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- W4246170238 abstract "Humans learn from statistical regularities in the environment. There has been increasing interest in recent decades regarding the mechanisms underlying such learning. We used Error-Driven Learning (EDL) to simulate participants’ trial-by-trial learning during exposure to a bimodal distribution of non-native lexical tones. This gave us estimates of the degree of expectation of upcoming stimuli over the course of the experiment. The expectation estimates were combined with Temporal Response Function fitting to generate a prediction of the trial-by-trial ERP waveform. The expectation estimates of certain EDL simulations were significantly more informative about the ERP signal than discrete stimulus conditions or overall stimulus probability. These results show that trial-by-trial changes in neural activity can be captured by EDL estimates of expectation. Hence, we tentatively conclude that learning of statistical regularities, as reflected in neural activity, involves continual updating of expectations depending on error-driven learning from cues in the environment." @default.
- W4246170238 created "2022-05-12" @default.
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- W4246170238 date "2021-10-14" @default.
- W4246170238 modified "2023-09-29" @default.
- W4246170238 title "Temporal response modelling uncovers electrophysiological correlates of trial-by-trial error-driven learning" @default.
- W4246170238 doi "https://doi.org/10.31234/osf.io/dg5mw" @default.
- W4246170238 hasPublicationYear "2021" @default.
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