Temporal dynamics of ensemble statistics calculation using a neural network model

Abstract

Computing summary or ensemble statistics of a visual scene is often automatic and a hard necessity for stable perceptual life of a cognitive agent. Although computationally the process should be as simple as applying a filter as it were to a perceived scene, the issue of mechanism of summary statistics is complicated by the fact that we can seamlessly switch from summarizing to individuation while computing the ensemble averages across multiple reference frames. In the current work we have investigated the possibility of a neural network that can also switch between individuation and summarization. We have chosen a computational model previously used for enumeration/individuation (Sengupta et al, 2014) in order to show possibility of extracting summary statistics using two different measures from the network. The results also shed a light on possible temporal dynamics of ensemble perception.