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The equation of an ellipse centered at the origin defines all points whose distances from the center maintain a constant ratio between the horizontal and vertical axes. This equation results in a smooth, closed curve that extends further along the x-axis than the y-axis, giving it a horizontal orientation. Such an ellipse demonstrates three kinds of symmetry: across the x-axis, across the y-axis, and about the origin. These symmetries are essential in understanding the graph's structure and...
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A Gaze-Driven Evolutionary Algorithm to Study Aesthetic Evaluation of Visual Symmetry.

Alexis D J Makin1, Marco Bertamini1, Andrew Jones1

  • 1Psychological Sciences, University of Liverpool, Liverpool, UK.

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|July 20, 2016
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Summary

People prefer visual symmetry, but not always perfection. This study used a gaze-driven evolutionary algorithm to show that while explicit focus on symmetry increases it, general preference allows for some imperfection, with no effect of familiarity.

Keywords:
Symmetryaestheticsevolutionary algorithmeye trackingpreference

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Area of Science:

  • Psychology
  • Computer Science
  • Human-Computer Interaction

Background:

  • Empirical evidence suggests a general human preference for visual symmetry.
  • Understanding the nuances of aesthetic preferences, such as tolerance for imperfection, is crucial.

Purpose of the Study:

  • To investigate automatic symmetry evaluation without explicit instruction.
  • To determine if perfect symmetry or slight imperfection is preferred.
  • To assess if familiarity diminishes symmetry preference.

Main Methods:

  • Utilized a gaze-driven evolutionary algorithm with genes for symmetry (deviation-symmetry) and orientation.
  • Employed eye-tracking technology to identify stimuli that captured and held observer attention.
  • Tracked changes in gene distribution over 20 generations to quantify preference shifts.

Main Results:

  • Explicitly seeking symmetry led to an increase in high-symmetry genes.
  • Seeking preferred patterns resulted in a moderate increase in symmetry, indicating tolerance for imperfection.
  • Free viewing and familiarity did not significantly alter symmetry preference or orientation.

Conclusions:

  • The evolutionary algorithm approach provides a viable quantitative measure for aesthetic preferences.
  • Human preference for symmetry is context-dependent, allowing for a degree of imperfection.
  • Familiarity and orientation do not appear to be primary drivers of symmetry preference in this paradigm.