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<p>The modern study of perceptual learning across humans, non-human animals, and artificial agents requires large-scale datasets with flexible, customizable, and controllable features for distinguishing between categories. To support this research, we developed the Oomplet Dataset Toolkit (ODT), an open-source, publicly available toolbox capable of generating 9.1 million unique visual stimuli across ten feature dimensions. Each stimulus is a cartoon-like humanoid character, termed an “Oomplet,” designed to be an instance within clearly defined visual categories that are engaging and suitable for use with diverse groups, including children. Experiments show that adults can use four to five of the ten dimensions as single classification criteria in simple perceptual discrimination tasks, underscoring the toolkit’s flexibility. With the ODT, researchers can dynamically generate large, novel stimulus sets to study perceptual learning across biological and artificial contexts.</p>

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The Oomplet dataset toolkit as a flexible and extensible system for large-scale, multi-category image generation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月18日

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  • John P. Kasarda,
  • Angela Zhang,
  • Hua Tong,
  • Yuan Tan,
  • Ruizi Wang,
  • Timothy Verstynen,
  • Michael J. Tarr

摘要

The modern study of perceptual learning across humans, non-human animals, and artificial agents requires large-scale datasets with flexible, customizable, and controllable features for distinguishing between categories. To support this research, we developed the Oomplet Dataset Toolkit (ODT), an open-source, publicly available toolbox capable of generating 9.1 million unique visual stimuli across ten feature dimensions. Each stimulus is a cartoon-like humanoid character, termed an “Oomplet,” designed to be an instance within clearly defined visual categories that are engaging and suitable for use with diverse groups, including children. Experiments show that adults can use four to five of the ten dimensions as single classification criteria in simple perceptual discrimination tasks, underscoring the toolkit’s flexibility. With the ODT, researchers can dynamically generate large, novel stimulus sets to study perceptual learning across biological and artificial contexts.