Figure \(5.6\): Hierarchy of visual detectors of increasing complexity achieves sophisticated perceptual categorization, with the higher levels being able to recognize 1000's of different objects, people, etc.
Figure \(5.7\): Felleman & Van Essen's (1991) diagram of the anatomical connectivity of visual processing pathways, starting with retinal ganglion cells (RGC) to the LGN of the thalamus, then primary visual cortex (V1) and on up.
The perceptual system provides an excellent example of the power of hierarchically organized layers of neural detectors, as we discussed in the Networks Chapter. Figure 5.6 summarizes this process, with associated cortical areas noted below each stage of processing. Figure 5.7 shows the actual anatomical connectivity patterns of all of the major visual areas, showing that information really is processed in a hierarchical fashion in the brain (although there are many interconnections outside of a strict hierarchy as well). Figure 5.8 puts these areas into their anatomical locations, showing more clearly the what vs where (ventral vs dorsal) split in visual processing. Here is a quick summary of the flow of information up the what side of the visual pathway (pictured on the right side of Figure 5.7):
- V1 -- primary visual cortex, which encodes the image in terms of oriented edge detectors that respond to edges (transitions in illumination) along different angles of orientation. We will see in Perception and Attention how these edge detectors develop through self-organizing learning, driven by the reliable statistics of natural images.
- V2 -- secondary visual cortex, which encodes combinations of edge detectors to develop a vocabulary of intersections and junctions, along with many other basic visual features (e.g., 3D depth selectivity, basic textures, etc), that provide the foundation for detecting more complex shapes. These V2 neurons also encode these features in a broader range of locations, starting a process that ends up with IT neurons being able to recognize an object regardless of where it appears in the visual field (i.e., invariant object recognition).
- V4 -- detects more complex shape features, over an even larger range of locations (and sizes, angles, etc).
- IT-posterior (PIT) -- detects entire object shapes, over a wide range of locations, sizes, and angles. For example, there is an area near the fusiform gyrus on the bottom surface of the temporal lobe, called the fusiform face area (FFA), that appears especially responsive to faces. As we saw in the Networks Chapter, however, objects are encoded in distributed representations over a broad range of areas in IT.
- IT-anterior (AIT) -- this is where visual information becomes extremely abstract and semantic in nature -- as shown in the Figure, it can encode all manner of important information about different people, places and things.
Figure \(5.8\): Division of What vs Where (ventral vs. dorsal) pathways in visual processing.
We'll explore a model of invariant object recognition in Perception and Attention that shows how this deep hierarchy of detectors can develop through learning. The Language Chapter builds upon this object recognition process to understand how words are recognized and translated into associated verbal motor outputs during reading, and associated with semantic knowledge as well.
The where aspect of visual processing going up in a dorsal directly through the parietal cortex (areas MT, VIP, LIP, MST) contains areas that are important for processing motion, depth, and other spatial features. As noted above, these areas are also critical for translating visual input into appropriate motor output, leading Goodale and Milner to characterize this as the howpathway. In Perception and Attention we'll see how this dorsal pathway can interact with the ventral what pathway in the context of visual attention, producing the characteristic effects of parietal damage in hemispatial neglect, for example.