Annual Review of Vision Science最新文献

The study of biological optics would be complicated enough if light only came in a single wavelength. However, altering the wavelength (or distribution of wavelengths) of light has multiple effects on optics, including on diffraction, scattering (of various sorts), transmission through and reflection by various media, fluorescence, and waveguiding properties, among others. In this review, we consider just one wavelength-dependent optical effect: longitudinal chromatic aberration (LCA). All vertebrate eyes that have been tested have significant LCA, with shorter (bluer) wavelengths of light focusing closer to the front of the eye than longer (redder) wavelengths. We consider the role of LCA in the visual system in terms of both how it could degrade visual acuity and how biological systems make use of it.

Animals live in visually complex environments. As a result, visual systems have evolved mechanisms that simplify visual processing and allow animals to focus on the information that is most relevant to adaptive decision making. This review explores two key mechanisms that animals use to efficiently process visual information: categorization and specialization. Categorization occurs when an animal's perceptual system sorts continuously varying stimuli into a set of discrete categories. Specialization occurs when particular classes of stimuli are processed using distinct cognitive operations that are not used for other classes of stimuli. We also describe a nonadaptive consequence of simplifying heuristics: visual illusions, where visual perception consistently misleads the viewer about the state of the external world or objects within it. We take an explicitly comparative approach by exploring similarities and differences in visual cognition across human and nonhuman taxa. Considering areas of convergence and divergence across taxa provides insight into the evolution and function of visual systems and associated perceptual strategies.

Our visual systems are remarkably adept at deriving the shape and material properties of surfaces even when only one image of a surface is available. This ability implies that a single image of a surface contains potent information about both surface shape and material. However, from a computational perspective, the problem of deriving surface shape and material is formally ill posed. Any given image could be due to many combinations of shape, material, and illumination. Early computational models required prior knowledge about two of the three scene variables to derive the third. However, such models are biologically implausible because our visual systems are tasked with extracting all relevant scene variables from images simultaneously. This review describes recent progress in understanding how the visual system solves this problem by identifying complex forms of image structure that support its ability to simultaneously derive the shape and material properties of surfaces from images.

The ventral visual pathway transforms retinal images into neural representations that support object understanding, including exquisite appreciation of precise 2D pattern shape and 3D volumetric shape. We articulate a framework for understanding the goals of this transformation and how they are achieved by neural coding at successive ventral pathway stages. The critical goals are (a) radical compression to make shape information communicable across axonal bundles and storable in memory, (b) explicit coding to make shape information easily readable by the rest of the brain and thus accessible for cognition and behavioral control, and (c) representational stability to maintain consistent perception across highly variable viewing conditions. We describe how each transformational step in ventral pathway vision serves one or more of these goals. This three-goal framework unifies discoveries about ventral shape processing into a neural explanation for our remarkable experience of shape as a vivid, richly detailed aspect of the natural world.

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.