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On theory integration: Toward developing affective components within cognitive architectures

Published online by Cambridge University Press:  08 June 2015

Justin M. Olds  and
Julian N. Marewski
Justin M. Olds
Affiliation:
Department of Organizational Behavior, Faculty of Business and Economics, University of Lausanne, Lausanne 1015, Switzerland. justin.olds@unil.chjulian.marewski@unil.chhttp://www.unil.ch
Julian N. Marewski
Affiliation:
Department of Organizational Behavior, Faculty of Business and Economics, University of Lausanne, Lausanne 1015, Switzerland. justin.olds@unil.chjulian.marewski@unil.chhttp://www.unil.ch
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Abstract

In The Cognitive-Emotional Brain, Pessoa (2013) suggests that cognition and emotion should not be considered separately. We agree with this and argue that cognitive architectures can provide steady ground for this kind of theory integration and for investigating interactions among underlying cognitive processes. We briefly explore how affective components can be implemented and how neuroimaging measures can help validate models and influence theory development.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 38 , 2015 , e82
Copyright
Copyright © Cambridge University Press 2015 

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References

Anderson, J. R. (2005) Human symbol manipulation within an integrated cognitive architecture. Cognitive Science 29:313–41.Google Scholar
Anderson, J. R. (2007) How can the human mind occur in the physical universe? Oxford University Press.Google Scholar
Anderson, J. R. & Lebiere, C. (1998) The atomic components of thought. Erlbaum.Google Scholar
Belavkin, R. V. (2001) Modelling the inverted-U effect in ACT-R. In: Proceedings of the 2001 Fourth International Conference on Cognitive Modeling, ed. Altmann, E. M., Cleeremans, A., Schunn, C. D. & Gray, W. D., pp. 275–76. Erlbaum.Google Scholar
Borst, J. P. & Anderson, J. R. (2014) Using the ACT-R Cognitive Architecture in combination with fMRI data. In: An introduction to model-based cognitive neuroscience, ed. Forstmann, B. U. & Wagenmakers, E.-J.. Springer.Google Scholar
Cochran, R. E., Lee, F. J. & Chown, E. (2006) Modeling emotion: Arousal's impact on memory. In: Proceedings of the 28th Annual Conference of the Cognitive Science Society, pp. 1133–38. Erlbaum.Google Scholar
Dancy, C. L., Ritter, F. E., Berry, K. & Klein, L. C. (2013) Using a cognitive architecture with a physiological substrate to represent effects of psychological stress on cognition. Computational and Mathematical Organization Theory. Available at: http://dx.doi.org/10.1007/s10588-014-9178-1.Google Scholar
Gunzelmann, G., Gross, J. B., Gluck, K. A. & Dinges, D. F. (2009) Sleep deprivation and sustained attention performance: Integrating mathematical and cognitive modeling. Cognitive Science 33:880–910.Google Scholar
Hudlicka, E. 2004. Beyond cognition: Modeling emotion in cognitive architectures. In: Proceedings of the sixth international conference on cognitive modeling, ICCCM 2004, Integrating models, ed. Lovett, M., Schunn, C., Lebiere, C. & Munro, P., pp. 118–23. Erlbaum.Google Scholar
Just, M. A. & Varma, S. (2007) The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition. Cognitive, Affective, and Behavioral Neuroscience 7(3):153–91.Google Scholar
Kleinsmith, L. J. & Kaplan, S. (1964) Interaction of arousal and recall interval in nonsense syllable paired-associate learning. Journal of Experimental Psychology 67:124.Google Scholar
Langley, P., Laird, J. E. & Rogers, S. (2009) Cognitive architectures: Research issues and challenges. Cognitive Systems Research 10:141–60.CrossRefGoogle Scholar
McGaugh, J. L. (2000) Memory – A century of consolidation. Science 287:248–51.Google Scholar
Meyer, D. E. & Kieras, D. E. (1997) A computational theory of executive cognitive processes and multiple-task performance: Part I. Basic mechanisms. Psychological Review 104:3.Google Scholar
Newell, A. (1973) You can't play 20 questions with nature and win: Projective comments on the papers of this symposium. In: Visual information processing, ed. Chase, W., pp. 283–308. Academic Press.Google Scholar
Newell, A. (1990) Unified theories of cognition. Harvard University Press.Google Scholar
Pessoa, L. (2013) The cognitive-emotional brain. From interactions to integration. MIT Press.Google Scholar
Reisenzein, R., Gratch, J., Hindriks, K., Hudlicka, E., Dastani, M., Lorini, E. & Meyer, J. J. (2013) Computational modeling of emotion: Towards improving the inter- and intradisciplinary exchange. IEEE Transactions on Affective Computing 1. 246–66.Google Scholar
Ritter, F. E., Reifers, A., Klein, L. & Schoelles, M. J. (2007) Lessons from defining theories of stress. In: Integrated models of cognitive systems, ed. Gray, W. D., pp. 254–62. Oxford University Press.Google Scholar
Salvucci, D. D. (2001) An integrated model of eye movements and visual encoding. Cognitive Systems Research 1:201–20.Google Scholar