7,429 research outputs found
Data from Culbertson et al. (2019) Children’s sensitivity to phonological and semantic cues during noun class learning
No full textData from Culbertson, J., Jarvinen, H., Haggarty, F., and Smith, K. (2019). Children’s sensitivity to phonological and semantic cues during noun class learning: Evidence for a phonological bias. Language, 95(2):268–293
Data from Culbertson et al. (2019) Children’s sensitivity to phonological and semantic cues during noun class learning
No full textData from Culbertson, J., Jarvinen, H., Haggarty, F., and Smith, K. (2019). Children’s sensitivity to phonological and semantic cues during noun class learning: Evidence for a phonological bias. Language, 95(2):268–293.Culbertson, Jennifer; Smith, Kenny. (2019). Data from Culbertson et al. (2019) Children’s sensitivity to phonological and semantic cues during noun class learning, [text]. University of Edinburgh. https://doi.org/10.7488/ds/2641
Saldana & Culbertson CogSci 2023 - Supplementary Materials
No full textData analysis to accompany Saldana, C., & Culbertson, J. (2023). Speakers' cognitive representations of gender and number morphology shape cross-linguistic tendencies in morpheme order. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 45, No. 45). Open access at https://escholarship.org/uc/item/4bj698b
Saldana & Culbertson CogSci 2023 - Supplementary Materials
No full textData analysis to accompany Saldana, C., & Culbertson, J. (2023). Speakers' cognitive representations of gender and number morphology shape cross-linguistic tendencies in morpheme order. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 45, No. 45). Open access at https://escholarship.org/uc/item/4bj698b
Jennifer-Angoh/Pine-Marten-Rodent: v.1.0.0
No full text<p>Code for article "How do microtine rodent abundance, snow and landscape parameters influence pine marten Martes martes population dynamics?". Authors: Siow Yan Jennifer Angoh Affiliation: Inland Norway University of Applied Sciences, NO-2480, Koppang, Norway. Corresponding author: Siow Yan Jennifer Angoh, [email protected], ORCID: <a href="https://orcid.org/0000-0002-3791-0150">https://orcid.org/0000-0002-3791-0150</a></p>
<p><strong>Full Changelog</strong>: <a href="https://github.com/Jennifer-Angoh/Pine-Marten-Rodent/commits/Pine-Marten-Rodent">https://github.com/Jennifer-Angoh/Pine-Marten-Rodent/commits/Pine-Marten-Rodent</a></p>
Data from "Language learners privilege structured meaning over surface frequency"
No full textAlthough it is widely agreed that learning the syntax of natural languages involves acquiring structure-dependent rules, recent work on acquisition has nevertheless attempted to characterize the outcome of learning primarily in terms of statistical generalizations about surface distributional information. In this paper we investigate whether surface statistical knowledge or structural knowledge of English is used to infer properties of a novel language under conditions of impoverished input. We expose learners to artificial-language patterns that are equally consistent with two possible underlying grammars—one more similar to English in terms of the linear ordering of words, the other more similar on abstract structural grounds. We show that learners’ grammatical inferences overwhelmingly favor structural similarity over preservation of superficial order. Importantly, the relevant shared structure can be characterized in terms of a universal preference for isomorphism in the mapping from meanings to utterances. Whereas previous empirical support for this universal has been based entirely on data from cross-linguistic language samples, our results suggest it may reflect a deep property of the human cognitive system—a property that, together with other structure-sensitive principles, constrains the acquisition of linguistic knowledge
Jennifer-Angoh/Pine-Marten-Rodent: v.1.0.1
No full text<p>Code for article "How do microtine rodent abundance, snow and landscape parameters influence pine marten Martes martes population dynamics?". Authors: Siow Yan Jennifer Angoh Affiliation: Inland Norway University of Applied Sciences, NO-2480, Koppang, Norway. Corresponding author: Siow Yan Jennifer Angoh, [email protected], ORCID: <a href="https://orcid.org/0000-0002-3791-0150">https://orcid.org/0000-0002-3791-0150</a></p>
<p><strong>Full Changelog</strong>: <a href="https://github.com/Jennifer-Angoh/Pine-Marten-Rodent/commits/Pine-Marten-Rodent">https://github.com/Jennifer-Angoh/Pine-Marten-Rodent/commits/Pine-Marten-Rodent</a></p>
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