Metacognitive networks and measures of consciousness

Antoine Pasquali, Bert Timmermans, Axel Cleeremans

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

Abstract

Subjective measures of awareness rest on the assumption that conscious knowledge is knowledge that participants know they possess. Post-decision wagering, recently proposed as an objective measure of awareness, raised a new controversy on determining the properties that should characterize the objectivity of an awareness measure. Indeed, if the method appears objective in many aspects – it does not require introspection but rather lies on instinct, it does not affect conscious states, it can be learned unconsciously –, it also shares some characteristics with subjective measures – it involves metacognitive content and particularly, it represents a decision about a decision. The lack of consensus on this topic leaded us to develop a new approach based on a novel theoretical aspect, causality, and to consider a causally independent mechanism that would give an agent the capability to know what knowledge it possesses. In this framework, any measure that would not necessarily rely on such mechanism in a given experimental situation should be considered as objective. We support our claim with a computational model based on metacognitive networks, and present three simulation studies in which neural networks learn to wager on their own performance. Results demonstrate a good fit to human data, although depending on the situation, post-decision wagering is implemented either as an objective or as a subjective measure of network’s knowledge. We discuss implications of our results for defining the nature of subjective and objective measures, as well as for our understanding of consciousness.
Original languageEnglish
Title of host publicationProceedings of the 31st Annual Conference of the Cognitive Science Society
EditorsNiels Taatgen, Hedderik van Rijn
Place of PublicationAustin TX
PublisherCognitive Science Society
Pages2620-2625
Number of pages6
ISBN (Print)978-0-9768318-5-3
Publication statusPublished - 2009

Fingerprint

Consciousness
Subjective measures
Objectivity
Simulation study
Computational model
Knowledge networks
Causality
Neural networks

Cite this

Pasquali, A., Timmermans, B., & Cleeremans, A. (2009). Metacognitive networks and measures of consciousness. In N. Taatgen, & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 2620-2625). Austin TX: Cognitive Science Society.

Metacognitive networks and measures of consciousness. / Pasquali, Antoine; Timmermans, Bert; Cleeremans, Axel.

Proceedings of the 31st Annual Conference of the Cognitive Science Society. ed. / Niels Taatgen; Hedderik van Rijn. Austin TX : Cognitive Science Society, 2009. p. 2620-2625.

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

Pasquali, A, Timmermans, B & Cleeremans, A 2009, Metacognitive networks and measures of consciousness. in N Taatgen & H van Rijn (eds), Proceedings of the 31st Annual Conference of the Cognitive Science Society. Cognitive Science Society, Austin TX, pp. 2620-2625.
Pasquali A, Timmermans B, Cleeremans A. Metacognitive networks and measures of consciousness. In Taatgen N, van Rijn H, editors, Proceedings of the 31st Annual Conference of the Cognitive Science Society. Austin TX: Cognitive Science Society. 2009. p. 2620-2625
Pasquali, Antoine ; Timmermans, Bert ; Cleeremans, Axel. / Metacognitive networks and measures of consciousness. Proceedings of the 31st Annual Conference of the Cognitive Science Society. editor / Niels Taatgen ; Hedderik van Rijn. Austin TX : Cognitive Science Society, 2009. pp. 2620-2625
@inproceedings{50e1050ee0514617bb011a8f058458dc,
title = "Metacognitive networks and measures of consciousness",
abstract = "Subjective measures of awareness rest on the assumption that conscious knowledge is knowledge that participants know they possess. Post-decision wagering, recently proposed as an objective measure of awareness, raised a new controversy on determining the properties that should characterize the objectivity of an awareness measure. Indeed, if the method appears objective in many aspects – it does not require introspection but rather lies on instinct, it does not affect conscious states, it can be learned unconsciously –, it also shares some characteristics with subjective measures – it involves metacognitive content and particularly, it represents a decision about a decision. The lack of consensus on this topic leaded us to develop a new approach based on a novel theoretical aspect, causality, and to consider a causally independent mechanism that would give an agent the capability to know what knowledge it possesses. In this framework, any measure that would not necessarily rely on such mechanism in a given experimental situation should be considered as objective. We support our claim with a computational model based on metacognitive networks, and present three simulation studies in which neural networks learn to wager on their own performance. Results demonstrate a good fit to human data, although depending on the situation, post-decision wagering is implemented either as an objective or as a subjective measure of network’s knowledge. We discuss implications of our results for defining the nature of subjective and objective measures, as well as for our understanding of consciousness.",
author = "Antoine Pasquali and Bert Timmermans and Axel Cleeremans",
note = "AP, BT & AC : equal contributions (shared first-authorship)",
year = "2009",
language = "English",
isbn = "978-0-9768318-5-3",
pages = "2620--2625",
editor = "Niels Taatgen and {van Rijn}, Hedderik",
booktitle = "Proceedings of the 31st Annual Conference of the Cognitive Science Society",
publisher = "Cognitive Science Society",

}

TY - GEN

T1 - Metacognitive networks and measures of consciousness

AU - Pasquali, Antoine

AU - Timmermans, Bert

AU - Cleeremans, Axel

N1 - AP, BT & AC : equal contributions (shared first-authorship)

PY - 2009

Y1 - 2009

N2 - Subjective measures of awareness rest on the assumption that conscious knowledge is knowledge that participants know they possess. Post-decision wagering, recently proposed as an objective measure of awareness, raised a new controversy on determining the properties that should characterize the objectivity of an awareness measure. Indeed, if the method appears objective in many aspects – it does not require introspection but rather lies on instinct, it does not affect conscious states, it can be learned unconsciously –, it also shares some characteristics with subjective measures – it involves metacognitive content and particularly, it represents a decision about a decision. The lack of consensus on this topic leaded us to develop a new approach based on a novel theoretical aspect, causality, and to consider a causally independent mechanism that would give an agent the capability to know what knowledge it possesses. In this framework, any measure that would not necessarily rely on such mechanism in a given experimental situation should be considered as objective. We support our claim with a computational model based on metacognitive networks, and present three simulation studies in which neural networks learn to wager on their own performance. Results demonstrate a good fit to human data, although depending on the situation, post-decision wagering is implemented either as an objective or as a subjective measure of network’s knowledge. We discuss implications of our results for defining the nature of subjective and objective measures, as well as for our understanding of consciousness.

AB - Subjective measures of awareness rest on the assumption that conscious knowledge is knowledge that participants know they possess. Post-decision wagering, recently proposed as an objective measure of awareness, raised a new controversy on determining the properties that should characterize the objectivity of an awareness measure. Indeed, if the method appears objective in many aspects – it does not require introspection but rather lies on instinct, it does not affect conscious states, it can be learned unconsciously –, it also shares some characteristics with subjective measures – it involves metacognitive content and particularly, it represents a decision about a decision. The lack of consensus on this topic leaded us to develop a new approach based on a novel theoretical aspect, causality, and to consider a causally independent mechanism that would give an agent the capability to know what knowledge it possesses. In this framework, any measure that would not necessarily rely on such mechanism in a given experimental situation should be considered as objective. We support our claim with a computational model based on metacognitive networks, and present three simulation studies in which neural networks learn to wager on their own performance. Results demonstrate a good fit to human data, although depending on the situation, post-decision wagering is implemented either as an objective or as a subjective measure of network’s knowledge. We discuss implications of our results for defining the nature of subjective and objective measures, as well as for our understanding of consciousness.

M3 - Conference contribution

SN - 978-0-9768318-5-3

SP - 2620

EP - 2625

BT - Proceedings of the 31st Annual Conference of the Cognitive Science Society

A2 - Taatgen, Niels

A2 - van Rijn, Hedderik

PB - Cognitive Science Society

CY - Austin TX

ER -