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data archive 161 disk0/00/00/04/49 2020-03-05 17:18:28 2020-03-05 17:32:13 2020-03-05 17:18:28 data_collection show Milin Petar P.Milin@bham.ac.uk 0000-0001-9708-7031 Harish Tayyar Madabushi H.TayyarMadabushi.1@bham.ac.uk 0000-0001-5260-3653 Croucher Michael michael.croucher@nag.co.uk Divjak Dagmar D.Divjak@bham.ac.uk 0000-0001-6825-8508 10col_artl Widrow-Hoff rule; Delta rule; Least Mean Square rule; Rescorla-Wagner rule; error-correction learning; adaptation; language In this paper we present the Widrow-Hoff rule and its applications to language data. After contextualizing the rule historically and placing it in the chain of neurally inspired artificial learning models, we explain its rationale and implementational considerations. Using a number of case studies we illustrate how the Widrow-Hoff rule offers unexpected opportunities for the computational simulation of a range of language phenomena that make it possible to approach old problems from a novel perspective. 2020 published University of Birmingham https://doi.org/10.25500/edata.bham.00000449 Dataset research-data@contacts.bham.ac.uk School of Languages, Cultures, Art History and Music, Department of Modern Languages School of Computer Science School of English, Drama and Creative Studies, Department of English Language and Linguistics lt RLA-2016-001 | Out of Our Minds: optimizing language learning with discriminative algorithms ru yes yes yes TRUE ds_other https://edata.bham.ac.uk/385/ en