Generic decoding of seen and imagined objects using hierarchical visual features

Understand

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.