Cover not available

In:Current Perspectives on Child Language Acquisition: How children use their environment to learn
Edited by Caroline F. Rowland, Anna L. Theakston, Ben Ambridge and Katherine E. Twomey
[Trends in Language Acquisition Research 27] 2020
► pp. 65–89

Get fulltext from our e-platform
JBE logo with link
PDF logo with download linkDownload Book PDF
authorization required

Insights from studying statistical learning

 | Max Planck Institute for Psycholinguistics
 | University of Lancaster
Published online: 17 September 2020
DOI logo with link
https://doi.org/10.1075/tilar.27.03fro
Abstract
Acquiring language is notoriously complex, yet for the majority of children this feat is accomplished with remarkable ease. Usage-based accounts of language acquisition suggest that this success can be largely attributed to the wealth of experience with language that children accumulate over the course of language acquisition. One field of research that is heavily underpinned by this principle of experience is statistical learning, which posits that learners can perform powerful computations over the distribution of information in a given input, which can help them to discern precisely how that input is structured, and how it operates. A growing body of work brings this notion to bear in the field of language acquisition, due to a developing understanding of the richness of the statistical information contained in speech. In this chapter we discuss the role that statistical learning plays in language acquisition, emphasising the importance of both the distribution of information within language, and the situation in which language is being learnt. First, we address the types of statistical learning that apply to a range of language learning tasks, asking whether the statistical processes purported to support language learning are the same or distinct across different tasks in language acquisition. Second, we expand the perspective on what counts as environmental input, by determining how statistical learning operates over the situated learning environment, and not just sequences of sounds in utterances. Finally, we address the role of variability in children’s input, and examine how statistical learning can accommodate (and perhaps even exploit) this during language acquisition.
Article outline
  • Preface
  • Introduction
  • Statistical processes for different language learning tasks
    • General statistical principles of language acquisition: Grouping and dividing
  • The role of the broader environment on learning
  • A note on cue variability
  • Conclusions
  • References
References (108)
References
Aristotle (350BCE, 1932). Posterior analytics. Translated by G. R. G. Mure, Works of Aristotle, Volume 2. Oxford: Oxford University Press.Google Scholar logo with link to Google Scholar
Ay, N., Flack, J., & Krakauer, D. (2007). Robustness and complexity co-constructed in multimodal signalling networks. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1479), 441–447. Google Scholar logo with link to Google Scholar
Bahrick, L. E., Lickliter, R., & Flom, R. (2004). Intersensory redundancy guides the development of selective attention, perception, and cognition in infancy. Current Directions in Psychological Science, 13, 99–102. Google Scholar logo with link to Google Scholar
Baldwin, D. A. (1991). Infants’ contribution to the achievement of joint reference. Child Development, 62, 875–890. Google Scholar logo with link to Google Scholar
Black, A., & Bergmann, C. (2017). Quantifying infants’ statistical word segmentation: A meta-analysis. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 124–129). Austin, TX: Cognitive Science Society.Google Scholar logo with link to Google Scholar
Brooks, R., & Meltzoff, A. N. (2008). Infant gaze following and pointing predict accelerated vocabulary growth through two years of age: A longitudinal, growth curve modeling study. Journal of Child Language, 35(1), 207–220. Google Scholar logo with link to Google Scholar
Cameron-Faulkner, T., Lieven, E. V., & Tomasello, M. (2003). A construction based analysis of child directed speech. Cognitive Science, 27(6), 843–873. Google Scholar logo with link to Google Scholar
Cartwright, T. A., & Brent, M. R. (1997). Syntactic categorization in early language acquisition: Formalizing the role of distributional analysis. Cognition, 63(2), 121–170. Google Scholar logo with link to Google Scholar
Chomsky, N. (1981). Lectures on government and binding. Dordrecht: Foris.Google Scholar logo with link to Google Scholar
(2005). Three factors in language design. Linguistic Inquiry, 36, 1–22. Google Scholar logo with link to Google Scholar
Clerkin, E. M., Hart, E., Rehg, J. M., Yu, C., Smith, L. B. (2017). Real-world visual statistics and infants’ first-learned object names. Philosophical Transactions of the Royal Society B, 372 (1711), 1–10. Google Scholar logo with link to Google Scholar
Crain, S., & Nakayama, M. (1987). Structure dependence in grammar formation. Language, 63(3), 522–543. Google Scholar logo with link to Google Scholar
Conwell, E. (2017). Prosodic disambiguation of noun/verb homophones in child-directed speech. Journal of Chid Language, 44(3), 734–751. Google Scholar logo with link to Google Scholar
Culter, A., & Norris, D.. (1988). The role of strong syllables in segmentation for lexical access. Journal of Experimental Psychology: Human Perception & Performance, 14, 113–121.Google Scholar logo with link to Google Scholar
Cunillera, T., Toro, J. M., Sebastian-Galles, N., & Rodruiguez-Fornells, A. (2006). The effects of stress and statistical cues on continuous speech segmentation: An event-related brain potential study. Brain Research, 1123(1), 168–178. Google Scholar logo with link to Google Scholar
De Diego-Balaguer, R., Rodriguez-Fornells, A. & Bachoud-Lévi, A. C. (2015). Prosodic cues enhance rule learning by changing speech segmentation mechanisms. Frontiers in Psychology, 6, 1478. Google Scholar logo with link to Google Scholar
De Diego-Balauger, R., Toro, J. M., Rodriguez-Fornells, A., & Bachoud-Levi, A.-C. (2007). Different neurophysiological mechanisms underlying word and rule extraction from speech. PLoS One, 2, 01175.Google Scholar logo with link to Google Scholar
Deutsch, D. (2013). Grouping mechanisms in music. In D. Deutsch (Ed.), The psychology of music (pp. 184–238). San Diego, CA: Elsevier. Google Scholar logo with link to Google Scholar
Dupoux, E. (2018). Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner. Cognition, 173, 43–59. Google Scholar logo with link to Google Scholar
Elman, J. L. (1993). Learning and developmentin neural networks: The importance of starting small. Cognition, 48, 71–99. Google Scholar logo with link to Google Scholar
Endress, A. D., Scholl, B. J., & Mehler, J. (2005). The role of salience in the extraction of algebraic rules. Journal of Psychology: General, 134(3), 406–419.Google Scholar logo with link to Google Scholar
Fernald, A. (1985). Four-month-old infants prefer to listen to motherese. Infant Behavior & Development, 8(2), 181–195. Google Scholar logo with link to Google Scholar
Fernald, A., & Kuhl, P. K. (1987). Acoustic determinants of infant preference for motherese speech. Infant Behavior & Development, 10(3), 279–293. Google Scholar logo with link to Google Scholar
Fery, C. & Schubö, F. (2010). Hierarchical prosodic structures in the intonation of center embedded relative clauses. The Linguistic Review, 27(3), 293–317. Google Scholar logo with link to Google Scholar
French, R. M., Addyman, C., & Mareschal, D. (2011). TRACX: A recognition-based connectionist framework for sequence segmentation and chunk extraction. Psychological Review, 118(4), 614–636. Google Scholar logo with link to Google Scholar
Freudenthal, D., Pine, J. M., & Gobet, F. (2006). Modeling the development of children’s use of optional infinitives in Dutch and English using MOSAIC. Cognitive Science, 30, 277–310. Google Scholar logo with link to Google Scholar
Fries, C. C. (1952). The structure of English. London: Longmans.Google Scholar logo with link to Google Scholar
Frost, R. L. A., Isbilen, E. S., Christiansen, M. H. & Monaghan, P. (2019). Testing the limits of non-adjacent dependency learning: Statistical segmentation and generalization across domains. In A. K. Goel, C. M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society. Montreal, QB: Cognitive Science Society.Google Scholar logo with link to Google Scholar
Frost, R. L. A., Jessop, A., Durrant, S., Peter, M. S., Bidgood, A., C., Pine, J. M., Rowland, C. F., & Monaghan, P. (2020). Non-adjacent dependency learning in infancy, and its link to language development. Cognitive Psychology, 120: 101291. Google Scholar logo with link to Google Scholar
Frost, R. L. A., & Monaghan, P. (2016). Simultaneous segmentation and generalisation of non-adjacent dependencies from continuous speech. Cognition, 147, 70–74. Google Scholar logo with link to Google Scholar
Frost, R. L. A., Monaghan, P., & Tatsumi, T. (2017). Domain-general mechanisms for speech segmentation: The role of duration information in language learning. Journal of Experimental Psychology: Human Perception and Performance, 43(3), 466–476.Google Scholar logo with link to Google Scholar
Gogate, L. J., Maganti, M., & Laing, K. (2013). Maternal naming of object wholes versus parts for preverbal infants: A fine-grained analysis of scaffolding at 6 to 8 months. Infant Behavior & Development, 36(3), 470–479. Google Scholar logo with link to Google Scholar
Gomez, R. L. (2002). Variability and detection of invariant structure. Psychological Science, 13(5), 431–436. Google Scholar logo with link to Google Scholar
Graf Estes, K., & Hurley, K. (2013). Infant-directed prosody helps infants map sounds to meanings. Infancy, 18(5), 797–824. Google Scholar logo with link to Google Scholar
Harris, Z. S. (1954). Distributional structure. Word, 10, 140–162. Google Scholar logo with link to Google Scholar
(1955). From phoneme to morpheme. Language, 31, 190–222. Google Scholar logo with link to Google Scholar
Hawthorne, K., & Gerken, L. (2014). From pauses to clauses: Prosody facilitates learning of syntactic constituency. Cognition, 133, 420 – 428. Google Scholar logo with link to Google Scholar
Hendrickson, A. T., & Perfors, A. (2019). Cross-situational learning in a Zipfian environment. Cognition, 189, 11–22. Google Scholar logo with link to Google Scholar
Hockema, S. A. (2006). Finding words in speech: An investigation of American English. Language Learning and Development, 2, 119–146. Google Scholar logo with link to Google Scholar
Hollich, G., Hirsh-Pasek, K., & Golinkoff, R. M. (2000). Breaking the language barrier: An emergentist coalition model for the origins of word learning. Monographs of the Society for Research in Child Development, 65(3, Serial No. 262).Google Scholar logo with link to Google Scholar
Houston-Price, C., Plunkett, K., & Duffy, H. (2006). The use of social and salience cues in early word learning. Journal of Experimental Child Psychology, 95, 27–55. Google Scholar logo with link to Google Scholar
Jackendoff, R. (2002). Foundations of language: Brain, meaning, grammar, evolution. Oxford: Oxford University Press. Google Scholar logo with link to Google Scholar
Johnson, E. K., & Jusczyk, P. W. (2001). Word segmentation by 8-month-olds: When speech cues count more than statistics. Journal of Memory and Language, 44(4), 548–567. Google Scholar logo with link to Google Scholar
Johnson, E. K., & Seidl, A. H. (2009). At 11 months, prosody still outranks statistics. Developmental Science, 12(1), 131–141. Google Scholar logo with link to Google Scholar
Johnson, E. K., & Tyler, M. D. (2010). Testing the limits of statistical learning for word segmentation. Developmental Science, 13(2), 339–345. Google Scholar logo with link to Google Scholar
Jusczyk, P. W., Cutler, A., Redanz, N. J. (1993). Infants’ preference for the predominant stress patterns of English words. Child Development, 64(3), 675–687. Google Scholar logo with link to Google Scholar
Kamper, H., Jansen, A., & Goldwater, S. (2016). Unsupervised word segmentation and lexicon discovery using acoustic word embeddings. In IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 24, 669–679. Google Scholar logo with link to Google Scholar
Kelly, M. H. (1992). Using sound to solve syntactic problems: The role of phonology in grammatical category assignments. Psychological Review, 99, 349–364. Google Scholar logo with link to Google Scholar
Kurumada, C., Meylan, S. C., & Frank, M. C. (2013). Zipfian frequency distributions facilitate word segmentation in context. Cognition, 127(3), 439–453. Google Scholar logo with link to Google Scholar
Lany, J. (2014). Judging words by their covers and the company they keep: Probabilistic cues support word learning. Child Development, 85(4), 1727–1739. Google Scholar logo with link to Google Scholar
Lieven, E. V., Behrens, H., Speares, J., & Tomasello, M. (2003). Early syntactic creativity: A usage-based approach. Journal of Child Language, 30(2), 333–370. Google Scholar logo with link to Google Scholar
Lieven, E. V., & Brandt, S. (2011). The constructivist approach. Infancia y Aprendizaje, 34(3), 281–296. Google Scholar logo with link to Google Scholar
Lieven, E. V., Salomo, D., & Tomasello, M. (2009). Two-year-old children’s production of multiword utterances: A usage-based analysis. Cognitive Linguistics, 20, 481–508. Google Scholar logo with link to Google Scholar
Ma, W., Golinkoff, R. M., Houston, D. M., & Hirsh-Pasek, K. (2011). Word learning in infant- and adult-directed speech. Language Learning and Development,7(3), 185–201. Google Scholar logo with link to Google Scholar
MacWhinney, B. J. (2000). The CHILDES project: Tools for analyzing talk (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar logo with link to Google Scholar
MacWhinney, B., Bates, E., & Kliegl, R. (1984). Cue validity and sentence interpretation in English, German, and Italian. Journal of Verbal Learning and Verbal Behaviour, 23, 127–150. Google Scholar logo with link to Google Scholar
Marchetto, E., & Bonatti, L. L. (2013). Words and possible words in early language acquisition. Cognitive Psychology 67(3), 130–150. Google Scholar logo with link to Google Scholar
(2015). Finding words and word structure in artificial speech: The development of infants’ sensitivity to morphosyntactic regularities. Journal of Child Language, 42(4), 873–902. Google Scholar logo with link to Google Scholar
Mattys, S. L., White, L., & Melhorn, J. F. (2005). Integration of multiple speech segmentation cues: A hierarchical framework. Journal of Experimental Psychology: General, 134, 477–500. Google Scholar logo with link to Google Scholar
McClelland, J. L., & Elman, J. L. (1986). The TRACE model of speech perception. Cognitive Psychology, 18(1), 1–86. Google Scholar logo with link to Google Scholar
McMurray, B., Horst, J. S., & Samuelson, L. K. (2012). Word learning emerges from the interaction of online referent selection and slow associative learning. Psychological Review, 119(4), 831–877. Google Scholar logo with link to Google Scholar
Meyer, M., & Baldwin, D. A.. (2013). Pointing as a socio-pragmatic cue to particular vs. generic reference. Language Learning and Development, 9(3), 245–265. Google Scholar logo with link to Google Scholar
Mintz, T. (2003). Frequent frames as a cue for grammatical categories in child directed speech. Cognition, 90, 91–117. Google Scholar logo with link to Google Scholar
Modrak, D. K. W. (2001). Aristotle’s theory of language and meaning. Cambridge: Cambridge University Press.Google Scholar logo with link to Google Scholar
Monaghan, P. (2017). Canalization of language structure from environmental constraints: A computational model of word learning from multiple cues. Topics in Cognitive Science, 9, 21–34.. Google Scholar logo with link to Google Scholar
Monaghan, P., Brand, J., Frost, R. L. A., & Taylor, G. (2017). Multiple variable cues in the environment promote accurate and robust word learning. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 817–822). Austin, TX: Cognitive Science Society.Google Scholar logo with link to Google Scholar
Monaghan, P., Chater, N., & Christiansen, M. H. (2005). The differential contribution of phonological and distributional cues in grammatical categorisation. Cognition, 96, 143–182. Google Scholar logo with link to Google Scholar
Monaghan, P., & Christiansen, M. H. (2010). Words in puddles of sound: Modelling psycholinguistic effects in speech segmentation. Journal of Child Language, 37, 545–564. Google Scholar logo with link to Google Scholar
Monaghan, P., Christiansen, M. H., & Chater, N. (2007). The Phonological Distributional Coherence Hypothesis: Cross-linguistic evidence in language acquisition. Cognitive Psychology, 55, 259–305. Google Scholar logo with link to Google Scholar
Monaghan, P. & Mattock, K. (2012). Integrating constraints for learning word referent mappings. Cognition, 123, 133–143. Google Scholar logo with link to Google Scholar
Monaghan, P., Mattock, K., Davies, R., & Smith, A. C. (2015). Gavagai is as gavagai does: Learning nouns and verbs from cross-situational statistics. Cognitive Science, 39, 1099–1112. Google Scholar logo with link to Google Scholar
Monaghan, P., Kalashnikova, M., & Mattock, K. (2017). Intrinsic and extrinsic cues to word learning. In G. Westermann & N. Mani (Eds.), Early word learning. Hove: Psychology Press. Google Scholar logo with link to Google Scholar
Moore, C., Angelopolous, M., & Bennett, P. (1999). Word learning in the context of referential and salience cues. Developmental Psychology, 35(1), 60–68. Google Scholar logo with link to Google Scholar
Mueller, J. L., Bahlmann, J., & Friederici, A. D. (2010). Learnability of embedded syntactic structures depends on prosodic cues. Cognitive Science, 34(2), 338–349. Google Scholar logo with link to Google Scholar
Nespor, M. & Vogel, I. (1986). Prosodic Phonology. Dordrecht: Foris PublicationsGoogle Scholar logo with link to Google Scholar
Newmeyer, F. J. (2017). Form and function in the evolution of grammar. Cognitive Science, 41, 259–276. Google Scholar logo with link to Google Scholar
Newport, E. L., & Aslin, R. (2004). Learning at a distance: Statistical learning of non-adjacent dependencies. Cognitive Psychology, 48(2), 127–162. Google Scholar logo with link to Google Scholar
Nixon, J. S. (submitted). Of mice and men: Is speech sound acquisition statistical or error- driven?
O’Brien, M. G., Jackson, C. N., Gardner, C. E. (2014). Cross-linguistic differences in prosodic cues to syntactic disambiguation in German and English. Applied Psycholinguistics, 35(1), 27–70. Google Scholar logo with link to Google Scholar
Pelucchi, B., Hay, J. F., Saffran, J. R. (2009). Statistical learning in a natural language by 8-month-old infants. Child Development, 80(3), 674–685. Google Scholar logo with link to Google Scholar
Perruchet, P., Tyler, M. D., Galland, N., & Peereman, R. (2004). Learning non- adjacent dependencies: No need for algebraic-like computations. Journal of Experimental Psychology, 133(4), 573–583). Google Scholar logo with link to Google Scholar
Perruchet, P., & Vinter, A. (1998). PARSER: A model for word segmentation. Journal of Memory and Language, 39(2), 246–263. Google Scholar logo with link to Google Scholar
Peña, M., Bonatti, L., Nespor, M., & Mehler, J. (2002). Signal-driven computations in speech processing. Science, 298, 604–607. Google Scholar logo with link to Google Scholar
Pinker, S. (1984). Language learnability and language development. Cambridge, MA: Harvard University Press.Google Scholar logo with link to Google Scholar
Pullum, G. K., & Scholz, B. (2002). Empirical assessment of stimulus poverty arguments. The Linguistic Review, 19, 9–50.Google Scholar logo with link to Google Scholar
Quine, W. V. O. (1960). Word and object. Cambridge, MA: The MIT PressGoogle Scholar logo with link to Google Scholar
Redington, M., Chater, N. & Finch, S. (1998). Distributional information: A powerful cue for acquiring syntactic structures. Cognitive Science, 22, 425–469. Google Scholar logo with link to Google Scholar
Rodriguez-Fornells, A., Cunillera, T., Mestres-Misse, A., & De Diego-Balauger, R. (2009). Neurophysiological mechanisms involved in language learning in adults. Philosophical Transactions of the Royal Society, B: Biological Sciences, 364(1536), 3711–3734. Google Scholar logo with link to Google Scholar
Saffran, J., Aslin, R., & Newport, E. (1996). Statistical learning by 8-month-old infants. Science, 274, 1926–1928. Google Scholar logo with link to Google Scholar
Saffran, J. R., Newport, E. L., & Aslin, R. N. (1996b). Word segmentation: The role of distributional cues. Journal of Memory and Language, 35(4), 606–621. Google Scholar logo with link to Google Scholar
Saffran, J. R., Newport, E. L., Aslin, R. N., Tunick, R. A., & Barrueco, S. (1997). Incidental language learning: Listening (and learning) out of the corner of your ear. Psychological Science, 8 (2), 101–105. Google Scholar logo with link to Google Scholar
Saksida, A., Langus, A., & Nespor, M. (2017). Co-occurrence statistics as a language-dependent cue for speech segmentation. Developmental Science, 20(3), e12390. Google Scholar logo with link to Google Scholar
Salverda, A. P., Dahan, D., & McQueen, J. M. (2003). The role of prosodic boundaries in the resolution of lexical embedding in speech comprehension. Cognition, 90, 51–89. Google Scholar logo with link to Google Scholar
Scott, R. M., & Fisher, C. (2012). 2.5-Year-olds use cross-situational consistency to learn verbs under referential uncertainty. Cognition, 122, 163–180. Google Scholar logo with link to Google Scholar
Shukla, M., White, K. S., & Aslin, R. (2011). Prosody guides the rapid mapping of auditory word forms onto visual objects in 6-mo-old infants. PNAS, 108(15), 6038–6043. Google Scholar logo with link to Google Scholar
Smith, L., & Yu, C. (2008). Infants rapidly learn word–referent mappings via cross-situational statistics. Cognition, 106, 1558–1568. Google Scholar logo with link to Google Scholar
Smith, L. B., Jayaraman, S., Clerkin, E., & Yu, C. (2018). The developing infant creates a curriculum for statistical learning. Trends in Cognitive Sciences, 22(4), 325–336. Google Scholar logo with link to Google Scholar
St Clair, M. C., Monaghan, P., & Christiansen, M. H. (2010). Learning grammatical categories from distributional cues: Flexible frames for language acquisition. Cognition, 116, 341–360. Google Scholar logo with link to Google Scholar
Stumper, B., Bannard, C., Lieven, E. V., & Tomasello, M. (2011). “Frequent frames” in German child-directed speech: A limited cue to grammatical categories. Cognitive Science, 35(6), 1190–1205. Google Scholar logo with link to Google Scholar
Swingley, D. (2005). Statistical clustering and the contents of infant vocabulary. Cognitive Psychology, 50(1), 86–132. Google Scholar logo with link to Google Scholar
Thiessen, E. D., & Saffran, J. R. (2003). When cues collide: Use of stress and statistical cues to word boundaries by 7- to 9-month-old infants. Developmental Psychology, 39, 706–716. Google Scholar logo with link to Google Scholar
Trotter, A. S., Frost, R. L. A., & Monaghan, P. (2019). Chained melody: Low-level acoustic cues as a guide to phrase structure in comprehension (Unpublished doctoral dissertation).Google Scholar logo with link to Google Scholar
Whitacre, J. (2010). Degeneracy: A link between evolvability, robustness and complexity in biological systems. Theoretical Biology and Medical Modelling, 7, 6. Google Scholar logo with link to Google Scholar
White, L., & Turk, A. E. (2010). English words on a Procrustean bed: Polysyllabic shortening reconsidered. Journal of Phonetics, 38(3), 459–471. Google Scholar logo with link to Google Scholar
Yu, C., & Smith, L. B. (2012). Modeling cross situational word referent learning: Prior questions. Psychological Review, 119(1), 21–39. Google Scholar logo with link to Google Scholar
Yurovsky, D., Smith, L. B., & Yu, C. (2013). Statistical word learning at scale: The baby’s view is better. Developmental Science, 16, 959–966.Google Scholar logo with link to Google Scholar
Yurovsky, D., Boyer, T. W., Smith, L. B., & Yu, C. (2013). Probabilistic cue combination: Less is more. Developmental Science, 16(2), 149–158. Google Scholar logo with link to Google Scholar
Zipf, G. K. (1935). Psycho-biology of languages. Cambridge, MA: The MIT Press.Google Scholar logo with link to Google Scholar
Cited by (2)

Cited by two other publications

Mirzaei, Azizullah, Mahshid Azizi Farsani & Heesun Chang
2023. Statistical learning of L2 lexical bundles through unimodal, bimodal, and multimodal stimuli. Language Teaching Research DOI logo
Munro, Natalie, Elise Baker, Sarah Masso, Lynn Carson, Taiying Lee, Anita M.-Y. Wong & Stephanie F. Stokes
2021. Vocabulary Acquisition and Usage for Late Talkers Treatment: Effect on Expressive Vocabulary and Phonology. Journal of Speech, Language, and Hearing Research 64:7 ► pp. 2682 ff. DOI logo

This list is based on CrossRef data as of 6 december 2025. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.

Mobile Menu Logo with link to supplementary files background Layer 1 prag Twitter_Logo_Blue