Abstract
As scientists, we carry out experiments to contribute to the knowledge of the world. Yet we have to make choices in our experimental design that abstract away from the real world, which can lead to selection bias, limiting our ability to translate our research findings into generalizable conclusions. For studies involving the presentation of objects, central choices are which objects are shown and - in case of visual stimuli - in what format object images should be presented (e.g. abstracted, cropped from background, or natural photographs). In this talk, I will discuss the THINGS initiative, which is a large-scale global initiative of researchers collecting behavioral and brain imaging datasets using the THINGS object concept and image dataset. I will highlight the motivation underlying the development of THINGS, the advantages and limitations in the object and image sampling strategy, and new insights enabled by this strategy about the behavioral and neural representation of objects. Further, I will discuss strategies that offer more generalizable conclusions for their small-scale laboratory experiments using THINGS images. Moving beyond THINGS, I will discuss ideas for future sampling approaches that may further narrow the gap between stimulus sampling and neural representations.