Feature distribution learning (FDL): A new method for studying visual ensembles perception with priming of attention shifts

Abstract

We discuss how priming of attention shifts has in recent studies proved to be a useful method for studying internal representations of visual ensembles. Attentional priming is very powerful in particular when role reversals between targets and distractors occur. Such role reversals can be used to assess how expected or unexpected a particular target is. This new method for studying representations of visual ensembles has revealed that observer’s representations are far more detailed than previous studies of ensemble perception have suggested where the emphasis has been on summary statistics, i.e., mean and variance. Observers can represent surprisingly complex distribution shapes such as whether a representation is bimodal or not. We discuss the details of how this feature distribution learning (FDL) method has been used to assess internal representations of visual ensembles. We also speculate that the method can prove to be an important implicit way of assessing how observers represent regularities in their environments.