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Turk-Browne NB, Aslin RN. Infant neuroscience: how to measure brain activity in the youngest minds. Trends Neurosci 2024; 47:338-354. [PMID: 38570212 DOI: 10.1016/j.tins.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 01/08/2024] [Accepted: 02/09/2024] [Indexed: 04/05/2024]
Abstract
The functional properties of the infant brain are poorly understood. Recent advances in cognitive neuroscience are opening new avenues for measuring brain activity in human infants. These include novel uses of existing technologies such as electroencephalography (EEG) and magnetoencephalography (MEG), the availability of newer technologies including functional near-infrared spectroscopy (fNIRS) and optically pumped magnetometry (OPM), and innovative applications of functional magnetic resonance imaging (fMRI) in awake infants during cognitive tasks. In this review article we catalog these available non-invasive methods, discuss the challenges and opportunities encountered when applying them to human infants, and highlight the potential they may ultimately hold for advancing our understanding of the youngest minds.
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Affiliation(s)
- Nicholas B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT 06520, USA; Wu Tsai Institute, Yale University, New Haven, CT 06510, USA.
| | - Richard N Aslin
- Department of Psychology, Yale University, New Haven, CT 06520, USA; Child Study Center, Yale School of Medicine, New Haven, CT 06520, USA
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2
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Hosseini EA, Schrimpf M, Zhang Y, Bowman S, Zaslavsky N, Fedorenko E. Artificial Neural Network Language Models Predict Human Brain Responses to Language Even After a Developmentally Realistic Amount of Training. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2024; 5:43-63. [PMID: 38645622 PMCID: PMC11025646 DOI: 10.1162/nol_a_00137] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 01/09/2024] [Indexed: 04/23/2024]
Abstract
Artificial neural networks have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans during language learning. Here, we use two complementary approaches to ask how the models' ability to capture human fMRI responses to sentences is affected by the amount of training data. First, we evaluate GPT-2 models trained on 1 million, 10 million, 100 million, or 1 billion words against an fMRI benchmark. We consider the 100-million-word model to be developmentally plausible in terms of the amount of training data given that this amount is similar to what children are estimated to be exposed to during the first 10 years of life. Second, we test the performance of a GPT-2 model trained on a 9-billion-token dataset to reach state-of-the-art next-word prediction performance on the human benchmark at different stages during training. Across both approaches, we find that (i) the models trained on a developmentally plausible amount of data already achieve near-maximal performance in capturing fMRI responses to sentences. Further, (ii) lower perplexity-a measure of next-word prediction performance-is associated with stronger alignment with human data, suggesting that models that have received enough training to achieve sufficiently high next-word prediction performance also acquire representations of sentences that are predictive of human fMRI responses. In tandem, these findings establish that although some training is necessary for the models' predictive ability, a developmentally realistic amount of training (∼100 million words) may suffice.
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Affiliation(s)
- Eghbal A. Hosseini
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Martin Schrimpf
- The MIT Quest for Intelligence Initiative, Cambridge, MA, USA
- Swiss Federal Institute of Technology, Lausanne, Switzerland
| | - Yian Zhang
- Computer Science Department, Stanford University, Stanford, CA, USA
| | - Samuel Bowman
- Center for Data Science, New York University, New York, NY, USA
- Department of Linguistics, New York University, New York, NY, USA
- Department of Computer Science, New York University, New York, NY, USA
| | - Noga Zaslavsky
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Language Science, University of California, Irvine, CA, USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- The MIT Quest for Intelligence Initiative, Cambridge, MA, USA
- Speech and Hearing Bioscience and Technology Program, Harvard University, Boston, MA, USA
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3
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Lavechin M, de Seyssel M, Métais M, Metze F, Mohamed A, Bredin H, Dupoux E, Cristia A. Modeling early phonetic acquisition from child-centered audio data. Cognition 2024; 245:105734. [PMID: 38335906 DOI: 10.1016/j.cognition.2024.105734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 12/29/2023] [Accepted: 01/22/2024] [Indexed: 02/12/2024]
Abstract
Infants learn their native language(s) at an amazing speed. Before they even talk, their perception adapts to the language(s) they hear. However, the mechanisms responsible for this perceptual attunement and the circumstances in which it takes place remain unclear. This paper presents the first attempt to study perceptual attunement using ecological child-centered audio data. We show that a simple prediction algorithm exhibits perceptual attunement when applied on unrealistic clean audio-book data, but fails to do so when applied on ecologically-valid child-centered data. In the latter scenario, perceptual attunement only emerges when the prediction mechanism is supplemented with inductive biases that force the algorithm to focus exclusively on speech segments while learning speaker-, pitch-, and room-invariant representations. We argue these biases are plausible given previous research on infants and non-human animals. More generally, we show that what our model learns and how it develops through exposure to speech depends exquisitely on the details of the input signal. By doing so, we illustrate the importance of considering ecologically valid input data when modeling language acquisition.
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Affiliation(s)
- Marvin Lavechin
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France; Cognitive Machine Learning Team, INRIA, Paris, France; Meta AI Research, Paris, France.
| | - Maureen de Seyssel
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France; Cognitive Machine Learning Team, INRIA, Paris, France; Laboratoire de linguistique formelle, Université de Paris, CNRS, Paris, France
| | - Marianne Métais
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France; Cognitive Machine Learning Team, INRIA, Paris, France
| | | | | | - Hervé Bredin
- Institut de Recherche en Informatique de Toulouse, Université de Toulouse, CNRS, Toulouse, France
| | - Emmanuel Dupoux
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France; Cognitive Machine Learning Team, INRIA, Paris, France; Meta AI Research, Paris, France
| | - Alejandrina Cristia
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France; Cognitive Machine Learning Team, INRIA, Paris, France
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4
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Swingley D, Algayres R. Computational Modeling of the Segmentation of Sentence Stimuli From an Infant Word-Finding Study. Cogn Sci 2024; 48:e13427. [PMID: 38528789 DOI: 10.1111/cogs.13427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 02/22/2024] [Accepted: 02/24/2024] [Indexed: 03/27/2024]
Abstract
Computational models of infant word-finding typically operate over transcriptions of infant-directed speech corpora. It is now possible to test models of word segmentation on speech materials, rather than transcriptions of speech. We propose that such modeling efforts be conducted over the speech of the experimental stimuli used in studies measuring infants' capacity for learning from spoken sentences. Correspondence with infant outcomes in such experiments is an appropriate benchmark for models of infants. We demonstrate such an analysis by applying the DP-Parser model of Algayres and colleagues to auditory stimuli used in infant psycholinguistic experiments by Pelucchi and colleagues. The DP-Parser model takes speech as input, and creates multiple overlapping embeddings from each utterance. Prospective words are identified as clusters of similar embedded segments. This allows segmentation of each utterance into possible words, using a dynamic programming method that maximizes the frequency of constituent segments. We show that DP-Parse mimics American English learners' performance in extracting words from Italian sentences, favoring the segmentation of words with high syllabic transitional probability. This kind of computational analysis over actual stimuli from infant experiments may be helpful in tuning future models to match human performance.
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5
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Vong WK, Wang W, Orhan AE, Lake BM. Grounded language acquisition through the eyes and ears of a single child. Science 2024; 383:504-511. [PMID: 38300999 DOI: 10.1126/science.adi1374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 12/31/2023] [Indexed: 02/03/2024]
Abstract
Starting around 6 to 9 months of age, children begin acquiring their first words, linking spoken words to their visual counterparts. How much of this knowledge is learnable from sensory input with relatively generic learning mechanisms, and how much requires stronger inductive biases? Using longitudinal head-mounted camera recordings from one child aged 6 to 25 months, we trained a relatively generic neural network on 61 hours of correlated visual-linguistic data streams, learning feature-based representations and cross-modal associations. Our model acquires many word-referent mappings present in the child's everyday experience, enables zero-shot generalization to new visual referents, and aligns its visual and linguistic conceptual systems. These results show how critical aspects of grounded word meaning are learnable through joint representation and associative learning from one child's input.
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Affiliation(s)
- Wai Keen Vong
- Center for Data Science, New York University, New York, NY, USA
| | - Wentao Wang
- Center for Data Science, New York University, New York, NY, USA
| | - A Emin Orhan
- Center for Data Science, New York University, New York, NY, USA
| | - Brenden M Lake
- Center for Data Science, New York University, New York, NY, USA
- Department of Psychology, New York University, New York, NY, USA
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6
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Meylan SC, Foushee R, Wong NH, Bergelson E, Levy RP. How adults understand what young children say. Nat Hum Behav 2023; 7:2111-2125. [PMID: 37884678 PMCID: PMC11033618 DOI: 10.1038/s41562-023-01698-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 08/11/2023] [Indexed: 10/28/2023]
Abstract
Children's early speech often bears little resemblance to that of adults, and yet parents and other caregivers are able to interpret that speech and react accordingly. Here we investigate how adult listeners' inferences reflect sophisticated beliefs about what children are trying to communicate, as well as how children are likely to pronounce words. Using a Bayesian framework for modelling spoken word recognition, we find that computational models can replicate adult interpretations of children's speech only when they include strong, context-specific prior expectations about the messages that children will want to communicate. This points to a critical role of adult cognitive processes in supporting early communication and reveals how children can actively prompt adults to take actions on their behalf even when they have only a nascent understanding of the adult language. We discuss the wide-ranging implications of the powerful listening capabilities of adults for theories of first language acquisition.
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Affiliation(s)
- Stephan C Meylan
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA.
| | - Ruthe Foushee
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Nicole H Wong
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Elika Bergelson
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - Roger P Levy
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
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7
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Qiao H, Zhao A. Artificial intelligence-based language learning: illuminating the impact on speaking skills and self-regulation in Chinese EFL context. Front Psychol 2023; 14:1255594. [PMID: 38022973 PMCID: PMC10652775 DOI: 10.3389/fpsyg.2023.1255594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 10/11/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction This study investigated the effectiveness of artificial intelligence-based instruction in improving second language (L2) speaking skills and speaking self-regulation in a natural setting. The research was conducted with 93 Chinese English as a foreign language (EFL) students, randomly assigned to either an experimental group receiving AI-based instruction or a control group receiving traditional instruction. Methods The AI-based instruction leveraged the Duolingo application, incorporating natural language processing technology, interactive exercises, personalized feedback, and speech recognition technology. Pre- and post-tests were conducted to assess L2 speaking skills and self-regulation abilities. Results The results of the study demonstrated that the experimental group, which received AI-based instruction, exhibited significantly greater improvement in L2 speaking skills compared to the control group. Moreover, participants in the experimental group reported higher levels of self-regulation. Discussion These findings suggest that AI-based instruction effectively enhances L2 speaking skills and fosters self-regulatory processes among language learners, highlighting the potential of AI technology to optimize language learning experiences and promote learners' autonomy and metacognitive strategies in the speaking domain. However, further research is needed to explore the long-term effects and specific mechanisms underlying these observed improvements.
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Affiliation(s)
- Hongliang Qiao
- School of Foreign Languages, Northeast Petroleum University, Daqing, China
| | - Aruna Zhao
- Department of Foreign Language, Baotou Teachers’ College, Inner Mongolia University of Science and Technology, Baotou, China
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8
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de Seyssel M, Lavechin M, Dupoux E. Realistic and broad-scope learning simulations: first results and challenges. JOURNAL OF CHILD LANGUAGE 2023; 50:1294-1317. [PMID: 37246513 DOI: 10.1017/s0305000923000272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
There is a current 'theory crisis' in language acquisition research, resulting from fragmentation both at the level of the approaches and the linguistic level studied. We identify a need for integrative approaches that go beyond these limitations, and propose to analyse the strengths and weaknesses of current theoretical approaches of language acquisition. In particular, we advocate that language learning simulations, if they integrate realistic input and multiple levels of language, have the potential to contribute significantly to our understanding of language acquisition. We then review recent results obtained through such language learning simulations. Finally, we propose some guidelines for the community to build better simulations.
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Affiliation(s)
- Maureen de Seyssel
- Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Études Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France
- Laboratoire de Linguistique Formelle, Université Paris Cité, CNRS, Paris, France
| | - Marvin Lavechin
- Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Études Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France
- Meta AI Research, Paris, France
| | - Emmanuel Dupoux
- Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Études Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France
- Meta AI Research, Paris, France
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9
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Frank MC. Bridging the data gap between children and large language models. Trends Cogn Sci 2023; 27:990-992. [PMID: 37659919 DOI: 10.1016/j.tics.2023.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/04/2023]
Abstract
Large language models (LLMs) show intriguing emergent behaviors, yet they receive around four or five orders of magnitude more language data than human children. What accounts for this vast difference in sample efficiency? Candidate explanations include children's pre-existing conceptual knowledge, their use of multimodal grounding, and the interactive, social nature of their input.
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Affiliation(s)
- Michael C Frank
- Department of Psychology, Stanford University, 450 Jane Stanford Way, Stanford, CA 94305, USA.
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10
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Cruz Blandón MA, Cristia A, Räsänen O. Introducing Meta-analysis in the Evaluation of Computational Models of Infant Language Development. Cogn Sci 2023; 47:e13307. [PMID: 37395673 DOI: 10.1111/cogs.13307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 07/04/2023]
Abstract
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 infants to those of models as a function of language experience and development. The present study aims to take concrete steps to address these needs by introducing the concept of comparing models with large-scale cumulative empirical data from infants, as quantified by meta-analyses conducted across a large number of individual behavioral studies. We formalize the connection between measurable model and human behavior, and then present a conceptual framework for meta-analytic evaluation of computational models. We exemplify the meta-analytic model evaluation approach with two modeling experiments on infant-directed speech preference and native/non-native vowel discrimination.
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Affiliation(s)
- María Andrea Cruz Blandón
- Unit of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University
| | | | - Okko Räsänen
- Unit of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University
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11
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Nikolaus M, Fourtassi A. Communicative Feedback in language acquisition. NEW IDEAS IN PSYCHOLOGY 2023. [DOI: 10.1016/j.newideapsych.2022.100985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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12
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Cristia A. A systematic review suggests marked differences in the prevalence of infant-directed vocalization across groups of populations. Dev Sci 2023; 26:e13265. [PMID: 35429106 DOI: 10.1111/desc.13265] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 03/25/2022] [Accepted: 03/28/2022] [Indexed: 12/15/2022]
Abstract
Anthropological reports have long suggested that speaking to young children is very infrequent in certain populations (notably farming ones), which is in line with scattered quantitative studies. A systematic review was undertaken to use available literature in order to estimate the extent of population variation. Database searches, expert lists, and citation searches led to the discovery of 29 reports on the frequency of vocalizations directed to infants aged 24 months or younger, based on systematic observations of spontaneous activity in the infant's natural environment lasting at least 30 min in length. Together, these studies provide evidence on 1314 infants growing up in a range of communities (urban, foraging, farming). For populations located outside of North America, the frequency with which vocalization was directed to urban infants was much higher than that for rural infants (including both foraging and farming, medians = 12.6 vs. 3.6% of observations contained infant-directed vocalization behaviors). We benchmarked this effect against socio-economic status (SES) variation in the United States, which was much smaller. Infants in high SES American homes were spoken to only slightly more frequently than those in low SES homes (medians = 16.4 vs. 15.1% of observations contained infant-directed vocalization behaviors). Although published research represents a biased sample of the world's populations, these results invite further cross-population research to understand the causes and effects of such considerable population group differences.
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Affiliation(s)
- Alejandrina Cristia
- Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Etudes cognitives, ENS, EHESS, CNRS, PSL University, Paris, France
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13
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Kühl N, Goutier M, Baier L, Wolff C, Martin D. Human vs. supervised machine learning: Who learns patterns faster? COGN SYST RES 2022. [DOI: 10.1016/j.cogsys.2022.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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14
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Synthesizing theories of human language with Bayesian program induction. Nat Commun 2022; 13:5024. [PMID: 36042196 PMCID: PMC9427767 DOI: 10.1038/s41467-022-32012-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 07/12/2022] [Indexed: 11/11/2022] Open
Abstract
Automated, data-driven construction and evaluation of scientific models and theories is a long-standing challenge in artificial intelligence. We present a framework for algorithmically synthesizing models of a basic part of human language: morpho-phonology, the system that builds word forms from sounds. We integrate Bayesian inference with program synthesis and representations inspired by linguistic theory and cognitive models of learning and discovery. Across 70 datasets from 58 diverse languages, our system synthesizes human-interpretable models for core aspects of each language’s morpho-phonology, sometimes approaching models posited by human linguists. Joint inference across all 70 data sets automatically synthesizes a meta-model encoding interpretable cross-language typological tendencies. Finally, the same algorithm captures few-shot learning dynamics, acquiring new morphophonological rules from just one or a few examples. These results suggest routes to more powerful machine-enabled discovery of interpretable models in linguistics and other scientific domains. Humans can infer rules for building words in a new language from a handful of examples, and linguists also can infer language patterns across related languages. Here, the authors provide an algorithm which models these grammatical abilities by synthesizing human-understandable programs for building words.
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15
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Abstract
Sentences contain structure that determines their meaning beyond that of individual words. An influential study by Ding and colleagues (2016) used frequency tagging of phrases and sentences to show that the human brain is sensitive to structure by finding peaks of neural power at the rate at which structures were presented. Since then, there has been a rich debate on how to best explain this pattern of results with profound impact on the language sciences. Models that use hierarchical structure building, as well as models based on associative sequence processing, can predict the neural response, creating an inferential impasse as to which class of models explains the nature of the linguistic computations reflected in the neural readout. In the current manuscript, we discuss pitfalls and common fallacies seen in the conclusions drawn in the literature illustrated by various simulations. We conclude that inferring the neural operations of sentence processing based on these neural data, and any like it, alone, is insufficient. We discuss how to best evaluate models and how to approach the modeling of neural readouts to sentence processing in a manner that remains faithful to cognitive, neural, and linguistic principles.
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Affiliation(s)
- Sanne Ten Oever
- Language and Computation in Neural Systems Group, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Karthikeya Kaushik
- Language and Computation in Neural Systems Group, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
| | - Andrea E. Martin
- Language and Computation in Neural Systems Group, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- * E-mail:
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Ludusan B, Cristia A, Mazuka R, Dupoux E. How much does prosody help word segmentation? A simulation study on infant-directed speech. Cognition 2021; 219:104961. [PMID: 34856424 DOI: 10.1016/j.cognition.2021.104961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 07/01/2021] [Accepted: 11/15/2021] [Indexed: 11/03/2022]
Abstract
Infants come to learn several hundreds of word forms by two years of age, and it is possible this involves carving these forms out from continuous speech. It has been proposed that the task is facilitated by the presence of prosodic boundaries. We revisit this claim by running computational models of word segmentation, with and without prosodic information, on a corpus of infant-directed speech. We use five cognitively-based algorithms, which vary in whether they employ a sub-lexical or a lexical segmentation strategy and whether they are simple heuristics or embody an ideal learner. Results show that providing expert-annotated prosodic breaks does not uniformly help all segmentation models. The sub-lexical algorithms, which perform more poorly, benefit most, while the lexical ones show a very small gain. Moreover, when prosodic information is derived automatically from the acoustic cues infants are known to be sensitive to, errors in the detection of the boundaries lead to smaller positive effects, and even negative ones for some algorithms. This shows that even though infants could potentially use prosodic breaks, it does not necessarily follow that they should incorporate prosody into their segmentation strategies, when confronted with realistic signals.
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Affiliation(s)
- Bogdan Ludusan
- Laboratory for Language Development, RIKEN Center for Brain Science, Japan; Laboratoire de Sciences Cognitives et Psycholinguistique, ENS Paris Sciences Lettres, EHESS, CNRS, France.
| | - Alejandrina Cristia
- Laboratoire de Sciences Cognitives et Psycholinguistique, ENS Paris Sciences Lettres, EHESS, CNRS, France
| | - Reiko Mazuka
- Laboratory for Language Development, RIKEN Center for Brain Science, Japan; Department of Psychology and Neuroscience, Duke University, USA
| | - Emmanuel Dupoux
- Laboratoire de Sciences Cognitives et Psycholinguistique, ENS Paris Sciences Lettres, EHESS, CNRS, INRIA, France
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17
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Giorgi I, Golosio B, Esposito M, Cangelosi A, Masala GL. Modeling Multiple Language Learning in a Developmental Cognitive Architecture. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2020.3033963] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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18
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Meylan SC, Bergelson E. Learning Through Processing: Toward an Integrated Approach to Early Word Learning. ANNUAL REVIEW OF LINGUISTICS 2021; 8:77-99. [PMID: 35481110 PMCID: PMC9037961 DOI: 10.1146/annurev-linguistics-031220-011146] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Children's linguistic knowledge and the learning mechanisms by which they acquire it grow substantially in infancy and toddlerhood, yet theories of word learning largely fail to incorporate these shifts. Moreover, researchers' often-siloed focus on either familiar word recognition or novel word learning limits the critical consideration of how these two relate. As a step toward a mechanistic theory of language acquisition, we present a framework of "learning through processing" and relate it to the prevailing methods used to assess children's early knowledge of words. Incorporating recent empirical work, we posit a specific, testable timeline of qualitative changes in the learning process in this interval. We conclude with several challenges and avenues for building a comprehensive theory of early word learning: better characterization of the input, reconciling results across approaches, and treating lexical knowledge in the nascent grammar with sufficient sophistication to ensure generalizability across languages and development.
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Affiliation(s)
- Stephan C Meylan
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Psychology and Neuroscience, Duke University, Durham, North Carolina, USA
| | - Elika Bergelson
- Department of Psychology and Neuroscience, Duke University, Durham, North Carolina, USA
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19
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SCALa: A blueprint for computational models of language acquisition in social context. Cognition 2021; 213:104779. [PMID: 34092384 DOI: 10.1016/j.cognition.2021.104779] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 04/24/2021] [Accepted: 05/12/2021] [Indexed: 11/21/2022]
Abstract
Theories and data on language acquisition suggest a range of cues are used, ranging from information on structure found in the linguistic signal itself, to information gleaned from the environmental context or through social interaction. We propose a blueprint for computational models of the early language learner (SCALa, for Socio-Computational Architecture of Language Acquisition) that makes explicit the connection between the kinds of information available to the social learner and the computational mechanisms required to extract language-relevant information and learn from it. SCALa integrates a range of views on language acquisition, further allowing us to make precise recommendations for future large-scale empirical research.
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20
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Ludusan B, Mazuka R, Dupoux E. Does Infant-Directed Speech Help Phonetic Learning? A Machine Learning Investigation. Cogn Sci 2021; 45:e12946. [PMID: 34018231 DOI: 10.1111/cogs.12946] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 12/17/2020] [Accepted: 12/31/2020] [Indexed: 11/28/2022]
Abstract
A prominent hypothesis holds that by speaking to infants in infant-directed speech (IDS) as opposed to adult-directed speech (ADS), parents help them learn phonetic categories. Specifically, two characteristics of IDS have been claimed to facilitate learning: hyperarticulation, which makes the categories more separable, and variability, which makes the generalization more robust. Here, we test the separability and robustness of vowel category learning on acoustic representations of speech uttered by Japanese adults in ADS, IDS (addressed to 18- to 24-month olds), or read speech (RS). Separability is determined by means of a distance measure computed between the five short vowel categories of Japanese, while robustness is assessed by testing the ability of six different machine learning algorithms trained to classify vowels to generalize on stimuli spoken by a novel speaker in ADS. Using two different speech representations, we find that hyperarticulated speech, in the case of RS, can yield better separability, and that increased between-speaker variability in ADS can yield, for some algorithms, more robust categories. However, these conclusions do not apply to IDS, which turned out to yield neither more separable nor more robust categories compared to ADS inputs. We discuss the usefulness of machine learning algorithms run on real data to test hypotheses about the functional role of IDS.
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Affiliation(s)
- Bogdan Ludusan
- Laboratory for Language Development, RIKEN Center for Brain Science.,Phonetics Workgroup, Faculty of Linguistics and Literary Studies, Bielefeld University
| | - Reiko Mazuka
- Laboratory for Language Development, RIKEN Center for Brain Science.,Department of Psychology and Neuroscience, Duke University
| | - Emmanuel Dupoux
- Laboratoire de Sciences Cognitives et Psycholiguistique, EHESS/ENS/PSL/CNRS/INRIA
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21
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De Luca G. Modelling Societal Knowledge in the Health Sector: Machine Learning and Google Trends. JOURNAL OF INNOVATION ECONOMICS & MANAGEMENT 2021. [DOI: 10.3917/jie.pr1.0092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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22
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Schatz T, Feldman NH, Goldwater S, Cao XN, Dupoux E. Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input. Proc Natl Acad Sci U S A 2021; 118:e2001844118. [PMID: 33510040 PMCID: PMC7924220 DOI: 10.1073/pnas.2001844118] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Before they even speak, infants become attuned to the sounds of the language(s) they hear, processing native phonetic contrasts more easily than nonnative ones. For example, between 6 to 8 mo and 10 to 12 mo, infants learning American English get better at distinguishing English and [l], as in "rock" vs. "lock," relative to infants learning Japanese. Influential accounts of this early phonetic learning phenomenon initially proposed that infants group sounds into native vowel- and consonant-like phonetic categories-like and [l] in English-through a statistical clustering mechanism dubbed "distributional learning." The feasibility of this mechanism for learning phonetic categories has been challenged, however. Here, we demonstrate that a distributional learning algorithm operating on naturalistic speech can predict early phonetic learning, as observed in Japanese and American English infants, suggesting that infants might learn through distributional learning after all. We further show, however, that, contrary to the original distributional learning proposal, our model learns units too brief and too fine-grained acoustically to correspond to phonetic categories. This challenges the influential idea that what infants learn are phonetic categories. More broadly, our work introduces a mechanism-driven approach to the study of early phonetic learning, together with a quantitative modeling framework that can handle realistic input. This allows accounts of early phonetic learning to be linked to concrete, systematic predictions regarding infants' attunement.
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Affiliation(s)
- Thomas Schatz
- Department of Linguistics, University of Maryland, College Park, MD 20742;
- University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742
| | - Naomi H Feldman
- Department of Linguistics, University of Maryland, College Park, MD 20742
- University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742
| | - Sharon Goldwater
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom
| | - Xuan-Nga Cao
- Cognitive Machine Learning, École Normale Supérieure-École des Hautes Études en Sciences Sociales-Paris Sciences et Lettres Research University-CNRS-Institut National de Recherche en Informatique et en Automatique, 75012 Paris, France
| | - Emmanuel Dupoux
- Cognitive Machine Learning, École Normale Supérieure-École des Hautes Études en Sciences Sociales-Paris Sciences et Lettres Research University-CNRS-Institut National de Recherche en Informatique et en Automatique, 75012 Paris, France
- Facebook A.I. Research, 75002 Paris, France
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23
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Pitti A, Quoy M, Boucenna S, Lavandier C. Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization. PLoS Comput Biol 2021; 17:e1008566. [PMID: 33600482 PMCID: PMC7891699 DOI: 10.1371/journal.pcbi.1008566] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 11/27/2020] [Indexed: 11/25/2022] Open
Abstract
We propose a developmental model inspired by the cortico-basal system (CX-BG) for vocal learning in babies and for solving the correspondence mismatch problem they face when they hear unfamiliar voices, with different tones and pitches. This model is based on the neural architecture INFERNO standing for Iterative Free-Energy Optimization of Recurrent Neural Networks. Free-energy minimization is used for rapidly exploring, selecting and learning the optimal choices of actions to perform (eg sound production) in order to reproduce and control as accurately as possible the spike trains representing desired perceptions (eg sound categories). We detail in this paper the CX-BG system responsible for linking causally the sound and motor primitives at the order of a few milliseconds. Two experiments performed with a small and a large audio database show the capabilities of exploration, generalization and robustness to noise of our neural architecture in retrieving audio primitives during vocal learning and during acoustic matching with unheared voices (different genders and tones).
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Affiliation(s)
- Alexandre Pitti
- Laboratoire ETIS, CY Cergy Paris University, ENSEA, CNRS, UMR8051, Cergy, France
| | - Mathias Quoy
- Laboratoire ETIS, CY Cergy Paris University, ENSEA, CNRS, UMR8051, Cergy, France
| | - Sofiane Boucenna
- Laboratoire ETIS, CY Cergy Paris University, ENSEA, CNRS, UMR8051, Cergy, France
| | - Catherine Lavandier
- Laboratoire ETIS, CY Cergy Paris University, ENSEA, CNRS, UMR8051, Cergy, France
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24
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Ji Z, Zou X, Huang T, Wu S. Unsupervised Few-Shot Feature Learning via Self-Supervised Training. Front Comput Neurosci 2020; 14:83. [PMID: 33178000 PMCID: PMC7592391 DOI: 10.3389/fncom.2020.00083] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 08/05/2020] [Indexed: 11/13/2022] Open
Abstract
Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. However, current few-shot learners are mostly supervised and rely heavily on a large amount of labeled examples. Unsupervised learning is a more natural procedure for cognitive mammals and has produced promising results in many machine learning tasks. In this paper, we propose an unsupervised feature learning method for few-shot learning. The proposed model consists of two alternate processes, progressive clustering and episodic training. The former generates pseudo-labeled training examples for constructing episodic tasks; and the later trains the few-shot learner using the generated episodic tasks which further optimizes the feature representations of data. The two processes facilitate each other, and eventually produce a high quality few-shot learner. In our experiments, our model achieves good generalization performance in a variety of downstream few-shot learning tasks on Omniglot and MiniImageNet. We also construct a new few-shot person re-identification dataset FS-Market1501 to demonstrate the feasibility of our model to a real-world application.
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Affiliation(s)
- Zilong Ji
- State Key Laboratory of Cognitive Neuroscience & Learning, Beijing Normal University, Beijing, China
| | - Xiaolong Zou
- School of Electronics Engineering & Computer Science, Peking University, Beijing, China
| | - Tiejun Huang
- School of Electronics Engineering & Computer Science, Peking University, Beijing, China
| | - Si Wu
- School of Electronics Engineering & Computer Science, Peking University, Beijing, China.,IDG/McGovern Institute for Brain Research, PKU-Tsinghua Center for Life Sciences, Peking University, Beijing, China
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25
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26
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Cristia A. Language input and outcome variation as a test of theory plausibility: The case of early phonological acquisition. DEVELOPMENTAL REVIEW 2020. [DOI: 10.1016/j.dr.2020.100914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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27
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Beguš G. Generative Adversarial Phonology: Modeling Unsupervised Phonetic and Phonological Learning With Neural Networks. Front Artif Intell 2020; 3:44. [PMID: 33733161 PMCID: PMC7861218 DOI: 10.3389/frai.2020.00044] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 05/19/2020] [Indexed: 11/30/2022] Open
Abstract
Training deep neural networks on well-understood dependencies in speech data can provide new insights into how they learn internal representations. This paper argues that acquisition of speech can be modeled as a dependency between random space and generated speech data in the Generative Adversarial Network architecture and proposes a methodology to uncover the network's internal representations that correspond to phonetic and phonological properties. The Generative Adversarial architecture is uniquely appropriate for modeling phonetic and phonological learning because the network is trained on unannotated raw acoustic data and learning is unsupervised without any language-specific assumptions or pre-assumed levels of abstraction. A Generative Adversarial Network was trained on an allophonic distribution in English, in which voiceless stops surface as aspirated word-initially before stressed vowels, except if preceded by a sibilant [s]. The network successfully learns the allophonic alternation: the network's generated speech signal contains the conditional distribution of aspiration duration. The paper proposes a technique for establishing the network's internal representations that identifies latent variables that correspond to, for example, presence of [s] and its spectral properties. By manipulating these variables, we actively control the presence of [s] and its frication amplitude in the generated outputs. This suggests that the network learns to use latent variables as an approximation of phonetic and phonological representations. Crucially, we observe that the dependencies learned in training extend beyond the training interval, which allows for additional exploration of learning representations. The paper also discusses how the network's architecture and innovative outputs resemble and differ from linguistic behavior in language acquisition, speech disorders, and speech errors, and how well-understood dependencies in speech data can help us interpret how neural networks learn their representations.
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Affiliation(s)
- Gašper Beguš
- Department of Linguistics, University of California, Berkeley, Berkeley, CA, United States
- Department of Linguistics, University of Washington, Seattle, WA, United States
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28
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Adolph KE. Oh, Behave!: PRESIDENTIAL ADDRESS, XXth International Conference on Infant Studies New Orleans, LA, US May 2016. INFANCY 2020; 25:374-392. [PMID: 33100922 PMCID: PMC7580788 DOI: 10.1111/infa.12336] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 10/30/2019] [Indexed: 01/18/2023]
Abstract
Behavior is essential for understanding infant learning and development. Although behavior is transient and ephemeral, we have the technology to make it tangible and enduring. Video uniquely captures and preserves the details of behavior and the surrounding context. By sharing videos for documentation and data reuse, we can exploit the tremendous opportuni-ties provided by infancy research and overcome the important challenges in studying behavior. The Datavyu video coding software and Databrary digital video library provide tools and infrastructure for mining and sharing the richness of video. This article is based on my Presidential Address to the International Congress on Infant Studies in New Orleans, May 22, 2016 (Video 1 at https://www.databrary.org/volume/955/slot/39352/-?asset=190106. Given that the article de-scribes the power of video for understanding behavior, I use video clips rather than static images to illustrate most of my points, and the videos are shared on the Databrary library.
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Affiliation(s)
- Karen E Adolph
- Department of Psychology, New York University, New York, NY, USA
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29
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Yuan L, Xiang V, Crandall D, Smith L. Learning the generative principles of a symbol system from limited examples. Cognition 2020; 200:104243. [DOI: 10.1016/j.cognition.2020.104243] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 02/19/2020] [Accepted: 02/20/2020] [Indexed: 10/24/2022]
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30
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Fourtassi A, Frank MC. How optimal is word recognition under multimodal uncertainty? Cognition 2020; 199:104092. [PMID: 32135386 DOI: 10.1016/j.cognition.2019.104092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 09/23/2019] [Accepted: 10/03/2019] [Indexed: 11/16/2022]
Abstract
Identifying a spoken word in a referential context requires both the ability to integrate multimodal input and the ability to reason under uncertainty. How do these tasks interact with one another? We study how adults identify novel words under joint uncertainty in the auditory and visual modalities, and we propose an ideal observer model of how cues in these modalities are combined optimally. Model predictions are tested in four experiments where recognition is made under various sources of uncertainty. We found that participants use both auditory and visual cues to recognize novel words. When the signal is not distorted with environmental noise, participants weight the auditory and visual cues optimally, that is, according to the relative reliability of each modality. In contrast, when one modality has noise added to it, human perceivers systematically prefer the unperturbed modality to a greater extent than the optimal model does. This work extends the literature on perceptual cue combination to the case of word recognition in a referential context. In addition, this context offers a link to the study of multimodal information in word meaning learning.
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Affiliation(s)
| | - Michael C Frank
- Department of Psychology, Stanford University, United States
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31
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32
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Guevara-Rukoz A, Cristia A, Ludusan B, Thiollière R, Martin A, Mazuka R, Dupoux E. Are Words Easier to Learn From Infant- Than Adult-Directed Speech? A Quantitative Corpus-Based Investigation. Cogn Sci 2018; 42:1586-1617. [PMID: 29851142 DOI: 10.1111/cogs.12616] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 12/04/2017] [Accepted: 02/26/2018] [Indexed: 11/26/2022]
Abstract
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 in IDS than in ADS. At the phonological level, we find an effect in the opposite direction: The IDS lexicon contains more distinctive words (such as onomatopoeias) than the ADS counterpart. Combining the acoustic and phonological metrics together in a global discriminability score reveals that the bigger separation of lexical categories in the phonological space does not compensate for the opposite effect observed at the acoustic level. As a result, IDS word forms are still globally less discriminable than ADS word forms, even though the effect is numerically small. We discuss the implication of these findings for the view that the functional role of IDS is to improve language learnability.
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Affiliation(s)
| | - Alejandrina Cristia
- Laboratoire de Sciences Cognitives et Psycholinguistique, ENS/EHESS/CNRS/PSL
| | - Bogdan Ludusan
- Laboratoire de Sciences Cognitives et Psycholinguistique, ENS/EHESS/CNRS/PSL
- Laboratory for Language Development, RIKEN Brain Science Institute
| | - Roland Thiollière
- Laboratoire de Sciences Cognitives et Psycholinguistique, ENS/EHESS/CNRS/PSL
| | - Andrew Martin
- Faculty of Letters, Department of English Literature and Language, Konan University
| | - Reiko Mazuka
- Laboratory for Language Development, RIKEN Brain Science Institute
- Department of Psychology and Neuroscience, Duke University
| | - Emmanuel Dupoux
- Laboratoire de Sciences Cognitives et Psycholinguistique, ENS/EHESS/CNRS/PSL
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