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Neurological Plausibility of AI-Generated Music for Commercial Environments: An In-Silico Cortical Investigation Using Wubble and TRIBE v2

Shaad Sufi

Abstract

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.

Neurological Plausibility of AI-Generated Music for Commercial Environments: An In-Silico Cortical Investigation Using Wubble and TRIBE v2

Abstract

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.

Paper Structure

This paper contains 28 sections, 6 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Research Question
  3. Contributions
  4. Related Work
  5. Music in Commercial Environments
  6. Music, Reward, and Neuroaesthetics
  7. AI-Generated Music Evaluation
  8. Whole-Brain Encoding Models
  9. Methods
  10. Study Design
  11. Stimulus Generation
  12. Audio Preprocessing
  13. TRIBE v2 Audio-Only Inference
  14. ROI Analysis
  15. Whole-Brain Contrast Analysis
  16. ...and 13 more sections

Figures (4)

  • Figure 1: Whole-cortex mean predicted activation for each prompt-conditioned Wubble track. Error bars show the per-track whole-cortex standard deviation across vertices of the mean map.
  • Figure 2: Parcel-wise cortical response profiles for the prompt-conditioned Wubble tracks. T4 shows the strongest frontal values and the largest shift toward less negative auditory-temporal parcel means.
  • Figure 3: Predicted cortical surface maps from TRIBE v2 for the low-arousal baseline-like condition T1, the strongest condition T4, and the T4$-$T1 contrast. These visualizations are model-predicted cortical maps on the fsaverage5 surface, not raw acquired MRI scans.
  • Figure 4: Whole-brain spatial correlation matrix between track-level mean cortical maps. Prompt manipulations change cortical state geometry without eliminating shared structure across all music conditions.