Binding problem

The consciousness and binding problem is the problem of how objects, background and abstract or emotional features are combined into a single experience. The binding problem refers to the overall encoding of our brain circuits for the combination of decisions, actions, and perception. It is considered a "problem" due to the fact that no complete model exists. The binding problem can be subdivided into four problems of perception, used in neuroscience, cognitive science and philosophy of mind. It includes general considerations on coordination, the subjective unity of perception, and variable binding. Attention is crucial in determining which phenomena appear to be bound together, noticed, and remembered. This specific binding problem is generally referred to as temporal synchrony. At the most basic level, all neural firing and its adaptation depends on specific consideration to timing (Feldman, 2010). At a much larger level, frequent patterns in large scale neural activity are a major diagnostic and scientific tool. A popular hypothesis mentioned by Peter Milner, in his 1974 article A Model for Visual Shape Recognition, has been that features of individual objects are bound/segregated via synchronization of the activity of different neurons in the cortex. The theory, called binding-by-synchrony (BBS), is hypothesized to occur through the transient mutual synchronization of neurons located in different regions of the brain when the stimulus is presented. Empirical testing of the idea was brought to light when von der Malsburg proposed that feature binding posed a special problem that could not be covered simply by cellular firing rates. However, it has been shown this theory may not be a problem since it was revealed that the modules code jointly for multiple features, countering the feature-binding issue. Temporal synchrony has been shown to be the most prevalent when regarding the first problem, "General Considerations on Coordination," because it is an effective method to take in surroundings and is good for grouping and segmentation.