Contour Integration in Dynamic Scenes: Impaired Detection Performance in Extended Presentations

Abstract

Since scenes in nature are highly dynamic, perception requires an on-going and robust integration of local information into global representations. In vision, contour integration (CI) is one of these tasks, and it is performed by our brain in a seemingly effortless manner. Following the rule of good continuation, oriented line segments are linked into contour percepts, thus supporting important visual computations such as the detection of object boundaries. This process has been studied almost exclusively using static stimuli, raising the question of whether the observed robustness and “pop-out” quality of CI carries over to dynamic scenes. We investigate contour detection in dynamic stimuli where targets appear at random times by Gabor elements aligning themselves to form contours. In briefly presented displays (230 ms), a situation comparable to classical paradigms in CI, performance is about 87%. Surprisingly, we find that detection performance decreases to 67% in extended presentations (about 1.9–3.8 s) for the same target stimuli. In order to observe the same reduction with briefly presented stimuli, presentation time has to be drastically decreased to intervals as short as 50 ms. Cueing a specific contour position or shape helps in partially compensating this deterioration, and only in extended presentations combining a location and a shape cue was more efficient than providing a single cue. Our findings challenge the notion of CI as a mainly stimulus-driven process leading to pop-out percepts, indicating that top-down processes play a much larger role in supporting fundamental integration processes in dynamic scenes than previously thought.

Change-detection training and its effects on visual processing skills

“…It is also possible that the focus on individual features that was trained in the change-detection task was inappropriate to lead to positive changes in integrating multiple features into wholes as needed in the contour integration task. Finally, our contour integration task allowed participants to respond within a relatively wide time window which further increases task difficulty, as it has been shown that the ability to detect a contour worsens with prolonged presentation 31 .…”

“…Contour integration is based on the Gestalt law of “good continuation”, referring to the preference in viewing an object as something that is smooth and continuous rather than something that is abrupt and disconnected through the integration of local elements to form a global contour 31 , 32 . Contour integration is a special case of visual grouping, which refers to the idea that the visual system tends to group elements into visual wholes through simple rules 33 .…”

The subconscious impact of line orientations in background images on memory of Chinese written characters

“…In modern science, researchers found that oriented lines or objects contribute significantly to visual perceptions [ 7 , 8 ]. Under certain circumstances, the lines and line orientations in an image affect people’s perception, behavior, and even decision-making [ 9 – 11 ].…”