基于成员间高阶认知交互的团队认知诊断建模

doi:10.3724/SP.J.1041.2026.0166

摘要/Abstract

摘要：

团队合作作为复杂社会系统中实现共同目标的关键行为模式, 其表现高度依赖于由成员间认知交互形成的团队认知。对团队认知的诊断测量不仅有助于探索团队成员间的认知交互规律, 还有助于识别影响团队行为表现的具体原因, 进而为实施有针对性干预、提升团队表现提供重要量化依据。然而, 目前尚缺乏具有团队认知诊断功能的心理测量模型。对此, 本研究构建了一个适用于分析团队合作作答数据的团队认知诊断模型——Team-DINA模型。实证研究和模拟研究结果表明新模型适用于分析团队合作作答数据, 可实现同时评估团队整体和成员个体的认知属性掌握情况和认知能力水平; 不仅填补了团队认知诊断领域的方法学空白, 还进一步拓展了认知诊断测量范式的适用范围。

关键词: 团队认知诊断, 团队合作, 团队认知, 认知诊断模型, DINA模型

Abstract:

Teamwork involves individuals collaborating to achieve shared goals that exceed individual capabilities. As a team-level construct, team cognition plays a critical role in effective teamwork by integrating individual expertise and fostering shared understanding. Identifying the causes of inefficiencies or poor performance in teams is essential for implementing targeted interventions and promoting the development of team cognition. However, traditional cognitive diagnosis models (CDMs), which are limited to individual problem-solving contexts, are inadequate for diagnosing team cognition in collaborative settings.

To address this, this study introduces a teamwork DINA (Team-DINA) model, an extension of the traditional DINA model for individual cognitive diagnosis, specifically designed to analyze teamwork response data. The proposed model constructs the formation mechanism of team members' individual cognitions into team cognition based on a higher-order latent structure. Further, it is hypothesized that individual cognition affects the mastery of individual cognitive attributes, while team cognition influences the mastery of team-level cognitive attributes. This allows for the simultaneous assessment of cognitive attribute mastery and the collective cognitive ability of both the team and its individual members. To demonstrate the practical applicability of the model, a dyadic teamwork matrix reasoning test was conducted. Additionally, two simulation studies were carried out to evaluate the psychometric performance of the model.

The results of the empirical study revealed that the Team-DINA model provides a deeper understanding of teamwork performance and identifies specific cognitive factors affecting team outcomes. The simulation studies highlighted two main findings: (1) The model demonstrates strong psychometric performance across various test scenarios, with improvements in model accuracy achieved by increasing the number of items and teams; (2) When the test includes an equal number of independent and collaborative response items, the model parameters are recovered in a balanced and appropriate manner, allowing for flexible adjustments of item proportions to enhance measurement accuracy.

Overall, the proposed Team-DINA model fills a critical methodological gap in the field of team cognition diagnosis and expands the application of cognitive diagnostic measurement paradigms from individual cognitive diagnosis to teamwork cognitive diagnosis.

Key words: teamwork cognitive diagnosis, teamwork, team cognition, cognitive diagnosis model, DINA model

中图分类号:

B841

詹沛达, 王志谋, 褚高红, 郝宁. (2026). 基于成员间高阶认知交互的团队认知诊断建模. 心理学报, 58(1), 166-179.

ZHAN Peida, WANG Zhimou, CHU Gaohong, HAO Ning. (2026). Teamwork cognitive diagnostic modeling with higher-order cognitive interaction between team members. Acta Psychologica Sinica, 58(1), 166-179.

图/表 13

参考文献 46

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团队	θ_A	θ_Β	θ_AΒ	ε_AΒ	α_A	α_Β	α_AΒ	Raw_A	Raw_B	Raw_AB	z₁	z₂	z₃
1	0.79	0.81	0.86	0.17	11111	11111	11111	9	9	9	9	118	17.01
2	0.48	0.40	0.09	−0.25	11111	11111	11101	8	7	6	9	141	17.11
7	−1.38	−1.48	−1.95	−0.63	11100	10100	01000	5	5	0	3	66	100.45
15	0.43	0.37	0.07	−0.23	11111	11111	11101	8	7	7	7	9	232.37
48	0.48	−0.43	0.29	0.29	11111	01100	11111	8	4	9	9	419	5.64
56	−0.17	0.40	0.35	0.18	01101	11111	11111	6	6	8	9	405	8.71
66	−0.17	−0.09	0.11	0.18	11101	11111	11111	6	5	8	9	468	48.12

团队	θ_A	θ_Β	θ_AΒ	ε_AΒ	α_A	α_Β	α_AΒ	Raw_A	Raw_B	Raw_AB	z₁	z₂	z₃
1	0.79	0.81	0.86	0.17	11111	11111	11111	9	9	9	9	118	17.01
2	0.48	0.40	0.09	−0.25	11111	11111	11101	8	7	6	9	141	17.11
7	−1.38	−1.48	−1.95	−0.63	11100	10100	01000	5	5	0	3	66	100.45
15	0.43	0.37	0.07	−0.23	11111	11111	11101	8	7	7	7	9	232.37
48	0.48	−0.43	0.29	0.29	11111	01100	11111	8	4	9	9	419	5.64
56	−0.17	0.40	0.35	0.18	01101	11111	11111	6	6	8	9	405	8.71
66	−0.17	−0.09	0.11	0.18	11101	11111	11111	6	5	8	9	468	48.12

背景变量	成员组成	组数
性别	男-男	7
	男-女	12
	女-女	48
独生子女	独生-独生	6
	独生-非独生	35
	非独生-非独生	26
学历	本科-本科	48
	本科-研究生	7
	研究生-研究生	12

背景变量	成员组成	组数
性别	男-男	7
	男-女	12
	女-女	48
独生子女	独生-独生	6
	独生-非独生	35
	非独生-非独生	26
学历	本科-本科	48
	本科-研究生	7
	研究生-研究生	12

ρ	T	I	g1			s1			g2			s2			g			s
ρ	T	I	Bias	RMSE	Cor	Bias	RMSE	Cor	Bias	RMSE	Cor	Bias	RMSE	Cor	Bias	RMSE	Cor	Bias	RMSE	Cor
0.6	50	10	0.042	0.069	0.775	0.064	0.104	0.768	0.060	0.092	0.668	0.118	0.153	0.634	0.051	0.081	0.751	0.091	0.128	0.702
		15	0.019	0.052	0.875	0.047	0.079	0.838	0.030	0.072	0.789	0.092	0.134	0.686	0.025	0.062	0.828	0.069	0.107	0.757
		30	0.015	0.043	0.925	0.037	0.073	0.830	0.025	0.062	0.854	0.072	0.115	0.709	0.020	0.053	0.896	0.054	0.094	0.765
	100	10	0.029	0.059	0.776	0.036	0.066	0.863	0.042	0.077	0.703	0.068	0.101	0.772	0.036	0.068	0.765	0.052	0.084	0.825
		15	0.011	0.038	0.910	0.026	0.054	0.885	0.021	0.052	0.876	0.037	0.073	0.824	0.016	0.045	0.901	0.031	0.064	0.863
		30	0.007	0.032	0.957	0.018	0.046	0.914	0.013	0.043	0.915	0.037	0.074	0.847	0.010	0.038	0.938	0.027	0.060	0.880
	200	10	0.018	0.044	0.839	0.020	0.048	0.911	0.025	0.054	0.748	0.035	0.068	0.872	0.022	0.049	0.842	0.028	0.058	0.901
		15	0.005	0.027	0.963	0.013	0.039	0.930	0.012	0.036	0.929	0.023	0.053	0.907	0.009	0.032	0.948	0.018	0.046	0.929
		30	0.002	0.021	0.982	0.011	0.033	0.953	0.007	0.029	0.959	0.018	0.048	0.922	0.005	0.025	0.972	0.015	0.041	0.940
0	50	10	0.038	0.076	0.736	0.074	0.107	0.763	0.058	0.093	0.551	0.118	0.157	0.627	0.048	0.085	0.669	0.096	0.132	0.703
		15	0.022	0.056	0.830	0.044	0.078	0.803	0.042	0.080	0.757	0.089	0.127	0.740	0.032	0.068	0.801	0.066	0.103	0.765
		30	0.014	0.043	0.922	0.036	0.069	0.865	0.024	0.063	0.860	0.080	0.120	0.729	0.019	0.053	0.889	0.058	0.094	0.783
	100	10	0.024	0.051	0.828	0.035	0.068	0.837	0.045	0.078	0.722	0.066	0.103	0.778	0.035	0.064	0.785	0.051	0.086	0.823
		15	0.011	0.038	0.930	0.022	0.053	0.888	0.025	0.052	0.848	0.046	0.081	0.824	0.018	0.045	0.906	0.034	0.067	0.866
		30	0.008	0.029	0.962	0.020	0.051	0.897	0.012	0.043	0.925	0.038	0.071	0.859	0.010	0.036	0.946	0.029	0.061	0.885
	200	10	0.020	0.045	0.841	0.021	0.046	0.900	0.034	0.061	0.771	0.047	0.071	0.903	0.027	0.053	0.836	0.034	0.059	0.909
		15	0.004	0.027	0.957	0.013	0.038	0.939	0.011	0.037	0.929	0.025	0.057	0.894	0.007	0.032	0.949	0.019	0.048	0.923
		30	0.003	0.022	0.980	0.009	0.032	0.956	0.007	0.030	0.963	0.018	0.047	0.916	0.005	0.026	0.972	0.014	0.040	0.939
-0.6	50	10	0.037	0.070	0.729	0.070	0.102	0.759	0.054	0.092	0.592	0.115	0.151	0.635	0.046	0.081	0.692	0.092	0.126	0.703
		15	0.019	0.056	0.822	0.046	0.081	0.802	0.035	0.075	0.788	0.101	0.143	0.654	0.027	0.065	0.812	0.074	0.112	0.715
		30	0.012	0.044	0.931	0.034	0.070	0.848	0.025	0.063	0.843	0.072	0.111	0.755	0.019	0.054	0.890	0.053	0.090	0.796
	100	10	0.028	0.056	0.814	0.039	0.073	0.850	0.045	0.078	0.751	0.057	0.104	0.745	0.036	0.067	0.782	0.048	0.088	0.791
		15	0.012	0.040	0.923	0.028	0.057	0.862	0.022	0.050	0.878	0.048	0.089	0.829	0.017	0.045	0.907	0.038	0.073	0.844
		30	0.007	0.031	0.959	0.017	0.045	0.919	0.013	0.044	0.923	0.035	0.068	0.858	0.010	0.038	0.940	0.026	0.057	0.890
	200	10	0.017	0.041	0.854	0.017	0.044	0.931	0.026	0.055	0.799	0.032	0.069	0.874	0.021	0.048	0.845	0.025	0.056	0.910
		15	0.004	0.028	0.952	0.010	0.038	0.948	0.011	0.037	0.919	0.017	0.053	0.874	0.008	0.032	0.945	0.014	0.045	0.925
		30	0.002	0.021	0.979	0.010	0.033	0.957	0.008	0.031	0.959	0.017	0.046	0.913	0.005	0.026	0.970	0.014	0.040	0.939