牌频率呈正相关的脑区。在控制性别和年龄的影响之后, 发现左侧额极灰质体积(r = 0.162, p < 0.05)、左侧后扣带回灰质体积(r = 0.140, p < 0.05)、左侧壳核灰质体积(r = 0.134, p < 0.05)和左侧脑岛灰质体积(r = 0.128, p < 0.05)仍然与换牌频率呈正相关。

表2   灰质体积与换牌频率呈正相关的脑区

脑区左/右半球体素数量pMNI坐标xMNI坐标yMNI坐标z
后扣带回17880.016-4422
后扣带回0.03-14-4834
楔前叶0.02-10-6426
楔前叶0.0210-5826
额中回15220.01-342432
脑岛0.04-3682
壳核0.02-28-2-8
额中回7920.01461050
额极1160.03-34402

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图3

图3   灰质体积与换牌频率正相关的脑区(R表示被试右侧区域)


表3图4显示了大脑灰质体积与换牌频率呈负相关的脑区。在控制性别和年龄的影响后, 右侧内侧颞叶灰质体积(r = -0.142, p < 0.05)和右侧脑岛灰质体积(r = -0.129, p < 0.05)与换牌频率仍呈负相关。

3.2.2 多体素分析结果

单变量分析仍有不少缺陷, 因此研究接着用多变量模式分析方法探究能够预测换牌频率的脑区。

多变量模式分析的结果显示, 左侧后扣带回、右侧额中回、右侧额极和右侧脑岛的灰质体积模式能预测换牌频率。表4图5呈现多变量模式分析可预测换牌频率的脑区。

表3   灰质体积与换牌频率呈负相关的脑区

脑区大脑半球体素数量pMNI坐标xMNI坐标yMNI坐标z
颞上回15410.0566-166
脑岛0.0434-2612
颞叶9980.016012-26
内侧颞叶3790.0218-10-22
颞叶2800.02-561630

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图4

图4   灰质体积与换牌频率负相关的脑区(R表示被试右侧区域)


表4   可以预测换牌频率的脑区

脑区大脑半球体素数量预测准确性MNI坐标xMNI坐标yMNI坐标z
额中回2220.18-382232
额中回1280.1648848
脑岛750.1630-2620
额极180.16165046
后扣带回100.15-2-4424

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图5

图5   可以预测换牌频率的脑区(R表示被试右侧区域)


在控制性别和年龄变量后, 左侧后扣带回的灰质体积(r = 0.116, p < 0.05)和额中回的灰质体积(r = 0.182, p < 0.05)与换牌频率呈正相关; 而右侧脑岛的灰质体积(r = -0.135, p < 0.05)和右侧额极的灰质体积(r = -0.139, p < 0.05)与换牌频率呈负相关。

3.3 中介效应分析结果

根据前面所述的研究结果, 接下来我们使用SPSS 22.0软件, 结合PROCESS插件(http://www. processmacro.org/index.html), 采用中介效应模型来考察坚持性和认知灵活性在换牌频率与脑结构之间的中介效应。选用非参数百分位Bootstrap方法, 使用Hayes编制的SPSS宏(PROCESS), 进行抽样5000次, 检查中介效应对应95%的置信区间。根据多元模式分析结果, 提取出四个脑区(左侧后扣带回、右侧额中回、右侧额极和右侧脑岛)中每个脑区的灰质体积数据作为自变量, 换牌频率作为因变量, 并加入坚持性和认知灵活性做多个中介效应模型。采用Bonferroni方法进行多重比较校正。研究结果显示, 坚持性可以中介后扣带回灰质体积与换牌频率的关系, 认知灵活性则可以中介额中回灰质体积与换牌频率的关系(图6)。

图6

图6   中介效应模型


4 讨论

本研究目的在于从认知和神经基础角度解释大脑结构对换牌决策的影响。首先, 行为结果表明换牌频率具有明显的个体差异, 坚持性人格分数与被试换牌频率呈负相关, 说明越有毅力的人越倾向于不改变自己最初的抉择, 而坚持性差的人则更喜欢不断改变自己的策略; 认知灵活性也与换牌频率呈负相关, 这意味着认知灵活性差的人换牌频率越低(即认知灵活性差的人更换自己的选择的频率低), 反之换牌频率越高(即认知灵活性好的人懂得在不同的情境下灵活选择)。这个结果与前人的研究相一致。比如, Macaskill和Hackenberg (2012)通过鸽子作为被试来探索坚持性在沉默成本中的影响发现, 在鸽子很难抉择何时按键是从笼子中逃脱的最佳时机或者自己是否该按键从笼子中逃脱时, 坚持待在笼子中是其做出的默认行为策略。还有研究认为认知灵活性水平不同的被试在模糊决策任务上的表现存在显著差异, 相较于高认知灵活性的被试, 低认知灵活性者掌握模糊任务规则的能力差, 直觉认识水平差, 并且更倾向于不断选择不利卡牌(Dong, Du, & Qi, 2016)。

接着, 本研究用VBM技术对换牌频率的脑结构基础进行单变量和多元模式分析。单变量分析发现额极、后扣带回、壳核和左侧脑岛与换牌频率呈正相关; 内侧颞叶和右侧脑岛与换牌频率呈负相关。多元模式分析的研究发现左侧后扣带回、右侧额中回、右侧额极和右侧脑岛的灰质体积模式能预测换牌频率。从以往研究结果来看, 这些脑区的功能与换牌行为密不可分。有研究表明后扣带回对未选择选项损失表征方面有重要作用(Wang et al., 2017)。额极主要参与表征未选择选项的奖赏概率, 计算可选选项的价值(尤其在遭遇损失时) (Xue, Juan, et al., 2012)。由此可见, 换牌频率行为是人们在猜牌时基于获取金额奖励的动机产生的, 这些动机会对选项的得失进行估值。脑岛能够表征不同的躯体和内脏感觉, 在情绪系统具有非常重要的作用; 壳核与强化学习和内隐学习有关(Baliki, Geha, & Apkarian, 2009; Ogino et al., 2007)。使用fMRI的研究发现, 当人们浏览到流露出厌恶情绪的人的图片时, 相比观看到面部表情为中性情绪的人的图片, 其脑岛和壳核被激活地更加强烈, 研究者进一步认为壳核和脑岛可能有知觉厌恶的功能, 是厌恶环路的组成部分(Wicker et al., 2003)。所以人们的换牌行为可能是对原先的选择产生了厌恶情绪, 而这种负强化导致其改变决策模式。内侧颞叶(medial temporal lobe, MTL)是记忆系统的重要部分, 记忆之前所得的奖赏与当前选项的关联会影响到其决策情况, 对评价未来奖赏有着重要的作用(Ishii, Ohara, Tobler, Tsutsui, & Iijima, 2012)。被试换与不换的决定是根据之前做出的反应以获得的奖励来预测当前要做出的选择是否能继续得到金钱奖赏, 这就需要他们回忆之前的行为结果并且对未来奖赏做出评价, 而这与内侧颞叶的功能恰恰相关。

本研究最后根据多元模式分析的结果, 提取出四个脑区(后扣带回、额中回、额极和脑岛)的每个脑区的灰质体积数据, 并加入坚持性和认知灵活性做多重中介模型发现, 坚持性可以中介换牌频率与后扣带回灰质体积的关系, 认知灵活性可以中介换牌频率与额中回灰质体积的关系。简而言之, 换牌频率是通过坚持性影响后扣带回灰质体积的变化, 也通过认知灵活性影响额中回灰质体积的改变。

通过中介模型中坚持性人格与脑形态学的相关分析的结果还表明, 坚持性得分与后扣带回灰质体积呈显著负相关, 说明了个体的坚持性越高, 后扣带回灰质体积越小。这与大多数的前人研究结果相似, 坚持性与边缘系统区域的灰质和白质体积显著相关, 如中央旁小叶, 楔前叶和扣带回(Gardini, Cloninger, & Venneri, 2009; Van Schuerbeek, Baeken, De Raedt, Mey, & Luypaert, 2011)。在绝大多数探索坚持性人格与后扣带回灰质体积的实验研究结果也与本研究所得一致, 表明坚持性与后扣带回的灰质体积呈显著的负相关(Gardini et al., 2009; Van Schuerbeek et al., 2011), 然而目前也存在个别研究认为坚持性与后扣带回、楔前叶和海马旁回的灰质体积呈正相关关系(Hakamata et al., 2006)。

根据中介模型, 认知灵活性在换牌频率与额中回的灰质体积的关系中也起着中介效应, 这表明换牌频率与额中回灰质体积呈负相关关系可能是通过认知灵活性实现的。研究人员普遍认为额中回与执行控制功能关系密切, 反应抑制的神经基础主要涉及背侧前扣带回皮层(ACC), 背外侧前额叶皮层(dlPFC), 额中回(MFG)等脑区(Aron, 2011; Marsh et al., 2006; Watanabe et al., 2002; Zheng, Oka, Bokura, & Yamaguchi, 2008)。国内的研究人员通过功能性磁共振成像技术研究正常被试进行威斯康星卡片分类测验任务时大脑的活动状态发现, 人脑被激活部分主要分布在双侧前额叶, 尤其是前额叶背外侧部(包括额上回及额中回, 即BA46, 9, 10区); 以及顶叶后下部(包括顶上区的BA 7区及顶下区的BA 40区)皮层(舒畅, 王高华, 王惠玲, 王晓萍, 2009; 舒畅等人, 2004)。本研究结果也表明额中回灰质体积与换牌频率呈显著正相关, 说明执行功能的强弱可能影响到了被试的换牌频率的高低, 认知灵活性高的人更倾向于在猜牌选择时不定向。这还与本研究的行为结果相符, 即认知灵活性越高, 越喜欢换牌。

5 结论

本研究的行为结果发现换牌频率确实是一个具有稳定的个体差异的行为特质, 并且与坚持性和认知灵活性呈负相关; 单变量和多变量体素形态学的数据结果都表明, 左侧后扣带回、右侧额中回、右侧额极和右侧脑岛区域的灰质体积可以预测被试的换牌频率; 进一步的中介模型显示, 个体的坚持性和认知灵活性在大脑结构与换牌频率之间扮演显著的中介作用。这些实验结果从认知机制和神经基础两方面阐释了换牌频率的个体差异。

总的来讲, 本研究首次从认知机制和脑结构两个角度对猜牌任务中存在的换牌频率的个体差异提供了一定的实验证据和理论指导。我们的理论虽然能够较好地解释本研究中的实验结果, 但对于其是否能完美阐释换牌频率的个体差异的神经机制仍有待进一步的实验验证。基于此, 我们建议未来的研究可进一步采用问卷调查和行为实验探讨背后的人格特质和认知能力等心理机制, 并综合多种方法技术, 如静息态、DTI、fMRI、fNIRS (李玉华, 陈睿, 何清华, 2018)等方法来更加全面地探索换牌频率个体差异的神经基础。

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Information-based functional brain mapping

Proceedings of the National Academy of Sciences of the United States of America, 103( 10), 3863-3868.

DOI:10.1073/pnas.0600244103      URL     PMID:16537458      [本文引用: 1]

The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a "searchlight," whose contents are analyzed multivariately at each location in the brain.

&Macaskill A.C, Hackenberg T.D . ( 2012).

The sunk cost effect with pigeons: Some determinants of decisions about persistence

Journal of the Experimental Analysis of Behavior, 97( 1), 85-100.

DOI:10.1901/jeab.2012.97-85      URL     PMID:22287806      Magsci    

The sunk cost effect occurs when an individual persists following an initial investment, even when persisting is costly in the long run. The current study used a laboratory model of the sunk cost effect. Two response alternatives were available: Pigeons could persist by responding on a schedule key with mixed ratio requirements, or escape by responding on a second key. In Experiment 1, mean response requirements for persistence and escape were varied across conditions. Pigeons persisted (committing the sunk cost error) when persisting increased the mean response requirement only slightly but not when persisting was sufficiently nonoptimal. Experiment 2 explored more systematically combinations of ratios and probabilities assigned to the schedule key. Persistence varied with the ratio of the mean global response requirements for persistence and escape. In Experiment 3, transitions between ratios were signaled. This reduced nonoptimal persistence, and produced some instances of a reverse sunk cost error scaping when persistence was optimal. In Experiment 4, it was optimal to escape after the second-smallest ratio ever presented. Pigeons escaped at approximately the optimal juncture, especially in conditions with added signals. Overall, this series of experiments suggests that the sunk cost error may arise in part because persistence is the default behavioral strategy in situations where the contingencies for escape and persistence are insufficiently disparate and/or it is relatively difficult to discriminate when to escape. The study also demonstrates the utility of animal models of complex decision making situations.

Marsh R., Zhu H., Schultz R. T., Quackenbush G., Royal J., Wang Z., Skudlarski P., & Peterson B. S . ( 2006).

A developmental fMRI study of self-regulatory control in Tourette's syndrome

Human Brain Mapping, 27(11), 848-863.

DOI:10.1002/hbm.20225      URL     PMID:2292452      [本文引用: 1]

We used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of self-regulatory control across development in healthy individuals performing the Stroop interference task. Proper performance of the task requires the engagement of self-regulatory control to inhibit an automatized response (reading) in favor of another, less automatic response (color naming). Functional MRI scans were acquired from a sample of 70 healthy individuals ranging in age from 7 to 57 years. We measured task-related regional signal changes across the entire cerebrum and conducted correlation analyses to assess the associations of signal activation with age and with behavioral performance. The magnitude of fMRI signal change increased with age in the right inferolateral prefrontal cortex (Brodmann area [BA] 44/45) and right lenticular nucleus. Greater activation of the right inferolateral prefrontal cortex also accompanied better performance. Activity in the right frontostriatal systems increased with age and with better response inhibition, consistent with the known functions of frontostriatal circuits in self-regulatory control. Age-related deactivations in the mesial prefrontal cortex (BA 10), subgenual anterior cingulate cortex (BA 24), and posterior cingulate cortex (BA 31) likely represented the greater engagement of adults in self-monitoring and free associative thought processes during the easier baseline task, consistent with the improved performance on this task in adults compared with children. Although we cannot exclude the possibility that age-related changes in reading ability or in the strategies used to optimize task performance were responsible for our findings, the correlations of brain activation with performance suggest that changes in frontostriatal activity with age underlie the improvement in self-regulatory control that characterizes normal human development. Hum Brain Mapp, 2006. 2006 Wiley-Liss, Inc.

Li M.H., & Bai X.J . ( 2005).

The research progress of the development of cognitive flexibility in executive function

Psychological exploration, 25(2), 35-38.

URL    

Abstrcat Recently decade,Executive function has been the research of hot spot and forward position. Cognitive flexibility is the main component of Executive function, but Cognitive flexibility also plays an important role in individual ability development and interacts effectively with the environment. So in this paper we try to explore the essence of Cognitive Flexibility, its development, its research method and the relations between Cognitive Flexibility and the education.

[ 李美华, 白学军 . ( 2005).

执行功能中认知灵活性发展的研究进展

心理学探新, 25( 2), 35-38.]

[本文引用: 1]

Li Y. H., Chen R., & He Q. H . ( 2018).

The application of functional Near-Infrared Spectroscopy in understanding the brain mechanism of social interaction

Journal of Psychological Science, 41( 2), 305-311.

URL    

Humans are social animals.Regardless of whether you are talking to others,working on a project,or having a conversation,you are practicing social interactions.According to the content,social interactions can be divided into non-verbal interactions,verbal interactions,and mixed interactions.This process also includes verbal or non-verbal emotional exchange.Understanding the brain mechanism of social interaction could potentially provide guidelines for better social interactions.As early as 2002,Brothers has put forward "the social brain hypothesis" according to the complexity and diversity of social activities of primates.He believes that animals,including primates,have the neural mechanism to know and understand other individuals,and the mechanism can help individual quickly deal with all kinds of information produced in the process of interacting with other individuals in society,so as to achieve the purposes of communication with high quality.However,due to the limitation of the previous research technology,most of the experimental paradigms presented individual stimulus through computer screen,or let the participants interact with an imaginary partner,and then study the brain response and neural mechanism of the target subjects.Although researchers have explored a series of brain regions that constitute the human brain by studies of lesion subjects,brain imaging,and electrophysiological techniques,such as the amygdala,orbitofrontal cortex,ventromedial prefrontal cortex,the mirror neuron system and so on,it is still not clear that how to play and how to transfer the information between the brain in the dynamic and real-time social interaction.Functional near-infrared spectroscopy imaging is a convenient and friendly technology which provides an important technique support for studying the brain mechanism of social interaction in the natural setting.f NIRS is an emerging brain functional imaging technology,which uses blood volume and oxygenation as the carrier of information in the brain tissue,in order to understand local brain activity by measuring distribution and changes of blood volume and blood oxygen in the cerebral cortex.The present study describes the application of f NIRS in understanding the neural basis of three types of social interactions:verbal,nonverbal,and mixed social interactions.In the past decade,f NIRS hyperscanning was used by many researchers to measure changes in several cortical regions during social interaction,including frontal lobe,parietal lobe,and temporal lobe.However,largely because of different tasks used and different brain regions measured,their results are not consistent.We discuss the inconsistency of results and suggest the future research directions.First,future studies should improve the technology of functional near-infrared spectroscopy imaging itself and polish the data analysis and evaluation methods.Future studies need to develop advanced f NIRS devices to cover larger brain regions.We should construct a generalized predictive model to investigate the deep brain activity inferred from surface brain signals according to functional connectivity.Second,future research should refine and enrich the task paradigm used to study the brain mechanism of social interaction.And future research should use a more standardized control condition to control other variables.Finally,we need to focus on the application of technology integration in this field,providing multimodal evidence for the exploration of the brain mechanisms of social interaction.By taking advantage of multi-modal functional brain imaging techniques,and providing converging evidences,we could learn the neural basis of social interaction deeper.

[ 李玉华, 陈睿, 何清华 . ( 2018).

功能性近红外光谱技术在社会互动脑机制研究中的应用

心理科学, 41( 2), 305-311.]

URL     [本文引用: 1]

Liu Z.N . ( 1999).

Clinical application of the Wisconsin card classification test

Foreign Medical Sciences (Section of Psychiatry), 26( 1), 6-9.

[ 刘哲宁 . ( 1999).

Wisconsin 卡片分类测验的临床运用

国外医学: 精神病学分册, 26( 1), 6-9.]

URL     [本文引用: 1]

本文简要介绍了Wisconsin卡片分类测验的结构与功能,有其在器质性脑损害和精神分裂症患者中的临床运用。

Ogino Y., Nemoto H., Inui K., Saito S., Kakigi Ryusuke., & Goto Fumio . ( 2007).

Inner experience of pain: Imagination of pain while viewing images showing painful events forms subjective pain representation in human brain

Cerebral Cortex, 17( 5), 1139-1146.

DOI:10.1093/cercor/bhl023      URL     PMID:16855007      [本文引用: 1]

Pain is an unpleasant sensation, and at the same time, it is always subjective and affective. Ten healthy subjects viewed 3 counterbalanced blocks of images from the International Affective Picture System: images showing painful events and those evoking emotions of fear and rest. They were instructed to imagine pain in their own body while viewing each image showing a painful event (the imagination of pain). Using functional magnetic resonance imaging, we compared cerebral hemodynamic responses during the imagination of pain with those to emotions of fear and rest. The results show that the imagination of pain is associated with increased activity in several brain regions involved in the pain-related neural network, notably the anterior cingulate cortex (), right anterior insula, cerebellum, posterior parietal cortex, and secondary somatosensory cortex region, whereas increased activity in the and amygdala is associated with the viewing of images evoking fear. Our results indicate that the imagination of pain even without physical injury engages the cortical representations of the pain-related neural network more specifically than emotions of fear and rest; it also engages the common representation (i.e., in ) between the imagination of pain and the emotion of fear.

Pittenger D.J . ( 2002).

The two paradigms of persistence

Genetic Social & General Psychology Monographs, 128( 3), 237.

DOI:10.1007/s00787-002-0272-4      URL     PMID:12401034      [本文引用: 1]

Persistence refers to the extent to which an individual pursues reinforcement that is no longer available. The most common generalization regarding persistence is the partial reinforcement extinction effect, which states that partial, rather than continuous, reinforcement creates the greatest level of persistence. Although the partial reinforcement effect is the most common effect in , exceptions exist, namely the generalized and the reversed partial reinforcement effect. Since the 1930s, psychologists have used 2 general paradigms for studying persistence in : the experimental paradigm and the cognitive/individual differences paradigm. For the experimental paradigm, the primary independent variable is the schedule of reinforcement used to establish the prior to the removal of reinforcement. Explanations of persistence from the experimental perspective depend on associative principles derived from various theories of . By contrast, the cognitive/individual differences paradigm treats persistence as a function of trait variables, including locus of control and self-esteem, or general cognitive processes, such as cognitive dissonance or social . In this article, the author reviews the status of the current literature on persistence and recommends directions for future research.

Shu C., Wang G. H., Wang H. L., & Wang X. P . ( 2009).

The role of anterior cingle in the Wisconsin card classification test

Chinese Journal of Behavioral Medicine and Brain Science, 18( 12), 1083-1084.

[ 舒畅, 王高华, 王惠玲, 王晓萍 . ( 2009).

前扣带回在威斯康星卡片分类测验操作中的作用

中华行为医学与脑科学杂志, 18( 12), 1083-1084.]

DOI:10.3760/cma.j.issn.1674-6554.2009.12.010      URL     [本文引用: 1]

执行功能(executive function)是指个体在实施目的行为过程中以动态、灵活的方式协调多个认知子系统活动的复杂认知过程.由于它可能与人类许多高级心理功能有密切关系,因而成为了当今认知神经科学研究的重要前沿课题.

Shu C., Wang G. H., Wang H. L., Wu G. Y.., Huang X., Sun J. M., & Li Q. Y . ( 2004).

Preliminary study on brain activity patterns in WCST operation

Chinese Journal of Nervous and Mental Diseases, 30( 3), 223-225.

[ 舒畅, 王高华, 王惠玲, 吴光耀, 黄雄, 孙骏谟, 李秋英 . ( 2004).

WCST 操作时脑活动模式的初步研究

中国神经精神疾病杂志, 30( 3), 223-225.]

DOI:10.3969/j.issn.1002-0152.2004.03.022      URL     [本文引用: 1]

目的利用功能性磁共振成像,研究正常人进行威斯康星卡片分类测验(Wiscosin card sorting test,WCST)时大脑的功能活动模式,探讨该测验中有关脑区的作用.方法用1.5 T的磁共振成像仪和EPI扫描序列,以WCST为刺激任务,对17名正常人进行脑功能性磁共振成像,观察脑激动区域.结果 13人成功完成研究.WCST的执行激活了一个复杂的大脑网络,激活主要分布在双侧前额叶,尤其是背外侧部.中央前回、前扣带回、Broca区、顶上小叶、视区、视觉联系皮质和下颞部皮质也激活,丘脑和小脑有部分区域激动.结论 WCST是有效反映额叶功能的认知测验,但也与其他脑区功能相关.

Van Schuerbeek P., Baeken C., De Raedt R., De Mey J., & Luypaert R . ( 2011).

Individual differences in local gray and white matter volumes reflect differences in temperament and character: A voxel-based morphometry study in healthy young females

Brain Research, 1371( 2), 32-42.

DOI:10.1016/j.brainres.2010.11.073      URL     PMID:21126511      [本文引用: 2]

78 Temperament and character are both reflected in local brain morphology. 78 Temperament correlates with morphology in those regions generating behavior. 78 Character correlates with morphology in those regions processing cognitive tasks. 78 A neurobiological basis for Cloninger's personality model is supported.

Wang Y., Ma N., He X. S., Li N., Wei Z. D., Yang L., … &. Zhang X.C . ( 2017).

Neural substrates of updating the prediction through prediction error during decision making

Neuroimage, 157, 1-12.

DOI:10.1016/j.neuroimage.2017.05.041      URL     PMID:28536046      [本文引用: 1]

Learning of prediction error (PE), including reward PE and risk PE, is crucial for updating the prediction in reinforcement learning (RL). Neurobiological and computational models of RL have reported extensive brain activations related to PE. However, the occurrence of PE does not necessarily predict updating the prediction, e.g., in a probability-known event. Therefore, the brain regions specifically engaged in updating the prediction remain unknown. Here, we conducted two functional magnetic resonance imaging (fMRI) experiments, the probability-unknown Iowa Gambling Task (IGT) and the probability-known risk decision task (RDT). Behavioral analyses confirmed that PEs occurred in both tasks but were only used for updating the prediction in the IGT. By comparing PE-related brain activations between the two tasks, we found that the rostral anterior cingulate cortex/ventral medial prefrontal cortex (rACC/vmPFC) and the posterior cingulate cortex (PCC) activated only during the IGT and were related to both reward and risk PE. Moreover, the responses in the rACC/vmPFC and the PCC were modulated by uncertainty and were associated with reward prediction-related brain regions. Electric brain stimulation over these regions lowered the performance in the IGT but not in the RDT. Our findings of a distributed neural circuit of PE processing suggest that the rACC/vmPFC and the PCC play a key role in updating the prediction through PE processing during decision making.

Watanabe J., Sugiura M., Sato K., Sato Y., Maeda Y., Matsue Y., … Kawashima R . ( 2002).

The human prefrontal and parietal association cortices are involved in NO-GO performances: An event-related fMRI study

Neuroimage, 17( 3), 1207-1216.

DOI:10.1006/nimg.2002.1198      URL     PMID:12414261      [本文引用: 1]

One of the important roles of the prefrontal cortex is inhibition of movement. We applied an event-related functional magnetic resonance imaging (fMRI) technique to observe changes in fMRI signals of the entire brain during a GO/NO-GO task to identify the functional fields activated in relation to the NO-GO decision. Eleven normal subjects participated in the study, which consisted of a random series of 30 GO and 30 NO-GO trials. The subjects were instructed to press a mouse button immediately after the GO signal was presented. However, they were instructed not to move when the NO-GO signal was presented. We detected significant changes in MR signals in relation to the preparation phases, GO responses, and NO-GO responses. The activation fields related to the NO-GO responses were located in the bilateral middle frontal cortices, left dorsal premotor area, left posterior intraparietal cortices, and right occipitotemporal area. The fields of activation in relation to the GO responses were found in the left primary sensorimotor, right cerebellar anterior lobule, bilateral thalamus, and the area from the anterior cingulate to the supplementary motor area (SMA). Brain activations related to the preparation phases were identified in the left dorsal premotor, left lateral occipital, right ventral premotor, right fusiform, and the area from the anterior cingulate to the SMA. The results indicate that brain networks consisting of the bilateral prefrontal, intraparietal, and occipitotemporal cortices may play an important role in executing a NO-GO response.

Wicker B., Keysers C., Plailly J., Royet J-P., Gallese V., & Rizzolatti G . ( 2003).

Both of us disgusted in my insula: The common neural basis of seeing and feeling disgust

Neuron, 40( 3), 655-664.

[本文引用: 1]

Xue G., He Q. H., Lei X. M., Chen C. H., Liu Y. Y., Chen C. S., … Bechara A . ( 2012).

The gambler’s fallacy is associated with weak affective decision making but strong cognitive ability

Plos One, 7( 10), e47019.

DOI:10.1371/journal.pone.0047019      URL     PMID:23071701      [本文引用: 1]

Humans demonstrate an inherent bias towards making maladaptive decisions, as shown by a phenomenon known as the gambler’s fallacy (GF). The GF has been traditionally considered as a heuristic bias supported by the fast and automatic intuition system, which can be overcome by the reasoning system. The present study examined an intriguing hypothesis, based on emerging evidence from neuroscience research, that the GF might be attributed to a weak affective but strong cognitive decision making mechanism. With data from a large sample of college students, we found that individuals’ use of the GF strategy was positively correlated with their general intelligence and executive function, such as working memory and conflict resolution, but negatively correlated with their affective decision making capacities, as measured by the Iowa Gambling Task. Our result provides a novel insight into the mechanisms underlying the GF, which highlights the significant role of affective mechanisms in adaptive decision-making.

Xue G., Juan C-H., Chang C-F., Lu Z-L., & Dong Q . ( 2012).

Lateral prefrontal cortex contributes to maladaptive decisions

Proceedings of the National Academy of Sciences of the United States of America, 109( 12), 4401-4406.

DOI:10.1073/pnas.1111927109      URL     Magsci     [本文引用: 3]

Humans consistently make suboptimal decisions involving random events, yet the underlying neural mechanisms remain elusive. Using functional MRI and a matching pennies game that captured subjects' increasing tendency to predict the break of a streak as it continued [i.e., the "gambler's fallacy" (GF)], we found that a strong blood oxygen level-dependent response in the left lateral prefrontal cortex (LPFC) to the current outcome preceded the use of the GF strategy 10 s later. Furthermore, anodal transcranial direct current stimulation over the left LPFC, which enhances neuronal firing rates and cerebral excitability, increased the use of the GF strategy, and made the decisions more "sticky." These results reveal a causal role of the LPFC in implementing suboptimal decision strategy guided by false world models, especially when such strategy requires great resources for cognitive control.

Xue G., Lu Z. L., Levin I. P., & Bechara A . ( 2010).

The impact of prior risk experiences on subsequent risky decision-making: The role of the insula

Neurolmage, 50( 2), 709-716.

DOI:10.1016/j.neuroimage.2009.12.097      URL     PMID:20045470      [本文引用: 2]

Risky decision-making is significantly affected by homeostatic states associated with different prior risk experiences, yet the neural mechanisms have not been well understood. Using functional MRI, we examined how gambling decisions and their underlying neural responses were modulated by prior risk experiences, with a focus on the insular cortex since it has been implicated in interoception, emotion and risky decision-making. Fourteen healthy young participants were scanned while performing a gambling task that was designed to simulate daily-life risk taking. Prior risk experience was manipulated by presenting participants with gambles that they were very likely to accept or gambles that they were unlikely to accept. A probe gamble, which was sensitive to individual's risk preference, was presented to examine the effect of prior risk experiences (Risk vs. Norisk) on subsequent risky decisions. Compared to passing on a gamble (Norisk), taking a gamble, especially winning a gamble (Riskwin), was associated with significantly stronger activation in the insular and dorsal medial prefrontal cortices. Decision making after Norisk was more risky and more likely to recruit activation of the insular and anterior cingulate cortices. This insular activity during decision making predicted the extent of risky decisions both within- and across-subjects, and was also correlated with an individual's personality trait of urgency. These findings suggest that the insula plays an important role in activating representations of homeostatic states associated with the experience of risk, which in turn exerts an influence on subsequent decisions.

Xue G., Lu Z. L., Levin I. P., Weller J. A., Li X. R., & Bechara Antoine . ( 2009).

Functional Dissociations of Risk and Reward Processing in the Medial Prefrontal Cortex

Cerebral Cortex, 19( 5), 1019-1027.

DOI:10.1093/cercor/bhn147      URL     PMID:2665154     

Making a risky decision is a complex process that involves evaluation of both the value of the options and the associated risk level. Yet the neural processes underlying these processes have not so far been clearly identified. Using functional magnetic resonance imaging and a task that simulates risky decisions, we found that the dorsal region of the medial prefrontal cortex (MPFC) was activated whenever a risky decision was made, but the degree of this activity across subjects was negatively correlated with their risk preference. In contrast, the ventral MPFC was parametrically modulated by the received gain/loss, and the activation in this region was positively correlated with an individual's risk preference. These results extend existing neurological evidence by showing that the dorsal and ventral MPFC convey different decision signals (i.e., aversion to uncertainty vs. approach to rewarding outcomes), where the relative strengths of these signals determine behavioral decisions involving risk and uncertainty.

Zheng D., Oka T., Bokura H., & Yamaguchi S . ( 2008).

The key locus of common response inhibition network for no-go and stop signals

Journal of Cognitive Neuroscience, 20( 8), 1434-1442.

DOI:10.1162/jocn.2008.20100      URL     PMID:18303978      [本文引用: 1]

Response inhibition is one of the highest evolved executive functions of human beings. Previous studies revealed a wide variety of brain regions related to response inhibition, although some of them may not be directly related to inhibition but to task-specific effects or noninhibitory cognitive functions such as attention, response competition, or error detection. Here, we conducted event-related functional magnetic resonance imaging studies in which all subjects performed both stop-signal and go/no-go tasks in order to explore key neural correlates within the response inhibition network irrelevant to task designs and other cognitive processes. The successful inhibition in the stop-signal and go/no-go tasks, respectively, activated a set of predominantly right-lateralized hemispheric cortices. The common inhibitory regions across the two tasks included the right middle prefrontal cortex in addition to the right middle occipital cortex. Correlation analysis was carried out within these areas between intensity of activation and behavioral performance in the two tasks. Only the region located in the middle prefrontal cortex showed significant correlations in both tasks. We believe this region is the key locus for execution of response inhibition in the distributed inhibitory neural network.

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