Abstract
EEG (electroencephalograph) provides information about the electrical fluctuations between neurons that characterize brain activity, and measurements of brain activity at resolutions approaching real time. On the other hand, cognitive architectures such as ACT-R would explain how all the components of the mind work together to generate coherent human cognition. Thus EEG/ERP (event-related potential) and ACT-R will provide two aspects to explore the cognitive processes and their neural basis. In this paper, we present a case study by combining EEG/ERP and ACT-R for investigating human computation mechanism. In particular, we focus on two digits addition tasks with or without carry, and systematically perform a set of behavior and EEG experiments, as well as with the help of ACT-R simulation. Preliminary results show the usefulness of our approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An Intergrated Theory of the Mind. Psychological Review 111(4), 1036–1060 (2004)
Anderson, J.R.: How can the Human Mind Occur in the Physical Universe. Oxford University Press, Oxford (2007)
Gazzaniga, M.S. (ed.): The Cognitive Neurosciences III. The MIT Press, Cambridge (2004)
Handy, T.C.: Event-Related Potentials, A Methods Handbook. The MIT Press, Cambridge (2004)
Liu, J., Jin, X., Tsui, K.C.: Autonomy Oriented Computing: From Problem Solving to Complex Systems Modeling. Springer, Heidelberg (2005)
Megalooikonomou, V., Herskovits, E.H.: Mining Structure-Function Associations in a Brain Image Database. In: Cios, K.J. (ed.) Medical Data Mining and Knowledge Discovery, pp. 153–179. Physica-Verlag (2001)
Mizuhara, H., Wang, L., Kobayashi, K., Yamaguchi, Y.: Long-range EEG Phase-synchronization During an Arithmetic Task Indexes a Coherent Cortical Network Simultaneously Measured by fMRI. NeuroImage 27(3), 553–563 (2005)
Mitchell, T.M., Hutchinson, R., Niculescu, R.S., Pereira, F., Wang, X., Just, M., Newman, S.: Learning to Decode Cognitive States from Brain Images. Machine Learning 57(1-2), 145–175 (2004)
Motomura, S., Hara, A., Zhong, N., Lu, S.: An Investigation of Human Problem Solving System: Computation as an Example. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 824–834. Springer, Heidelberg (2007)
Newell, A., Simon, H.A.: Human Problem Solving. Prentice-Hall, Englewood Cliffs (1972)
Qin, Y., et al.: Predicting the Practice Effects on the Blood Oxygenation Level-dependent (BOLD) Function of fMRI in a Symbolic Manipulation Task. PNAS 100, 4951–4956 (2003)
Qin, Y., Bothell, D., Anderson, J.R.: ACT-R meets fMRI. In: Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Li, K. (eds.) Web Intelligence Meets Brain Informatics. LNCS (LNAI), vol. 4845, pp. 205–222. Springer, Heidelberg (2007)
Sohn, M.-H., Douglass, S.A., Chen, M.-C., Anderson, J.R.: Characteristics of Fluent Skills in a Complex, Dynamic Problem-solving Task. Human Factors 47(4), 742–752 (2005)
Sommer, F.T., Wichert, A. (eds.): Exploratory Analysis and Data Modeling in Functional Neuroimaging. The MIT Press, Cambridge (2003)
Sternberg, R.J., Lautrey, J., Lubart, T.I.: Models of Intelligence. American Psychological Association (2003)
Ward, L.M.: Synchronous Neural Oscillations and Cognitive Processes. TRENDS in Cognitive Sciences 7(12), 553–559 (2003)
Zadeh, L.A.: Precisiated Natural Language (PNL). AI Magazine 25(3), 74–91 (Fall 2004)
Zhong, N., Wu, J.L., Nakamaru, A., Ohshima, M., Mizuhara, H.: Peculiarity Oriented fMRI Brain Data Analysis for Studying Human Multi-Perception Mechanism. Cognitive Systems Research 5(3), 241–256 (2004)
Zhong, N.: Impending Brain Informatics Research from Web Intelligence Perspective. International Journal of Information Technology and Decision Making 5(4), 713–727 (2006)
Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Li, K. (eds.): Web Intelligence Meets Brain Informatics. LNCS (LNAI), vol. 4845, pp. 1–31. Springer, Heidelberg (2007)
Zhong, N., Motomura, S.: Agent-Enriched Data Mining: A Case Study in Brain Informatics. IEEE Intelliegnt Systems 24(3), 38–45 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Motomura, S., Ojima, Y., Zhong, N. (2009). EEG/ERP Meets ACT-R: A Case Study for Investigating Human Computation Mechanism. In: Zhong, N., Li, K., Lu, S., Chen, L. (eds) Brain Informatics. BI 2009. Lecture Notes in Computer Science(), vol 5819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04954-5_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-04954-5_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04953-8
Online ISBN: 978-3-642-04954-5
eBook Packages: Computer ScienceComputer Science (R0)