Theory + Neuroscience
Integrates prosody, affective neuroscience and attention theory to justify tonal signals as early prioritization cues in cognition.
Bridging Human Voice Tonality and AI Attention Mechanisms to Reintroduce the Human Layer to Intelligence
This paper proposes Tonality As Attention - a unifying theory linking prosodic weighting in human communication and selective attention mechanisms in artificial intelligence, revealing tone as the bridge where listening becomes response.
Tonality as Attention reframes vocal tonality from a secondary acoustic feature into a first-class attention modality. It provides methodological guidance for tonal embeddings, ethical licensing (Tonalityprint™) and evaluation metrics (e.g., HASI) so AI systems can listen - and respond - with human-level attunement. A framework bridging emotion, computation and cognition through tonality.
Integrates prosody, affective neuroscience and attention theory to justify tonal signals as early prioritization cues in cognition.
Practical pathways for tonal embeddings, calibration corpora and attention bias modules that modulate transformer weights in multimodal models.
Tonalityprint™ outlines consent, compensation and sonic-forget mechanisms to protect vocal sovereignty and enforce ethical licensing.
This white paper (Version 1.0 - October 2025) is structured for researchers and product teams exploring multimodal alignment, expressive AI and human-centered voice strategies. It includes theoretical foundations, implementation pathways, evaluation proposals and recommendations for ethical deployment.
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Tonality as Attention™ builds upon the foundational concept introduced in the 2017 paper “Attention Is All You Need” et al. This white paper extends the principle of selective attention from machine learning architectures to human vocal communication - exploring how prosody, tone, and emotional reciprocity may inform the next generation of multimodal alignment between human and artificial cognition.
Curated sources that informed this framework (selected): Vuilleumier (2005), Bahdanau et al. (2015), Vaswani et al. (2017), Schirmer & Kotz (2006), Tsai et al. (2019). Full references are in the White Paper Manuscript.
If you are a research lab, founder, brand, or studio exploring tone-aware AI, dataset licensing, or advisory engagements, Ronda is available for strategic partnerships and high-impact advisory. Premium engagements and limited lab partnerships are available - contact for rates and scopes.
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