Mean-Feature and Centroid Estimations: A study on how the human visual system processes two classes ofsummary statistical representations.

When presented with a complex scene our visual system relies on perceptions that occur pre-attentively, or in just a fraction of a second. This ability, known as summary statistical representations, allows us to get the gist of a group of items. The studies presented in this thesis are focused on two of these summary statistical representations, the centroid and the mean-feature estimations, and are designed to understand mean-feature estimations in the centroid paradigm framework. First, we compared observers’ performances between mean-size and centroid estimations. There were two different centroid tasks: in one, observers were asked to give more weight to larger items and in the other they were asked to ignore size. In a third task, observers judged the mean size of the same stimuli used in the centroid tasks. Results from this study showed that observers were able to either ignore or use size to make centroid responses efficiently, but that mean-size judgments were inefficient. To asses the generality of these results, in a second study we asked observers to estimate the mean luminance of a group of items and compared their performance when asked to estimate the centroid giving more weight to lighter items and less weight to darker items or to estimate the centroid ignoring the luminance of the items. Again, we found high Efficiency in both centroid tasks, but low Efficiency in the mean-luminance task. Finally, we explored whether the distinction between the ventral-dorsal streams of visual processing as described by Goodale & Milner (1992) might provide a plausible explanation for the difference in performances between the mean-size and centroid task. We found that when the relative mean size of one of three groups was used to select and guide movements (dorsal processing) to that group, observers were again able to use size information efficiently