Effect of human-intelligent system cognitive fit on human-machine collaborative decision-making performance

doi:10.16265/j.cnki.issn1003-3033.2026.08.0813

Abstract

Abstract:

To address constrained human-machine collaborative decision-making performance caused by cognitive differences between operators and intelligent systems in coal mine intelligent monitoring tasks, this study, grounded in cognitive load theory, develops a theoretical model of the effects of human-intelligent system cognitive fit on human-machine collaborative decision-making performance. Task complexity, including information load and task structure, and system transparency, including information transparency and process transparency, are introduced as contextual moderators. Using a multi-stage questionnaire survey, empirical data were collected from frontline coal mine workers. The results show that human-intelligent system cognitive fit has a significant positive effect on human-machine collaborative decision-making performance (β=0.253, p<0.001). Information load, task structure, information transparency, and process transparency all significantly and positively moderate the relationship between cognitive fit and human-machine collaborative decision-making performance. Specifically, higher levels of these contextual factors strengthen the positive effect of cognitive fit on collaborative decision-making performance. This effect is particularly pronounced under conditions of high information load and high process transparency.

Key words: human-intelligent system, cognitive fit, human-machine collaborative decision-making performance, task complexity, system transparency

CLC Number:

X912.9安全人机学

Niu Lixia, Li Bo, Li Guo, Pan Lihang, Zhang Guojian. Effect of human-intelligent system cognitive fit on human-machine collaborative decision-making performance[J]. China Safety Science Journal, 2026, 36(8): 21-28.

Figures/Tables 6

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Table 1

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Fig.2

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References 33

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模型	χ²	df	χ²/v	CFI	TLI	RMSEA	SRMR
6因子(A,B,C,D,E,F)	415.444	391	1.063	0.996	0.995	0.011	0.0721
5因子(A+B,C,D,E,F)	832.83	395	2.108	0.929	0.922	0.052	0.0569
4因子(A+B,C+D,E,F)	1 529.406	399	3.833	0.818	0.801	0.083	0.1257
3因子(A+B,C+D,E+F)	1 814.689	402	4.514	0.772	0.753	0.092	0.1358
2因子(A+B+C+D,E+F)	3 008.348	404	7.446	0.58	0.548	0.125	0.1292
1因子(A+B+C+D+E+F)	3 928.319	405	9.7	0.432	0.389	0.145	0.1407

变量	均值	标准差	相关系数
变量	均值	标准差	1	2	3	4	5	6	7	8	9	10	11
性别	1.14	0.35	1	—	—	—	—	—	—	—	—	—	—
年龄	2.76	1.15	0.162^**	1	—	—	—	—	—	—	—	—	—
工龄	2.98	1.29	0.102^*	0.879^**	1	—	—	—	—	—	—	—	—
学历	1.70	0.74	0.230^**	0.042	0.023	1	—	—	—	—	—	—	—
岗位类型	2.17	1.17	0.361^**	0.073	0.031	0.436^**	1	—	—	—	—	—	—
A	3.83	0.75	0.047	-0.029	-0.016	0.085	-0.019	1	—	—	—	—	—
F	3.71	0.87	-0.028	-0.068	-0.069	0.009	-0.005	0.271^**	1	—	—	—	—
B	2.36	1.09	-0.127	0.052	0.080	-0.066	-0.042	0.305	-0.19	1	—	—	—
C	2.38	1.09	-0.128	0.077	0.089	-0.073	-0.041	0.266	-0.176	0.655^**	1	—	—
D	1.94	0.88	0.005	0.050	0.072	-0.052	-0.032	-0.352	-0.298	0.289^**	0.265^**	1	—
E	2.01	0.91	-0.045	0.031	0.052	-0.127^**	-0.053	-0.303	-0.278	0.241^**	0.214^**	0.696^**	1

变量类型	变量	人机协同决策绩效
变量类型	变量	模型1	模型2	模型3	模型4	模型5
控制变量	性别	-0.047	-0.043	-0.055	-0.060	-0.057
	年龄	-0.044	-0.051	-0.043	-0.031	-0.043
	工龄	0.014	0.037	0.024	0.012	0.016
	学历	0.001	0.003	-0.002	-0.001	0.005
	岗位类型	-0.008	-0.036	-0.026	-0.007	-0.013
自变量	“人-智能系统”认知适配	0.253^***	0.122^**	0.157^**	0.207^***	0.192^***
调节变量	信息透明度	—	—	—	-0.087^*	—
	过程透明度	—	—	—	—	-0.072
	信息负荷	—	-0.142^***	—	—	—
	任务结构	—	—	-0.125^**	—	—
交互项	“人-智能系统”认知适配×信息负荷	—	0.218^***	—	—	—
	“人-智能系统”认知适配×任务结构	—	—	0.182^***	—	—
	“人-智能系统”认知适配×信息透明度	—	—	—	0.087^*	—
	“人-智能系统”认知适配×过程透明度	—	—	—	—	0.117^**
	决定系数R²	0.068	0.141	0.123	0.084	0.088
	决定系数变化量ΔR²		0.037	0.026	0.007	0.012
	F统计量F	4.887^***	8.289^***	7.067^***	4.625^***	4.844^***
	F值变化量ΔF	27.326^***	17.529^***	12.14^***	2.932^**	5.107^***

调节变量水平	效应值	标准误	95%置信区间
调节变量水平	效应值	标准误	低限	高限
低信息负荷	0.181 4	0.095	0.105 4	0.268 2
高信息负荷	0.414 0	0.076 5	0.263 5	0.564 5
低任务结构	0.182 8	0.092 6	0.119 2	0.284 8
高任务结构	0.379 4	0.071 8	0.038 3	0.520 6
低信息透明度	0.138 5	0.102 4	0.062 9	0.339 9
高信息透明度	0.392 1	0.090 2	0.214 7	0.569 5
低过程透明度	0.116 6	0.103 8	0.116 6	0.291 5
高过程透明度	0.418 2	0.088 5	0.244 2	0.592 1