Convolutional neural networks (CNNs) were inspired by early findings in the study of
biological vision. They have since become successful tools in computer vision and state-of …

Human vision is still largely unexplained. Computer vision made impressive progress on this
front, but it is still unclear to which extent artificial neural networks approximate human object …

Pattern recognition system developers have looked in multiple directions over the years and
designed a broad spectrum of methodologies for face identification and verification, both in …

After years of experience, humans become experts at perceiving letters. Is this visual
capacity attained by learning specialized letter features, or by reusing general visual …

Recent neuroimaging studies have shown that the visual cortex plays an important role in
representing the affective significance of visual input. The origin of these affect-specific …

Whether face processing depends on unique, domain-specific neurocognitive mechanisms
or domain-general object recognition mechanisms has long been debated. Directly testing …

Inferotemporal cortex (IT) in humans and other primates is topo-graphically organized,
containing multiple hierarchically-organized areas selective for particular domains, such as …

There has been much debate over how the functional organization of vision develops.
Contemporary theories that are inspired by analyzing neural data with machine learning …

Gow's (2012) dual lexicon model suggests that the primary purpose of words is to mediate
the mappings between acoustic-phonetic input and other forms of linguistic representation …

In order to develop object recognition algorithms, which can approach human-level
recognition performance, researchers have been studying how the human brain performs …