Development and validation of an AI-generated real-world object stimuli set.

The availability of real-world object stimuli that meet researchers' requirements is an ongoing challenge in visual cognition research. While numerous manually curated object stimulus sets exist, stimulus features such as size, color, and orientation tend to vary widely within a given set and may not be suitable for studies with specific requirements regarding these parameters. However, recent advances in artificial intelligence (AI) can facilitate the generation of highly realistic, custom-made stimuli. Building on these developments, the present study aimed to share a set of 200 AI-generated images of everyday objects for research use. The objects were oriented as though 'placed' on a flat surface, such that they could be naturally embedded in virtual scenes. Moreover, they were created in greyscale and suitable for rendering in different colors. Here, we report the method used to efficiently generate the stimuli, as well as the results from a validation study in which we assessed the nameability, perceived realism and familiarity of the stimuli in a sample of 45 younger (18-35) and 45 older (65-85) adults. As anticipated, the majority of the stimuli were rated highly across all three measures, and no significant age differences were observed. The results thus validated most of the stimuli for future research. The stimuli, each in seven colors, and the corresponding validation scores are openly available for future use. Low-level image statistics of mean brightness and contrast for each image are also included in the dataset.