Perceiving Slope and Acceleration: Evidence for Variable Tempo Sampling in Pitch-Based Sonification of Functions

TL;DR

This paper introduces a novel variable tempo sampling method for pitch-based sonification that significantly improves the perception of data slope and acceleration, making data interpretation more accurate and user-preferred.

Contribution

It presents the Variable Tempo sampling approach and demonstrates its superiority over traditional methods in perceiving data derivatives in sonification.

Findings

Variable Tempo improves slope perception accuracy.

Variable Tempo enables over 13 times finer acceleration detection.

Participants prefer and find Variable Tempo less mentally demanding.

Abstract

Sonification offers a non-visual way to understand data, with pitch-based encodings being the most common. Yet, how well people perceive slope and acceleration-key features of data trends-remains poorly understood. Drawing on people's natural abilities to perceive tempo, we introduce a novel sampling method for pitch-based sonification to enhance the perception of slope and acceleration in univariate functions. While traditional sonification methods often sample data at uniform x-spacing, yielding notes played at a fixed tempo with variable pitch intervals (Variable Pitch Interval), our approach samples at uniform y-spacing, producing notes with consistent pitch intervals but variable tempo (Variable Tempo). We conducted psychoacoustic experiments to understand slope and acceleration perception across three sampling methods: Variable Pitch Interval, Variable Tempo, and a Continuous (no sampling) baseline. In slope comparison tasks, Variable Tempo was more accurate than the other methods when modulated by the magnitude ratio between slopes. For acceleration perception, just-noticeable differences under Variable Tempo were over 13 times finer than with other methods. Participants also commonly reported higher confidence, lower mental effort, and a stronger preference for Variable Tempo compared to other methods. This work contributes models of slope and acceleration perception across pitch-based sonification techniques, introduces Variable Tempo as a novel and preferred sampling method, and provides promising initial evidence that leveraging timing can lead to more sensitive, accurate, and precise interpretation of derivative-based data features.