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  1. Introducing Meta‐analysis in the Evaluation of Computational Models of Infant Language Development.María Andrea Cruz Blandón, Alejandrina Cristia & Okko Räsänen - 2023 - Cognitive Science 47 (7):e13307.
    Computational models of child language development can help us understand the cognitive underpinnings of the language learning process, which occurs along several linguistic levels at once (e.g., prosodic and phonological). However, in light of the replication crisis, modelers face the challenge of selecting representative and consolidated infant data. Thus, it is desirable to have evaluation methodologies that could account for robust empirical reference data, across multiple infant capabilities. Moreover, there is a need for practices that can compare developmental trajectories of (...)
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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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  • Fast mapping word meanings across trials: Young children forget all but their first guess.Athulya Aravind, Jill de Villiers, Amy Pace, Hannah Valentine, Roberta Golinkoff, Kathy Hirsh-Pasek, Aquiles Iglesias & Mary Sweig Wilson - 2018 - Cognition 177 (C):177-188.
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  • The Role of Feedback in the Statistical Learning of Language‐Like Regularities.Felicity F. Frinsel, Fabio Trecca & Morten H. Christiansen - 2024 - Cognitive Science 48 (3):e13419.
    In language learning, learners engage with their environment, incorporating cues from different sources. However, in lab‐based experiments, using artificial languages, many of the cues and features that are part of real‐world language learning are stripped away. In three experiments, we investigated the role of positive, negative, and mixed feedback on the gradual learning of language‐like statistical regularities within an active guessing game paradigm. In Experiment 1, participants received deterministic feedback (100%), whereas probabilistic feedback (i.e., 75% or 50%) was introduced in (...)
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  • Cross‐situational Learning From Ambiguous Egocentric Input Is a Continuous Process: Evidence Using the Human Simulation Paradigm.Yayun Zhang, Daniel Yurovsky & Chen Yu - 2021 - Cognitive Science 45 (7):e13010.
    Recent laboratory experiments have shown that both infant and adult learners can acquire word‐referent mappings using cross‐situational statistics. The vast majority of the work on this topic has used unfamiliar objects presented on neutral backgrounds as the visual contexts for word learning. However, these laboratory contexts are much different than the real‐world contexts in which learning occurs. Thus, the feasibility of generalizing cross‐situational learning beyond the laboratory is in question. Adapting the Human Simulation Paradigm, we conducted a series of experiments (...)
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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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  • Cross‐Situational Word Learning With Multimodal Neural Networks.Wai Keen Vong & Brenden M. Lake - 2022 - Cognitive Science 46 (4).
    Cognitive Science, Volume 46, Issue 4, April 2022.
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  • Cross-situational and ostensive word learning in children with and without autism spectrum disorder.Courtney E. Venker - 2019 - Cognition 183 (C):181-191.
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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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  • Cross‐Situational Learning of Phonologically Overlapping Words Across Degrees of Ambiguity.Karen E. Mulak, Haley A. Vlach & Paola Escudero - 2019 - Cognitive Science 43 (5):e12731.
    Cross‐situational word learning (XSWL) tasks present multiple words and candidate referents within a learning trial such that word–referent pairings can be inferred only across trials. Adults encode fine phonological detail when two words and candidate referents are presented in each learning trial (2 × 2 scenario; Escudero, Mulak, & Vlach, ). To test the relationship between XSWL task difficulty and phonological encoding, we examined XSWL of words differing by one vowel or consonant across degrees of within‐learning trial ambiguity (1 × (...)
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  • What Children with Developmental Language Disorder Teach Us About Cross‐Situational Word Learning.Karla K. McGregor, Erin Smolak, Michelle Jones, Jacob Oleson, Nichole Eden, Timothy Arbisi-Kelm & Ronald Pomper - 2022 - Cognitive Science 46 (2):e13094.
    Children with developmental language disorder (DLD) served as a test case for determining the role of extant vocabulary knowledge, endogenous attention, and phonological working memory abilities in cross-situational word learning. First-graders (Mage = 7 years; 3 months), 44 with typical development (TD) and 28 with DLD, completed a cross-situational word-learning task comprised six cycles, followed by retention tests and independent assessments of attention, memory, and vocabulary. Children with DLD scored lower than those with TD on all measures of learning and (...)
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  • What Children with Developmental Language Disorder Teach Us About Cross‐Situational Word Learning.Karla K. McGregor, Erin Smolak, Michelle Jones, Jacob Oleson, Nichole Eden, Timothy Arbisi-Kelm & Ronald Pomper - 2022 - Cognitive Science 46 (2):e13094.
    Cognitive Science, Volume 46, Issue 2, February 2022.
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  • Explicit but Not Implicit Memory Predicts Ultimate Attainment in the Native Language.Miquel Llompart & Ewa Dąbrowska - 2020 - Frontiers in Psychology 11.
    The present paper examines the relationship between explicit and implicit memory and ultimate attainment in the native language. Two groups of native speakers of English with different levels of academic attainment (i.e., high vs. low) took part in three language tasks which assessed grammar, vocabulary and collocational knowledge, as well as phonological short-term memory (assessed using a forward digit-span task), explicit associative memory (assessed using a paired-associates task) and implicit memory (assessed using a deterministic serial reaction time task). Results revealed (...)
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  • Goldilocks Forgetting in Cross-Situational Learning.Paul Ibbotson, Diana G. López & Alan J. McKane - 2018 - Frontiers in Psychology 9:387015.
    Given that there is referential uncertainty (noise) when learning words, to what extent can forgetting filter some of that noise out, and be an aid to learning? Using a Cross Situational Learning model we find a U-shaped function of errors indicative of a “Goldilocks” zone of forgetting: an optimum store-loss ratio that is neither too aggressive or too weak, but just the right amount to produce better learning outcomes. Forgetting acts as a high-pass filter that actively deletes (part of) the (...)
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  • Cross-situational learning in a Zipfian environment.Andrew T. Hendrickson & Amy Perfors - 2019 - Cognition 189 (C):11-22.
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  • Comparing cross-situational word learning, retention, and generalisation in children with autism and typical development.Calum Hartley, Laura-Ashleigh Bird & Padraic Monaghan - 2020 - Cognition 200 (C):104265.
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  • Prediction error boosts retention of novel words in adults but not in children.Chiara Gambi, Martin J. Pickering & Hugh Rabagliati - 2021 - Cognition 211 (C):104650.
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  • The Growth of Children's Semantic and Phonological Networks: Insight From 10 Languages.Abdellah Fourtassi, Yuan Bian & Michael C. Frank - 2020 - Cognitive Science 44 (7):e12847.
    Children tend to produce words earlier when they are connected to a variety of other words along the phonological and semantic dimensions. Though these semantic and phonological connectivity effects have been extensively documented, little is known about their underlying developmental mechanism. One possibility is that learning is driven by lexical network growth where highly connected words in the child's early lexicon enable learning of similar words. Another possibility is that learning is driven by highly connected words in the external learning (...)
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  • How optimal is word recognition under multimodal uncertainty?Abdellah Fourtassi & Michael C. Frank - 2020 - Cognition 199 (C):104092.
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