Predicting eye movements in a contour detection task
© Ernst et al; licensee BioMed Central Ltd. 2012
Published: 16 July 2012
An important task for the visual system is grouping local image elements into meaningful objects. One fundamental process for performing this task is contour integration, in which collinearly aligned local edges are merged into global contours. Models for contour integration often use iterative algorithms to explain how this cognitive process is performed in the brain. By employing an association field (AF) which quantifies how strongly two oriented edge elements are linked to be part of a contour, such a model integrates edge elements in a recurrent manner. This process generates saliency maps for contours of increasing lengths as time proceeds.
Recently, we developed a probabilistic model of contour integration which explains human contour detection behavior to a previously unprecedented degree . Given this performance, we wondered whether the model might also explain the spatiotemporal dynamics of contour integration. Measuring eye movements can be a useful method to test the corresponding model predictions, hypothesizing that subsequent fixations of subjects preferentially visit ‘hotspots’ of neural activity which dynamically emerge during the integration process.
The optimal model was then used to predict potential locations for saccade targets which we compared to fixation trajectories of observers for stimuli from the second task in which no contour was present. For edge elements near saccade targets, the model predicts a probability to belong to a contour which is two times higher than for other edge elements. Thus, the statistical analysis shows that fixations are indeed not random, but are likely to occur on locations judged salient by the model. This result confirms both the validity of our model and the hypothesis that saccades on random Gabor fields preferentially visit locations with edge configurations similar to contours.
This work was supported by a PhD and a Postdoctoral fellowship of the Research Foundation Flanders (FWO-Vlaanderen), awarded to NVH and FH, respectively, and a Methusalem grant from the Flemish Government (METH/08/02), awarded to JW. We wish to thank Roger Watt for providing computer code for stimulus generation.
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