Modelling attention within a complete cognitive architecture

doi:10.1017/CBO9780511974519.009

9 - Modelling attention within a complete cognitive architecture

Published online by Cambridge University Press: 04 February 2011

Georgi Stojanov and

Andrea Kulakov

Edited by

Claudia Roda

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Claudia Roda: Affiliation:
The American University of Paris, France

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Summary

Human attention is a complex phenomenon (or a set of related phenomena) that occurs at different levels of cognition (from low-level perceptual processes to higher perceptual and cognitive processes). Since the dawn of modern psychology through cognitive sciences to fields like Human–Computer Interaction (HCI), attention has been one of the most controversial research topics. Attempts to model attentional processes often show their authors' implicit construal of related cognitive phenomena and even their overall meta-theoretical stands about what cognition is. Moreover, the modelling of attention cannot be done in isolation from related cognitive phenomena like curiosity, motivation, anticipation and awareness, to mention but a few. For these reasons we believe that attention models are best presented within a complete cognitive architecture where most authors' assumptions will be made explicit.

In this chapter we first present several attempts to model attention within a complete cognitive architecture. Several known cognitive architectures (ACT-R, Fluid Concepts, LIDA, DUAL, Novamente AGI and MAMID) are reviewed from the point of view of their treatment of attentional processes. Before presenting our own take on attention modelling, we briefly present the meta-theoretical approach of interactivism as advocated by Mark Bickhard.

We then give a description of a cognitive architecture that we have been developing in the last ten years. We present some of the cognitive phenomena that we have modelled (expectations, routine behaviour, planning, curiosity and motivation) and what parts of the architecture can be seen as involved in the attentional processes.

Type: Chapter
Information: Human Attention in Digital Environments , pp. 210 - 242

DOI: https://doi.org/10.1017/CBO9780511974519.009 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2011

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References

Anderson, J. R. 1993. Rules of the Mind. Hillsdale, NJ: ErlbaumGoogle Scholar

Anderson, J. R., Matessa, M., and Douglass, S. 1995. The ACT-R theory and visual attention, in Proceedings of the 17th Annual Conference of the Cognitive Science Society, Hillsdale, NJ: Lawrence Erlbaum Associates: 61–5Google Scholar

Anderson, J. R., Matessa, M., and Lebiere, C. 1997. ACT-R: A theory of higher level cognition and its relation to visual attention, Human–Computer Interaction 12(4): 439–62CrossRef Google Scholar

Baars, B. J. 1988. A Cognitive Theory of Consciousness. Cambridge: Cambridge University PressGoogle Scholar

Bickhard, M. H. (1980a). Cognition, Convention, and Communication. New York: Praeger PublishersGoogle Scholar

Bickhard, M. H. (1980b). A model of developmental and psychological processes, Genetic Psychology Monographs 102: 61–116Google Scholar

Bickhard, M. H. 1993. Representational content in humans and machines, Journal of Experimental and Theoretical Artificial Intelligence 5: 285–333CrossRef Google Scholar

Bickhard, M. H. 1998. Levels of representationality, Journal of Experimental and Theoretical Artificial Intelligence 10(2): 179–215CrossRef Google Scholar

Bickhard, M. H., and Terveen, L. 1995. Foundational Issues in Artificial Intelligence and Cognitive Science: Impasse and Solution. Amsterdam: ElsevierGoogle Scholar

Breazeal, C. 2000. Sociable machines: Expressive social exchange between humans and robots. Sc.D. dissertation, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

Broadbent, D. 1958. Perception and Communication. London: Pergamon PressCrossRef Google Scholar

Campbell, D. T. 1990. Levels of organization, downward causation, and the selection-theory approach to evolutionary epistemology. In Greenberg, G. and Tobach, E. (eds.), Theories of the Evolution of Knowing. Hillsdale, NJ: Lawrence Erlbaum Associates: 1–17Google Scholar

Carpenter, G. A., Grossberg, S., and Rosen, D. B. 1991. FuzzyART: Fast stable learning and categorization of analog patterns by an adaptive resonance system, Neural Networks 4: 759–71CrossRef Google Scholar

Chalmers, D., French, R., and Hofstadter, D. 1992. High-level perception, representation, and analogy: A critique of artificial intelligence methodology, Journal of Experimental and Theoretical Artificial Intelligence: 4(3): 169–93CrossRef Google Scholar

Dixon, N. 1981. Preconscious Processing. Chichester: WileyGoogle Scholar

Drescher, G. 1991. Made-up Minds: A Constructivist Approach to Artificial Intelligence. Cambridge, MA: MIT PressGoogle Scholar

Franklin, S. 2003. IDA: A conscious artifact? Journal of Consciousness Studies 10: 47–66Google Scholar

Franklin, S. 2007. A foundational architecture for artificial general intelligence, in B. Goertzel and P. Wang (eds.), Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, Proceedings of the AGI Workshop, 2006, Amsterdam: IOS Press: 36–54Google Scholar

French, R. M. 1992, TableTop: An emergent, stochastic computer model of analogy-making. Ph.D. thesis, University of Michigan

Goertzel, B., and Pennachin, C. 2007. The Novamente artificial intelligence engine, in B. Goertzel, and P. Wang, (eds.), Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, Proceedings of the AGI Workshop, 2006, Amsterdam: IOS Press: 63–129CrossRef Google Scholar

Grossberg, S. 1978. A theory of visual coding, memory, and development, in E. L. J. Leeuwenberg, and H. F. J. M. Buffart, (eds.), Formal Theories of Visual Comparative Analysis. New York: Wiley: 7–26Google Scholar

Hofstadter, D. 1996. Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought. New York: Basic BooksGoogle Scholar

Hudlicka, E. 2005. Modeling interactions between metacognition and emotion in a cognitive architecture, in Proceedings of the AAAI Spring Symposium on Metacognition in Computation. AAAI Technical Report SS-05–04. Menlo Park, CA: AAAI Press: 55–61Google Scholar

James, W. 1890. The Principles of Psychology: 404–5. Retrieved 29 September 2009 from: http://psychclassics.asu.edu/James/Principles/prin11.htm

Kokinov, B. 1994. The DUAL cognitive architecture: A hybrid multi-agent approach, in A. G. Cohn (ed.), Proceedings of the 11th European Conference on Artificial Intelligence (ECAI '94). London: Wiley: 203–7Google Scholar

Kokinov, B. 1997. Micro-level hybridization in the cognitive architecture DUAL, in R. Sun, and F. Alexander, (eds.), Connectionist-Symbolic Integration: From Unified to Hybrid Architectures. Hillsdale, NJ: Lawrence Erlbaum Associates: 197–208Google Scholar

Kokinov, B., and Petrov, A. 2001. Integration of memory and reasoning in analogy-making: The AMBR model, in D. Gentner, K. J. Holyoak and B. N. Kokinov, (eds.), The Analogical Mind: Perspectives from Cognitive Science. Cambridge, MA: MIT PressGoogle Scholar

Kulakov, A. 1998. Vygovorotsky: A model of an anticipative and analogy-making actor. M.Sc. thesis, New Bulgarian University, Sofia

Kulakov, A., and Stojanov, G. 2002. Structures, inner values, hierarchies and stages: Essentials for developmental robot architectures, in Proceedings of the 2nd International Workshop on Epigenetic Robotics: Modelling Cognitive Development in Robotic Systems, Edinburgh: 63–9Google Scholar

Kulakov, A., Stojanov, G., and Davcev, D. 2002. A model of an expectancy-driven and analogy-making actor, in Proceedings of the 2nd International Conference on Development and Learning, Cambridge, MA: 61–8Google Scholar

Langley, P., and Choi, D. 2006. A unified cognitive architecture for physical agents, in Proceedings of the AAAI 2006, Boston: 1469–74Google Scholar

Minsky, M. 1986. The Society of Mind. New York: Simon and SchusterGoogle Scholar

Nestor, A., and Kokinov, B. 2004. Towards active vision in the DUAL cognitive architecture, International Journal of Information Theories and Applications 11(1): 11Google Scholar

Nevill-Manning, C. G., and Witten, I. H. 1997. Identifying hierarchical structure in sequences: A linear-time algorithm, Journal of Artificial Intelligence Research 7: 67–82Google Scholar

Newell, A. 1990. Unified Theories of Cognition. Cambridge, MA: Harvard University PressGoogle Scholar

Posner, M. I. 1980. Orienting of attention, Quarterly Journal of Experimental Psychology 32: 3–25CrossRef Google Scholar PubMed

Posner, M. I., and Petersen, S. E. 1990. The attention system of the human brain, Annual Review of Neuroscience 13: 25–42CrossRef Google Scholar PubMed

Rybak, I. A., Gusakova, V. I., Golovan, A. V., Podladchikova, L. N., and Shevtsova, N. A. 1998. A model of attention-guided visual perception and recognition, Vision Research 38: 2387–400CrossRef Google Scholar PubMed

Salvucci, D. D. 2000. A model of eye movements and visual attention, in Proceedings of the International Conference on Cognitive Modeling, Veenendaal, The Netherlands: Universal Press: 252–9Google Scholar

Salvucci, D. D., Kushleyeva, Y., and Lee, F. J. 2004. Toward an ACT-R general executive for human multitasking, in Proceedings of the 6th International Conference on Cognitive Modeling, Pittsburgh: 267–72Google Scholar

Stojanov, G. 1997. Expectancy theory and interpretation of the EXG curves in context of machine and biological intelligence, Ph.D. thesis, University Sts Cyril and Methodius, Skopje, Macedonia

Stojanov, G. 2001. Petitagé: A case study in developmental robotics. Proceedings of the 1st International Workshop on Epigenetic Robotics, Lund University Cognitive Studies, 85Google Scholar

Stojanov, G., and Kulakov, A. 2003. Interactivist approach to representation in epigenetic agents, in Proceedings of the 3rd International Workshop on Epigenetic Robotics, Lund, Sweden: 123–30Google Scholar

Stojanov, G., and Kulakov, A. 2006. On curiosity in intelligent robotic systems, in Proceedings of the 2006 AAAI Fall Symposia, Arlington, VA, 12–15 October: 44–51Google Scholar

Stojanov, G., and Trajkovski, G., and Kulakov, A., 2006. Interactivism in artificial intelligence (AI) and intelligent robotics, New Ideas in Psychology 24(2): 163–85CrossRef Google Scholar

Styles, E. A. 2005. Attention, Perception, and Memory: An Integrated Introduction. Hove: Psychology PressGoogle Scholar

Treisman, A. M., and Gelade, G. 1980. A feature-integration theory of attention, Cognitive Psychology 12: 97–136CrossRef Google Scholar

Treisman, A. M., and Sato, S. 1990. Conjunction search revisited, Journal of Experimental Psychology: Human Perception and Performance 16: 459–78Google Scholar

Tversky, A., and Gati, I. 1978. Studies of similarity, in E. Rosch, and B. B. Lloyd, (eds.), Cognition and Categorization. Hillsdale, NJ: Lawrence Erlbaum Associates: 79–98Google Scholar

Wang, H., and Fan, J. 2007. Human attentional networks: A connectionist model, Journal of Cognitive Neuroscience 19(10): 1678–89CrossRef Google Scholar PubMed

Wolfe, J. M. 1994. Guided search 2.0: A revised model of visual search, Psychonomic Bulletin and Review 1: 202–38CrossRef Google Scholar PubMed

Wolfe, J. M. 1999. In attentional amnesia. In Coltheart, V. (ed.), Fleeting Memories. Cambridge, MA: MIT Press: 71–94Google Scholar

Wolfe, J. M., and Bennett, S. C. 1997. Preattentive object files. Vision Research 37: 25–44CrossRef Google Scholar PubMed

Wolfe, J. M., and Horowitz, T. S. 2004. What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience 5(6): 495–501CrossRef Google Scholar PubMed