Hypothetically Modeled Perceptual Sensory Modality of Human Visual Selective Attention Scheme by PFC-Based Network
The Selective Attention Scheme has attracted renowned interest in the field of sensorimotor control and visual recognition problems. Especially, selective attention is crucial in terms of saving computational cost for constructing a sensorimotor control system, as the amount of sensory inputs over the system far exceeds its information processing capacity. In fact, selective attention plays an integral role in sensory information processing, enhancing neuronal responses to important or task-relevant stimuli at the expense of the neuronal responses to irrelevant stimuli. To compute human selective attention scheme, we assume that each attention modeled as a probabilistic class must correctly be learned to yield the relationship with different sensory inputs by learning schemes in the first place (sensory modality). Afterwards, their learned probabilistic attention classes can straightforwardly be used for the control property of selecting attention (shifting attention). In this paper, the soundness of proposed human selective attention scheme has been shown in particular with perceptual sensory modality. The scheme is actually realized by a neural network, namely PFC-based network.
KeywordsSelective Attention Sensory Modality Neuronal Response Probabilistic Class Information Processing Capacity
Unable to display preview. Download preview PDF.
- 4.H. J. Heinze, G. R. Mangun, W. Burchert, H. Hinrichs, M. Scholz, T. F. Munte, A. Gos, M. Scherg, S. Jahannes, H. Hundeshagen, M. S. Gazzaniga, S.A. Hillyard: Combined spatial and temporal imaging of brain activity during visual selective attention in humans. Nature 372 (1994) 543–546.CrossRefGoogle Scholar
- 6.L. Itti, C. Koch, E. Neibur: A model of saliency-based visual attention for rapid scene analysis. Proceed. of Image Understanding Workshop 11 (1999) 1254–1259.Google Scholar
- 7.T. Koshizen, Y. Ueda H. Tsujino: New conscious sensorimotor control system induced by human selective attention mechanism with minimum variance theory. Technical Report of Honda R&D Co. Ltd. (In preparation).Google Scholar
- 8.D. LaBerge M. S. Buchsbaum:Attentional processing: the brain’s art of mindfulness. (1995) MA: Harvard University Press.Google Scholar
- 9.G.R. Mangun, S.A. Hillyard, S. J. Luck: Attention and performance XIV:synergies in experimental psychology, artificial intelligence, and cognitive neuroscience. Cambridge MA: MIT Press (1993) 219–243.Google Scholar
- 11.W. Schultz: Predictive reward signal of dopamine neurons. J. of Neurophysiology 80 (1990) 1–27.Google Scholar
- 12.R.P.N. Rao: Predictive sequence learning in recurrent neocortical circuits. Advances in neural information processing systems 12 (2000) 164–170.Google Scholar
- 13.Y. Weiss: Slow and Smooth: a Bayesian theory for the combination of local motion signals in human vision. Technical report of Massachusetts Institute of Technology A. I.Memo 1624 (1998).Google Scholar