Top-Down modulation of neural responses in visual perception: a computational exploration
Visual perception is typically performed in the context of a task or goal. Nonetheless, visual processing has traditionally been conceptualized in terms of a fixed, task-independent hierarchy of feature detectors. We explore the computational implications of allowing early visual processing to be task modulated. Using artificial neural networks, we show that significant improvements in task accuracy can be obtained by allowing the weights to be modulated by task. The primary benefits are obtained under resource-limited processing. A relatively modest task-based modulation of weights and activities can lead to a large performance boost, suggesting an efficient means of increasing effective cortical capacity.
KeywordsNeural network Top-down processing Visual perception Control Computational modeling Cognitive psychology
This research was supported by NSF BCS 0339103 and NSF CSE-SMA 0509521. We thank Ben Pearre, Avleen Singh, and Thomas Strohmann for helpful comments on a draft of this manuscript.
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