Emergent neuronal mechanisms mediating covert attention in convolutional neural networks.

ABSTRACT: Covert visual attention allows the brain to select different regions of the visual world without eye movements. Predictive cues of a target location orient covert attention and improve perceptual performance. In most computational models, researchers explicitly incorporate an attentional mechanism that alters processing at the attended location (gain, noise reduction, divisive normalization, biased competition, Bayesian priors). Here, we assess the emergent neuronal mechanisms of Convolutional Neural Networks (CNNs) that exhibit behavioral signatures of covert attention, despite lacking a built-in attention mechanism. We use neuroscience-inspired approaches to analyze 1.8 M units of CNNs trained on the cueing paradigm. Consistent with neurophysiology, we show early layers with retinotopic

SUBMITTER: Srivastava S 

PROVIDER: S-EPMC12646230 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature