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

doi:10.3724/SP.J.1041.2018.01449

摘要/Abstract

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.

Key words: card switching frequency, the card guessing task, persistence, cognitive flexibility, decision making, voxel-based morphometry, repeated binary-choice

中图分类号:

孙雅晨, 张汉其, 李勇辉, 薛贵, 何清华. (2018). 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, 50(12), 1449-1459.

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

图/表 10

参考文献 44

1	Aron A.R . ( 2011). From reactive to proactive and selective control: Developing a richer model for stopping inappropriate responses. Biological Psychiatry, 69( 12), e55-e68. doi: 10.1016/j.biopsych.2010.07.024
2	Baliki M. N., Geha P. Y., & Apkarian A. V . ( 2009). Parsing pain perception between nociceptive representation and magnitude estimation. Journal of Neurophysiology, 101( 2), 875-87.
3	Bechara A., Damasio H., Tranel D., & Damasio A. R . ( 1997). Deciding advantageously before knowing the advantageous strategy. Science, 275( 5304), 1293.
4	Bechara A., & Damasio A.R . ( 2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52( 2), 336-372. doi: 10.1016/j.geb.2004.06.010 URL
5	, , Bereczkei T., &Czibor A. , ( 2014). Personality and situational factors differently influence high Mach and low Mach persons’ decisions in a social dilemma game. Personality and Individual Differences, 64, 168-173. doi: 10.1016/j.paid.2014.02.035 URL
6	Clark L., Bechara A., Damasio H., Aitken M. R. F., Sahakian B. J., & Robbins T. W . ( 2008). Differential effects of insular and ventromedial prefrontal cortex lesions on risky decision-making. Brain, 131( 5), 1311-1322. doi: 10.1093/brain/awn066 URL pmid: 2367692
7	Cloninger C. R., Svrakic D. M., & Przybeck T. R . ( 1993). A psychobiological model of temperament and character. Archives of general psychiatry, 50( 12), 975-990. doi: 10.1001/archpsyc.1993.01820240059008. URL pmid: 8250684
8	Daw N. D., O’doherty J. P., Dayan P., Seymour B., & Dolan R. J . ( 2006). Cortical substrates for exploratory decisions in humans. Nature, 441( 7095), 876-879.
9	De Martino B., Kumaran D., Seymour B., & Dolan R. J . ( 2006). Frames, Biases, and Rational Decision-Making in the Human Brain. Science, 313( 5787), 684-687. doi: 10.1126/science.1128356 URL pmid: 16888142
10	Dong X., Du X., & Qi B . ( 2016). Conceptual knowledge influences decision making differently in individuals with high or low cognitive flexibility: An ERP study. Plos One, 11( 8), e0158875. doi: 10.1371/journal.pone.0158875 URL pmid: 27479484
11	Feather N.T . ( 1962). The study of persistence. Psychological Bulletin, 59( 2), 94-115.
12	Gardini S., Cloninger C. R., & Venneri A . ( 2009). Individual differences in personality traits reflect structural variance in specific brain regions. Brain Research Bulletin, 79( 5), 265-270. doi: 10.1016/j.brainresbull.2009.03.005 URL pmid: 19480986
13	Green C. S., Benson C., Kersten D., & Schrater P . ( 2010). Alterations in choice behavior by manipulations of world model. Proceedings of the National Academy of Sciences of the United States of America, 107( 37), 16401-16406. doi: 10.1073/pnas.1001709107 URL pmid: 20805507
14	Hakamata Y., Lwase M., Lwata H., Kobayashi T., Tamaki T., Nishio M., … Lnada T . ( 2006). Regional brain cerebral glucose metabolism and temperament: A positron emission tomography study. Neuroscience Letters, 396( 1), 33-37. doi: 10.1016/j.neulet.2005.11.017 URL pmid: 16356648
15	Hanke M., Halchenko Y. O., Sederberg P. B., Hanson S. J., Haxby J. V., & Pollmann S . ( 2009). PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7( 1), 37-53. doi: 10.1007/s12021-008-9041-y URL pmid: 2664559
16	Heath A. C., Cloninger C. R., & Martin N. G . ( 1994). Testing a model for the genetic structure of personality: A comparison of the personality systems of Cloninger and Eysenck. Journal of personality and social psychology, 66( 4), 762-775. doi: 10.1037/0022-3514.66.4.762 URL pmid: 8189351
17	Heaton R., Chelune G., Talley J., Kay G., & Curtiss G . ( 1993). Wisconsin Card Sorting Test Manual: Revised and expanded. Odessa, FL: Psychological Assessment Resources.
18	Ishii H., Ohara S., Tobler P. N., Tsutsui K., & Iijima T . ( 2012). Inactivating anterior insular cortex reduces risk taking. Journal of Neuroscience the Official Journal of the Society for Neuroscience, 32( 45), 16031-16039. doi: 10.1523/JNEUROSCI.2278-12.2012 URL pmid: 23136439
19	Jimura K., & Poldrack R.A . ( 2012). Analyses of regional- average activation and multivoxel pattern information tell complementary stories. Neuropsychologia, 50( 4), 544-552. doi: 10.1016/j.neuropsychologia.2011.11.007 URL pmid: 22100534
20	Jung Y. C., Ku J., Namkoong K., Lee W., Kim S. I., & Kim J. J . ( 2010). Human orbitofrontal-striatum functional connectivity modulates behavioral persistence. Neuroreport, 21( 7), 502-6. doi: 10.1097/WNR.0b013e3283383482 URL pmid: 20357688
21	Kriegeskorte N., Goebel R., & Bandettini P . ( 2006). 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
22	&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
23	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
24	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.
25	[ 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.]
26	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.
27	[ 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.]
28	Liu Z.N . ( 1999). Clinical application of the Wisconsin card classification test. Foreign Medical Sciences (Section of Psychiatry), 26( 1), 6-9.
29	[ 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.]
30	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
31	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
32	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.
33	[ 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
34	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.
35	[ 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
36	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
37	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
38	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
39	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.
40	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
41	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
42	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
43	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
44	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

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

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

脑区	大脑半球	体素数量	p	MNI坐标x	MNI坐标y	MNI坐标z
颞上回	右	1541	0.05	66	-16	6
脑岛	右		0.04	34	-26	12
颞叶	右	998	0.01	60	12	-26
内侧颞叶	右	379	0.02	18	-10	-22
颞叶	左	280	0.02	-56	16	30

脑区	大脑半球	体素数量	p	MNI坐标x	MNI坐标y	MNI坐标z
颞上回	右	1541	0.05	66	-16	6
脑岛	右		0.04	34	-26	12
颞叶	右	998	0.01	60	12	-26
内侧颞叶	右	379	0.02	18	-10	-22
颞叶	左	280	0.02	-56	16	30

[1]	邱天, 江南, 陆静怡. 切忌班门弄斧?低估在评价者擅长领域展现能力的好处[J]. 心理学报, 2023, 55(5): 766-780.
[2]	卓利楠, 曾祥玉, 吴冰, 牛荣荣, 于萍, 王玮文. 内侧前额皮层−伏隔核环路在决策冲动中的作用：基于动物模型的研究[J]. 心理学报, 2023, 55(4): 556-571.
[3]	刘洪志, 杨钘兰, 李秋月, 魏子晗. 跨期决策中的维度差异偏好：眼动证据[J]. 心理学报, 2023, 55(4): 612-625.
[4]	胡馨允, 沈悦, 戴俊毅. 系列决策任务中的策略转换：来自爱荷华赌博任务的证据[J]. 心理学报, 2023, 55(11): 1793-1805.
[5]	李梅, 李琎, 张冠斐, 钟毅平, 李红. 承诺水平与社会距离对信任投资的影响：来自行为与ERPs的证据[J]. 心理学报, 2023, 55(11): 1859-1871.
[6]	张宝山, 金豆, 马梦佳, 徐冉. 消极刻板印象对老年人医疗决策的影响及归因偏差的作用[J]. 心理学报, 2022, 54(8): 951-963.
[7]	冉雅璇, 张谱月, 陈斯允, 项典典. 有“新”同享：共有消费促进对不熟悉产品的选择^*[J]. 心理学报, 2022, 54(8): 979-995.
[8]	潘玥安, 姜云鹏, 郭茂杰, 吴瑕. 不确定性和预期有效性对运动方向感知决策的影响[J]. 心理学报, 2022, 54(6): 595-603.
[9]	占友龙, 肖啸, 谭千保, 李琎, 钟毅平. 声誉关注与社会距离对伤害困境中道德决策的影响：来自行为与ERPs的证据[J]. 心理学报, 2022, 54(6): 613-627.
[10]	何怡娟, 胡馨木, 买晓琴. 共情关怀对公平决策的影响——来自ERP的证据[J]. 心理学报, 2022, 54(4): 385-397.
[11]	蒋元萍, 江程铭, 胡天翊, 孙红月. 情绪对跨期决策的影响：来自单维占优模型的解释[J]. 心理学报, 2022, 54(2): 122-140.
[12]	刘洪志, 李兴珊, 李纾, 饶俪琳. 基于期望值最大化的理论何时失效：风险决策中为自己-为所有人决策差异的眼动研究[J]. 心理学报, 2022, 54(12): 1517-1531.
[13]	冯霞, 冯成志. 认知灵活性对概率类别学习的影响[J]. 心理学报, 2022, 54(11): 1340-1353.
[14]	成晓君, 刘美焕, 潘亚峰. 责任共担促进新手的互动决策获益：超扫描研究[J]. 心理学报, 2022, 54(11): 1391-1402.
[15]	熊承清, 许佳颖, 马丹阳, 刘永芳. 囚徒困境博弈中对手面部表情对合作行为的影响及其作用机制[J]. 心理学报, 2021, 53(8): 919-932.