Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
PLoS One. 2012;7(7):e41738. doi: 10.1371/journal.pone.0041738. Epub 2012 Jul 25.
Neuro-imaging holds great potential for predicting choice behavior from brain responses. In this study we used both traditional mass-univariate and state-of-the-art multivariate pattern analysis to establish which brain regions respond to preferred packages and to what extent neural activation patterns can predict realistic low-involvement consumer choices. More specifically, this was assessed in the context of package-induced binary food choices. Mass-univariate analyses showed that several regions, among which the bilateral striatum, were more strongly activated in response to preferred food packages. Food choices could be predicted with an accuracy of up to 61.2% by activation patterns in brain regions previously found to be involved in healthy food choices (superior frontal gyrus) and visual processing (middle occipital gyrus). In conclusion, this study shows that mass-univariate analysis can detect small package-induced differences in product preference and that MVPA can successfully predict realistic low-involvement consumer choices from functional MRI data.
神经影像学在从大脑反应预测选择行为方面具有巨大潜力。在这项研究中，我们同时使用了传统的大规模单变量和最先进的多变量模式分析来确定哪些大脑区域对首选包装做出反应，以及神经激活模式在多大程度上可以预测现实的低参与度消费者选择。更具体地说，这是在包装引起的二元食品选择的背景下评估的。大规模单变量分析表明，几个区域，包括双侧纹状体，对喜欢的食品包装的反应更强烈。通过先前发现与健康食品选择（额上回）和视觉处理（中枕叶）相关的大脑区域的激活模式，可以达到高达 61.2%的准确性来预测食物选择。总之，这项研究表明，大规模单变量分析可以检测到产品偏好的微小包装诱导差异，并且 MVPA 可以成功地从功能性磁共振成像数据预测现实的低参与度消费者选择。
