Object recognition is a key function in both human and machine vision. While recent studies have achieved fMRI decoding of seen and imagined contents, the prediction is limited to training examples. We present a decoding approach for arbitrary objects, using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features including those from a convolutional neural network can be predicted from fMRI patterns and that greater accuracy is achieved for low/high-level features with lower/higher-level visual areas, respectively. Predicted features are used to identify seen/imagined object categories (extending beyond decoder training) from a set of computed features for numerous object images. Furthermore, the decoding of imagined objects reveals progressive recruitment of higher to lower visual representations. Our results demonstrate a homology between human and machine vision and its utility for brain-based information retrieval.
@article{arxiv.1510.06479,
title = {Generic decoding of seen and imagined objects using hierarchical visual features},
author = {Tomoyasu Horikawa and Yukiyasu Kamitani},
journal= {arXiv preprint arXiv:1510.06479},
year = {2016}
}