This paper describes a network-oriented model based on the neuro-scientist Graziano's Attention Schema Theory for consciousness. This theory describes an attention schema as an internal model of the attention process supporting the control of attention, similar to how our mind uses a body schema as an internal model of the body to control its movements. The Attention Schema Theory comes with a number of testable predictions. After designing a neuro-logically inspired temporal-causal network model for the Attention schema Theory, a few simulations were conducted to verify some of these predictions. One prediction is that a noticeable attention control deficit occurs when using attention without awareness. Another is that a noticeable attention control deficit occurs when using only bottom-up influence (from the sensory representations) without any top-down influence (for example, from goal or control states). The presented model is illustrated by a scenario where a hunter imagines (using internal simulation) a prey which he wants to attend to and catch, but shortly after he or she imagines a predator which he then wants to attend to and avoid. The outcomes of the simulations support the predictions that were made.
|Title of host publication||Natural and Artificial Computation for Biomedicine and Neuroscience, Proc. of the 7th International Work-Conference on the Interplay between Natural and Artificial Computation, IWINAC'17|
|Editors||J.M. Ferrandez Vicente|
|Number of pages||12|
|Publication status||Published - 19 Jun 2017|
|Name||Lecture Notes in Computer Science|