Topic: visual targets

Our attention falters, which is commonly known as ‘attentional blink’ (AB), after spotting a target; as a result, the subsequent target often goes unnoticed. Attentional demand also affects the size of the pupillary aperture. In previous studies, differential pupillary dilation averaged across trials within a short critical epoch distinguished AB. In contrast, we examined the pupil dynamics on a trial-by-trial basis. Healthy young volunteers performed a task, wherein two visual targets embedded in a series of distractors were presented in a rapid succession. The participants were asked to iden

Hearing and vision collectively provide the spatial information on which we rely for interacting with our environment, and presumably interact already at early stages of the sensory pathways. It has recently been shown that saccadic eye movements towards visual targets induce low-frequent displacements of the tympanic membrane, termed Eye movement-related eardrum oscillations (EMREOs). These EMREOs carry parametric information about the direction and amplitude of these saccades, and may shape how sounds are transduced in the ear. Here we asked whether EMREOs emerge similarly regardless of the

The acquisition of new motor skills engages rich neural processes that are still not fully characterized. Behaviorally, learning of a new skill (e.g., the ability to tie up shoelaces) involves two seemingly independent components: (1) the selection of successful heuristics in the form of high-level strategies that efficiently solve the task at hand, and (2) the gradual increase in motor proficiency associated with faster, smoother, and more precise movements. While motor heuristics and motor proficiency both play a significant role in skill learning, it is still unknown how they are orchestrat

This study investigates whether retinotopic mapping can be integrated into classical computational architectures that model biological visual pathways, specifically deep convolutional neural networks (dCNNs), to enhance image categorization and localization performance. The study retrains a retinotopic dCNN on the ImageNet task, which involves classifying images into 1000 labels.The study's primary finding is that standard deep CNNs are highly effective in processing retinotopic inputs, despite the significant transformation of visual inputs that this mapping entails (A). The ResNet101 network