Crowding, the inability to recognize objects in clutter, sets a fundamental limit on conscious
visual perception and object recognition throughout most of the visual field. Despite how …

Crowding has become a hot topic in vision research, and some fundamentals are now
widely agreed upon. For the classical crowding task, one would likely agree with the …

Deep neural networks (DNNs) have had extraordinary successes in classifying
photographic images of objects and are often described as the best models of biological …

Numerous non-invasive experimental “blinding” methods exist for suppressing the
phenomenal awareness of visual stimuli. Not all of these suppressive methods occur at, and …

In crowding, the perception of a target strongly deteriorates when neighboring elements are
presented. Crowding is usually assumed to have the following characteristics.(a) Crowding …

In crowding, perception of a target is strongly deteriorated by nearby elements. Crowding is
often explained by pooling models predicting that adding flankers increases crowding. In …

We enjoy the illusion that visual resolution is high across the entire field of vision. However,
this illusion can be easily dispelled by trying to identify objects in a cluttered environment out …

Visual crowding refers to phenomena in which the perception of a peripheral target is
strongly affected by nearby flankers. Observers often report seeing the stimuli as “jumbled …

In object recognition, features are thought to be processed in a hierarchical fashion from low-
level analysis (edges and lines) to complex figural processing (shapes and objects). Here …

Classically, visual processing is described as a cascade of local feedforward computations.
Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such …