用脑模型验证AI生成音乐的神经合理性，发现不同风格可引发特定大脑反应。
Neurological Plausibility of AI-Generated Music for Commercial Environments: An In-Silico Cortical Investigation Using Wubble and TRIBE v2
- 结合生成音乐与脑编码模型，模拟不同提示下音乐的大脑响应。
- 高唤醒流行音乐激发最强前额叶与听觉皮层激活，达0.0704和0.1102。
- 结果支持可系统调控神经响应，适合用于商业音乐的神经预筛选。
背景音乐影响商业环境中的注意力、情绪与行为，但其神经合理性尚不明确。本研究通过在硅环境中结合Wubble生成音乐系统与公开的TRIBE v2全脑编码模型，评估提示驱动的零售音乐的皮层响应。五首纯器乐曲分别对应低至高唤醒、稀疏至密集编排、中性至正向情感提示，经响度归一化后，仅用音频输入进行TRIBE v2推理。分析聚焦于听觉、上颞、颞顶及下额叶等HCP区域的fsaverage5皮层预测。快节奏明亮大调流行风格产生最大全皮层平均激活（0.0402），最强前额叶区域复合响应（0.0704），并在IFJa（0.1102）、IFJp（0.0995）、A5（0.0188）和面积45（0.0015）中达到最高均值。成对空间相关性在0.787至0.974之间，表明提示变化调节了预测皮层状态，而非单一非特异性反应。预测皮层图谱进一步显示低唤醒与高唤醒条件间存在显著空间差异。结果支持谨慎的神经合理性主张：提示驱动的AI音乐可系统性改变与显著性及估值相关的听觉-颞-前额皮层模式。尽管未验证皮层下奖赏参与或消费者行为，该研究提供了基于生物启发皮层代理的商业音乐生成神经预筛选与优化的可复现框架。
原文摘要 · Abstract (English)
Background music shapes attention, affect, and approach behavior in commercial environments, yet the neural plausibility of AI-generated music for such settings remains poorly characterized. We present an in-silico pilot study that combines Wubble, a generative music system, with TRIBE v2, a publicly released whole-brain encoding model, to estimate cortical response profiles for prompt-conditioned retail music. Five fully instrumental tracks were generated to span low-to-high arousal, sparse-to-dense arrangement, and neutral-to-positive valence prompts, then analyzed with audio-only TRIBE v2 inference on loudness-normalized waveforms. Analysis focused on fsaverage5 cortical predictions summarized over auditory, superior temporal, temporo-parietal, and inferior frontal HCP parcels. The fast bright major-pop condition produced the largest whole-cortex mean activation (0.0402), the strongest prefrontal ROI composite response (0.0704), and the highest parcel means in IFJa (0.1102), IFJp (0.0995), A5 (0.0188), and area 45 (0.0015). Pairwise spatial correlations ranged from 0.787 to 0.974, indicating that prompt variation modulated predicted cortical states rather than yielding a single undifferentiated response profile. Predicted cortical surface maps further revealed visually distinct spatial organization between low-arousal and high-arousal conditions. These results support a cautious claim of cortical neurological plausibility: prompt-conditioned AI music can systematically shift predicted auditory-temporal-prefrontal patterns relevant to salience and valuation. Although the study does not establish subcortical reward engagement or consumer behavior, it provides a reproducible framework for neural pre-screening and pre-optimization of commercial music generation against biologically informed cortical proxies.
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