Recognition of Computer-Generated Trees

Abstract

This research assessed the importance of rendering specific details when creating a virtual forest. Specifically, we examined memory for computer-generated trees using a modified recognition task in which participants were shown a target tree, engaged in a distractor task, and then ranked the similarity of seven foils to the original tree they had seen. Five of the foils represented changes on only one dimension of the tree whereas the other two foils represented modifications to either five features previously identified as salient or all nine tree features. Results showed that similarity rankings were largely based on overall structural similarity of the trees as opposed to similarity on smaller details such as branch thickness or leaf size. Additionally, perceived similarity rankings varied as a function of the symmetry of the tree. Virtual forests need to show realism for different features depending on the forest type.