A mouse-tracking classification task to measure the unhealthy = tasty intuition

TL;DR

This paper introduces a new mouse-tracking task to measure the belief that unhealthy food is tasty, which could help understand food preferences and decision-making.

Contribution

A novel mouse-tracking classification task is introduced to measure the 'unhealthy = tasty intuition' with process-based behavioral data.

Findings

The task correlates with explicit UTI scores and predicts actual food consumption.

The task is sensitive to contextual manipulations and can be adapted for other beliefs.

Three studies validate the task as a useful tool for studying lay beliefs and decision-making.

Abstract

Understanding food preferences plays a crucial role in addressing both health concerns, such as obesity, and environmental concerns, such as climate change. Recognizing the impact of lay beliefs on food preferences is essential in addressing these challenges. One prevalent belief is the “unhealthy = tasty intuition” (UTI), the belief that taste and health in food do not go together. While self-report scales and behavioral tasks are commonly used to measure such beliefs, they have distinct methodological purposes: scales are better suited for assessing stable, trait-like constructs, whereas tasks capture more dynamic processes and are well suited for experimental manipulation. This paper introduces a mouse-tracking classification task that provides a process-based behavioral index of UTI, providing a novel approach for assessing implicit beliefs about the relationship between taste and health in food. Three studies validate the task, demonstrating correlations between explicit UTI scores and task performance. Additionally, the task predicts actual food consumption and, importantly, exhibits sensitivity to contextual manipulations. Because this task can be adapted to measure other beliefs, it is a valuable tool for researchers working on individual lay beliefs and decision-making processes. To that end, a template of the task is provided to help other researchers build on this work.

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