| Title |
Quaddles: A multidimensional 3-D object set with parametrically controlled and customizable features
|
|---|---|
| Published in |
Behavior Research Methods, August 2018
|
| DOI | 10.3758/s13428-018-1097-5 |
| Pubmed ID | |
| Authors |
Marcus R. Watson, Benjamin Voloh, Milad Naghizadeh, Thilo Womelsdorf |
| Abstract |
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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 34 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 18% |
| Student > Ph. D. Student | 5 | 15% |
| Researcher | 4 | 12% |
| Student > Bachelor | 3 | 9% |
| Professor > Associate Professor | 2 | 6% |
| Other | 4 | 12% |
| Unknown | 10 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 6 | 18% |
| Psychology | 5 | 15% |
| Computer Science | 4 | 12% |
| Engineering | 2 | 6% |
| Business, Management and Accounting | 1 | 3% |
| Other | 5 | 15% |
| Unknown | 11 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 December 2019.
All research outputs
#4,621,327
of 25,385,509 outputs
Outputs from Behavior Research Methods
#592
of 2,526 outputs
Outputs of similar age
#81,776
of 340,782 outputs
Outputs of similar age from Behavior Research Methods
#21
of 47 outputs
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