Cortical brain regions associated with color processing: an FMRI study

To clarify whether the neural pathways concerning color processing are the same for natural objects, for artifacts objects and for non-objects we examined brain responses measured with functional magnetic resonance imaging (FMRI) during a covert naming task including the factors color (color vs. black&white (B&W)) and stimulus type (natural vs. artifacts vs. non-objects). Our results indicate that the superior parietal lobule and precuneus (BA 7) bilaterally, the right hippocampus and the right fusifom gyrus (V4) make part of a network responsible for color processing both for natural objects and artifacts, but not for non-objects. When color objects (both natural and artifacts) were contrasted with color non-objects we observed activations in the right parahippocampal gyrus (BA 35/36), the superior parietal lobule (BA 7) bilaterally, the left inferior middle temporal region (BA 20/21) and the inferior and superior frontal regions (BA 10/11/47). These additional activations s uggest that colored objects recruit brain regions that are related to visual semantic information/retrieval and brain regions related to visuo-spatial processing. Overall, the results suggest that color information is an attribute that can improve object recognition (behavioral results) and activate a specific neural network related to visual semantic information that is more extensive than for B&W objects during object recognitio

Cortical Brain Regions Associated with Color Processing: An FMRi Study

To clarify whether the neural pathways concerning color processing are the same for natural objects, for artifacts objects and for non-objects we examined brain responses measured with functional magnetic resonance imaging (FMRI) during a covert naming task including the factors color (color vs. black&white (B&W)) and stimulus type (natural vs. artifacts vs. non-objects). Our results indicate that the superior parietal lobule and precuneus (BA 7) bilaterally, the right hippocampus and the right fusifom gyrus (V4) make part of a network responsible for color processing both for natural objects and artifacts, but not for non-objects. When color objects (both natural and artifacts) were contrasted with color non-objects we observed activations in the right parahippocampal gyrus (BA 35/36), the superior parietal lobule (BA 7) bilaterally, the left inferior middle temporal region (BA 20/21) and the inferior and superior frontal regions (BA 10/11/47). These additional activations suggest that colored objects recruit brain regions that are related to visual semantic information/retrieval and brain regions related to visuo-spatial processing. Overall, the results suggest that color information is an attribute that can improve object recognition (behavioral results) and activate a specific neural network related to visual semantic information that is more extensive than for B&W objects during object recognition

The Constructive Nature of Color Vision and Its Neural Basis

Our visual world is made up of colored surfaces. The color of a surface is physically determined by its reflectance, i.e., how much energy it reflects as a function of wavelength. Reflected light, however, provides only ambiguous information about the color of a surface as it depends on the spectral properties of both the surface and the illumination. Despite the confounding effects of illumination on the reflected light, the visual system is remarkably good at inferring the reflectance of a surface, enabling observers to perceive surface colors as stable across illumination changes. This capacity of the visual system is called color constancy and it highlights that color vision is a constructive process. The research presented here investigates the neural basis of some of the most relevant aspects of the constructive nature of human color vision using machine learning algorithms and functional neuroimaging. The experiments demonstrate that color-related prior knowledge influences neural signals already in the earliest area of visual processing in the cortex, area V1, whereas in object imagery, perceived color shared neural representations with the color of the imagined objects in human V4. A direct test for illumination-invariant surface color representation showed that neural coding in V1 as well as a region anterior to human V4 was robust against illumination changes. In sum, the present research shows how different aspects of the constructive nature of color vision can be mapped to different regions in the ventral visual pathway

The Complementary Brain: From Brain Dynamics To Conscious Experiences

How do our brains so effectively achieve adaptive behavior in a changing world? Evidence is reviewed that brains are organized into parallel processing streams with complementary properties. Hierarchical interactions within each stream and parallel interactions between streams create coherent behavioral representations that overcome the complementary deficiencies of each stream and support unitary conscious experiences. This perspective suggests how brain design reflects the organization of the physical world with which brains interact, and suggests an alternative to the computer metaphor suggesting that brains are organized into independent modules. Examples from perception, learning, cognition, and action are described, and theoretical concepts and mechanisms by which complementarity is accomplished are summarized.Defense Advanced Research Projects and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (ITI-97-20333); Office of Naval Research (N00014-95-1-0657

The Complementary Brain: A Unifying View of Brain Specialization and Modularity

Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-I-0409); National Science Foundation (ITI-97-20333); Office of Naval Research (N00014-95-I-0657

The crack of dawn : perceptual functions and neural mechanisms that mark the transition from unconscious processing to conscious vision

There is conscious vision, and there is unconscious visual processing. So far so good. But where lies the boundary between the two? What are the visual functions that shape the transition from “processing in the dark” to having a conscious visual percept? And what are the neural mechanisms that carry that transition? I review the findings on feature detection, object categorization, interference, inference, Gestalt grouping, and perceptual organization, and examine to what extent these functions correlate with the presence or absence of conscious vision. It turns out that a surprisingly large set of visual functions is executed unconsciously, indicating that unconscious vision is much “smarter” than we might intuitively think. Only when these unconscious mechanisms fail, and more elaborate and incremental processing steps are required, is consciousness necessary. The function of conscious vision may be to add a final layer to our interpretation of the world, to solve relatively “new” visual problems, and to enable visual learning

Visual recognition of objects : behavioral, computational, and neurobiological aspects

I surveyed work on visual object recognition and perception. In animals, vision has been studied mainly on the behavioral and neurobiological levels. Behavioral data typically show what the visual system, by itself or together with the rest of the organism, is capable of. They show, for example, that humans can recognie objects regardless of size and position, but that rotated objects pose problems. Important insights into the organization of behavior have also been provided by people who suffered localized brain damage. We have learned that the brain is divided into areas subserving different and relatively well-defined behaviors. The visual system itself is also organized in different subsystems; the visual cortex alone contains nearly twenty maps of the visual field. And individual neurons respond selectively to visual stimuli, e.g., the orientation of line segments, color, direction of motion, and, most intriguingly, faces. The question is how the actions of all these neurons produce the behavior we observe. How do neurons represent the shape of objects such that they can be recognized? Before we can answer the question, we have to understand the computational aspect of shape representation, the nature of the problem as it were. Many methods for representing shape have been explored, mainly by computer scientists, but so far no satisfactory answers have been found