When the human eye sees a word or an object, it uses a network of detectors, organized in layers, going from the least complex to the most. The bottom layer, is concerned with features, and therefore is called a feature net.These detectors range in their need for strong or weak inputs in order to make them fire. Each detector has an activation level, a response threshold, and a baseline activation level. The activation level tells us how active a specific detector is at that moment. The response threshold tells us how high the activation level must get in order to warrant a response from the detector. The baseline activation level is the detectors activation level when the detector is not receiving any inputs. If a detector is used all the time, its baseline activation level will be higher, so it will require fewer inputs in order to fire.
A feature net can be used to explain object recognition also. When a person sees an object, they immediately see the object as it's component shape, or geons. This means, that before our minds even register what an object is, it organizes the object by shape, and then determines what the object is. This is in itself a feature net. When we see an object as a geon, it is in its simplest form, and we continue to add features until the object is whole.
Feature nets help us understand a few behaviors. First, they help explain why we recognize patterns and object very quickly, and with relatively very few inputs. Our language is also very redundant. They allow us to be able to glance at familiar letters, and fill in the rest. Therefore, we do not have to scrutinize over every word we read. The feature net also helps us recognize three-dimensional objects, and sounds. Feature nets do however have their limitations. First, we do experience some errors. Sometimes the wrong inputs get detected and we therefore recognize what we are seeing incorrectly. This system does correct itself, and usually …

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