Area summation experiments measure the improvement in detectability of contrast as stimuli get larger. But what are the limits of this improvement, and what strategy does an observer use to integrate contrast across space? We employed a reverse correlation technique to directly estimate the size of the pooling region, and to compare two different strategies: signal selection (MAXing) and signal combination (summing). Stimuli were regular arrays of (27×27) grating patches, the contrasts of which were determined individually from a normal distribution (mean 32%, SD 10%) on each trial interval. Observers detected a contrast increment applied to a square subset of the patches (1 to 27
elements wide). Each observer completed 2000 2IFC trials per target size using a blocked method of constant stimuli design. We correlated the observer’s trial-by-trial responses with the contrast difference of each patch across trial intervals. This produced a map, akin to a classification image, that revealed the patches contributing to the observer’s decisions. But this standard approach cannot distinguish between summing and MAXing strategies. We therefore directly compared (again using correlation) trial-by-trial model predictions of observer responses for both strategies across a range of pooling windows. Summing target contrasts over space produced the strongest correlation with human behaviour, and provided an estimated pooling region of 9-13 grating cycles. This supports earlier work that had reached similar conclusions using more traditional techniques. Furthermore, individual differences in maximum pooling region correctly predicted the rank ordering across observers of the
magnitude of area summation at detection threshold.
|Number of pages||1|
|Publication status||Published - Dec 2012|
|Event||AVA Christmas Meeting - London, United Kingdom|
Duration: 18 Dec 2012 → …