Anticipation for learning, cognition and education

On the Horizon

Volumn 12, Issue 3, 2004, Pages 111-116

  Butz, Martin V ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Behaviour; Cognition; Learning; Sensory process psychology

Indexed keywords

EID: 84986166639     PISSN: 10748121     EISSN: 20541708     Source Type: Journal    
DOI: 10.1108/10748120410555359     Document Type: Article

References (35)

1. Blakemore, S.-J. and Decety, J. (2001), “From the perception of action to the understanding of intention”, Nature Reviews Neuroscience, Vol. 2 No. 1, pp. 561-567.
2. Blakemore, S.-J., Frith, C.D. and Wolpert, D.M. (2001), “The cerebellum is involved in predicting the sensory consequences of action”, Brain Imaging, Vol. 12 No. 9, pp. 1879-1884.
3. Bock, K., Irwin, D.E., Davidson, D.J. and Levelt, W.J.M. (2003), “Minding the clock”, Journal of Memory and Language, Vol. 48, pp. 653-685.
4. Butz, M.V. and Hoffmann, J. (2002), “Anticipations control behavior: animal behavior in an anticipatory learning classifier system”, Adaptive Behavior, Vol. 10, pp. 75-96.
5. Butz, M.V., Sigaud, O. and Gérard, P. (2003), “Anticipatory behavior: exploiting knowledge about the future to improve current behavior”, in Ed. Butz, M.V., Ed. Sigaud, O. and Ed. Gérard, P. (Eds), Anticipatory Behavior in Adaptive Learning Systems: Foundations, Theories, and Systems, Springer-Verlag, Berlin, Heidelberg, pp. 1-10.
6. Camacho, E.F. and Bordons, C. (Eds) (1999), Model Predictive Control, Springer-Verlag, Berlin/Heidelberg.
7. Colwill, R.M. and Rescorla, R.A. (1985), “Post-conditioning devaluation of a reinforcer affects instrumental learning”, Journal of Experimental Psychology: Animal Behavior Processes, Vol. 11 No. 1, pp. 120-132.
8. Goldberg, D.E. (2002), The Design of Innovation: Lessons from and for Competent Genetic Algorithms, Kluwer Academic Publishers, Boston, MA.
9. Graesser, A.C., Singer, M. and Trabasso, T. (1994), “Constructing inferences during narrative text comprehension”, Psychological Review, Vol. 101, pp. 371-395.
10. Griffin, Z.M. and Bock, J.K. (2000), “What the eyes say about speaking”, Psychological Science, Vol. 11, pp. 274-279.
11. Haykin, S. (Ed.) (2001), Kalman Filtering and Neural Networks, John Wiley & Sons, New York, NY.
12. Hoffmann, J. (1993), Vorhersage und Erkenntnis: Die Funktion von Antizipationen in der menschlichen Verhaltenssteuerung und Wahrnehmung (Anticipation and Cognition: The Function of Anticipations in Human Behavioral Control and Perception), Hogrefe, Göttingen.
13. Hoffmann, J. (2003), “Anticipatory behavioral control”, in Ed. Butz, M.V., Ed. Sigaud, O. and Ed. Gérard, P. (Eds), Anticipatory Behavior in Adaptive Learning Systems: Foundations, Theories, and Systems, Springer-Verlag, Heidelberg, pp. 44-65.
14. Kaelbling, L.P., Littman, M.L. and Moore, A.W. (1996), “Reinforcement learning: a survey”, Journal of Artificial Intelligence Research, Vol. 4, pp. 237-285.
15. Kambe, G., Rayner, K. and Duffy, S.A. (2001), “Global context effects on processing lexically ambiguous words: evidence from eye fixations”, Memory and Cognition, Vol. 29 No. 2, pp. 363-372.
16. Kunde, W. (2001), “Response-effect compatibility in manual choice reaction tasks”, Journal of Experimental Psychology: Human Perception and Performance, Vol. 27, pp. 387-394.
17. Kunde, W., Koch, I. and Hoffmann, J. (2004), “Anticipated action effects affect the selection, initiation and execution of actions”, Quarterly Journal of Experimental Psychology: Section A: Human Experimental Psychology, Vol. 57 No. 1, pp. 87-106.
18. McKoon, G. and Ratcliff, R. (1992), “Inference during reading”, Psychological Review, Vol. 99, pp. 440-466.
19. Prensky, M. (2003), “e-Nough! ‘e-Learning’ is a misnomer – it's mostly just ‘e-Teaching’. For any teaching to reliably and consistently produce the results we want, we still have a lot to learn about learning”, On the Horizon, Vol. 11 No. 1, pp. 26-32.
20. Rescorla, R.A. (1991), “Associative relations in instrumental learning: the 18th Bartlett Memorial Lecture”, Quarterly Journal of Experimental Psychology, Vol. 43(B), pp. 1-23.
21. Rescorla, R.A. (1995), “Full preservation of a response-outcome association through training with a second outcome”, Quarterly Journal of Experimental Psychology, Vol. 48(B), pp. 252-261.
22. Schubotz, R.I. and von Cramon, D.Y. (2001), “Functional organization of the lateral premotor cortex: fMRI reveals different regions activated by anticipation of object properties, location and speed”, Cognitive Brain Research, Vol. 11 No. 1, pp. 97-112.
23. Simons, D.J. and Chabris, C.F. (1999), “Gorillas in our midst: sustained inattentional blindness for dynamic events”, Perception, Vol. 28 No. 9, pp. 1059-1074.
24. Skinner, B. (1971), Beyond Freedom and Dignity, Bantam/Vintage, New York, NY.
25. Stickgold, R. (1998), “Sleep: off-line memory reprocessing”, Trends in Cognitive Sciences, Vol. 2 No. 12, pp. 484-492.
26. Sutton, R.S. (1990), “Integrated architectures for learning, planning, and reacting based on approximating dynamic programming”, in Ed. Porter, B.W. and Ed. Mooney, R.J. (Eds), Proceedings of the 7th International Conference on Machine Learning, Austin, TX, 21-23 June, Morgan Kaufmann, San Francisco, CA, pp. 216-224.
27. Sutton, R.S. and Barto, A.G. (1998), Reinforcement Learning: An introduction, MIT Press, Cambridge, MA.
28. Sutton, R.S., Precup, D. and Singh, S. (1999), “Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning”, Artificial Intelligence, Vol. 112 No. 1-2, pp. 181-211.
29. Thistlethwaite, D. (1951), “A critical review of latent learning and related experiments”, Psychological Bulletin, Vol. 48 No. 2, pp. 97-129.
30. Tolman, E.C. (1932), Purposive Behavior in Animals and Men, Appleton, New York, NY.
31. Tolman, E.C. (1959), “Principles of purposive behavior”, in Ed. Koch, S. (Ed.), Psychology: A Study of Science, McGraw-Hill, New York, NY, pp. 92-157.
32. Watson, J. (1913), “Psychology as a behaviorist views it”, Psychological Review, Vol. 20 No. 5, pp. 158-177.
33. Wolpert, D.H. and Macready, W.G. (1995), “No free lunch theorems for search” (Tech. Rep. No. SFI-TR-95-02-010). Santa Fe Institute, Santa Fe, NM.
34. Wolpert, D.H. and Macready, W.G. (1997), “No free lunch theorems for optimization”, IEEE Transactions on Evolutionary Computation, Vol. 1 No. 1, pp. 67-82.
35. Wolpert, D.M., Ghahramani, Z. and Flanagan, J.R. (2001), “Perspectives and problems in motor learning”, Trends in Cognitive Sciences, Vol. 5 No. 11, pp. 487-494.