Task representations emerge rapidly throughout human cortex, with parallel object representations in occipitotemporal cortex that are increasingly dominated by task in higher visual areas.

Tactile edge orientation processing during object manipulation is many times better than previously thought based on perceptual studies.

THINGS-data reflects three large-scale neuroimaging and behavioral datasets of object processing in humans, comprising densely sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1854 objects.

Context-based object recognition causally relies on both scene- and object-selective cortex, with scene-selective cortex generating expectations (at 160-200 ms after onset) that disambiguate object representations in object-selective cortex (at 260-300 ms after onset).

Reducing visibility with higher image presentation rates increases recurrent processing demands along the visual processing pathway to resolve object recognition.

Neurite arbors of VGluT3-expressing amacrine cells (VG3-ACs) process visual information locally uniformly detecting object motion while varying in contrast preferences; and in spite of extensive overlap between arbors of neighboring cells population activity in the VG3-AC plexus encodes stimulus positions with subcellular precision.

The human entorhinal-hippocampal circuitry is characterized by an information-specific functional organization where two routes, that are preferentially connected to the parahippocampal cortex or the perirhinal and retrosplenial cortices, divide the entorhinal cortex as well as hippocampal subiculum and CA1 subregions.

When all features of conflicting sensory input are task-irrelevant, the brain can still process its sensory information, whereas conflict detection requires that minimally one stimulus feature is task-relevant or associated with a response.

Neuronal recordings from rat visual cortex reveal an object-processing pathway, along which neuronal representations become increasingly capable of supporting recognition of visual objects in spite of variation in their appearance.

Consistent spatial clustering and fine-scale neural tuning to different face parts were found within the face-processing regions, which may be the neural implementation of efficient neural computation for face identification.