dc.contributor.author | Magnuson, James S. | |
dc.contributor.author | Crinnion, Anne Marie | |
dc.contributor.author | Luthra, Sahil | |
dc.contributor.author | Gaston, Phoebe | |
dc.contributor.author | Grubb, Samantha | |
dc.date | 2024-11-07 | |
dc.date.accessioned | 2024-07-02T12:48:38Z | |
dc.date.available | 2024-07-02T12:48:38Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Magnuson, J.S., Crinnion, A.M., Luthra, S., Gaston, P., & Grubb, S. (2024). Contra assertions, feedback improves word recognition: How feedback and lateral inhibition sharpen signals over noise. Cognition, 242:105661. Doi:10.1016/j.cognition.2023.105661 | es_ES |
dc.identifier.citation | Cognition | |
dc.identifier.issn | 0010-0277 | |
dc.identifier.uri | http://hdl.handle.net/10810/68737 | |
dc.description | Available online 7 November 2023 | es_ES |
dc.description.abstract | Whether top-down feedback modulates perception has deep implications for cognitive theories. Debate has been vigorous in the domain of spoken word recognition, where competing computational models and agreement on at least one diagnostic experimental paradigm suggest that the debate may eventually be resolvable. Norris and Cutler (2021) revisit arguments against lexical feedback in spoken word recognition models. They also incorrectly claim that recent computational demonstrations that feedback promotes accuracy and speed under noise (Magnuson et al., 2018) were due to the use of the Luce choice rule rather than adding noise to inputs (noise was in fact added directly to inputs). They also claim that feedback cannot improve word recognition because feedback cannot distinguish signal from noise. We have two goals in this paper. First, we correct the record about the simulations of Magnuson et al. (2018). Second, we explain how interactive activation models selectively sharpen signals via joint effects of feedback and lateral inhibition that boost lexically-coherent sublexical patterns over noise. We also review a growing body of behavioral and neural results consistent with feedback and inconsistent with autonomous (non-feedback) architectures, and conclude that parsimony supports feedback. We close by discussing the potential for synergy between autonomous and interactive approaches. | es_ES |
dc.description.sponsorship | This research was supported in part by U.S. National Science Foundation grants BCS-PAC 1754284, BCS-PAC 2043903, and NRT 1747486 (PI: JSM). This research was also supported in part by the Basque Government, Spain through the BERC 2022-2025 program and by the Spanish State Research Agency, Spain through BCBL Severo Ochoa excellence accreditation CEX2020-001010-S and through project PID2020-119131GB-I00 (BLIS). SL was supported by an National Science Foundation, USA Graduate Research Fellowship and by NIH, USA NRSA 1F32DC020625-01. AMC and PG were supported by NIH, USA T32 DC017703 (E. Myers and I.-M. Eigsti, PIs). We thank Arty Samuel, Jay McClelland, Thomas Hannagan, and Rachel Theodore for comments that improved this work. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | ELSEVIER | es_ES |
dc.relation | info:eu-repo/grantAgreement/GV/BERC2022-2025 | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/CEX2020-001010-S | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/PID2020-119131GB-I00 | es_ES |
dc.rights | info:eu-repo/semantics/embargoedAccess | es_ES |
dc.subject | spoken word recognition | es_ES |
dc.subject | Computational models | es_ES |
dc.subject | Language processing | es_ES |
dc.subject | Neural Networks | es_ES |
dc.title | Contra assertions, feedback improves word recognition: How feedback and lateral inhibition sharpen signals over noise | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2023 Elsevier B.V. All rights reserved. | es_ES |
dc.relation.publisherversion | https://www.sciencedirect.com/journal/cognition | es_ES |
dc.identifier.doi | 10.1016/j.cognition.2023.105661 | |