Neurological Plausibility of AI-Generated Music for Commercial Environments: An In-Silico Cortical Investigation Using Wubble and TRIBE v2
This work addresses the problem of assessing the neurological plausibility of AI-generated music for commercial environments, providing a reproducible framework for neural pre-screening, though it is incremental as it focuses on cortical proxies without establishing subcortical or behavioral effects.
The study investigated whether AI-generated music for commercial settings can produce neurologically plausible cortical responses by using a generative music system and a whole-brain encoding model to analyze five instrumental tracks across different arousal and valence prompts. The results showed that prompt variation modulated predicted cortical activation patterns, with the fast bright major-pop condition eliciting the strongest responses in auditory, temporal, and prefrontal regions, supporting a cautious claim of cortical plausibility.
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.