Comparing Global and Limited sampling Strategies in Size-averaging a Set of Items

Many studies have shown that our visual system may construct a “statistical summary representation” over groups of visual objects. Although there is a general understanding that human observers can accurately represent sets of a variety of features, many questions on how the statistical summary is computed still remain unanswered. This study investigated sampling properties of visual information used by human observers in deriving an average representation of a set of items. We presented three models of ideal observers to perform a size averaging task: a global averaging model without item noise (GAM1), a global averaging model with item noise (GAM2), and a limited sampling model (LSM). We compared the performance of the ideal observer of each model to the performance of human observers using statistical efficiency analysis. Our results suggest that average size of items in a set may be computed without representing individual items, discarding the limited sampling model.