Deep neural networks (DNNs) trained on object recognition provide the best current models
of high-level visual cortex. What remains unclear is how strongly experimental choices, such …

Whenever a visual scene is cast onto the retina, much of it will appear degraded due to poor
resolution in the periphery; moreover, optical defocus can cause blur in central vision …

Achievement of human-level image recognition by deep neural networks (DNNs) has
spurred interest in whether and how DNNs are brain-like. Both DNNs and the visual cortex …

To interact with real-world objects, any effective visual system must jointly code the unique
features defining each object. Despite decades of neuroscience research, we still lack a firm …

Deep neural networks (DNNs) trained on object recognition provide the best current models
of high-level visual areas in the brain. What remains unclear is how strongly network design …

Learning to read places a strong challenge on the visual system. Years of expertise lead to a
remarkable capacity to separate similar letters and encode their relative positions, thus …

Deep neural networks (DNNs) are promising models of the cortical computations supporting
human object recognition. However, despite their ability to explain a significant portion of …

The analogy between the brain and deep neural networks (DNNs) has sparked interest in
neuroscience. Although DNNs have limitations, they remain valuable for modeling specific …

Feed-forward deep convolutional neural networks (DCNNs) are, under specific conditions,
matching and even surpassing human performance in object recognition in natural scenes …

Arousal levels strongly affect task performance. Yet, what arousal level is optimal for a task
depends on its difficulty. Easy task performance peaks at higher arousal levels, whereas …