Visual selective attention, the ability to allocate limited processing capacity towards the most relevant sensory stimuli, is essential for survival in complex, dynamic environments. When countless photons hit the retina, how does the brain filter this vast influx of information to extract what is truly behaviorally relevant? This central question has driven decades of research, leading to the identification of key brain regions, neuronal correlates of attentional modulation, and computational frameworks such as saliency and priority maps. Yet, despite this progress, the circuit-level mechanisms underlying selective attention remain poorly understood.

Open questions include:
• How do specific neuronal types contribute to attention?
• How do cortical and subcortical circuits interact?
• How are attentional states regulated by neuromodulators?
• How does attention interact with other functions such as reward processing and decision-making?

Recent methodological innovations now offer unprecedented opportunities to address these questions. Large-scale neuronal recordings with high-density electrode arrays and two-photon calcium imaging, combined with genetically tractable animal models such as rodents and genetically modified non-human primates, offer powerful ways to dissect attentional circuitry. In parallel, machine learning–based behavioral analysis enables precise mapping of motor and cognitive variables onto neural activity. Together, these approaches allow researchers to probe selective attention at the level of neural networks, defined neuronal subtypes, and synaptic mechanisms.

This Research Topic seeks contributions that advance our understanding of the neural circuits underlying visual selective attention. We welcome Original Research Articles, Brief Research Reports, and Reviews that provide novel insights into the circuit basis of both goal-driven and stimulus-driven attention, including but not limited to:

• Comparative architecture of visual attentional pathways and circuits across species: evolutionary conservation and divergence in attentional pathways.

• Laminar- and projection-specific mechanisms of attentional modulation in neocortex and superior colliculus, including interactions with pulvinar and brainstem nuclei.

• Circuit-level contributions of inhibitory and excitatory neurons in the initiation, maintenance, and termination of attentional states.

• Neuromodulatory influence of attentional states transitions and network flexibility: how cholinergic, dopaminergic, noradrenergic, and serotonergic systems modulate attention-related circuits across timescales, task demands, and arousal levels.

• Functional coupling between attention and reward, motivation, and decision-making circuits: how value-based signals shape visual attention through corticostriatal, frontoparietal, and neuromodulatory interactions, particularly under conditions of conflict or uncertainty.

• Large-scale neuronal ensemble recordings, behavioral monitoring, and causal manipulations during attention tasks. Use of Neuropixels or similar high-density probes and 2-photon calcium imaging to analyze spatiotemporal patterns of attentional modulation across distributed cortical and subcortical networks.

• Closed-Loop behavioral tracking and neural decoding in attention paradigms combining real-time behavioral monitoring (pose estimation, eye tracking, pupillometry) with neural recordings.

• Temporal dynamics and differential circuit mechanisms underlying rapid attentional shifts and sustained focus, including the role of feedforward vs. feedback loops.

• Neural circuit dysfunction in attention-related disorders (from ADHD to schizophrenia): disruption and imbalances that contribute to pathological attentional states across neurodevelopmental and psychiatric disorders.

• Development and validation of novel theories, biologically grounded computational models of saliency, priority maps and analytical approaches: how attentional selection emerges from circuit interactions and resource allocation.

• Development of behavioral paradigms for studying visual selective attention

Are you interested in contributing a manuscript to this Research Topic?

Neural circuit mechanics underlying visual attention