Our visual environment is not random, but follows compositional rules according to what objects are usually found where. Despite the growing interest in how such semantic and syntactic rules - a scene grammar - enable effective attentional guidance and object perception, no common image database containing highly-controlled object-scene modifications has been publically available. Such a database is essential in minimizing the risk that low-level features drive high-level effects of interest, which is being discussed as possible source of controversial study results. To generate the first database of this kind - SCEGRAM - we took photographs of 62 real-world indoor scenes in six consistency conditions that contain semantic and syntactic (both mild and extreme) violations as well as their combinations. Importantly, always two scenes were paired, so that an object was semantically consistent in one scene (e.g., ketchup in kitchen) and inconsistent in the other (e.g., ketchup in bathroom). Low-level salience did not differ between object-scene conditions and was generally moderate. Additionally, SCEGRAM contains consistency ratings for every object-scene condition, as well as object-absent scenes and object-only images. Finally, a cross-validation using eye-movements replicated previous results of longer dwell times for both semantic and syntactic inconsistencies compared to consistent controls. In sum, the SCEGRAM image database is the first to contain well-controlled semantic and syntactic object-scene inconsistencies that can be used in a broad range of cognitive paradigms (e.g., verbal and pictorial priming, change detection, object identification, etc.) including paradigms addressing developmental aspects of scene grammar. SCEGRAM can be retrieved for research purposes from http://www.scenegrammarlab.com/research/scegram-database/ .
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