Annual Review of Vision Science最新文献

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.

Ocular accommodation, the autofocus mechanism of the human eye, is fundamental for the achievement and maintenance of clear vision across viewing distances. Together with its close ally, vergence eye movements, this mechanism also ensures that binocular single vision is achieved at all these distances. Several dimensions of this mechanism have been investigated for well over a century. The present article summarizes this large volume of work under three themes: (a) biomechanics and neural control of the accommodative apparatus, (b) its behavioral properties, and (c) control-engineering modeling endeavors that offer a theoretical framework for gaining insights into the functioning of this mechanism. Built into these themes is a discussion on the development of accommodation, its loss with aging (presbyopia), sensory cues that aid the generation of these responses, and the technologies available for the measurement of these responses. The article also raises several unresolved questions for future research.

I enjoy studying the brain, a passion I inherited from my Italian mentors (Lamberto Maffei and Adriana Fiorentini) and Australian colleagues (John Ross and David Burr) when I was a young physics student. Looking back on the development of my career, I believe that my motivation in pursuing challenging research came from the great excitement that arose when we were close to understanding a problem. When this happened, I did not care whether I was without a real job or that I lacked the recognition I deserved as a female scientist in a highly competitive, male-dominated field. This professional joy, mixed also with family joys, such as being married to a scientist with whom I shared the same passion, was my strength. In this review, I briefly outline the values instilled by my family, teachers, peers, and research tutors, from the perspective of a woman from then-underdeveloped Southern Italy approaching a science career in the 1970s.

In humans, occipital strokes invariably damage the primary visual cortex (V1), causing a loss of conscious vision over large portions of the visual field. This unfortunate experiment of nature affects a significant proportion of all stroke victims, but there is a lack of accepted vision restoration therapies clinically, despite a rich history of studies into the resulting visual deficit and the perceptual abilities that paradoxically survive in affected portions of the visual field. Over the last two decades, the clinical dogma that V1-damaged adult visual systems cannot recover has been challenged by accumulating evidence that visual retraining to detect or discriminate stimuli in the blind field can restore perceptual abilities. This review summarizes key developments in training approaches, some of the mechanistic insights they have revealed, and limitations and opportunities that have emerged.

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.

Visual image reconstruction, the decoding of perceptual content from brain activity into images, has advanced significantly with the integration of deep neural networks (DNNs) and generative models. This review traces the field's evolution from early classification approaches to sophisticated reconstructions that capture detailed, subjective visual experiences, emphasizing the roles of hierarchical latent representations, compositional strategies, and modular architectures. Despite notable progress, challenges remain, such as achieving true zero-shot generalization for unseen images and accurately modeling the complex, subjective aspects of perception. We discuss the need for diverse datasets, refined evaluation metrics aligned with human perceptual judgments, and compositional representations that strengthen model robustness and generalizability. Ethical issues, including privacy, consent, and potential misuse, are underscored as critical considerations for responsible development. Visual image reconstruction offers promising insights into neural coding and enables new psychological measurements of visual experiences, with applications spanning clinical diagnostics and brain-machine interfaces.