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  1. How to Make the Most out of Very Little.Charles Yang - 2020 - Topics in Cognitive Science 12 (1):136-152.
    Yang returns to the problem of referential ambiguity, addressed in the opening paper by Gleitman and Trueswell. Using a computational approach, he argues that “big data” approaches to resolving referential ambiguity are destined to fail, because of the inevitable computational explosion needed to keep track of contextual associations present when a word is uttered. Yang tests several computational models, two of which depend on one‐trial learning, as described in Gleitman and Trueswell’s paper. He concludes that such models outperform cross‐situational learning (...)
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  • A computational model of the cultural co-evolution of language and mindreading.Marieke Woensdregt, Chris Cummins & Kenny Smith - 2020 - Synthese 199 (1-2):1347-1385.
    Several evolutionary accounts of human social cognition posit that language has co-evolved with the sophisticated mindreading abilities of modern humans. It has also been argued that these mindreading abilities are the product of cultural, rather than biological, evolution. Taken together, these claims suggest that the evolution of language has played an important role in the cultural evolution of human social cognition. Here we present a new computational model which formalises the assumptions that underlie this hypothesis, in order to explore how (...)
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  • The Role of Saliency in Learning First Words.Eugenia Wildt, Katharina J. Rohlfing & Ingrid Scharlau - 2019 - Frontiers in Psychology 10.
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  • Explicit and implicit memory representations in cross-situational word learning.Felix Hao Wang - 2020 - Cognition 205 (C):104444.
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  • Being suspicious of suspicious coincidences: The case of learning subordinate word meanings.Felix Hao Wang & John Trueswell - 2022 - Cognition 224 (C):105028.
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  • The Pursuit of Word Meanings.Jon Scott Stevens, Lila R. Gleitman, John C. Trueswell & Charles Yang - 2017 - Cognitive Science 41 (S4):638-676.
    We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed Pursuit, uses an associative learning mechanism to estimate word-referent probability but pursues and tests the best referent-meaning at any given time. Pursuit is found to perform as well as global models under many conditions extracted from (...)
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  • Reference as an Interactive Achievement: Sequential and Longitudinal Analyses of Labeling Interactions in Shared Book Reading and Free Play.Vivien Heller & Katharina J. Rohlfing - 2017 - Frontiers in Psychology 8.
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  • Are Words Easier to Learn From Infant‐ Than Adult‐Directed Speech? A Quantitative Corpus‐Based Investigation.Adriana Guevara-Rukoz, Alejandrina Cristia, Bogdan Ludusan, Roland Thiollière, Andrew Martin, Reiko Mazuka & Emmanuel Dupoux - 2018 - Cognitive Science 42 (5):1586-1617.
    We investigate whether infant‐directed speech (IDS) could facilitate word form learning when compared to adult‐directed speech (ADS). To study this, we examine the distribution of word forms at two levels, acoustic and phonological, using a large database of spontaneous speech in Japanese. At the acoustic level we show that, as has been documented before for phonemes, the realizations of words are more variable and less discriminable inIDSthan inADS. At the phonological level, we find an effect in the opposite direction: TheIDSlexicon (...)
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  • Easy Words: Reference Resolution in a Malevolent Referent World.Lila R. Gleitman & John C. Trueswell - 2018 - Topics in Cognitive Science 12 (1):22-47.
    Gleitman and Trueswell’s “The easy words” forms a pair with their earlier paper, “Hard words,” completing a circle in which the authors ask how “easy” words (e.g., concrete nouns) are learned. They take up the hypothesis of “cross‐situational learning,” and argue that accumulating observations actually hinders learning if the mechanism requires holding all exemplars in memory over time. They present an alternative hypothesis, “Propose but Verify,” wherein people use one‐trial learning to confirm or disconfirm their current hypothesis—a mechanism distinctly different (...)
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  • Mapping Word to World in ASL: Evidence from a Human Simulation Paradigm.Allison Fitch, Sudha Arunachalam & Amy M. Lieberman - 2021 - Cognitive Science 45 (12):e13061.
    Across languages, children map words to meaning with great efficiency, despite a seemingly unconstrained space of potential mappings. The literature on how children do this is primarily limited to spoken language. This leaves a gap in our understanding of sign language acquisition, because several of the hypothesized mechanisms that children use are visual (e.g., visual attention to the referent), and sign languages are perceived in the visual modality. Here, we used the Human Simulation Paradigm in American Sign Language (ASL) to (...)
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  • Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner.Emmanuel Dupoux - 2018 - Cognition 173 (C):43-59.
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  • Do Backward Associations Have Anything to Say About Language?Thomas F. Chartier & Isabelle Dautriche - 2023 - Cognitive Science 47 (4):e13282.
    In this letter, we argue against a recurring idea that early word learning in infants is related to the low-level capacity for backward associations—a notion that suggests a cognitive gap with other animal species. Because backward associations entail the formation of bidirectional associations between sequentially perceived stimulus pairs, they seemingly mirror the label-referent bidirectional mental relations underlying the lexicon of natural language. This appealing but spurious resemblance has led to various speculations on language acquisition, in particular regarding early word learning, (...)
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  • Adjacent and Non‐Adjacent Word Contexts Both Predict Age of Acquisition of English Words: A Distributional Corpus Analysis of Child‐Directed Speech.Lucas M. Chang & Gedeon O. Deák - 2020 - Cognitive Science 44 (11):e12899.
    Children show a remarkable degree of consistency in learning some words earlier than others. What patterns of word usage predict variations among words in age of acquisition? We use distributional analysis of a naturalistic corpus of child‐directed speech to create quantitative features representing natural variability in word contexts. We evaluate two sets of features: One set is generated from the distribution of words into frames defined by the two adjacent words. These features primarily encode syntactic aspects of word usage. The (...)
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  • Consequences of phonological variation for algorithmic word segmentation.Caroline Beech & Daniel Swingley - 2023 - Cognition 235 (C):105401.
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  • A distributional perspective on the gavagai problem in early word learning.Richard N. Aslin & Alice F. Wang - 2021 - Cognition 213 (C):104680.
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