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Research attention is currently focused not only on external perception processes, but also to "interoception", considered as the process of receiving, accessing and appraising internal bodily signals. Maintaining desired physiological states is critical for an organism's well-being and survival.
Feature integration theory is a theory of attention developed in 1980 by Anne Treisman and Garry Gelade that suggests that when perceiving a stimulus, features are "registered early, automatically, and in parallel, while objects are identified separately" and at a later stage in processing.
The review argues that perceptual load theory has been misconstrued as a hybrid solution to the early selection versus late selection debate, and that it is instead an early selection model: selection occurs because attention is necessary for semantic processing, and the difference between high-load and low-load conditions is a result of the ...
Additional research proposes the notion of a moveable filter. The multimode theory of attention combines physical and semantic inputs into one theory. Within this model, attention is assumed to be flexible, allowing different depths of perceptual analysis. [28] Which feature gathers awareness is dependent upon the person's needs at the time. [3]
Perceptual learning is a more in-depth relationship between experience and perception. Different perceptions of the same sensory input may arise in individuals with different experiences or training. This leads to important issues about the ontology of sensory experience, the relationship between cognition and perception. An example of this is ...
In 1980, Treisman and Gelade published their seminal paper on Feature Integration Theory (FIT). [15] One key element of FIT is that early stages of object perception encode features such as color, form, and orientation as separate entities; focused attention combines these distinct features into perceived objects.
The brain not only uses the process of attention, but it also builds a set of information, or a representation, descriptive of attention. That representation, or internal model, is the attention schema. In the theory, the attention schema provides the requisite information that allows the machine to make claims about consciousness.
The paper introduced a new deep learning architecture known as the transformer, based on the attention mechanism proposed in 2014 by Bahdanau et al. [4] It is considered a foundational [5] paper in modern artificial intelligence, as the transformer approach has become the main architecture of large language models like those based on GPT.