The Squealer: Sensification of model exploration and model misfit

2026-06-29

AI Summary

This work addresses the lack of intuitive, immediate feedback on fitting errors in existing model-fitting approaches. It proposes an interactive fitting framework that integrates visual and auditory feedback: as users manipulate parametric curves, the system synthesizes audio in real time, with greater model-data discrepancies producing louder and more dissonant sounds. This is the first approach to incorporate auditory cues into model exploration, enabling multisensory assessment of fit quality. Combining interactive visualization, real-time audio synthesis, and Gaussian process regression, the method demonstrates effectiveness and generalizability across four diverse case studies—golf putting, dilution experiments, cosmological parameter estimation, and temperature data fitting—significantly enhancing users’ intuitive perception of model misfit.

Abstract

We introduce a method for visual and auditory feedback when exploring the fit of a model to data. Starting with a best-fit curve fit to data, the user can drag the curve to a new position and the computer will emit a squeal, becoming louder and more unpleasant as the discrepancy between curve and data increases. We demonstrate with four examples: a two-parameter curve fit to golf putting data, a four-parameter curve fit to dilution assays, a fit to cosmological data sensitive to the parameters of the Big Bang model, and a nonparametric Gaussian process fit to temperature readings.

Problem

• model exploration
• model misfit
• sensification
• curve fitting
• data visualization

Innovation

• sensification
• model misfit
• auditory feedback
• interactive visualization
• Gaussian process