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Abstract. In this paper, we investigate the application of adaptive ensemble models of Extreme Learning Machines (ELMs) to the problem of one-step ahead prediction in (non)stationary time series. We verify that the method works on stationary time series and test the adaptivity of the ensemble model on a nonstationary time series. In the experiments, we show that the adaptive ensemble model achieves a test error comparable to the best methods, while keeping adaptivity. Moreover, it has low computational cost.

Introduction When listening to a narrative, the verbal expressions translate into meanings and flow of mental imagery. However, the same narrative can be heard quite differently based on differences in listeners' previous experiences and knowledge. We capitalized on such differences to disclose brain regions that support transformation of narrative into individualized propositional meanings and associated mental imagery by analyzing brain activity associated with behaviorally assessed individual meanings elicited by a narrative. Methods Sixteen right‐handed female subjects were instructed to list words that best described what had come to their minds while listening to an eight‐minute narrative during functional magnetic resonance imaging (fMRI). The fMRI data were analyzed by calculating voxel‐wise intersubject correlation (ISC) values. We used latent semantic analysis (LSA) enhanced with Wordnet knowledge to measure semantic similarity of the produced words between subjects. Finally, we predicted the ISC with the semantic similarity using representational similarity analysis. Results We found that semantic similarity in these word listings between subjects, estimated using LSA combined with WordNet knowledge, predicting similarities in brain hemodynamic activity. Subject pairs whose individual semantics were similar also exhibited similar brain activity in the bilateral supramarginal and angular gyrus of the inferior parietal lobe, and in the occipital pole. Conclusions Our results demonstrate, using a novel method to measure interindividual differences in semantics, brain mechanisms giving rise to semantics and associated imagery during narrative listening. During listening to a captivating narrative, the inferior parietal lobe and early visual cortical areas seem, thus, to support elicitation of individual meanings and flow of mental imagery.