Grammar-Weighted Futures: Modeling Human Expectation with Open Goals

Behavioral and neural evidence suggests that human expectations in language and music are not driven by surface statistics alone: readers and listeners also anticipate how open goals will be completed, as a subject awaits its verb and a chord progression awaits its return to the home chord. We present Grammar- Weighted Futures, a model in which such anticipation constrains the probability of the next event. A fixed surface sequence model, which predicts events well without exposing open goals, samples continuations; a probabilistic grammar, which represents open goals explicitly but predicts events poorly, scores how well each continuation completes the goals of the observed prefix; and these scores reweight the surface prediction of the next event. Tested against listeners’ surprise ratings for chord progressions with Transformer, LSTM and variable-order surface models, our proposed grammar weighting improves the prediction of held-out ratings with all three. In language, an exploratory implementation improves the prediction of word-predictability ratings over GPT-2 alone. These results indicate that expectations about how open goals will be completed shape human expectation in abstract cognitive domains such as language and music beyond surface statistics.