语义距离与创造性思维关系的元分析

doi:10.3724/SP.J.1042.2023.00519

摘要/Abstract

摘要：

自然语言处理的发展为探究语义距离与创造性思维的关系提供了可靠且有效的研究方法。近些年关于两者之间关系的研究逐渐增多, 但研究结论并不一致。本研究基于创造力联想理论及扩散激活模型, 通过元分析的方法探讨了语义距离与创造性思维的整体关系, 并且分析了以往研究结论不一致的原因。本文经过文献检索和筛选后获得14项研究, 提取r值作为效应值(共53个效应值, 4729个独立样本), 并使用随机效应模型进行了元分析。结果显示：语义距离与创造性思维存在中等程度的正相关(r = 0.379, 95% CI [0.300, 0.452]); 二者的相关强度受到被试年龄和创造性思维不同测量指标的调节。研究结果表明语义距离与创造性思维关系密切, 同时解释了以往研究结论不一致的原因。上述结果不仅能为更深入地探讨创造性思维的认知神经机制提供新的研究视角和理论解释, 而且有助于更全面地理解语义距离与创造性思维二者的关系及其边界条件, 为更好地解释、预测和提升创造力提供科学依据和重要启示。

关键词: 创造性思维, 语义距离, 创造力测量, 元分析

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

中图分类号:

B842

李亚丹, 杜颖, 谢聪, 刘春宇, 杨毅隆, 李阳萍, 邱江. (2023). 语义距离与创造性思维关系的元分析. 心理科学进展 , 31(4), 519-534.

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

图/表 4

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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^*