Reply by Gattinoni et al. to Hedenstierna et al., to Maley et al., to Fowler et al., to Bhatia and Mohammed, to Bos, to Koumbourlis and Motoyama, and to Haouzi et al.
Reply by Gattinoni <i>et al.</i> to Hedenstierna <i>et al.</i>, to Maley <i>et al.</i>, to Fowler <i>et al.</i>, to Bhatia and Mohammed, to Bos, to Koumbourlis and Motoyama, and to Haouzi <i>et al.</i>
Project description:Peripheral vision is fundamentally limited by the spacing between objects. When asked to report a target's identity, observers make erroneous reports that sometimes match the identity of a nearby distractor and sometimes match a combination of target and distractor features. The classification of these errors has previously been used to support competing 'substitution' [1] or 'averaging' [2] models of the phenomenon known as 'visual crowding'. We recently proposed a single model in which both classes of error occur because observers make their reports by sampling from a biologically-plausible population of weighted responses within a region of space around the target [3]. It is critical to note that there is no probabilistic substitution or averaging process in our model; instead, we argue that neither substitution nor averaging occur, but that these are misclassifications of the distribution of reports that emerge when a population response distribution is sampled. This is a fundamentally different way of thinking about crowding, and on this basis we claim to have provided a mechanism unifying categorically distinct perceptual errors. Our goal was not to model all crowding phenomena, such as the release from crowding when target and flanks differ in color or depth [4]. Pachai et al.[5] have suggested that our model is not unifying because it inaccurately predicts perceptual performance for a particular stimulus. Although we agree that our model does not predict their data, this specific demonstration overlooks the critical aspect of the model: perceptual reports are drawn from a weighted population code. We show that Pachai et al.'s [5] own data actually provide evidence for the population code we have described [3], and we suggest a biologically-plausible analysis of their stimuli that provides a computational basis for their 'grouping' account of crowding.