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Experimental And Computational Investigation of the Effects of Variable RSI on Sequence LearningAuthors: Sneha Kummetha,Pramod Sivaram Kaushik,Anuj Kumar Shukla,Bapi Raju Surampudi Conference: 2018 Conference on Cognitive Computational Neuroscience (CCN-2018 2018) Location Philadelphia, Pennsylvania, USA Date: 2018-09-05 Report no: IIIT/TR/2018/100 AbstractIn this study, we investigated the effects of variable Response-to-Stimulus interval (RSI) on sequence learning using both empirical and computational methods. In the empirical study, the serial reaction time task (SRT) was conducted which was followed by free generation and recognition tasks. Results showed that learning becomes explicit with increase in RSI despite its varying temporal nature. We constructed a computational model based on modified Elman network architecture to obtain a functional account of the empirical findings. The model illustrates how explicit learning could emerge due to a longer temporal window between stimuli which could potentially give insights into the mechanisms of sequence learning in variable RSI conditions. Full paper: pdf Centre for Cognitive Science |
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