Neural mechanisms of object-based attention.

By Elias H. Cohen, Frank Tong. 2015

262 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Neuroscience

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In Cognitive Neuroscience

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, background): “Similarly, studies on top-down attention demonstrate the ability of the visual cortex to separate out objects even when they are highly overlapping and transparent [4].”
Toward an Integration of Deep Learning and Neuroscience (2016, background): “Certain forms of attention may require a complex interaction between brain areas, e.g., to determine targets of attention based on higher-level properties that are represented across multiple areas, like the identity and spatial location of a speciﬁc face (Baldauf and Desimone, 2014).”
Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas (2016, unknown): “Between human frontal cortex and high-level ventral visual cortex, gamma-band coherence shows a 20 ms phase lead of frontal cortex (Baldauf and Desimone, 2014).”
Foundations of Vision (2018, background): “A magnetoencephalography study investigated the relative timing of attentional modulations across the brain by presenting an overlapping face and house that flickered at different rates (Baldauf & Desimone, 2014).”
Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks (2017, background): “One possibility is that FPCNB represents information about task context in working memory and that the DAN translates this information into commands to guide the deployment of spatial attention to specific objects and locations (65, 66).”
Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms (2015, background): “In contrast, when the partici-A pant concentrates on one feature (Zhou and Desimone, 2011; Baldauf and Desimone, 2014), this system is less active.”
Towards Robust Image Classification Using Sequential Attention Models (2019, background): “Furthermore (and possibly tightly connected to the fovea) the system has a strong attentional bottleneck which has been researched in many different works [44, 7].”
Theta-Phase Connectivity between Medial Prefrontal and Posterior Areas Underlies Novel Instructions Implementation (2022, background): “This finding hints at the need for a more fine-grained ROIs definition, and further research should investigate this issue for instance by using independent functional localizers (Baldauf and Desimone, 2014; Kok et al., 2017; Senoussi et al., 2020a; González-García et al., 2021).”