A meta-analysis of the relationship between semantic distance and creative thinking

doi:10.3724/SP.J.1042.2023.00519

Abstract

Abstract:

The development of natural language processing has offered reliable and valid research methods for exploring the relationship between semantic distance and creative thinking. There are more and more studies in this direction in recent years. However, the research findings are inconsistent in this line of research. It remains unknown whether semantic distance could predict creative thinking and whether this relationship is influenced by other potential factors. Therefore, a more comprehensive study is necessary to investigate the relationship between semantic distance and creativity. To fulfill such a research gap, the present study, based on the Associative Theory of Creativity and the Spreading-Activation Model, has investigated the relationship between semantic distance and creative thinking by using a meta-analysis method. The reasons for the inconsistency of previous studies in this line of research were also analyzed. The current research has involved 14 studies and extracted 53 independent effect sizes from 4729 subjects. Since the heterogeneity test showed that there was significant heterogeneity (I² = 92.21), the random effect model was used for the meta-analysis. Results of funnel plots, Egger’s test of regression, and Fail-Safe Number (Nfs) showed that there were low levels of publication bias in the selected publications. The sensitivity analysis also suggested that the meta-analysis in the present study had acceptable reliability. The main effect analysis suggested that there was a moderate level of correlation between semantic distance and creative thinking (r = 0.379, 95% CI [0.300, 0.452]). Further meta-regression analysis found that the correlation was moderated by the age of participants and dimensions of creative thinking. Specifically, the results suggested that the correlation between semantic distance and creative thinking decreased with the increase in the age of participants. In addition, the flexibility (r = 0.473, 95%CI [0.180, 0.688]) had a higher correlation coefficient with semantic distance than originality (r = 0.328, 95%CI [0.197, 0.447]) and fluency (r = 0.447, 95%CI [0.338, 0.545]). However, elaboration had a negative correlation with semantic distance (r = -0.533, 95%CI [-0.664, -0.372]). The current study showed that semantic distance was associated with creative thinking. It has been suggested that the moderation effects of the age of participants and dimensions of creative thinking might be the potential reasons for the inconsistent results in previous studies. In theory, the present study provides new perspectives and explanations for exploring cognitive and neural mechanisms of creativity. It contributes to the exploration of the relationship between semantic distance and creative thinking. The current study offers better scientific evidence and important implications for interpreting, predicting, and improving creativity. In practice, the current study contributes to quantifying creative thinking by using semantic distance. Future explorations should investigate the relationships between semantic distance and domain-specific creativity (e.g., scientific creativity and artistic creativity), as well as the influence of other higher cognitive functions (e.g., cognitive control) on these relationships.

Key words: creative thinking, semantic distance, the measure of creativity, meta-analysis

CLC Number:

B842

LI Yadan, DU Ying, XIE Cong, LIU Chunyu, YANG Yilong, LI Yangping, QIU Jiang. A meta-analysis of the relationship between semantic distance and creative thinking[J]. Advances in Psychological Science, 2023, 31(4): 519-534.

Figures/Tables 4

References 109

	(标*是纳入元分析的文献)
[1]	贡喆, 刘昌, 沈汪兵. (2016). 有关创造力测量的一些思考. 心理科学进展, 24(1), 31-45.
[2]	徐雪芬, 辛涛. (2013). 创造力测量的研究取向和新进展. 清华大学教育研究, 34(1), 54-63.
[3]	张亚利, 李森, 俞国良. (2019). 自尊与社交焦虑的关系: 基于中国学生群体的元分析. 心理科学进展, 27(6), 1005-1018.
[4]	* Acar S., & Runco M. A. (2014). Assessing associative distance among ideas elicited by tests of divergent thinking. Creativity Research Journal, 26(2), 229-238. doi: 10.1080/10400419.2014.901095 URL
[5]	Acar S., & Runco M. A. (2015). Thinking in multiple directions: Hyperspace categories in divergent thinking. Psychology of Aesthetics, Creativity, and the Arts, 9(1), 41-53. doi: 10.1037/a0038501 URL
[6]	Acar S., & Runco M. A. (2019). Divergent thinking: New methods, recent research, and extended theory. Psychology of Aesthetics, Creativity, and the Arts, 13(2), 153-158. doi: 10.1037/aca0000231 URL
[7]	Badre D., & Wagner A. D. (2007). Left ventrolateral prefrontal cortex and the cognitive control of memory. Neuropsychologia, 45(13), 2883-2901. doi: 10.1016/j.neuropsychologia.2007.06.015 pmid: 17675110
[8]	Baronchell, Ferrer-i-Cancho, R., Pastor-Satorras, R., Chater, N., & Christiansen, M. H. (2013). Networks in cognitive science. Trends in Cognitive Sciences, 17(7), 348-360. doi: 10.1016/j.tics.2013.04.010 pmid: 23726319
[9]	Beaty R. E., Benedek M., Barry K. S., & Silvia P. J. (2015). Default and executive network coupling supports creative idea production. Scientific Reports, 5(1), 1-14.
[10]	Beaty R. E., Chen Q. L., Christensen A. P., Kenett Y. N., Silvia P. J., Benedek M., & Schacter D. L. (2020). Default network contributions to episodic and semantic processing during divergent creative thinking: A representational similarity analysis. Neuroimage, 209, 116499. doi: 10.1016/j.neuroimage.2019.116499 URL
[11]	Beaty R. E., Christensen A. P., Benedek M., Silvia P. J., & Schacter D. L. (2017). Creative constraints: Brain activity and network dynamics underlying semantic interference during idea production. NeuroImage, 148, 189-196. doi: S1053-8119(17)30012-5 pmid: 28082106
[12]	* Beaty R. E., & Johnson D. R. (2021). Automating creativity assessment with SemDis: An open platform for computing semantic distance. Behavior Research Methods, 53(2), 757-780. doi: 10.3758/s13428-020-01453-w
[13]	Beaty R. E., Nusbaum E. C., & Silvia. J. S. (2014). Does insight problem solving predict real-world creativity? Psychology of Aesthetics, Creativity, and the Arts, 8(3), 287-292. doi: 10.1037/a0035727 URL
[14]	Beaty R. E., Silvia P. J., Nusbaum E. C., Jauk E., & Benedek M. (2014). The roles of associative and executive processes in creative cognition. Memory & Cognition, 42(7), 1186-1197. doi: 10.3758/s13421-014-0428-8 URL
[15]	* Beketayev K., & Runco M. (2016). Scoring divergent thinking tests by computer with a semantics-based algorithm. Europe's Journal of Psychology, 12(2), 210-220. doi: 10.5964/ejop.v12i2.1127 pmid: 27298632
[16]	* Benedek M., Kenett Y. N., Umdasch K., Anaki D., Faust M., & Neubauer A. C. (2017). How semantic memory structure and intelligence contribute to creative thought: A network science approach. Thinking & Reasoning, 23(2), 158-183.
[17]	Benedek M., Könen T., & Neubauer A. C. (2012). Associative abilities underlying creativity. Psychology of Aesthetics, Creativity, and the Arts, 6(3), 273-281. doi: 10.1037/a0027059 URL
[18]	Benedek M., & Neubauer A. C. (2013). Revisiting Mednick's model on creativity-related differences in associative hierarchies. Evidence for a common path to uncommon thought. The Journal of Creative Behavior, 47(4), 273-289. doi: 10.1002/jocb.35 URL
[19]	Borenstein M., Hedges L. V., Higgins J. P. T., & Rothstein H. R. (2010). A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods, 1(2), 97-111. doi: 10.1002/jrsm.12 pmid: 26061376
[20]	Bossomaier T., Harré M., Knittel A., & Snyder A. (2009). A semantic network approach to the Creativity Quotient (CQ). Creativity Research Journal, 21(1), 64-71. doi: 10.1080/10400410802633517 URL
[21]	Chen L., Wu J., Hartwigsen G., Li Z., Wang P., & Feng L. (2021). The role of a critical left fronto-temporal network with its right-hemispheric homologue in syntactic learning based on word category information. Journal of Neurolinguistics, 58, 100977. doi: 10.1016/j.jneuroling.2020.100977 URL
[22]	Chen Q., Beaty R. E., & Qiu J. (2020). Mapping the artistic brain: Common and distinct neural activations associated with musical, drawing, and literary creativity. Human Brain Mapping, 41(12), 3403-3419. doi: 10.1002/hbm.25025 pmid: 32472741
[23]	Christensen A. P., & Kenett Y. N. (2021). Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. Psychological Methods. Advance online publication.
[24]	Clark P. M., & Mirels H. L. (1970). Fluency as a pervasive element in the measurement of creativity. Journal of Educational Measurement, 7(2), 83-86. doi: 10.1111/jedm.1970.7.issue-2 URL
[25]	Cohen J. (1988). CHAPTER 4-Differences between Correlation Coefficients. In J. Cohen (Ed.), Statistical Power Analysis for the Behavioral Sciences (pp. 109-143). Salt Lake City, UT: Academic Press.
[26]	Collins A. M., & Loftus E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82(6), 407-428. doi: 10.1037/0033-295X.82.6.407 URL
[27]	Dubossarsky H., de Deyne S., & Hills T. T. (2017). Quantifying the structure of free association networks across the life span. Developmental Psychology, 53(8), 1560-1570. doi: 10.1037/dev0000347 pmid: 28569517
[28]	Dumas D., & Dunbar K. N. (2014). Understanding fluency and originality: A latent variable perspective. Thinking Skills and Creativity, 14, 56-67. doi: 10.1016/j.tsc.2014.09.003 URL
[29]	Dumas D., Organisciak P., & Doherty M. (2021). Measuring divergent thinking originality with human raters and text-mining models: A psychometric comparison of methods. Psychology of Aesthetics, Creativity, and the Arts, 15(4), 645-663. doi: 10.1037/aca0000319 URL
[30]	Egger M., Davey G. D., Schneider M., & Minder C. (1997). Bias in meta-analysis detected by a simple, graphical test. British Medical Journal, 315(7109), 629-634. doi: 10.1136/bmj.315.7109.629 URL
[31]	* Forster E. A., & Dunbar K. N. (2009). Creativity evaluation through latent semantic analysis. Proceedings of the Annual Conference of the Cognitive Science Society, 2009, 602-607.
[32]	* Forthmann B., Oyebade O., Ojo A., Günther F., & Holling H. (2018). Application of latent semantic analysis to divergent thinking is biased by elaboration. Journal of Creative Behavior, 53(4), 559-575. doi: 10.1002/jocb.v53.4 URL
[33]	Forthmann B., Oyebade O., Ojo A., Günther F., & Holling H. (2019). Application of latent semantic analysis to divergent thinking is biased by elaboration. The Journal of Creative Behavior, 53(4), 559-575. doi: 10.1002/jocb.v53.4 URL
[34]	Forthmann B., Wilken A., Doebler P., & Holling H. (2019). Strategy induction enhances creativity in figural divergent thinking. The Journal of Creative Behavior, 53(1), 18-29. doi: 10.1002/jocb.2019.53.issue-1 URL
[35]	Fox K. C., Spreng R. N., Ellamil M., Andrews-Hanna J. R., & Christoff K. (2015). The wandering brain: Meta-analysis of functional neuroimaging studies of mind-wandering and related spontaneous thought processes. NeuroImage, 111, 611-621. doi: 10.1016/j.neuroimage.2015.02.039 pmid: 25725466
[36]	Goncalves M., Cardoso C., & Badke-Schaub P. (2013). Inspiration peak: Exploring the semantic distance between design problem and textual inspirational stimuli. International Journal of Design Creativity and Innovation, 1(4), 215-232. doi: 10.1080/21650349.2013.799309 URL
[37]	* Gray K., Anderson S., Chen E. E., Kelly J. M., Christian M. S., Patrick J., … Lewis K. (2019). "Forward Flow": A new measure to quantify free thought an predict creativity. American Psychologist, 74(5), 539-554. doi: 10.1037/amp0000391 URL
[38]	Green A. E. (2016). Creativity, Within reason: Semantic distance and dynamic state creativity in relational thinking and reasoning. Current Directions in Psychological Science, 25(1), 28-35. doi: 10.1177/0963721415618485 URL
[39]	Green A. E., Cohen M. S., Kim J. U., & Gray J. R. (2012). An explicit cue improves creative analogical reasoning. Intelligence, 40(6), 598-603. doi: 10.1016/j.intell.2012.08.005 URL
[40]	Green A. E., Cohen M. S., Raab H. A., Yedibalian C. G., & Gray J. R. (2015). Frontopolar activity and connectivity support dynamic conscious augmentation of creative state. Human Brain Mapping, 36(3), 923-934. doi: 10.1002/hbm.22676 pmid: 25394198
[41]	Greenhouse J. B., & Iyengar S. (2009). Sensitivity analysis and diagnostics. In Cooper, Hedges, & Valentine (Eds.), The handbook of research synthesis and meta-analysis (2nd ed.). New York, NY: Russell Sage Foundation.
[42]	Gruszka A., & Necka E. (2002). Priming and acceptance of close and remote associations by creative and less creative people. Creativity Research Journal, 14(2), 193-205. doi: 10.1207/S15326934CRJ1402_6 URL
[43]	Guilford J. P. (1950). Creativity. American Psychologist, 5(9), 444-454. pmid: 14771441
[44]	Han L., Wang C., Yao D., Wang B., Zhang Z., & Liu J. (2020). Clinical efficacy and safety of Danhong injection for the treatment of chronic heart failure: A protocol for systematic review. Medicine, 99(14), e19526.
[45]	* Hass R. W. (2017). Tracking the dynamics of divergent thinking via semantic distance: Analytic methods and theoretical implications. Memory & Cognition, 45(2), 233-244. doi: 10.3758/s13421-016-0659-y URL
[46]	He L., Kenett Y. N., Zhuang K., Liu C., Zeng R., Yan T., Huo T., & Qiu J. (2020). The relation between semantic memory structure, associative abilities, and verbal and figural creativity. Thinking & Reasoning, 27(2), 268-293.
[47]	* Heinen D. J., & Johnson D. R. (2018). Semantic distance: An automated measure of creativity that is novel and appropriate. Psychology of Aesthetics, Creativity, and the Arts, 12(2), 144-156. doi: 10.1037/aca0000125 URL
[48]	Higgins P. T., Thompson S. G., Decks J. J., & Altman D. G. (2003). Measuring inconsistency in meta-analyses. British Medical Journal, 327(7414), 557-560. doi: 10.1136/bmj.327.7414.557 URL
[49]	Hocevar D. (1980). Intelligence, divergent thinking, and creativity. Intelligence, 4(1), 25-40. doi: 10.1016/0160-2896(80)90004-5 URL
[50]	Hocevar D., & Michael W. B. (1979). The effects of scoring formulas on the discriminant validity of tests of divergent thinking. Educational and Psychological Measurement, 39(4), 917-921. doi: 10.1177/001316447903900427 URL
[51]	Hoeve M., Stams G. J. J. M., Put C. E. V. D., Dubas J. S., Laan P. H. V. D., & Gerris J. R. M. (2012). A meta-analysis of attachment to parents and delinquency. Journal of Abnormal Child Psychology, 40(5), 771-785. doi: 10.1007/s10802-011-9608-1 pmid: 22278802
[52]	Johnson D. R., Cuthbert A. S., & Tynan M. E. (2021). The neglect of idea diversity in creative idea generation and evaluation. Psychology of Aesthetics, Creativity, and the Arts, 15(1), 125-135. doi: 10.1037/aca0000235 URL
[53]	Kaufman J. C., & Sternberg R. J.(Eds.) (2010). The Cambridge handbook of creativity. Cambridge University Press.
[54]	Kavé G., & Halamish V. (2015). Doubly blessed: Older adults know more vocabulary and know better what they know. Psychology Aging, 30(1), 68-73. doi: 10.1037/a0038669 URL
[55]	Kenett Y. N. (2018). Going the extra creative mile: The role of semantic distance in creativity-theory, research, and measurement. In R. Jung & O. Vartanian (Eds.), The Cambridge handbook of the neuroscience of creativity (pp. 233-248). Cambridge University Press.
[56]	Kenett Y. N. (2019). What can quantitative measures of semantic distance tell us about creativity? Current Opinion in Behavioral Sciences, 27, 11-16. doi: 10.1016/j.cobeha.2018.08.010
[57]	Kenett Y. N., Anaki D., & Faust M. (2014). Investigating the structure of semantic networks in low and high creative persons. Frontiers in Human Neuroscience, 8, 407. doi: 10.3389/fnhum.2014.00407 pmid: 24959129
[58]	Kenett Y. N., Levi E., Anaki D., & Faust M. (2017). The semantic distance task: Quantifying semantic distance with semantic network path length. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(9), 1470-1489. doi: 10.1037/xlm0000391 URL
[59]	Kenett Y. N., Levy O., Kenett D. Y., Stanley H. E., Faust M., & Havlin S. (2018). Flexibility of thought in high creative individuals represented by percolation analysis. Psychological and Cognitive Sciences, 115(5), 867-872.
[60]	Kisamore J. L., & Brannick M. T. (2008). An illustration of the consequences of meta-analysis model choice. Organizational Research Methods, 11(1), 35-53. doi: 10.1177/1094428106287393 URL
[61]	Kleinmintz O. M., Ivancovsky T., & Shamay-Tsoory S. G. (2019). The two-fold model of creativity: The neural underpinnings of the generation and evaluation of creative ideas. Current Opinion in Behavioral Sciences, 27, 131-138. doi: 10.1016/j.cobeha.2018.11.004
[62]	Lee S. (2008). Commentary: Reliability and validity of uniqueness scoring in creativity assessment. Psychology of Aesthetics, Creativity, and the Arts, 2(2), 103-108. doi: 10.1037/1931-3896.2.2.103 URL
[63]	Leon S. A., Altmann L. J. P., Abrams L., Gonzalez Rothi L. J., & Heilman K. M. (2019). Novel associative processing and aging: Effect on creative production. Aging, Neuropsychology, and Cognition, 26(6), 807-822. doi: 10.1080/13825585.2018.1532067
[64]	Li D., Yang D. -L., An J., Jiao J., Zhou Y. -M., Wu Q. -J., & Wang X. -X. (2016). Effect of assisted hatching on pregnancy outcomes: A systematic review and meta-analysis of randomized controlled trials. Scientific Reports, 6, 31228-31228. doi: 10.1038/srep31228 pmid: 27503701
[65]	Light R. J., & Pillemer D. B. (1984). Summing up: The science of reviewing research. Harvard University Press.
[66]	* Liu M., Wang Y., Li J., Zhuang X., Chen X., Li X., Liao X., & Wang L. (2020). Opposite effect of ablation on early/late-phase thromboembolic incidence in patients with atrial fibrillation: A meta-analysis on more than 100 000 individuals. Clinical Cardiology, 43(6), 594-605. doi: 10.1002/clc.23354 pmid: 32159241
[67]	Macaskill P., Walter S. D., & Irwig L. (2010). A comparison of methods to detect publication bias in meta-analysis. Statistics in Medicine, 20(4), 641-654. doi: 10.1002/(ISSN)1097-0258 URL
[68]	Marron T., & Faust M. (2018). Free association, divergent thinking, and creativity: Cognitive and neural perspectives. In R. Jung & O. Vartanian (Eds.), The Cambridge handbook of the neuroscience of creativity (Cambridge Handbooks in Psychology, pp. 261-280). Cambridge University Press.
[69]	Marron T. R., Lerner Y., Berant E., Kinreich S., Shapira-Lichter I., Hendler T., & Faust M. (2018). Chain free association, creativity, and the default mode network. Neuropsychologia, 118, 40-58. doi: S0028-3932(18)30111-8 pmid: 29555561
[70]	Mednick S. (1962). The associative basis of the creative process. Psychological Review, 69(3), 220-232. doi: 10.1037/h0048850 URL
[71]	Mednick S. (1968). The remote associates test. The Journal of Creative Behavior, 2(3), 213-214. doi: 10.1002/jocb.1968.2.issue-3 URL
[72]	* Murray S., Liang N., Brosowsky N., & Seli P. (2021). What are the benefits of mind wandering to creativity? Psychology of Aesthetics, Creativity, and the Arts. Advance online publication.
[73]	Nikolakopoulou A., Mavridis D., & Salanti G. (2014). How to interpret meta-analysis models: Fixed effect and random effects meta-analyses. Evidence Based Mental Health, 17(2), 64.
[74]	Orwig W., Diez I., Vannini P., Beaty R., & Sepulcre J. (2021). Creative connections: Computational semantic distance captures individual creativity and resting-state functional connectivity. Journal of Cognitive Neuroscience, 33(3), 499-509. doi: 10.1162/jocn_a_01658 pmid: 33284079
[75]	Orwin R. G., & Vevea J. L. (1994). Evaluating coding decisions. In L. V. H. Cooper & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis (pp. 177-203). New York, NY: Russell Sage Foundation.
[76]	Paulsen J. S., Romero R., Chan A., Davis A. V., Heaton R. K., & Jeste D. V. (1996). Impairment of the semantic network in schizophrenia. Psychiatry Research, 63(2), 109-121. doi: 10.1016/0165-1781(96)02901-0 URL
[77]	Plucker J. A., & Makel M. C. (2010). Assessment of creativity. In J. C. Kaufman & R. J. Sternberg (Eds.), The Cambridge handbook of creativity (pp. 48-73). Cambridge University Press.
[78]	* Prabhakaran R., Green A. E., & Gray J. R. (2014). Thin slices of creativity: Using single-word utterances to assess creative cognition. Behavior Research Methods, 46(3), 641-659. doi: 10.3758/s13428-013-0401-7 pmid: 24163211
[79]	Ralph M. A. L., Jefferies E., Patterson K., & Rogers T. T. (2017). The neural and computational bases of semantic cognition. Nature Reviews Neuroscience, 18(1), 42-55. doi: 10.1038/nrn.2016.150 pmid: 27881854
[80]	Reiter-Palmon R., Forthmann B., & Barbot B. (2019). Scoring divergent thinking tests: A review and systematic framework. Psychology of Aesthetics, Creativity, and the Arts, 13(2), 144-152. doi: 10.1037/aca0000227 URL
[81]	Rosenthal R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638-641. doi: 10.1037/0033-2909.86.3.638 URL
[82]	* Rossmann E., & Fink A. (2010). Do creative people use shorter associative pathways? Personality and Individual Differences, 49(8), 891-895. doi: 10.1016/j.paid.2010.07.025 URL
[83]	Rothenberg A. (1973). Word association and creativity. Psychological Reports, 33(1), 3-12. pmid: 4728480
[84]	Runco M. A., Abdulla A. M., Paek S. H., Al-Jasim F. A., & Alsuwaidi H. N. (2016). Which test of divergent thinking is best? Creativity. Theories-Research- Applications, 3(1), 4-18. doi: 10.1515/ctra-2016-0001 URL
[85]	Runco M. A. (2002). Creativity. In V. S. Ramachandran (Ed.), Encyclopedia of the human brain (pp. 83-87). Salt Lake City, UT: Academic Press.
[86]	Runco M. A., Pritzker S. R., & Reiterpalmon R. (1999). Encyclopedia of creativity. Salt Lake City, UT: Academic Press.
[87]	Ruth J. E., & Birren J. E. (1985). Creativity in adulthood and old age: Relations to intelligence, sex and mode of testing. International Journal of Behavioral Development, 8(1), 99-109. doi: 10.1177/016502548500800107 URL
[88]	Schilling M. A. (2005). A" small-world" network model of cognitive insight. Creativity Research Journal, 17(2-3), 131-154. doi: 10.1207/s15326934crj1702&3_2 URL
[89]	Shimonaka Y., & Nakazato K. (2007). Creativity and factors affecting creative ability in adulthood and old age. Japanese Journal of Educational Psychology, 55(2), 231-243.
[90]	Siew C. S. Q., Wulff D. U., Beckage N. M., & Kenett Y. N. (2019). Cognitive network science: A review of research on cognition through the lens of network representations, processes, and dynamics. Complexity, 2019, 2108423.
[91]	Silvia P. J. (2015). Intelligence and creativity are pretty similar after all. Educational Psychology Review, 27(4), 599-606. doi: 10.1007/s10648-015-9299-1 URL
[92]	Silvia P. J., Beaty R. E., & Nusbaum E. C. (2013). Verbal fluency and creativity: General and specific contributions of broad retrieval ability (Gr) factors to divergent thinking. Intelligence Norwood Mutidisciplinary Journal, 41(5), 328-340.
[93]	Silvia P. J., Winterstein B. P., Willse J. T., Barona C. M., Cram J. T., Hess K. I., Martinez J. L., & Richard C. A. (2008). Assessing creativity with divergent thinking tasks: Exploring the reliability and validity of new subjective scoring methods. Psychology of Aesthetics, Creativity, and the Arts, 2(2), 68-85. doi: 10.1037/1931-3896.2.2.68 URL
[94]	Simon A., & Bock O. (2016). Influence of divergent and convergent thinking on visuomotor adaptation in young and older adults. Human Movement Science, 46, 23-29. doi: 10.1016/j.humov.2015.11.020 pmid: 26707677
[95]	Sun J. R., Kong C. F., Qu X. K., Deng C., Lou Y. N., & Jia L. Q. (2020). Efficacy and safety of probiotics in irritable bowel syndrome: A systematic review and meta-analysis. Saudi Journal of Gastroenterology, 26(2), 66-77.
[96]	* Tempest G. D., & Radel R. (2019). Put on your (fNIRS) thinking cap: Frontopolar activation during augmented state creativity. Behavioural Brain Research, 373, 112082. doi: 10.1016/j.bbr.2019.112082
[97]	Terrin N., Schmid C. H., & Lau J. (2005). In an empirical evaluation of the funnel plot, researchers could not visually identify publication bias. Journal of Clinical Epidemiology, 58(9), 894-901. doi: 10.1016/j.jclinepi.2005.01.006 pmid: 16085192
[98]	Torrance E. P. (1965). Rewarding creative behavior. Denver, CO: Prentice Hall.
[99]	Torrance E. P. (1972). Predictive validity of the torrance tests of creative thinking. The Journal of Creative Behavior, 6(4), 236-252. doi: 10.1002/jocb.1972.6.issue-4 URL
[100]	Torrance E. P. (1988). The nature of creativity as manifest in its testing. In R. J. Sternberg (Ed.), The nature of creativity: Contemporary psychological perspectives (pp. 43-75). Cambridge University Press.
[101]	Verhaeghen P. (2003). Aging and vocabulary scores: A meta-analysis. Psychology and Aging, 18(2), 332-339. doi: 10.1037/0882-7974.18.2.332 pmid: 12825780
[102]	Viechtbauer W. (2007). Publication bias in meta-analysis:Prevention, assessment and adjustments. Psychometrika, 72(2), 269-271.
[103]	Volle E. (2018). Associative and controlled cognition in divergent thinking: Theoretical, experimental, neuroimaging evidence, and new directions. In R. E. Jung & O. Vartanian (Eds.), The Cambridge handbook of the neuroscience of creativity (pp. 333-360). Cambridge University Press.
[104]	Wang P., Wijnants M. L., & Ritter S. M. (2018). What enables novel thoughts? The temporal structure of associations and its relationship to divergent thinking. Frontiers in Psychology, 9, 1771. doi: 10.3389/fpsyg.2018.01771 pmid: 30319488
[105]	Weinberger A. B., Iyer H., & Green A. E. (2016). Conscious augmentation of creative state enhances "real" creativity in open-ended analogical reasoning. PLoS One, 11(3), e0150773.
[106]	Wu C. H., Cheng Y., Ip H. M., & McBride-Chang C. (2005). Age differences in creativity: Task structure and knowledge base. Creativity Research Journal, 17(4), 321-326. doi: 10.1207/s15326934crj1704_3 URL
[107]	Wulff D. U., de Deyne S., Jones M. N., & Mata R. (2019). New perspectives on the aging lexicon. Trends in Cognitive Sciences, 23(8), 686-698. doi: S1364-6613(19)30124-X pmid: 31288976
[108]	Zhang W., & Niu W. (2013). Creativity in the later life: Factors associated with the creativity of the Chinese elderly. Journal of Creative Behavior, 47(1), 60-76. doi: 10.1002/jocb.23 URL
[109]	Zortea M., Menegola B., Villavicencio A., & de Salles J. F. (2014). Graph analysis of semantic word association among children, adults, and the elderly. Psicologian- Reflexao e Critica, 27(1), 90-99.

研究者	被试群体	样本量	平均年龄	效应值	测量指标
Gray et al., 2019(A)	演员	30	41.03	r = 0.46	总分
Gray et al., 2019(B)	大学生	211	19.76	r = 0.24	总分
Gray et al., 2019(C)	美国代表样本	517	15.69	r = 0.12	总分
Gray et al., 2019(D)	表演系非表演系学生	167	19.44	t = 3.49	总分
Gray et al., 2019(E)	专业演员互联网工作者	104	38.89 32.88	t = 4.34	总分
Gray et al., 2019(F)	职业企业家会计	296	54.53 50.73	t = 3.70	总分
Gray et al., 2019(G)	随机被试	1397	-	r = 0.19	总分
Gray et al., 2019(H)	推特上知名人士	95	-	r = 0.22	总分
Prabhakaran et al., 2014(A)	大学生	183	22.1	r = 0.47	总分
Prabhakaran et al., 2014(B)	大学生	183	22.1	r = 0.43	流畅性
Prabhakaran et al., 2014(C)	大学生	183	22.1	r = 0.42	灵活性
Prabhakaran et al., 2014(D)	大学生	183	22.1	r = 0.48	独创性
Prabhakaran et al., 2014(E)	大学生	183	22.1	r = 0.40	总分
Prabhakaran et al., 2014(F)	大学生	183	22.1	r = 0.18	总分
Forthmann et al., 2018(A)	青少年	29	14.47	r = 0.699	流畅性
Forthmann et al., 2018(B)	青少年	29	14.47	r = 0.789	流畅性
Forthmann et al., 2018(C)	青少年	29	14.47	r = 0.648	总分
Forthmann et al., 2018(D)	青少年	29	14.47	r = 0.417	总分
Forthmann et al., 2018(E)	青少年	29	14.47	r = 0.562	总分
Forthmann et al., 2018(F)	青少年	29	14.47	r = 0.462	总分
Beketayev & Runco, 2016(A)	网络随机	250	33.65	r = 0.74	灵活性
Beketayev & Runco, 2016(B)	网络随机	250	33.65	r = 0.36	独创性
Forster & Dunbar, 2009(A)	大学生	61	18.75	r = 0.417	流畅性
Forster & Dunbar, 2009(B)	大学生	61	18.75	r = 0.66	流畅性
Forster & Dunbar, 2009(C)	大学生	61	18.75	r = 0.31	流畅性
Forster & Dunbar, 2009(D)	大学生	61	18.75	r =−0.42	精致性
Forster & Dunbar, 2009(E)	大学生	61	18.75	r =−0.46	精致性
Forster & Dunbar, 2009(F)	大学生	61	18.75	r =−0.60	精致性
Forster & Dunbar, 2009(G)	大学生	61	18.75	r =−0.63	精致性
Hass, 2017	大学生	226	-	r = 0.40	总分
Heinen & Johnson, 2018(A)	网络随机	62	37	r = 0.47	总分
Heinen & Johnson, 2018(B)	网络随机	121	37	r = 0.77	总分
Acar & Runco, 2014(A)	大学生	54	26.28	r = 0.29	流畅性
Acar & Runco, 2014(B)	大学生	54	26.28	r = 0.28	流畅性
Acar & Runco, 2014(C)	大学生	54	26.28	r = 0.27	总分
Acar & Runco, 2014(D)	大学生	54	26.28	r = 0.35	总分
Acar & Runco, 2014(E)	大学生	54	26.28	r = 0.31	总分
Mathias & Kenett, 2017(A)	大学生	89	25	r = 0.21	流畅性
Mathias & Kenett, 2017(B)	大学生	89	25	r = 0.41	总分
Mathias & Kenett, 2017(C)	大学生	89	25	r = 0.37	流畅性
Mathias & Kenett, 2017(D)	大学生	89	25	r = 0.24	总分
Rossmann & Fink, 2010	大学生	106	23.03	r = 0.22	独创性
Tempest & Rémi, 2019(A)	德语系大学生	29	22.9	r = 0.524	流畅性
Tempest & Rémi, 2019(B)	德语系大学生	29	22.9	r = 0.468	灵活性
Tempest & Rémi, 2019(C)	德语系大学生	29	22.9	r = 0.449	流畅性
Tempest & Rémi, 2019(D)	德语系大学生	29	22.9	r = 0.406	灵活性
Tempest & Rémi, 2019(E)	德语系大学生	29	22.9	r = 0.609	总分
Beaty & Johnson, 2021(A)	大学生	171	22.63	r = 0.91	总分
Beaty & Johnson, 2021(B)	大学生	142	19.60	r = 0.75	总分
Cheng Liu, 2021E1	大学生	189	19.30	r = 0.205	灵活性
Cheng Liu, 2021E2	大学生	189	19.30	r = 0.211	独创性
Cheng Liu, 2021E3	大学生	189	19.30	r = 0.203	总分
Murray, 2021	网络随机	200	40.24	r = 0.290	总分

研究者	被试群体	样本量	平均年龄	效应值	测量指标
Gray et al., 2019(A)	演员	30	41.03	r = 0.46	总分
Gray et al., 2019(B)	大学生	211	19.76	r = 0.24	总分
Gray et al., 2019(C)	美国代表样本	517	15.69	r = 0.12	总分
Gray et al., 2019(D)	表演系非表演系学生	167	19.44	t = 3.49	总分
Gray et al., 2019(E)	专业演员互联网工作者	104	38.89 32.88	t = 4.34	总分
Gray et al., 2019(F)	职业企业家会计	296	54.53 50.73	t = 3.70	总分
Gray et al., 2019(G)	随机被试	1397	-	r = 0.19	总分
Gray et al., 2019(H)	推特上知名人士	95	-	r = 0.22	总分
Prabhakaran et al., 2014(A)	大学生	183	22.1	r = 0.47	总分
Prabhakaran et al., 2014(B)	大学生	183	22.1	r = 0.43	流畅性
Prabhakaran et al., 2014(C)	大学生	183	22.1	r = 0.42	灵活性
Prabhakaran et al., 2014(D)	大学生	183	22.1	r = 0.48	独创性
Prabhakaran et al., 2014(E)	大学生	183	22.1	r = 0.40	总分
Prabhakaran et al., 2014(F)	大学生	183	22.1	r = 0.18	总分
Forthmann et al., 2018(A)	青少年	29	14.47	r = 0.699	流畅性
Forthmann et al., 2018(B)	青少年	29	14.47	r = 0.789	流畅性
Forthmann et al., 2018(C)	青少年	29	14.47	r = 0.648	总分
Forthmann et al., 2018(D)	青少年	29	14.47	r = 0.417	总分
Forthmann et al., 2018(E)	青少年	29	14.47	r = 0.562	总分
Forthmann et al., 2018(F)	青少年	29	14.47	r = 0.462	总分
Beketayev & Runco, 2016(A)	网络随机	250	33.65	r = 0.74	灵活性
Beketayev & Runco, 2016(B)	网络随机	250	33.65	r = 0.36	独创性
Forster & Dunbar, 2009(A)	大学生	61	18.75	r = 0.417	流畅性
Forster & Dunbar, 2009(B)	大学生	61	18.75	r = 0.66	流畅性
Forster & Dunbar, 2009(C)	大学生	61	18.75	r = 0.31	流畅性
Forster & Dunbar, 2009(D)	大学生	61	18.75	r =−0.42	精致性
Forster & Dunbar, 2009(E)	大学生	61	18.75	r =−0.46	精致性
Forster & Dunbar, 2009(F)	大学生	61	18.75	r =−0.60	精致性
Forster & Dunbar, 2009(G)	大学生	61	18.75	r =−0.63	精致性
Hass, 2017	大学生	226	-	r = 0.40	总分
Heinen & Johnson, 2018(A)	网络随机	62	37	r = 0.47	总分
Heinen & Johnson, 2018(B)	网络随机	121	37	r = 0.77	总分
Acar & Runco, 2014(A)	大学生	54	26.28	r = 0.29	流畅性
Acar & Runco, 2014(B)	大学生	54	26.28	r = 0.28	流畅性
Acar & Runco, 2014(C)	大学生	54	26.28	r = 0.27	总分
Acar & Runco, 2014(D)	大学生	54	26.28	r = 0.35	总分
Acar & Runco, 2014(E)	大学生	54	26.28	r = 0.31	总分
Mathias & Kenett, 2017(A)	大学生	89	25	r = 0.21	流畅性
Mathias & Kenett, 2017(B)	大学生	89	25	r = 0.41	总分
Mathias & Kenett, 2017(C)	大学生	89	25	r = 0.37	流畅性
Mathias & Kenett, 2017(D)	大学生	89	25	r = 0.24	总分
Rossmann & Fink, 2010	大学生	106	23.03	r = 0.22	独创性
Tempest & Rémi, 2019(A)	德语系大学生	29	22.9	r = 0.524	流畅性
Tempest & Rémi, 2019(B)	德语系大学生	29	22.9	r = 0.468	灵活性
Tempest & Rémi, 2019(C)	德语系大学生	29	22.9	r = 0.449	流畅性
Tempest & Rémi, 2019(D)	德语系大学生	29	22.9	r = 0.406	灵活性
Tempest & Rémi, 2019(E)	德语系大学生	29	22.9	r = 0.609	总分
Beaty & Johnson, 2021(A)	大学生	171	22.63	r = 0.91	总分
Beaty & Johnson, 2021(B)	大学生	142	19.60	r = 0.75	总分
Cheng Liu, 2021E1	大学生	189	19.30	r = 0.205	灵活性
Cheng Liu, 2021E2	大学生	189	19.30	r = 0.211	独创性
Cheng Liu, 2021E3	大学生	189	19.30	r = 0.203	总分
Murray, 2021	网络随机	200	40.24	r = 0.290	总分

调节变量	类别	k	r	95% CI	Q_w	Q_b	p
创造性思维测量指标	总分	28	0.430^***	0.326, 0.523	447.369^***
	流畅性	12	0.447^***	0.338, 0.545	25.666^**
	灵活性	5	0.473^**	0.180, 0.688	63.436^***	89.380	0.000
	精致性	4	−0.533^***	−0.664, −0.372	1.433
	独创性	4	0.328^***	0.197, 0.447	10.542^*

调节变量	类别	k	r	95% CI	Q_w	Q_b	p
创造性思维测量指标	总分	28	0.430^***	0.326, 0.523	447.369^***
	流畅性	12	0.447^***	0.338, 0.545	25.666^**
	灵活性	5	0.473^**	0.180, 0.688	63.436^***	89.380	0.000
	精致性	4	−0.533^***	−0.664, −0.372	1.433
	独创性	4	0.328^***	0.197, 0.447	10.542^*