A Neurologically Inspired Network Model for Graziano's Attention Schema Theory for Consciousness

Erik van den Boogaard, J. Treur, Maxim Turpijn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
LanguageEnglish
Title of host publicationNatural and Artificial Computation for Biomedicine and Neuroscience, Proc. of the 7th International Work-Conference on the Interplay between Natural and Artificial Computation, IWINAC'17
EditorsJ.M. Ferrandez Vicente
PublisherSpringer
Pages10-21
Number of pages12
StatePublished - 19 Jun 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10337

Cite this

van den Boogaard, E., Treur, J., & Turpijn, M. (2017). A Neurologically Inspired Network Model for Graziano's Attention Schema Theory for Consciousness. In J. M. Ferrandez Vicente (Ed.), 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 (pp. 10-21). (Lecture Notes in Computer Science; Vol. 10337). Springer.
van den Boogaard, Erik ; Treur, J. ; Turpijn, Maxim. / A Neurologically Inspired Network Model for Graziano's Attention Schema Theory for Consciousness. 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. editor / J.M. Ferrandez Vicente. Springer, 2017. pp. 10-21 (Lecture Notes in Computer Science).
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title = "A Neurologically Inspired Network Model for Graziano's Attention Schema Theory for Consciousness",
abstract = "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.",
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van den Boogaard, E, Treur, J & Turpijn, M 2017, A Neurologically Inspired Network Model for Graziano's Attention Schema Theory for Consciousness. in JM Ferrandez Vicente (ed.), 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. Lecture Notes in Computer Science, vol. 10337, Springer, pp. 10-21.

A Neurologically Inspired Network Model for Graziano's Attention Schema Theory for Consciousness. / van den Boogaard, Erik; Treur, J.; Turpijn, Maxim.

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. ed. / J.M. Ferrandez Vicente. Springer, 2017. p. 10-21 (Lecture Notes in Computer Science; Vol. 10337).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A Neurologically Inspired Network Model for Graziano's Attention Schema Theory for Consciousness

AU - van den Boogaard,Erik

AU - Treur,J.

AU - Turpijn,Maxim

PY - 2017/6/19

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N2 - 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.

AB - 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.

M3 - Conference contribution

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van den Boogaard E, Treur J, Turpijn M. A Neurologically Inspired Network Model for Graziano's Attention Schema Theory for Consciousness. In Ferrandez Vicente JM, editor, 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. Springer. 2017. p. 10-21. (Lecture Notes in Computer Science).