How Long to Get to Thè`gist'' of Real-world Natural Scenes?

This study aimed at assessing the processing time of a natural scene in a fast categorization task of its context or``gist''. In Experiment 1, human subjects performed 4 go/no-go categorization tasks in succession with colour pictures of real-world scenes belonging to 2 natural categories: ``Sea'' and``mountain'', and 2 artificial categories: ``Indoor'' and``urban''. Experiment 2 used colour and grey-level scenes in the same tasks to assess the role of colour cues on performance. Pictures were flashed for 26 ms. Both experiments showed that the gist of real-world scenes can be extracted with high accuracy (>90%), short median RT (400± 460 ms) and early responses triggered with latencies as short as 260±300 ms. Natural scenes were processed faster than artificial scenes. Categories for which colour could have a diagnostic value were processed faster in colour than in grey. Finally, processing speed is compared for scene and object categorization tasks. Natural scenes are more than a simple collection of objects. However, much of the research on scene processing has been devoted to the understanding of object processing in scenes, leaving aside the question of how we process the whole scene itself. This issue is important given that we do not only process objects but we also analyse the context in which they appear. Global coarse information about a scene (mainly its category, or gist, and its spatial structure, or layout) is relatively crucial in memory-free models of scene perception in which little information is integrated across saccades. According to this idea, perception is constructed by integrating abstract scene representations with volatile object PhD grant from the French government. We thank Anne-Sophie Paroissien for her very valuable help running the experimental sessions in Experiment 1.