What happens if you play hard techno or Death metal to a person that never listen to music? I would bet they won’t appreciate it. Musical taste is usually built over time. People usually start listening music with something “simple” like radio pop, then they like some songs and dive deeper into a genre, starting with easy pop-like songs, and then discovering lots of sub-genres. Switching from metal to techno is also usually through more predictable slower songs. Our journey through genres is a gradual process of building a predictive model in our brain.
The Musical Brain
Our brains are constantly predicting what comes next in a melody or rhythm. As neuroscientist Daniel Levitin explains in “This Is Your Brain on Music”, our brain uses regions like the cerebellum to track musical sequences and predict what’s next. The pleasure we get from music comes from the interplay between expectation and surprise. A song that is too predictable is boring; one that is completely unpredictable is just noise.
The emotional power of music lies in how an artist skillfully plays with this predictive process. When we experience that thrilling moment of tension and release—like a dramatic “drop” in electronic music or a powerful chorus—our brain’s reward system is activated. The pleasure isn’t just in getting the prediction right; it’s in the dynamic between what we expect and what we get:
- Confirmation: When a song resolves as our brain predicts, it provides a sense of comfort and satisfaction.
- Surprise: When a musician introduces an unexpected chord or a syncopated beat, it violates our expectations, creating a “prediction error” that jolts our attention.
- Resolution: The true artistry is in the masterful resolution of that surprise. When the unexpected is woven back into a satisfying pattern, our brain experiences a powerful emotional release.
David Huron’s book, “Sweet Anticipation,” offers a more detailed framework for this experience called ITPRA theory:
- Imagination: We anticipate where the music might go.
- Tension: We feel a buildup as an expected moment approaches.
- Prediction: We get a small reward when our prediction is correct.
- Reaction: We have a fast, reflexive response to a surprise.
- Appraisal: We consciously reflect on the musical outcome.
The AI Parallel
This process of prediction, surprise, and reward is exactly how we train Large Language Models. An LLM’s primary goal is to predict the next token based on all previous tokens. Its training is driven by minimizing prediction error using a cross-entropy loss function, which penalizes the model more heavily for being surprised by the correct next token. After this loss is low enough, we usually do another step of reinforcement learning fine tuning, where we ask to generate several complete answers, and select the one that’s closer aligns to what we expect from the model.
LLMs have some of ITPRA theory elements. Inference time compute gives us Imagination, and the context window builds Tension. Prediction is their core function. But they lack a sophisticated Reaction and Appraisal. When faced with a surprising or ambiguous prompt, a model doesn’t get curious; it just makes its most probable guess. It doesn’t appraise its own answers for insight, only for alignment with its training data. Can we do better?
- Reaction as Active Curiosity: When an LLM is “surprised” by a confusing prompt, its reaction shouldn’t be to guess. It should be to ask a clarifying question. This turns a passive prediction error into an active search for more information. This is a hot topic, and people build routers to direct different prompts to different models, for example
- Appraisal as Intrinsic Motivation: We can reward a model not just for being “correct,” but for being useful or insightful. By creating a reward signal for reducing uncertainty, we can teach the model to appraise its own lines of reasoning. It would learn to seek the sweet spot between boringly simple and frustratingly complex.
Final thought
If you want someone to like your music, don’t play your favorite song first. You have to let them build a good prediction system. Start with similar music they already like, and gradually introduce songs with more elements from your taste. When your tastes converge, you can shock them with your favorite song