A library for innovative category exemplars (ALICE) database: Streamlining research with printable 3D novel objects.

IF 4.6 2区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Behavior Research Methods Pub Date : 2024-10-01 Epub Date: 2024-08-02 DOI:10.3758/s13428-024-02458-5

Alice Xu, Ji Y Son, Catherine M Sandhofer

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Abstract

This paper introduces A Library for Innovative Category Exemplars (ALICE) database, a resource that enhances research efficiency in cognitive and developmental studies by providing printable 3D objects representing 30 novel categories. Our research consists of three experiments to validate the novelty and complexity of the objects in ALICE. Experiment 1 assessed the novelty of objects through adult participants' subjective familiarity ratings and agreement on object naming and descriptions. The results confirm the general novelty of the objects. Experiment 2 employed multidimensional scaling (MDS) to analyze perceived similarities between objects, revealing a three-dimensional structure based solely on shape, indicative of their complexity. Experiment 3 used two clustering techniques to categorize objects: k-means clustering for creating nonoverlapping global categories, and hierarchical clustering for allowing global categories that overlap and have a hierarchical structure. Through stability tests, we verified the robustness of each clustering method and observed a moderate to good consensus between them, affirming the strength of our dual approach in effectively and accurately delineating meaningful object categories. By offering easy access to customizable novel stimuli, ALICE provides a practical solution to the challenges of creating novel physical objects for experimental purposes.

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创新类别范例库（ALICE）数据库：利用可打印的三维新颖物体简化研究。

本文介绍了创新类别示例库（ALICE）数据库，该资源通过提供代表 30 个新颖类别的可打印三维对象，提高了认知和发展研究的效率。我们的研究包括三个实验，以验证 ALICE 中对象的新颖性和复杂性。实验 1 通过成年参与者对物体的主观熟悉程度评分以及对物体命名和描述的一致性来评估物体的新颖性。结果证实了对象的普遍新颖性。实验 2 采用多维标度（MDS）分析了物体之间的相似性，发现了一种仅基于形状的三维结构，表明了物体的复杂性。实验 3 采用了两种聚类技术对物体进行分类：K-均值聚类用于创建不重叠的全局类别，而分层聚类则允许全局类别重叠并具有层次结构。通过稳定性测试，我们验证了每种聚类方法的稳健性，并观察到它们之间达成了适度到良好的共识，从而肯定了我们的双重方法在有效、准确地划分有意义的对象类别方面的优势。ALICE 可以方便地获取可定制的新奇刺激物，为解决为实验目的创建新奇物理对象所面临的挑战提供了切实可行的解决方案。

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来源期刊

Behavior Research Methods Multiple-

CiteScore

10.30

自引率

9.30%

发文量

266

期刊介绍： Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.