31 found
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  1. Number as a cognitive technology: Evidence from Pirahã language and cognition.Michael C. Frank, Daniel L. Everett, Evelina Fedorenko & Edward Gibson - 2008 - Cognition 108 (3):819-824.
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  2.  78
    Modeling human performance in statistical word segmentation.Michael C. Frank, Sharon Goldwater, Thomas L. Griffiths & Joshua B. Tenenbaum - 2010 - Cognition 117 (2):107-125.
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  3.  41
    The length of words reflects their conceptual complexity.Molly L. Lewis & Michael C. Frank - 2016 - Cognition 153 (C):182-195.
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  4.  15
    An integrative account of constraints on cross-situational learning.Daniel Yurovsky & Michael C. Frank - 2015 - Cognition 145 (C):53-62.
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  5.  14
    The role of developmental change and linguistic experience in the mutual exclusivity effect.Molly Lewis, Veronica Cristiano, Brenden M. Lake, Tammy Kwan & Michael C. Frank - 2020 - Cognition 198 (C):104191.
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  6.  26
    Development of infants’ attention to faces during the first year.Michael C. Frank, Edward Vul & Scott P. Johnson - 2009 - Cognition 110 (2):160-170.
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  7.  17
    Pre-linguistic segmentation of speech into syllable-like units.Okko Räsänen, Gabriel Doyle & Michael C. Frank - 2018 - Cognition 171 (C):130-150.
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  8.  61
    Three ideal observer models for rule learning in simple languages.Michael C. Frank & Joshua B. Tenenbaum - 2011 - Cognition 120 (3):360-371.
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  9.  59
    A rose in any other font would not smell as sweet: Effects of perceptual fluency on categorization.Daniel M. Oppenheimer & Michael C. Frank - 2008 - Cognition 106 (3):1178-1194.
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  10.  15
    Moderated Online Data-Collection for Developmental Research: Methods and Replications.Aaron Chuey, Mika Asaba, Sophie Bridgers, Brandon Carrillo, Griffin Dietz, Teresa Garcia, Julia A. Leonard, Shari Liu, Megan Merrick, Samaher Radwan, Jessa Stegall, Natalia Velez, Brandon Woo, Yang Wu, Xi J. Zhou, Michael C. Frank & Hyowon Gweon - 2021 - Frontiers in Psychology 12.
    Online data collection methods are expanding the ease and access of developmental research for researchers and participants alike. While its popularity among developmental scientists has soared during the COVID-19 pandemic, its potential goes beyond just a means for safe, socially distanced data collection. In particular, advances in video conferencing software has enabled researchers to engage in face-to-face interactions with participants from nearly any location at any time. Due to the novelty of these methods, however, many researchers still remain uncertain about (...)
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  11.  21
    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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  12.  35
    From partners to populations: A hierarchical Bayesian account of coordination and convention.Robert D. Hawkins, Michael Franke, Michael C. Frank, Adele E. Goldberg, Kenny Smith, Thomas L. Griffiths & Noah D. Goodman - 2023 - Psychological Review 130 (4):977-1016.
  13.  66
    Zipfian frequency distributions facilitate word segmentation in context.Chigusa Kurumada, Stephan C. Meylan & Michael C. Frank - 2013 - Cognition 127 (3):439-453.
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  14.  6
    Relational reasoning and generalization using nonsymbolic neural networks.Atticus Geiger, Alexandra Carstensen, Michael C. Frank & Christopher Potts - 2023 - Psychological Review 130 (2):308-333.
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  15.  19
    Characterizing the Dynamics of Learning in Repeated Reference Games.Robert D. Hawkins, Michael C. Frank & Noah D. Goodman - 2020 - Cognitive Science 44 (6):e12845.
    The language we use over the course of conversation changes as we establish common ground and learn what our partner finds meaningful. Here we draw upon recent advances in natural language processing to provide a finer‐grained characterization of the dynamics of this learning process. We release an open corpus (>15,000 utterances) of extended dyadic interactions in a classic repeated reference game task where pairs of participants had to coordinate on how to refer to initially difficult‐to‐describe tangram stimuli. We find that (...)
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  16.  26
    The Interactions of Rational, Pragmatic Agents Lead to Efficient Language Structure and Use.Benjamin N. Peloquin, Noah D. Goodman & Michael C. Frank - 2020 - Topics in Cognitive Science 12 (1):433-445.
    Despite their diversity, human languages share consistent properties and regularities. Wherefrom does this consistency arise? And does it tell us something about the problem that all languages need to solve? The authors provide an intriguing analyses which focuses on the “communicative function of ambiguity” whose resolution entailed an equally intriguing “speaker–listener cross‐entropy objective for measuring the efficiency of linguistic systems from first principles of efficient language use.”.
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  17.  47
    Throwing out the Bayesian baby with the optimal bathwater: Response to Endress.Michael C. Frank - 2013 - Cognition 128 (3):417-423.
  18.  29
    The Role of Design and Training in Artifact Expertise: The Case of the Abacus and Visual Attention.Mahesh Srinivasan, Katie Wagner, Michael C. Frank & David Barner - 2018 - Cognitive Science 42 (S3):757-782.
    Previous accounts of how people develop expertise have focused on how deliberate practice transforms the cognitive and perceptual representations and processes that give rise to expertise. However, the likelihood of developing expertise with a particular tool may also depend on the degree to which that tool fits pre‐existing perceptual and cognitive abilities. The present studies explored whether the abacus—a descendent of the first human computing devices—may have evolved to exploit general biases in human visual attention, or whether developing expertise with (...)
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  19.  4
    Predicting Age of Acquisition for Children's Early Vocabulary in Five Languages Using Language Model Surprisal.Eva Portelance, Yuguang Duan, Michael C. Frank & Gary Lupyan - 2023 - Cognitive Science 47 (9):e13334.
    What makes a word easy to learn? Early‐learned words are frequent and tend to name concrete referents. But words typically do not occur in isolation. Some words are predictable from their contexts; others are less so. Here, we investigate whether predictability relates to when children start producing different words (age of acquisition; AoA). We operationalized predictability in terms of a word's surprisal in child‐directed speech, computed using n‐gram and long‐short‐term‐memory (LSTM) language models. Predictability derived from LSTMs was generally a better (...)
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  20. Informative communication in word production and word learning.Michael C. Frank, Noah D. Goodman, Peter Lai & Joshua B. Tenenbaum - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
     
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  21.  27
    Markers of Topical Discourse in Child‐Directed Speech.Hannah Rohde & Michael C. Frank - 2014 - Cognitive Science 38 (8):1634-1661.
    Although the language we encounter is typically embedded in rich discourse contexts, many existing models of processing focus largely on phenomena that occur sentence-internally. Similarly, most work on children's language learning does not consider how information can accumulate as a discourse progresses. Research in pragmatics, however, points to ways in which each subsequent utterance provides new opportunities for listeners to infer speaker meaning. Such inferences allow the listener to build up a representation of the speakers' intended topic and more generally (...)
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  22.  18
    Corrigendum to “Three ideal observer models for rule learning in simple languages” [Cognition 120 (3) (2011) 360–371].Michael C. Frank & Joshua B. Tenenbaum - 2014 - Cognition 132 (3):501.
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  23.  17
    Habituation Reflects Optimal Exploration Over Noisy Perceptual Samples.Anjie Cao, Gal Raz, Rebecca Saxe & Michael C. Frank - 2023 - Topics in Cognitive Science 15 (2):290-302.
    This paper presents the Rational Action, Noisy Choice for Habituation (RANCH) model. The model was evaluated with adult looking time collected from a paradigm analogous to the infant habituation paradigm. And the model captured key patterns of looking time documented in developmental research: habituation and dishabituation.
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  24.  9
    How optimal is word recognition under multimodal uncertainty?Abdellah Fourtassi & Michael C. Frank - 2020 - Cognition 199 (C):104092.
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  25.  17
    A Bayesian decision-making framework for replication.Tom E. Hardwicke, Michael Henry Tessler, Benjamin N. Peloquin & Michael C. Frank - 2018 - Behavioral and Brain Sciences 41.
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  26.  13
    Exploring Patterns of Stability and Change in Caregivers' Word Usage Across Early Childhood.Hang Jiang, Michael C. Frank, Vivek Kulkarni & Abdellah Fourtassi - 2022 - Cognitive Science 46 (7):e13177.
    The linguistic input children receive across early childhood plays a crucial role in shaping their knowledge about the world. To study this input, researchers have begun applying distributional semantic models to large corpora of child‐directed speech, extracting various patterns of word use/co‐occurrence. Previous work using these models has not measured how these patterns may change throughout development, however. In this work, we leverage natural language processing methods—originally developed to study historical language change—to compare caregivers' use of words when talking to (...)
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  27.  25
    Linguistic structure emerges through the interaction of memory constraints and communicative pressures.Molly L. Lewis & Michael C. Frank - 2016 - Behavioral and Brain Sciences 39.
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  28. Learning the Meanings of Function Words From Grounded Language Using a Visual Question Answering Model.Eva Portelance, Michael C. Frank & Dan Jurafsky - 2024 - Cognitive Science 48 (5):e13448.
    Interpreting a seemingly simple function word like “or,” “behind,” or “more” can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural‐network‐based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the (...)
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  29.  36
    Introducing a fund for open-access fees.Steven Sloman, Albert Kim, Jean-François Bonnefon, Johan Wagemans, Michael C. Frank, Jennifer E. Arnold, Gregory Murphy, Manos Tsakiris, Jacob Feldman, Stella F. Lourenco & Karen Wynn - 2016 - Cognition 154 (C):iii-iv.
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  30.  18
    Avoiding frostbite: It helps to learn from others.Michael Henry Tessler, Noah D. Goodman & Michael C. Frank - 2017 - Behavioral and Brain Sciences 40.
    Machines that learn and think like people must be able to learn from others. Social learning speeds up the learning process and – in combination with language – is a gateway to abstract and unobservable information. Social learning also facilitates the accumulation of knowledge across generations, helping people and artificial intelligences learn things that no individual could learn in a lifetime.
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  31.  16
    Improving the generalizability of infant psychological research: The ManyBabies model.Ingmar Visser, Christina Bergmann, Krista Byers-Heinlein, Rodrigo Dal Ben, Wlodzislaw Duch, Samuel Forbes, Laura Franchin, Michael C. Frank, Alessandra Geraci, J. Kiley Hamlin, Zsuzsa Kaldy, Louisa Kulke, Catherine Laverty, Casey Lew-Williams, Victoria Mateu, Julien Mayor, David Moreau, Iris Nomikou, Tobias Schuwerk, Elizabeth A. Simpson, Leher Singh, Melanie Soderstrom, Jessica Sullivan, Marion I. van den Heuvel, Gert Westermann, Yuki Yamada, Lorijn Zaadnoordijk & Martin Zettersten - 2022 - Behavioral and Brain Sciences 45.
    Yarkoni's analysis clearly articulates a number of concerns limiting the generalizability and explanatory power of psychological findings, many of which are compounded in infancy research. ManyBabies addresses these concerns via a radically collaborative, large-scale and open approach to research that is grounded in theory-building, committed to diversification, and focused on understanding sources of variation.
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