Many studies of vision and cognition require novel three-dimensional object sets defined by a parametric feature space. Creating such sets and verifying that they are suitable for a given task, however, can be time-consuming and effortful. Here we present a new set of multidimensional objects, Quaddles, designed for studies of feature-based learning and attention, but adaptable for many research purposes. Quaddles have features that are all equally visible from any angle around the vertical axis and can be designed to be equally discriminable along feature dimensions; these objects do not show strong or consistent response biases, with a small number of quantified exceptions. They are available as two-dimensional images, rotating videos, and FBX object files suitable for use with any modern video game engine. We also provide scripts that can be used to generate hundreds of thousands of further Quaddles, as well as examples and tutorials for modifying Quaddles or creating completely new object sets from scratch, with the aim to speed up the development time of future novel-object studies.
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Autotronics Research Lab (ARL), Faculty of Engineering, Ain Shams University, CairoAutotronics Research Lab (ARL), Faculty of Engineering, Ain Shams University, Cairo
Arafa M.
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Osama A.
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Abdelaziz M.
Ghoneima M.
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Autotronics Research Lab (ARL), Faculty of Engineering, Ain Shams University, Cairo
Mechatronics Engineering Department, Faculty of Engineering, Ain Shams University, CairoAutotronics Research Lab (ARL), Faculty of Engineering, Ain Shams University, Cairo
Ghoneima M.
García F.
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Intelligent Systems Laboratory, Universidad Carlos III de Madrid, Leganés, MadridAutotronics Research Lab (ARL), Faculty of Engineering, Ain Shams University, Cairo
García F.
Maged S.A.
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Autotronics Research Lab (ARL), Faculty of Engineering, Ain Shams University, Cairo
Mechatronics Engineering Department, Faculty of Engineering, Ain Shams University, CairoAutotronics Research Lab (ARL), Faculty of Engineering, Ain Shams University, Cairo