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Nonlinear neural network dynamics accounts for human confidence in a sequence of perceptual decisions
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In: ISSN: 2045-2322 ; EISSN: 2045-2322 ; Scientific Reports ; https://hal.archives-ouvertes.fr/hal-02138028 ; Scientific Reports, Nature Publishing Group, 2020 (2020)
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Modeling Language Change ; Modeling Language Change: The Pitfall of Grammaticalization
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In: Language in Complexity – The Emerging Meaning ; https://hal.archives-ouvertes.fr/hal-01481919 ; Language in Complexity – The Emerging Meaning, Springer, pp.49-72, 2017, 978-3-319-29481-0. ⟨10.1007/978-3-319-29483-4_3⟩ ; http://link.springer.com/chapter/10.1007%2F978-3-319-29483-4_3 (2017)
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Frequency patterns of semantic change: Corpus-based evidence of a near-critical dynamics in language change
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In: https://halshs.archives-ouvertes.fr/halshs-01483599 ; 2017 (2017)
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Frequency patterns of semantic change: Corpus-based evidence of a near-critical dynamics in language change ...
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Représentation du langage et modèles d'évolution linguistique : la grammaticalisation comme perspective
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In: ISSN: 1248-9433 ; EISSN: 1965-0906 ; Revue TAL ; https://halshs.archives-ouvertes.fr/halshs-01241353 ; Revue TAL, ATALA (Association pour le Traitement Automatique des Langues), 2015, 55 (3), pp.47-71 (2015)
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Perception of categories: from coding efficiency to reaction times
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In: ISSN: 0006-8993 ; EISSN: 0006-8993 ; Brain Research ; https://hal.archives-ouvertes.fr/hal-00569013 ; Brain Research, Elsevier, 2012, 1434, pp.47-61. ⟨10.1016/j.brainres.2011.08.014⟩ (2012)
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The acquisition of allophonic rules: statistical learning with linguistic constraints.
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In: ISSN: 0010-0277 ; EISSN: 1873-7838 ; Cognition ; https://hal.archives-ouvertes.fr/hal-00143852 ; Cognition, Elsevier, 2006, 101 (3), pp.B31-41. ⟨10.1016/j.cognition.2005.10.006⟩ (2006)
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Abstract:
International audience ; Phonological rules relate surface phonetic word forms to abstract underlying forms that are stored in the lexicon. Infants must thus acquire these rules in order to infer the abstract representation of words. We implement a statistical learning algorithm for the acquisition of one type of rule, namely allophony, which introduces context-sensitive phonetic variants of phonemes. This algorithm is based on the observation that different realizations of a single phoneme typically do not appear in the same contexts (ideally, they have complementary distributions). In particular, it measures the discrepancies in context probabilities for each pair of phonetic segments. In Experiment 1, we test the algorithm's performances on a pseudo-language and show that it is robust to statistical noise due to sampling and coding errors, and to non-systematic rule application. In Experiment 2, we show that a natural corpus of semiphonetically transcribed child-directed speech in French presents a very large number of near-complementary distributions that do not correspond to existing allophonic rules. These spurious allophonic rules can be eliminated by a linguistically motivated filtering mechanism based on a phonetic representation of segments. We discuss the role of a priori linguistic knowledge in the statistical learning of phonology.
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Keyword:
[SCCO.PSYC]Cognitive science/Psychology; [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences; MESH: Humans; MESH: Linguistics; MESH: Models; MESH: Phonetics; MESH: Verbal Learning; Statistical
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URL: https://doi.org/10.1016/j.cognition.2005.10.006
https://hal.archives-ouvertes.fr/hal-00143852
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