Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …

Recently it was argued that in addition to top-down and bottom-up processes, lingering
biases of selection history play a major role in visual selection (Awh, Belopolsky & …

We aim to model the top-down attention of a convolutional neural network (CNN) classifier
for generating task-specific attention maps. Inspired by a top-down human visual attention …

We introduce a simple yet surprisingly powerful model to incorporate attention in action
recognition and human object interaction tasks. Our proposed attention module can be …

Emotional arousal enhances perception and memory of high-priority information but impairs
processing of other information. Here, we propose that, under arousal, local glutamate levels …

While signatures of attention have been extensively studied in sensory systems, the neural
sources and computations responsible for top-down control of attention are largely …

Modeling visual attention-particularly stimulus-driven, saliency-based attention-has been a
very active research area over the past 25 years. Many different models of attention are now …

Highlights•Lingering biases of previous selection episodes play a major role in attentional
selection.•Statistical learning of the environmental regularities biases attentional …

A study that goes beyond the debate over functional specialization to describe the ways that
emotion and cognition interact and are integrated in the brain. The idea that a specific brain …

The ability of the human brain to learn is exceptional. Yet, learning is typically quite specific
to the exact task used during training, a limiting factor for practical applications such as …