Effects of AI viewpoint divergence on group creative performance: Cognitive-neural mechanisms

doi:10.3724/SP.J.1041.2026.2200

Abstract

Abstract: Generative AI is increasingly integrated into collaborative work, yet how characteristics of AI-generated viewpoints shape group creative performance remains insufficiently understood. AI viewpoint divergence refers to the extent to which AI-generated viewpoints are distributed across semantic space and differ in content. Drawing primarily on the Motivated Information Processing in Groups model, this study examined whether high versus low AI viewpoint divergence differentially affects the novelty and usefulness of group ideas in scientific and everyday creativity tasks. It also examined whether these effects are associated with cognitive processing variables and prefrontal neural indicators during human-AI co-creation.
We designed a dyad-AI collaborative paradigm in which two participants worked with AI to complete one scientific and one everyday creativity task. We recruited 180 participants. After excluding one dyad because one participant did not follow the task instructions, the final sample comprised 89 dyads (44 in the high-divergence condition and 45 in the low-divergence condition). We used a 2 × 2 mixed design, with AI viewpoint divergence as a between-dyad factor and task type as a within-dyad factor. Group creative performance was assessed in terms of novelty and usefulness. We also measured AI utilization strategies, perspective-taking strategies, and semantic features of human-generated and group-generated ideas, and used fNIRS hyperscanning to measure prefrontal inter-brain synchronization and intra-brain functional connectivity.
Manipulation checks showed that AI-generated viewpoints in the high-divergence condition were more semantically dispersed than those in the low-divergence condition. High AI viewpoint divergence increased the novelty of group ideas, but this benefit emerged primarily in the everyday creativity task. In contrast, high AI viewpoint divergence reduced the usefulness of group ideas across both task types. Mediation analyses showed that AI exclusion-based generation strategies and semantic features of human ideas mediated the association between AI viewpoint divergence and group creative performance. Brain-behavior integration analyses further indicated that prefrontal inter-brain synchronization and intra-brain functional connectivity were indirectly associated with novelty and usefulness through AI utilization strategies, perspective-taking strategies, and semantic features.
These findings indicate that AI viewpoint divergence does not uniformly enhance group creativity. Instead, its effects depend on task constraints and on how groups search for, select, adopt, and semantically reorganize AI-generated information. The study extends the application of the Motivated Information Processing in Groups model to human-AI co-creation and provides empirical evidence for cognitive processing pathways and neural associations linking AI viewpoint divergence to the distinct outcomes of novelty and usefulness in group creative performance. Practically, AI support for creative collaboration should be calibrated to task openness, domain constraints, and the stage-specific demands of idea generation and evaluation.

Key words: AI viewpoint divergence, group creativity, human-AI co-creation, fNIRS hyperscanning, cognitive-neural mechanisms

ZHOU Zhihao, QIAO Xinuo, ZHANG Wenyu, TONG Shuai, HAO Ning. (2026). Effects of AI viewpoint divergence on group creative performance: Cognitive-neural mechanisms. Acta Psychologica Sinica, 58(11), 2200-2217.

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