Annual Review of Vision Science最新文献

Everybody loves illusions. At times, the content on the internet seems to be mostly about illusions-shoes, dresses, straight lines looking bent. This attraction has a long history. Almost 2,000 years ago, Ptolemy marveled at how the sail of a distant boat could appear convex or concave. This sense of marvel continues to drive our fascination with illusions; indeed, few other corners of science can boast of such a large reach. However, illusions not only draw in the crowds; they also offer insights into visual processes. This review starts with a simple definition of illusions as conflicts between perception and cognition, where what we see does not agree with what we believe we should see. This mismatch can be either because cognition has misunderstood how perception works or because perception has misjudged the visual input. It is the perceptual errors that offer the chance to track the development of perception across visual regions. Unfortunately, the effects of illusions in different brain regions cannot be isolated in any simple way: Top-down projections from attention broadcast the expected perceptual properties everywhere, obscuring the critical evidence of where the illusion and perception emerge. The second part of this review then highlights the roadblocks to research raised by attention and describes current solutions for accessing what illusions can offer.

Patterns of brain activity contain meaningful information about the perceived world. Recent decades have welcomed a new era in neural analyses, with computational techniques from machine learning applied to neural data to decode information represented in the brain. In this article, we review how decoding approaches have advanced our understanding of visual representations and discuss efforts to characterize both the complexity and the behavioral relevance of these representations. We outline the current consensus regarding the spatiotemporal structure of visual representations and review recent findings that suggest that visual representations are at once robust to perturbations, yet sensitive to different mental states. Beyond representations of the physical world, recent decoding work has shone a light on how the brain instantiates internally generated states, for example, during imagery and prediction. Going forward, decoding has remarkable potential to assess the functional relevance of visual representations for human behavior, reveal how representations change across development and during aging, and uncover their presentation in various mental disorders.

Using natural scenes is an approach to studying the visual and eye movement systems approximating how these systems function in everyday life. This review examines the results from behavioral and neurophysiological studies using natural scene viewing in humans and monkeys. The use of natural scenes for the study of cerebral cortical activity is relatively new and presents challenges for data analysis. Methods and results from the use of natural scenes for the study of the visual and eye movement cortex are presented, with emphasis on new insights that this method provides enhancing what is known about these cortical regions from the use of conventional methods.

Deep neural networks (DNNs) are machine learning algorithms that have revolutionized computer vision due to their remarkable successes in tasks like object classification and segmentation. The success of DNNs as computer vision algorithms has led to the suggestion that DNNs may also be good models of human visual perception. In this article, we review evidence regarding current DNNs as adequate behavioral models of human core object recognition. To this end, we argue that it is important to distinguish between statistical tools and computational models and to understand model quality as a multidimensional concept in which clarity about modeling goals is key. Reviewing a large number of psychophysical and computational explorations of core object recognition performance in humans and DNNs, we argue that DNNs are highly valuable scientific tools but that, as of today, DNNs should only be regarded as promising-but not yet adequate-computational models of human core object recognition behavior. On the way, we dispel several myths surrounding DNNs in vision science.

The superior colliculus (SC) is a subcortical brain structure that is relevant for sensation, cognition, and action. In nonhuman primates, a rich history of studies has provided unprecedented detail about this structure's role in controlling orienting behaviors; as a result, the primate SC has become primarily regarded as a motor control structure. However, as in other species, the primate SC is also a highly visual structure: A fraction of its inputs is retinal and complemented by inputs from visual cortical areas, including the primary visual cortex. Motivated by this, recent investigations are revealing the rich visual pattern analysis capabilities of the primate SC, placing this structure in an ideal position to guide orienting movements. The anatomical proximity of the primate SC to both early visual inputs and final motor control apparatuses, as well as its ascending feedback projections to the cortex, affirms an important role for this structure in active perception.

This narrative review summarizes the literature on factors related to eye care access and utilization in the United States. Using the Healthy People 2030 framework, this review investigates social determinants of health associated with general and follow-up engagement, screenings, diagnostic visits, treatment, technology, and teleophthalmology. We provide hypotheses for these documented eye care disparities, featuring qualitative, patient-centered research. Lastly, we provide recommendations in the hopes of appropriately eliminating these disparities and reimagining eye care.

Rod and cone photoreceptors degenerate in retinitis pigmentosa and age-related macular degeneration, robbing the visual system of light-triggered signals necessary for sight. However, changes in the retina do not stop with the photoreceptors. A stereotypical set of morphological and physiological changes, known as remodeling, occur in downstream retinal neurons. Some aspects of remodeling are homeostatic, with structural or functional changes compensating for partial loss of visual inputs. However, other aspects are nonhomeostatic, corrupting retinal information processing to obscure vision mediated naturally by surviving photoreceptors or artificially by vision-restoration technologies. In this review, I consider the mechanism of remodeling and its consequences for residual and restored visual function; discuss the role of retinoic acid, a critical molecular trigger of detrimental remodeling; and discuss strategies for suppressing retinoic acid biosynthesis or signaling as therapeutic possibilities for mitigating vision loss.