Stimulus Manipulation and Assessment

Benton Facial Recognition Test

Description
A test used for determining face discrimination abilities. Participants are presented with a target face above 6 test faces, and are asked to identify the target face from the test faces.

Link

The BERT is available at the following repository – https://osf.io/vza3m/?view_only=404f6d1971924759b126d46cba1d25b7


Image Similarity Toolbox

Description
A Matlab toolbox for computing image similarities using a variety of computer vision algorithms. 

Link
The IST is available at the following link – https://github.com/tarrlab/image-similarity-toolbox-master 

Misc by Christoph Redies

Link
https://osf.io/ud8pk/

Order & Complexity Toolbox for Aesthetics

Description

The Order & Complexity Toolbox for Aesthetics (OCTA) is an open-source Python toolbox to create multi-element displays. The elements in a display can vary qualitatively (i.e., different types) and quantitatively (i.e., different levels) in order and complexity, based on regularity and variety along multiple element features (e.g., shape, size, color, orientation).

Link

Point-and-click application: https://elinevg.shinyapps.io/OCTA_toolbox/ Resources related to OCTA (incl. manual and example stimuli): https://elinevg.github.io/OCTA/

Paper

Van Geert, E., Bossens, C. & Wagemans, J. (2022). The Order & Complexity Toolbox for Aesthetics (OCTA): A systematic approach to study the relations between order, complexity, and aesthetic appreciation. Behavior Research Methods. https://doi.org/10.3758/s13428-022-01900-w 

SHINE Toolbox

Description
The SHINE Toolbox can be used to control for low-level image properties.

Link:

The toolbox can be accessed here.

SSIM Index for Image Quality Assessment

Description
The Structural SIMilarity (SSIM) index is a method for measuring the similarity between two images. The SSIM index can be viewed as a quality measure of one of the images being compared, provided the other image is regarded as of perfect quality. 

Link
The SSIM can be accessed here –  https://www.cns.nyu.edu/~lcv/ssim/