Deep daxes: mutual exclusivity arises through both learning biases and pragmatic strategies in neural networks

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• dc.contributor.author Gulordava, Kristina
• dc.contributor.author Brochhagen, Thomas
• dc.contributor.author Boleda, Gemma
• dc.date.accessioned 2021-09-27T11:34:35Z
• dc.date.available 2021-09-27T11:34:35Z
• dc.date.issued 2020
• dc.description.abstract Children’s tendency to associate novel words with novel referents has been taken to reflect a bias toward mutual exclusivity. This tendency may be advantageous both as (1) an ad-hoc referent selection heuristic to single out referents lacking a label and as (2) an organizing principle of lexical acquisition. This paper investigates under which circumstances cross-situational neural models can come to exhibit analogous behavior to children, focusing on these two possibilities and their interaction. To this end, we evaluate neural networks’ on both symbolic data and, as a first, on large-scale image data. We find that constraints in both learning and selection can foster mutual exclusivity, as long as they put words in competition for lexical meaning. For computational models, these findings clarify the role of available options for better performance in tasks where mutual exclusivity is advantageous. For cognitive research, they highlight latent interactions between word learning, referent selection mechanisms, and the structure of stimuli of varying complexity: symbolic and visual
• dc.description.sponsorship This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 715154), and from the Spanish Ramon´y Cajal programme (grant RYC-2015-18907). We thankfully acknowledge the computer resources at CTE-POWER and the technical support provided by Barcelona Supercomputing Center (RES-IM-2019-3-0006). We are grateful to the NVIDIA Corporation for the donation of GPUs used for this research.
• dc.format.mimetype application/pdf
• dc.identifier.citation Gulordava K, Brochhagen T, Boleda G. Deep daxes: mutual exclusivity arises through both learning biases and pragmatic strategies in neural networks. In: Denison S, Mack M, Xu Y, Armstrong BC. Proceedings for the 42nd Annual Meeting of the Cognitive Science Society; 2020 Jul 29 - 1 Aug. [S.l]: Cognitive Science Society, 2020. p. 2089-95.
• dc.identifier.uri http://hdl.handle.net/10230/48508
• dc.language.iso eng
• dc.publisher Cognitive Science Society
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/715154
• dc.rights ©2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0
• dc.subject.keyword Neural networks
• dc.subject.keyword Mutual exclusivity
• dc.subject.keyword Acquisition
• dc.subject.keyword Pragmatics
• dc.subject.keyword Learning biases
• dc.subject.keyword Lexical meaning
• dc.subject.keyword Referent selection
• dc.title Deep daxes: mutual exclusivity arises through both learning biases and pragmatic strategies in neural networks
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion