Topic: feature-based attention

Many visual phenomena suggest that humans use top-down generative or reconstructive processes to create visual percepts (e.g., imagery, object completion, pareidolia), but little is known about the role reconstruction plays in robust object recognition. We built an iterative encoder-decoder network that generates an object reconstruction and used it as top-down attentional feedback to route the most relevant spatial and feature information to feed-forward object recognition processes. We tested this model using the challenging out-of-distribution digit recognition dataset, MNIST-C, where 15 di

Detecting a specific object in a visual scene is a task that the human visual system performs every day. To perform this target-specific object detection, it is necessary to filter out all unwanted objects during the processing, so that only the specific object can be detected. For this purpose, attention-related mechanisms along the visual pathway have been proposed to allow focusing on the desired object. Beuth and Hamker (2015) [1], using a biologically inspired recurrent model simulating lower and higher layers of the visual cortex, the frontal eye field, and the prefrontal cortex, showed