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.

通过神经网络提取视觉特征，可以用 fMRI 预测任意物体类别，这在大脑信息检索上有实际应用。