Visual attention prioritizes relevant stimuli in complex environments through top-down (goal-directed) and bottom-up (stimulus-driven) mechanisms within cortical networks. This review explores the neural mechanisms underlying visual attention, focusing on how attentional control is encoded and decoded from prefrontal signals in both spatial and temporal domains. Decoding methods enable real-time tracking of covert visual attention from prefrontal activity with high spatial and temporal resolution, as a neurophysiological proxy of the attentional spotlight. This research provides insights into stimulus selection mechanisms, proactive and reactive suppression of irrelevant stimuli, the rhythmic nature of attentional shifts and attentional saccades, the balance between focus and flexibility, and the variation of these processes along epochs of sustained attention. Additionally, the review highlights how recurrent neural networks in the prefrontal cortex contribute to supporting these attention dynamics. These findings collectively offer a comprehensive model of attention that integrates dynamic prioritization processes at short and longer timescales.

Retinal prostheses aim at restoring sight to patients blinded by atrophy of photoreceptors using electrical stimulation of the inner retinal neurons. Bipolar cells can be targeted using subretinal implants, and their responses are then relayed to the central visual pathways via the retinal neural network, preserving many features of natural signal processing. Epiretinal implants stimulate the output retinal layer-ganglion cells-and encode visual information directly in spiking patterns.Several companies and academic groups have demonstrated that electrical stimulation of the degenerate retina can elicit visual percepts. However, most failed to consistently and safely achieve an acceptable level of performance. Recent clinical trials demonstrated that subretinal photovoltaic arrays in patients visually impaired by age-related macular degeneration can provide letter acuity matching their 100 μm pixel pitch, corresponding to 20/420 acuity. Electronic zoom enabled patients to read smaller fonts. This review describes the concepts, technologies, and clinical outcomes of current systems and provides an outlook into future developments.

The contributions of surface reflectance and incident illumination are entangled in the light reflected to the eye. Historically, the extent to which the perception of one determines the other has long been debated, particularly in empirical studies of surface lightness and color constancy. Despite enormous progress in physical measurements of the spatial, spectral, and temporal properties of natural illumination, and in the ability to generate and control in real time artificial light of an almost infinite variety of spectra, the questions of whether and how people perceive the illumination as a distinct entity with its own color, and the interdependence of perceived surface color on perceived illumination, remain open. Given the rise in novel lighting interventions that modulate illumination spectra in order to improve health, well-being, productivity, and culture, it has become increasingly important to understand the two-way interaction between the visual and nonvisual sensing of illumination.

Since Lettvin and colleagues' seminal discovery of bug detector neurons in the frog retina, understanding how retinal circuits support behavioral demands has been a central goal of visual neuroscience. Recent advances in machine learning, genetic tools, and neural recording have transformed our understanding of these circuits, particularly in the mouse retina. With a focus on mice, we examine how species-specific visual sampling strategies determine the behavioral relevance of retinal computations and review recent insights into circuits underlying reflexive behaviors, threat detection, prey capture, color vision, and night vision. We also highlight how the behavioral state itself influences retinal processing through direct neuromodulation and pupillary changes, challenging the traditional view of purely feedforward retinal processing in mammals. These findings confirm the retina as a sophisticated computational engine whose circuits have evolved to meet species-specific behavioral demands. While Lettvin's discovery of dedicated retinal circuits for innate behaviors launched the field, new tools now promise to expand our understanding of retinal contributions to naturalistic and flexible behaviors across species.

Over the past decade and a half, a new understanding has emerged of the role of vision during the critical period in the primary visual cortex. Rather than driving competition for cortical space, vision is now understood to inform the establishment of feature conjunctions that cannot be constructed intrinsically. Longitudinal imaging studies reveal that the establishment of these higher-order feature detectors is a remarkably dynamic process involving the gain and elimination of neurons from functional groups (e.g., binocular neurons with nonlinear response tuning). Experience exerts its influence selectively on this developing circuitry; some pathways require experience for normal development, while others appear to be intrinsically established. This difference drives the network dynamism that is exploited to construct novel cortical representations that best encode our local environment and inform our actions in it.

The primate brain excels at transforming photons into knowledge. When light strikes the back of the eye, opsin molecules within rods and cones absorb photons, triggering a change in membrane potential. This energy transfer initiates a cascade of neural events that endows us with useful knowledge. This knowledge manifests as subjectively experienced perceptual interpretations and mostly pertains to the 3D structure of the visual environment and the affordances of the objects within the scene. However, some of this knowledge instead pertains to the quality of these interpretations and contributes to our sense of confidence in perceptual decisions. Because such confidence reflects knowledge about knowledge, psychologists consider this the domain of metacognition. Here, we examine what is known about the neuronal basis of perceptual decision confidence, with a focus on vision. We review the crucial computational processes and neural operations that underlie and constrain the transformation of photons into visual metacognition.

Information visualization is central to how humans communicate. Designers produce visualizations to represent information about the world, and observers construct interpretations based on the visual input as well as their heuristics, biases, prior knowledge, and beliefs. Several layers of processing go into the design and interpretation of visualizations. This review focuses on processes that observers use for interpretation: perceiving visual features and their interrelations, mapping those visual features onto the concepts they represent, and comprehending information about the world based on observations from visualizations. Observers are more effective at interpreting visualizations when the design is well-aligned with the way their perceptual and cognitive systems naturally construct interpretations. By understanding how these systems work, it is possible to design visualizations that play to their strengths and thereby facilitate visual communication.

Our brains encode many features of the sensory world into memories: We can sing along with songs we have heard before, interpret spoken and written language composed of words we have learned, and recognize faces and objects. Where are these memories stored? Each area of cerebral cortex has a huge number of local recurrent excitatory-excitatory synapses, as many as 500 million per cubic millimeter. Here I outline evidence for the theory that cortical recurrent connectivity in sensory cortex is a substrate for sensory memories. Evidence suggests that the local recurrent network encodes the structure of natural sensory input and that it does so via active filtering, transforming network inputs to boost or select those associated with natural sensation. Active filtering is a form of predictive processing-in which the cortical recurrent network selectively amplifies some input patterns and attenuates others-and a form of memory.

Crowding is ubiquitous: When objects are surrounded by other elements, their perception may be impaired depending on factors such as the proximity of the surrounding elements and the grouping of elements and targets. Crowding research aims to identify these factors, for instance, which elements interfere with one another and how close they need to be to cause crowding. Traditionally, crowding was thought to occur only within narrow temporal and spatial limits around the target. Recent studies, however, reveal that crowding may result from both low- and high-level processes, such as perceptual grouping and timing, as well as the arrangement of complex visual stimuli. This review highlights these new insights, suggesting that overall organization, as well as both feedforward and feedback processes, plays a role. Crowding emerges as a highly complex and dynamic phenomenon, underscoring the need for a more integrated approach to fully capture its intricacies, which may carry broader implications not only for crowding but also for vision science as a whole.

Layer 6 corticothalamic (L6 CT) pyramidal neurons send feedback projections from the primary visual cortex to both first- and higher-order visual thalamic nuclei. These projections provide direct excitation and indirect inhibition through thalamic interneurons and neurons in the thalamic reticular nucleus. Although the diversity of L6 CT pathways has long been recognized, emerging evidence suggests multiple subnetworks with distinct connectivity, inputs, gene expression gradients, and intrinsic properties. Here, we review the structure and function of L6 CT circuits in development, plasticity, visual processing, and behavior, considering computational perspectives on their functional roles. We focus on recent research in mice, where a rich arsenal of genetic and viral tools has advanced the circuit-level understanding of the multifaceted roles of L6 CT feedback in shaping visual thalamic activity.