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  1. Predictability and Variation in Language Are Differentially Affected by Learning and Production.Aislinn Keogh, Simon Kirby & Jennifer Culbertson - 2024 - Cognitive Science 48 (4):e13435.
    General principles of human cognition can help to explain why languages are more likely to have certain characteristics than others: structures that are difficult to process or produce will tend to be lost over time. One aspect of cognition that is implicated in language use is working memory—the component of short‐term memory used for temporary storage and manipulation of information. In this study, we consider the relationship between working memory and regularization of linguistic variation. Regularization is a well‐documented process whereby (...)
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  • The crosslinguistic acquisition of sentence structure: Computational modeling and grammaticality judgments from adult and child speakers of English, Japanese, Hindi, Hebrew and K'iche'.Ben Ambridge, Tomoko Tatsumi, Laura Doherty, Ramya Maitreyee, Colin Bannard, Soumitra Samanta, Stewart McCauley, Inbal Arnon, Shira Zicherman, Dani Bekman, Amir Efrati, Ruth Berman, Bhuvana Narasimhan, Dipti Misra Sharma, Rukmini Bhaya Nair, Kumiko Fukumura, Seth Campbell, Clifton Pye, Pedro Mateo Pedro, Sindy Fabiola Can Pixabaj, Mario Marroquín Pelíz & Margarita Julajuj Mendoza - 2020 - Cognition 202 (C):104310.
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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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  • Understanding the Phonetic Characteristics of Speech Under Uncertainty—Implications of the Representation of Linguistic Knowledge in Learning and Processing.Fabian Tomaschek & Michael Ramscar - 2022 - Frontiers in Psychology 13.
    The uncertainty associated with paradigmatic families has been shown to correlate with their phonetic characteristics in speech, suggesting that representations of complex sublexical relations between words are part of speaker knowledge. To better understand this, recent studies have used two-layer neural network models to examine the way paradigmatic uncertainty emerges in learning. However, to date this work has largely ignored the way choices about the representation of inflectional and grammatical functions in models strongly influence what they subsequently learn. To explore (...)
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  • The Keys to the Future? An Examination of Statistical Versus Discriminative Accounts of Serial Pattern Learning.Fabian Tomaschek, Michael Ramscar & Jessie S. Nixon - 2024 - Cognitive Science 48 (2):e13404.
    Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences—and the relations between the elements they comprise—are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the learning of sequences are rarely investigated. We present three experiments that seek to examine these mechanisms during a typing task. Experiments 1 and 2 tested learning during typing single letters on each trial. (...)
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  • Relationships Between Language Structure and Language Learning: The Suffixing Preference and Grammatical Categorization.Michelle C. St Clair, Padraic Monaghan & Michael Ramscar - 2009 - Cognitive Science 33 (7):1317-1329.
    It is a reasonable assumption that universal properties of natural languages are not accidental. They occur either because they are underwritten by genetic code, because they assist in language processing or language learning, or due to some combination of the two. In this paper we investigate one such language universal: the suffixing preference across the world’s languages, whereby inflections tend to be added to the end of words. A corpus analysis of child‐directed speech in English found that suffixes were more (...)
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  • Of mice and men: Speech sound acquisition as discriminative learning from prediction error, not just statistical tracking.Jessie S. Nixon - 2020 - Cognition 197 (C):104081.
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  • Prediction and error in early infant speech learning: A speech acquisition model.Jessie S. Nixon & Fabian Tomaschek - 2021 - Cognition 212 (C):104697.
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  • Towards cognitively plausible data science in language research.Petar Milin, Dagmar Divjak, Strahinja Dimitrijević & R. Harald Baayen - 2016 - Cognitive Linguistics 27 (4):507-526.
    Name der Zeitschrift: Cognitive Linguistics Jahrgang: 27 Heft: 4 Seiten: 507-526.
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  • Towards cognitively plausible data science in language research.Petar Milin, Dagmar Divjak, Strahinja Dimitrijević & R. Harald Baayen - 2016 - Cognitive Linguistics 27 (4):507-526.
    Over the past 10 years, Cognitive Linguistics has taken a quantitative turn. Yet, concerns have been raised that this preoccupation with quantification and modelling may not bring us any closer to understanding how language works. We show that this objection is unfounded, especially if we rely on modelling techniques based on biologically and psychologically plausible learning algorithms. These make it possible to take a quantitative approach, while generating and testing specific hypotheses that will advance our understanding of how knowledge of (...)
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  • The Effects of Feature-Label-Order and Their Implications for Symbolic Learning.Michael Ramscar, Daniel Yarlett, Melody Dye, Katie Denny & Kirsten Thorpe - 2010 - Cognitive Science 34 (6):909-957.
    Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning—in particular, word learning—in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features from a (...)
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  • Simulating the Acquisition of Verb Inflection in Typically Developing Children and Children With Developmental Language Disorder in English and Spanish.Daniel Freudenthal, Michael Ramscar, Laurence B. Leonard & Julian M. Pine - 2021 - Cognitive Science 45 (3):e12945.
    Children with developmental language disorder (DLD) have significant deficits in language ability that cannot be attributed to neurological damage, hearing impairment, or intellectual disability. The symptoms displayed by children with DLD differ across languages. In English, DLD is often marked by severe difficulties acquiring verb inflection. Such difficulties are less apparent in languages with rich verb morphology like Spanish and Italian. Here we show how these differential profiles can be understood in terms of an interaction between properties of the input (...)
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  • Developmental Changes in Cross‐Situational Word Learning: The Inverse Effect of Initial Accuracy.Stanka A. Fitneva & Morten H. Christiansen - 2017 - Cognitive Science 41 (S1):141-161.
    Intuitively, the accuracy of initial word-referent mappings should be positively correlated with the outcome of learning. Yet recent evidence suggests an inverse effect of initial accuracy in adults, whereby greater accuracy of initial mappings is associated with poorer outcomes in a cross-situational learning task. Here, we examine the impact of initial accuracy on 4-year-olds, 10-year-olds, and adults. For half of the participants most word-referent mappings were initially correct and for the other half most mappings were initially incorrect. Initial accuracy was (...)
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  • What makes the past perfect and the future progressive? Experiential coordinates for a learnable, context-based model of tense and aspect.Dagmar Divjak, Petar Milin, Adnane Ez-Zizi & Laurence Romain - 2022 - Cognitive Linguistics 33 (2):251-289.
    We examined how language supports the expression of temporality within sentence boundaries in English, which has a rich inventory of grammatical means to express temporality. Using a computational model that mimics how humans learn from exposure we explored what the use of different tense and aspect combinations reveals about the interaction between our experience of time and the cognitive demands that talking about time puts on the language user. Our model was trained on n-grams extracted from the BNC to select (...)
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  • The Role of Lexical Frequency in the Acceptability of Syntactic Variants: Evidence From that‐ Clauses in Polish.Dagmar Divjak - 2017 - Cognitive Science 41 (2):354-382.
    A number of studies report that frequency is a poor predictor of acceptability, in particular at the lower end of the frequency spectrum. Because acceptability judgments provide a substantial part of the empirical foundation of dominant linguistic traditions, understanding how acceptability relates to frequency, one of the most robust predictors of human performance, is crucial. The relation between low frequency and acceptability is investigated using corpus‐ and behavioral data on the distribution of infinitival and finite that‐complements in Polish. Polish verbs (...)
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  • The Other Side of Cognitive Control: Can a Lack of Cognitive Control Benefit Language and Cognition?Evangelia G. Chrysikou, Jared M. Novick, John C. Trueswell & Sharon L. Thompson-Schill - 2011 - Topics in Cognitive Science 3 (2):253-256.
    Cognitive control refers to the regulation of mental activity to support flexible cognition across different domains. Cragg and Nation (2010) propose that the development of cognitive control in children parallels the development of language abilities, particularly inner speech. We suggest that children’s late development of cognitive control also mirrors their limited ability to revise misinterpretations of sentence meaning. Moreover, we argue that for certain tasks, a tradeoff between bottom-up (data-driven) and top-down (rule-based) thinking may actually benefit performance in both children (...)
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  • When unsupervised training benefits category learning.Franziska Bröker, Bradley C. Love & Peter Dayan - 2022 - Cognition 221 (C):104984.
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  • The Discriminative Lexicon: A Unified Computational Model for the Lexicon and Lexical Processing in Comprehension and Production Grounded Not in Composition but in Linear Discriminative Learning.R. Harald Baayen, Yu-Ying Chuang, Elnaz Shafaei-Bajestan & James P. Blevins - 2019 - Complexity 2019:1-39.
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  • An amorphous model for morphological processing in visual comprehension based on naive discriminative learning.R. Harald Baayen, Petar Milin, Dusica Filipović Đurđević, Peter Hendrix & Marco Marelli - 2011 - Psychological Review 118 (3):438-481.
  • Direct Versus Indirect Causation as a Semantic Linguistic Universal: Using a Computational Model of English, Hebrew, Hindi, Japanese, and K'iche’ Mayan to Predict Grammaticality Judgments in Balinese.I. Nyoman Aryawibawa, Yana Qomariana, Ketut Artawa & Ben Ambridge - 2021 - Cognitive Science 45 (4):e12974.
    The aim of this study was to test the claim that languages universally employ morphosyntactic marking to differentiate events of more‐ versus less‐direct causation, preferring to mark them with less‐ and more‐ overt marking, respectively (e.g., Somebody broke the window vs. Somebody MADE the window break; *Somebody cried the boy vs. Somebody MADE the boy cry). To this end, we investigated whether a recent computational model which learns to predict speakers’ by‐verb relative preference for the two causatives in English, Hebrew, (...)
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  • Granularity and the acquisition of grammatical gender: How order-of-acquisition affects what gets learned.Inbal Arnon & Michael Ramscar - 2012 - Cognition 122 (3):292-305.
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  • How Do Children Restrict Their Linguistic Generalizations? An (Un‐)Grammaticality Judgment Study.Ben Ambridge - 2013 - Cognitive Science 37 (3):508-543.
    A paradox at the heart of language acquisition research is that, to achieve adult-like competence, children must acquire the ability to generalize verbs into non-attested structures, while avoiding utterances that are deemed ungrammatical by native speakers. For example, children must learn that, to denote the reversal of an action, un- can be added to many verbs, but not all (e.g., roll/unroll; close/*unclose). This study compared theoretical accounts of how this is done. Children aged 5–6 (N = 18), 9–10 (N = (...)
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  • Balancing information-structure and semantic constraints on construction choice: building a computational model of passive and passive-like constructions in Mandarin Chinese.Ben Ambridge & Li Liu - 2021 - Cognitive Linguistics 32 (3):349-388.
    A central tenet of cognitive linguistics is that adults’ knowledge of language consists of a structured inventory of constructions, including various two-argument constructions such as the active, the passive and “fronting” constructions. But how do speakers choose which construction to use for a particular utterance, given constraints such as discourse/information structure and the semantic fit between verb and construction? The goal of the present study was to build a computational model of this phenomenon for two-argument constructions in Mandarin. First, we (...)
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  • A Computational Learning Semantics for Inductive Empirical Knowledge.Kevin T. Kelly - 2014 - In Alexandru Baltag & Sonja Smets (eds.), Johan van Benthem on Logic and Information Dynamics. Springer International Publishing. pp. 289-337.
    This chapter presents a new semantics for inductive empirical knowledge. The epistemic agent is represented concretely as a learner who processes new inputs through time and who forms new beliefs from those inputs by means of a concrete, computable learning program. The agent’s belief state is represented hyper-intensionally as a set of time-indexed sentences. Knowledge is interpreted as avoidance of error in the limit and as having converged to true belief from the present time onward. Familiar topics are re-examined within (...)
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