Evidence map›Paper›PMID 40610642›Full record

ArticleBehavior research methods2025

A toolbox for generating multidimensional 3D objects with fine-controlled feature space: Quaddle 2.0.

Xuan Wen, Leo Malchin, Thilo Womelsdorf

Abstract read
In one paragraph

Article in Behavior research methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Xuan WenDepartment of Psychology, Vanderbilt University, Nashville, TN, 37240, USA. xuan.wen@vanderbilt.edu.ORCID http://orcid.org/0009-0004-5703-2625
Leo MalchinDepartment of Psychology, Vanderbilt University, Nashville, TN, 37240, USA.
Thilo WomelsdorfDepartment of Psychology, Vanderbilt University, Nashville, TN, 37240, USA. thilo.womelsdorf@vanderbilt.edu.

Funding

Muscarinic modulation of RDoC constructs in primate behavior and fronto-striatal circuitsR01MH129641 · NIMH · VANDERBILT UNIVERSITY · PI Thilo Womelsdorf · 2022 to 2026
$3.3M
NIMH NIH HHS R01 MH129641
6 · The paper itself

Abstract

Multidimensional 3D-rendered objects are an important component of vision research and video-gaming applications, but it has remained challenging to parametrically control and efficiently generate those objects. Here, we describe a toolbox for controlling and efficiently generating 3D-rendered objects composed of 10 separate visual feature dimensions that can be fine-adjusted using Python scripts. The toolbox defines objects as multidimensional feature vectors with primary dimensions (object body related features), secondary dimensions (head related features), and accessory dimensions (including arms, ears, or beaks). The toolbox interfaces with the freely available Blender software to create objects. The toolbox makes it possible to gradually morph features of multiple feature dimensions, determine the desired feature similarity among objects, and automatize the generation of multiple objects in 3D object and 2D image formats. We document the use of multidimensional objects in a sequence learning task that embeds objects in a 3D-rendered augmented reality environment controlled by the gaming engine Unity. Together, the toolbox features enable the efficient generation of multidimensional objects with fine control of low-level features and higher-level object similarity useful for visual cognitive research and immersive visual environments.

Indexed as

Imaging, Three-DimensionalSoftwareAugmented RealityHumansVideo GamesVirtual RealityAugmented realityNaturalistic stimuliObject recognitionPsychophysical experimentsVideo gamesVirtual reality

Identifiers

PMID40610642
PMCPMC12226688

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.