Li Ziyi, Liu Lulu, Hu Ying, Zhang Lushuang, Xie Xingxu, Luo Jing
Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China.
School of Design, Hunan University, Changsha, 410000, China.
Neurosci Bull. 2025 Jun 24. doi: 10.1007/s12264-025-01431-2.
Exploring the mechanisms underlying willingness to buy (WTB) will help us identify neural indicators for predicting the performance of innovative products. Using functional magnetic resonance imaging, we asked participants to view products created by combining two components, including high applicability new combinations (HANCs), which provide a novel and practical application; and low applicability new combinations (LANCs), which provide no additional value. First, we found that WTB generally involves activation of the parahippocampal gyrus. For HANC, activation in the pars opercularis of the inferior frontal gyrus (IFG oper) is associated with WTB. Second, representational similarity analysis revealed that for HANC, the interrelation between the elements and combinations in the IFG oper predicts WTB. Third, multivoxel pattern analysis found that classification accuracy in the IFG oper predicts the difference in WTB between HANCs and LANCs. In conclusion, WTB requires default mode network-based associative processing. For HANC products, executive control network-based processes are necessary for value construction.
探索购买意愿（WTB）背后的机制将有助于我们识别预测创新产品性能的神经指标。我们使用功能磁共振成像，让参与者查看由两种组件组合而成的产品，包括具有高适用性的新组合（HANC），其提供新颖且实用的应用；以及低适用性的新组合（LANC），其不提供额外价值。首先，我们发现购买意愿通常涉及海马旁回的激活。对于HANC，额下回岛盖部（IFG oper）的激活与购买意愿相关。其次，表征相似性分析表明，对于HANC，IFG oper中元素与组合之间的相互关系可预测购买意愿。第三，多体素模式分析发现，IFG oper中的分类准确率可预测HANC与LANC之间购买意愿的差异。总之，购买意愿需要基于默认模式网络的联想处理。对于HANC产品，基于执行控制网络的过程对于价值构建是必要的。
