Working memory and perception

Proceedings - IEEE International Workshop on Robot and Human Interactive Communication

Volumn 2005, Issue , 2005, Pages 686-691

  Wilkes, D M ^a   Tugcu, M ^a   Hunter, J E ^a   Noelle, D ^a  

^a VANDERBILT UNIVERSITY   (United States)

Author keywords

Computer vision; Learning; Perception; Robot vision; Working memory

Indexed keywords

COMPUTER PROGRAMMING; COMPUTER VISION; LEARNING SYSTEMS; MATHEMATICAL MODELS; ROBOTS; SENSORY PERCEPTION;

COMPUTATIONAL NEUROSCIENCE MODELS; LEARNING; PERCEPTUAL MODULES; WORKING MEMORY;

ROBOTICS;

EID: 33746613665     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROMAN.2005.1513859     Document Type: Conference Paper

References (15)

1. Fuster, J. M. and Alexander, G. E. (1971). Neuron activity related to short-term memory. Science, 173:652-654.
2. Funahashi, S., Bruce, C. J., and Golman-Rakic, P. S. (1989). Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex. Journal of Neurophysiology, 61:331-349.
3. Miller, E. K. and Desimone, R. (1994). Parallel neuronal mechanisms for short-term memory. Science, 263:520-522.
4. O'Reilly, R. C., Braver, T. S., and Cohen, J. D. (1999). A biologically based computational model of working memory. In (Miyake and Shah, 1999), chapter 11, pages 375-411.
5. Brooks, R.A., "A robust layered control system for a mobile robot", IEEE Journal of Robotics and Automation, vol. 2, no. 1, pp. 14-23, 1986.
6. Peters, R.A., Hambuchen, K.E., Kawamura, K., and Wilkes, D.M., "The sensory egosphere as a short-term memory for humanoids," Proc. IEEE-RAS Int. Conf. Humanoid Robots, Tokyo, pp. 451-459, November, 2001.
7. Kawamura, K., Koku, A.B., Wilkes, D.M., Peters, R.A., and Sekmen, A.,"Toward egocentric navigation," Int. J. of Robotics and Automation, vol. 17, no. 4, pp. 135-145, 2002.
8. Sutton, R. S. (1988). Learning to predict by the method of temporal differences. Machine Learning, 3:9-44.
9. Montague, P. R., Dayan, P., and Sejnowski, T. J. (1996). A framework for mesencephalic dopamine systems based on predictive hebbian learning. Journal of Neuroscience, 16:1936-1947.
10. Braver, T. S. and Cohen, J. D. (2000). On the control of control: The role of dopamine in regulating prefrontal function and working memory. In Monsell, S. and Driver, J., editors, Control of Cognitive Processes, volume XVIII of Attention and Performance, chapter 31, pages 713-737. MIT Press.
11. O'Reilly, R. C., Noelle, D. C., Braver, T. S., and Cohen, J. D. (2002). Prefrontal cortex and dynamic categorization tasks: Representational organization and neuromodulatory control. Cerebral Cortex, 12:246-257.
12. Sutton, R.S. and Barto, A.G., Reinforcement learning: an introduction. MIT Press, Cambridge, Massachusetts, 1998.
13. Oliver, N.M., Rosario, B., and Pentland, A.P., "A Bayesian computer vision system for modeling human interactions," IEEE Trans. PAMI, vol. 22, no. 8, pp. 831-843.
14. Hongeng, S., Bremond, F., and Nevatia, R., "Bayesian framework for video surveillance application," Proc. ICPR 2004, pp. 164-170, August, 2004.
15. Piater, J.H. and Grupen, R.A., "Feature learning for recognition with Bayesian networks," Proc. ICPR 2004, pp. 17-20, August, 2004.