IntroductionThe research in consumer neuroscience has identified computational methods, particularly artificial intelligence (AI) and machine learning, as a significant frontier for advancement. Previously, we utilized functional magnetic resonance imaging (fMRI) and artificial neural networks (ANNs) to model brain processes related to brand preferences in a paradigm exempted from motor actions. In the current study, we revisit this data, introducing recent advancements in explainable artificial intelligence (xAI) to gain insights into this domain. By integrating fMRI data analysis, machine learning, and xAI, our study aims to search for functional brain networks that support brand perception and, ultimately, search for brain networks that disentangle between preferred and indifferent brands, focusing on the early processing stages.MethodsWe applied independent component analysis (ICA) to overcome the expected fMRI data’s high dimensionality, which raises hurdles in AI applications. We extracted pertinent features from the returned ICs. An ANN is then trained on this data, followed by pruning and retraining processes. We then apply explanation techniques, based on path-weights and Shapley values, to make the network more transparent, explainable, and interpretable, and to obtain insights into the underlying brain processes.ResultsThe fully connected ANN model obtained an accuracy of 54.6%, which dropped to 50.4% after pruning. However, the retraining process allowed it to surpass the fully connected network, achieving an accuracy of 55.9%. The path-weights and Shapley-based analysis concludes that, regarding brand perception, the expected initial participation of the primary visual system is followed. Other brain areas participate in early processing and discriminate between preferred and indifferent brands, such as the cuneal and the lateral occipital cortices.DiscussionThe most important finding is that a split between processing brands|preferred from brands|indifferent may occur during early processing stages, still in the visual system. However, we found no evidence of a “decision pipeline” that would yield if a brand is preferred or indifferent. The results suggest the existence of a “tagging”-like process in parallel flows in the extrastriate. Network training dynamics aggregate specific processes within the hidden nodes by analyzing the model’s hidden layer. This yielded that some nodes contribute to both global brand appraisal and specific brand category classification, shedding light on the neural substrates of decision-making in response to brand stimuli.

中文翻译：

---

神经营销/消费者神经科学中的可解释人工智能 (xAI)：品牌认知的功能磁共振成像研究

简介消费者神经科学的研究已经确定计算方法，特别是人工智能（AI）和机器学习，是一个重要的进步前沿。此前，我们利用功能磁共振成像（fMRI）和人工神经网络（ANN）在不受运动动作影响的范例中对与品牌偏好相关的大脑过程进行建模。在当前的研究中，我们重新审视这些数据，介绍可解释人工智能（xAI）的最新进展，以深入了解该领域。通过整合功能磁共振成像数据分析、机器学习和 xAI，我们的研究旨在寻找支持品牌感知的功能性大脑网络，并最终寻找区分首选品牌和中性品牌的大脑网络，重点关注早期处理阶段。独立成分分析（ICA）来克服预期的功能磁共振成像数据的高维性，这给人工智能应用带来了障碍。我们从返回的 IC 中提取了相关特征。然后使用这些数据训练人工神经网络，然后进行修剪和再训练过程。然后，我们应用基于路径权重和 Shapley 值的解释技术，使网络更加透明、可解释和可解释，并深入了解底层的大脑过程。结果全连接的 ANN 模型获得了 54.6% 的准确率，修剪后下降至50.4%。然而，再训练过程让它超越了全连接网络，达到了 55.9% 的准确率。路径权重和基于 Shapley 的分析得出的结论是，就品牌认知而言，主要视觉系统的预期初始参与是遵循的。其他大脑区域参与早期处理并区分首选品牌和无关品牌，例如楔骨和枕外侧皮质。讨论最重要的发现是，在早期处理阶段，可能会发生处理品牌|首选品牌|无关品牌|之间的分裂，仍然在视觉系统中。然而，我们没有发现任何证据表明“决策渠道”会在某个品牌受到青睐或漠不关心时产生影响。结果表明，在纹外平行流中存在类似“标记”的过程。网络训练动态通过分析模型的隐藏层来聚合隐藏节点内的特定过程。这使得一些节点对全球品牌评估和特定品牌类别分类做出贡献，揭示了响应品牌刺激的决策的神经基础。