Abstract
The THINGS database is a freely available stimulus set that has the potential to facilitate the generation of theory that bridges multiple areas within cognitive neuroscience. The database consists of 26,107 high quality digital photos that are sorted into 1,854 concepts. While a valuable resource, relatively few technical details relevant to the design of studies in cognitive neuroscience have been described. We present an analysis of two key low-level properties of THINGS images, luminance and luminance contrast. These image statistics are known to influence common physiological and neural correlates of perceptual and cognitive processes. In general, we found that the distributions of luminance and contrast are in close agreement with the statistics of natural images reported previously. However, we found that image concepts are separable in their luminance and contrast: we show that luminance and contrast alone are sufficient to classify images into their concepts with above chance accuracy. We describe how these factors may confound studies using the THINGS images, and suggest simple controls that can be implemented a priori or post-hoc. We discuss the importance of using such natural images as stimuli in psychological research.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Data and code repository: https://osf.io/v8a3q/
In addition to changes to improve clarity of writing, the major revision involved changing how images were classified based on luminance and contrast. I now use a leave-one-out approach that prevents circularity in this analysis and provides an unbiased estimate of classification accuracy that generalises to new, unseen images. All analysis files in the repository reflect this update.
https://osf.io/v8a3q/?view_only=656f7f3953c2465ba2435793b1a6a478