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Title: Combining low-level perception with expectations in CHREST
Authors: Lane, PCR
Sykes, A
Gobet, F
Keywords: Expectation;CHREST;Computational modelling;Perceptual modality;Perceptual learning;Cross-modal;Classification;Categorization;Noisy visual input
Issue Date: 2003
Publisher: Proceedings of the European Cognitive Science Conference 2003
Citation: Proceedings of the European Cognitive Science Conference 2003 (pp. 205-210). Mahwah, NJ: Erlbaum.
Abstract: The ability of humans to reliably perceive and recognise objects relies on an interaction between information seen in the visual image and prior expectations. We describe an extension to the CHREST computational model which enables it to learn and combine information from multiple input modalities. Simulations demonstrate the presence of quantitative effects on recognition ability due to cross-modal interactions. Our simulations with CHREST illustrate how expectations can improve classification accuracy, reduce classification time, and enable words to be reconstructed from noisy visual input.
Appears in Collections:Psychology
Dept of Life Sciences Research Papers

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