Repository logo
 
Publication

PASAR: an integrated model of prediction, anticipation, sensation, attention and response for artificial sensorimotor systems

2012Journal article Restricted access
Simple item page
dc.contributor.authorMathews, Zenon
dc.contributor.authorBermúdez i Badia, Sergi
dc.contributor.authorVerschureab, Paul F.M.J
dc.date.accessioned2019-12-03T11:25:52Z
dc.date.available2019-12-03T11:25:52Z
dc.date.issued2012
dc.description.abstractA wide range of neuroscientific studies suggest the existence of cognitive mechanisms like attention, prediction, anticipation and strong vertical interactions between different hierarchical layers of the brain while performing complex tasks. Despite advances in both cognitive brain research and in the development of brain-inspired artificial cognitive systems, the interplay of these key ingredients of cognition remain largely elusive and unquantified in complex real-world tasks. Furthermore, it has not yet been demonstrated how a self-contained hierarchical cognitive system acting under limited resource constraints can quantifiably benefit from the incorporation of top–down and bottom–up attentional mechanisms. In this context, an open fundamental question is how a data association mechanism can integrate bottom–up sensory information and top–down knowledge. Here, building on the Distributed Adaptive Control (DAC) architecture, we propose a single framework for integrating these different components of cognition and demonstrate the framework’s performance in solving real-world and simulated robot tasks. Using the model we quantify the interactions between prediction, anticipation, attention and memory. Our results support the strength of a complete system that incorporates attention, prediction and anticipation mechanisms compared to incomplete systems for real-world and complex tasks. We unveil the relevance of transient memory that underlines the utility of the above mechanisms for intelligent knowledge management in artificial sensorimotor systems. These findings provide concrete predictions for physiological and psychophysical experiments to validate our model in biological cognitive systems.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMathews, Z., Bermúdez i Badia, S., & Verschure, P. F. (2012). PASAR: an integrated model of prediction, anticipation, sensation, attention and response for artificial sensorimotor systems. Information Sciences, 186(1), 1-19.pt_PT
dc.identifier.doi10.1016/j.ins.2011.09.042pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/2609
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.subjectAutonomous cognitive agentspt_PT
dc.subjectAttentionpt_PT
dc.subjectLimited resourcespt_PT
dc.subjectData integrationpt_PT
dc.subjectAnticipatory behaviorpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titlePASAR: an integrated model of prediction, anticipation, sensation, attention and response for artificial sensorimotor systemspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage19pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleInformation Sciencespt_PT
oaire.citation.volume186(1)pt_PT
person.familyNameBermúdez i Badia
person.givenNameSergi
person.identifier239789
person.identifier.ciencia-idCA17-5E88-2B37
person.identifier.orcid0000-0003-4452-0414
person.identifier.ridC-8681-2018
person.identifier.scopus-author-id6506360007
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationef8f1e3b-3c09-4817-80d0-d96aa88051a2
relation.isAuthorOfPublication.latestForDiscoveryef8f1e3b-3c09-4817-80d0-d96aa88051a2

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
PASAR An integrated model of prediction anticipation sensation attention and response for artificial sensorimotor systems.pdf
Size:
1.46 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Artigos em revistas internacionais