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There exist studies suggesting deeper multisensory convergences than those at the sensory-specific cortices, which were listed earlier. This convergence of multiple sensory modalities is known as multisensory integration. Sensory processing deals with how the brain processes sensory input from multiple sensory modalities.
Most sensory systems have a quiescent state, that is, the state that a sensory system converges to when there is no input. [ citation needed ] This is well-defined for a linear time-invariant system , whose input space is a vector space, and thus by definition has a point of zero.
Sensory processing disorder (SPD), formerly known as sensory integration dysfunction, is a condition in which multisensory input is not adequately processed in order to provide appropriate responses to the demands of the environment.
The neocortex in the mammalian brain has parcellations that primarily process sensory input from one modality. For example, primary visual area, V1, or primary somatosensory area, S1. These areas mostly deal with low-level stimulus features such as brightness, orientation, intensity, etc.
Sensory neurons, also known as afferent neurons, are neurons in the nervous system, ... again not allowing the brain to process auditory input correctly. [15] Temperature
People with sensory processing issues may benefit from a sensory diet of activities and accommodations designed to prevent sensory overload and retrain the brain to process sensory input more typically. It is important in situations of sensory overload to calm oneself and return to a normal level. [6]
The cocktail party effect illustrates how the brain inhibits input from environmental stimuli, while still processing sensory input from the attended stimulus. The cocktail party effect demonstrates sensory gating in hearing, but the other senses also go through the same process protecting primary cortical areas from being overwhelmed.
Predictive coding was initially developed as a model of the sensory system, where the brain solves the problem of modelling distal causes of sensory input through a version of Bayesian inference. It assumes that the brain maintains an active internal representations of the distal causes, which enable it to predict the sensory inputs. [5]