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January 18, 2026Intelligent Decision Technologies0 citations

Ratio preference prediction system for mouse design: Insights from golden ratio and gender differences

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CCChih-Ping Chen

Key Points

  • This investigation explores how accurately users can identify computer mice designed using the golden ratio and examines gender-based preferences.
  • Analyzed a database of 233 Genius brand mice to select samples with varied length-to-width ratios.
  • Conducted absolute and relative judgment tests with 99 university students (62 females, 37 males).
  • Utilized chi-square tests to assess recognition accuracy and polynomial regression analysis to explore preference trends.
  • Participants struggled to identify the mouse closest to the golden ratio despite visual aids.
  • No significant difference in recognition accuracy was found between male (32%) and female (34%) participants.
  • A polynomial regression showed a strong relationship (R 2 = 99.91%) between mouse ratio and participants’ preferences.

Abstract

This investigation aims to explore users’ accuracy in identifying computer mice designed with the golden ratio and their aesthetic preferences, with a specific focus on gender differences. First, the study used a database of 233 Genius brand mice and selected five samples with length-to-width ratios ranging from 1.42 to 1.78. A total of 99 university students (62 females and 37 males) participated in absolute and relative judgment tests. Results showed that most participants failed to correctly identify the mouse with a ratio closest to the golden ratio (approximately 1:1.62), even when provided with 2D or 3D visual references. The chi-square test showed that the difference in recognition accuracy between males (32%) and females (34%) was not statistically significant (p = 0.34). In addition, there was no correlation between aesthetic preference and whether the mouse followed the golden ratio. These findings suggest that golden ratio design is neither easily recognizable nor a key factor in product preference, and designers need not overly rely on it to enhance product appeal. Finally, polynomial regression analysis was employed to investigate the effect of the ratio on the preferences of all participants. The model fit was very high (R 2 = 99.91%, p-value = 0.038), showing that the mouse ratio was associated with all participants’ preferences. According to the results, if a ratio/preference prediction system for mouse design is developed in the future, a mouse design with an aspect ratio of 1.5 to 1.56 can serve as an important reference indicator for preference prediction.

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Cite This Study

Chih-Ping Chen (2026) studied this question.

synapsesocial.com/papers/696c7877eb60fb80d1396a25https://doi.org/10.1177/18724981251413020
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