How long to get to the 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

Rapid visual categorization of natural scene contexts with equalized amplitude spectrum and increasing phase noise

This study aimed to determine the extent to which rapid visual context categorization relies on global scene statistics, such as diagnostic amplitude spectrum information. We measured performance in a Natural vs. Man-made context categorization task using a set of achromatic photographs of natural scenes equalized in average luminance, global contrast, and spectral energy. Results suggest that the visual system might use amplitude spectrum characteristics of the scenes to speed up context categorization processes. In a second experiment, we measured performance impairments with a parametric degradation of phase information applied to power spectrum averaged scenes. Results showed that performance accuracy was virtually unaffected up to 50% of phase blurring, but then rapidly fell to chance level following a sharp sigmoid curve. Response time analysis showed that subjects tended to make their fastest responses based on the presence of diagnostic man-made information; if no man-made characteristics enable to reach rapidly a decision threshold, because of a natural scene display or a high level of noise, the alternative decision for a natural response became increasingly favored. This two-phase strategy could maximize categorization performance if the diagnostic features of man-made environments tolerate higher levels of noise than natural features, as proposed recently.\r\n\r\nfree access to paper here: http://journalofvision.org/9/1/2/\r\