  Interdisciplinary research is often at the core of scientific progress. This
dissertation explores some advantageous synergies between machine learning,
cognitive science and neuroscience. In particular, this thesis focuses on
vision and images. The human visual system has been widely studied from both
behavioural and neuroscientific points of view, as vision is the dominant sense
of most people. In turn, machine vision has also been an active area of
research, currently dominated by the use of artificial neural networks. This
work focuses on learning representations that are more aligned with visual
perception and the biological vision. For that purpose, I have studied tools
and aspects from cognitive science and computational neuroscience, and
attempted to incorporate them into machine learning models of vision.
  A central subject of this dissertation is data augmentation, a commonly used
technique for training artificial neural networks to augment the size of data
sets through transformations of the images. Although often overlooked, data
augmentation implements transformations that are perceptually plausible, since
they correspond to the transformations we see in our visual world -- changes in
viewpoint or illumination, for instance. Furthermore, neuroscientists have
found that the brain invariantly represents objects under these
transformations. Throughout this dissertation, I use these insights to analyse
data augmentation as a particularly useful inductive bias, a more effective
regularisation method for artificial neural networks, and as the framework to
analyse and improve the invariance of vision models to perceptually plausible
transformations. Overall, this work aims to shed more light on the properties
of data augmentation and demonstrate the potential of interdisciplinary
research.

Machine Learning;Computer Vision and Pattern Recognition