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Lany J, Karaman F, Hay JF. A changing role for transitional probabilities in word learning during the transition to toddlerhood? Dev Psychol 2024; 60:567-581. [PMID: 38271022 PMCID: PMC10922822 DOI: 10.1037/dev0001641] [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] [Indexed: 01/27/2024]
Abstract
Infants' sensitivity to transitional probabilities (TPs) supports language development by facilitating mapping high-TP (HTP) words to meaning, at least up to 18 months of age. Here we tested whether this HTP advantage holds as lexical development progresses, and infants become better at forming word-referent mappings. Two groups of 24-month-olds (N = 64 and all White, tested in the United States) first listened to Italian sentences containing HTP and low-TP (LTP) words. We then used HTP and LTP words, and sequences that violated these statistics, in a mapping task. Infants learned HTP and LTP words equally well. They also learned LTP violations as well as LTP words, but learned HTP words better than HTP violations. Thus, by 2 years of age sensitivity to TPs does not lead to an HTP advantage but rather to poor mapping of violations of HTP word forms. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
- Jill Lany
- Department of Psychological Sciences, University of Liverpool
| | | | - Jessica F Hay
- Department of Psychology, University of Tennessee, Knoxville
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Karaman F, Lany J, Hay JF. Can Infants Retain Statistically Segmented Words and Mappings Across a Delay? Cogn Sci 2024; 48:e13433. [PMID: 38528792 PMCID: PMC10977659 DOI: 10.1111/cogs.13433] [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: 10/04/2022] [Revised: 11/01/2023] [Accepted: 02/20/2024] [Indexed: 03/27/2024]
Abstract
Infants are sensitive to statistics in spoken language that aid word-form segmentation and immediate mapping to referents. However, it is not clear whether this sensitivity influences the formation and retention of word-referent mappings across a delay, two real-world challenges that learners must overcome. We tested how the timing of referent training, relative to familiarization with transitional probabilities (TPs) in speech, impacts English-learning 23-month-olds' ability to form and retain word-referent mappings. In Experiment 1, we tested infants' ability to retain TP information across a 10-min delay and use it in the service of word learning. Infants successfully mapped high-TP but not low-TP words to referents. In Experiment 2, infants readily mapped the same words even when they were unfamiliar. In Experiment 3, high- and low-TP word-referent mappings were trained immediately after familiarization, and infants readily remembered these associations 10 min later. In sum, although 23-month-old infants do not need strong statistics to map word forms to referents immediately, or to remember those mappings across a delay, infants are nevertheless sensitive to these statistics in the speech stream, and they influence mapping after a delay. These findings suggest that, by 23 months of age, sensitivity to statistics in speech may impact infants' language development by leading word forms with low coherence to be poorly mapped following even a short period of consolidation.
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Affiliation(s)
- Ferhat Karaman
- Department of Psychology, Uşak University, Turkey
- Department of Linguistics, University of California, Los Angeles
| | - Jill Lany
- Department of Psychology, Lancaster University, UK
| | - Jessica F. Hay
- Department of Psychology, University of Tennessee, Knoxville
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3
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Endress AD. Hebbian learning can explain rhythmic neural entrainment to statistical regularities. Dev Sci 2024:e13487. [PMID: 38372153 DOI: 10.1111/desc.13487] [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: 04/28/2023] [Revised: 12/26/2023] [Accepted: 01/29/2024] [Indexed: 02/20/2024]
Abstract
In many domains, learners extract recurring units from continuous sequences. For example, in unknown languages, fluent speech is perceived as a continuous signal. Learners need to extract the underlying words from this continuous signal and then memorize them. One prominent candidate mechanism is statistical learning, whereby learners track how predictive syllables (or other items) are of one another. Syllables within the same word predict each other better than syllables straddling word boundaries. But does statistical learning lead to memories of the underlying words-or just to pairwise associations among syllables? Electrophysiological results provide the strongest evidence for the memory view. Electrophysiological responses can be time-locked to statistical word boundaries (e.g., N400s) and show rhythmic activity with a periodicity of word durations. Here, I reproduce such results with a simple Hebbian network. When exposed to statistically structured syllable sequences (and when the underlying words are not excessively long), the network activation is rhythmic with the periodicity of a word duration and activation maxima on word-final syllables. This is because word-final syllables receive more excitation from earlier syllables with which they are associated than less predictable syllables that occur earlier in words. The network is also sensitive to information whose electrophysiological correlates were used to support the encoding of ordinal positions within words. Hebbian learning can thus explain rhythmic neural activity in statistical learning tasks without any memory representations of words. Learners might thus need to rely on cues beyond statistical associations to learn the words of their native language. RESEARCH HIGHLIGHTS: Statistical learning may be utilized to identify recurring units in continuous sequences (e.g., words in fluent speech) but may not generate explicit memory for words. Exposure to statistically structured sequences leads to rhythmic activity with a period of the duration of the underlying units (e.g., words). I show that a memory-less Hebbian network model can reproduce this rhythmic neural activity as well as putative encodings of ordinal positions observed in earlier research. Direct tests are needed to establish whether statistical learning leads to declarative memories for words.
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Affiliation(s)
- Ansgar D Endress
- Department of Psychology, City, University of London, London, UK
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Abreu R, Postarnak S, Vulchanov V, Baggio G, Vulchanova M. The association between statistical learning and language development during childhood: A scoping review. Heliyon 2023; 9:e18693. [PMID: 37554804 PMCID: PMC10405008 DOI: 10.1016/j.heliyon.2023.e18693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 07/09/2023] [Accepted: 07/25/2023] [Indexed: 08/10/2023] Open
Abstract
The statistical account of language acquisition asserts that language is learned through computations on the statistical regularities present in natural languages. This type of account can predict variability in language development measures as arising from individual differences in extracting this statistical information. Given that statistical learning has been attested across different domains and modalities, a central question is which modality is more tightly yoked with language skills. The results of a scoping review, which aimed for the first time at identifying the evidence of the association between statistical learning skills and language outcomes in typically developing infants and children, provide preliminary support for the statistical learning account of language acquisition, mostly in the domain of lexical outcomes, indicating that typically developing infants and children with stronger auditory and audio-visual statistical learning skills perform better on lexical competence tasks. The results also suggest that the relevance of statistical learning skills for language development is dependent on sensory modality.
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Affiliation(s)
- Regina Abreu
- Language Acquisition and Language Processing Lab, Norwegian University of Science and Technology – Trondheim, Norway
| | | | - Valentin Vulchanov
- Language Acquisition and Language Processing Lab, Norwegian University of Science and Technology – Trondheim, Norway
| | - Giosuè Baggio
- Language Acquisition and Language Processing Lab, Norwegian University of Science and Technology – Trondheim, Norway
| | - Mila Vulchanova
- Language Acquisition and Language Processing Lab, Norwegian University of Science and Technology – Trondheim, Norway
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Dal Ben R, Prequero IT, Souza DDH, Hay JF. Speech Segmentation and Cross-Situational Word Learning in Parallel. Open Mind (Camb) 2023; 7:510-533. [PMID: 37637304 PMCID: PMC10449405 DOI: 10.1162/opmi_a_00095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 07/06/2023] [Indexed: 08/29/2023] Open
Abstract
Language learners track conditional probabilities to find words in continuous speech and to map words and objects across ambiguous contexts. It remains unclear, however, whether learners can leverage the structure of the linguistic input to do both tasks at the same time. To explore this question, we combined speech segmentation and cross-situational word learning into a single task. In Experiment 1, when adults (N = 60) simultaneously segmented continuous speech and mapped the newly segmented words to objects, they demonstrated better performance than when either task was performed alone. However, when the speech stream had conflicting statistics, participants were able to correctly map words to objects, but were at chance level on speech segmentation. In Experiment 2, we used a more sensitive speech segmentation measure to find that adults (N = 35), exposed to the same conflicting speech stream, correctly identified non-words as such, but were still unable to discriminate between words and part-words. Again, mapping was above chance. Our study suggests that learners can track multiple sources of statistical information to find and map words to objects in noisy environments. It also prompts questions on how to effectively measure the knowledge arising from these learning experiences.
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Affiliation(s)
- Rodrigo Dal Ben
- Universidade Federal de São Carlos, São Carlos, São Paulo, Brazil
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Endress AD, Johnson SP. Hebbian, correlational learning provides a memory-less mechanism for Statistical Learning irrespective of implementational choices: Reply to Tovar and Westermann (2022). Cognition 2023; 230:105290. [PMID: 36240613 DOI: 10.1016/j.cognition.2022.105290] [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: 06/12/2022] [Revised: 08/30/2022] [Accepted: 09/17/2022] [Indexed: 11/07/2022]
Abstract
Statistical learning relies on detecting the frequency of co-occurrences of items and has been proposed to be crucial for a variety of learning problems, notably to learn and memorize words from fluent speech. Endress and Johnson (2021) (hereafter EJ) recently showed that such results can be explained based on simple memory-less correlational learning mechanisms such as Hebbian Learning. Tovar and Westermann (2022) (hereafter TW) reproduced these results with a different Hebbian model. We show that the main differences between the models are whether temporal decay acts on both the connection weights and the activations (in TW) or only on the activations (in EJ), and whether interference affects weights (in TW) or activations (in EJ). Given that weights and activations are linked through the Hebbian learning rule, the networks behave similarly. However, in contrast to TW, we do not believe that neurophysiological data are relevant to adjudicate between abstract psychological models with little biological detail. Taken together, both models show that different memory-less correlational learning mechanisms provide a parsimonious account of Statistical Learning results. They are consistent with evidence that Statistical Learning might not allow learners to learn and retain words, and Statistical Learning might support predictive processing instead.
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Affiliation(s)
| | - Scott P Johnson
- Department of Psychology, University of California, Los Angeles, United States of America
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Ellis EM, Borovsky A, Elman JL, Evans JL. Toddlers' Ability to Leverage Statistical Information to Support Word Learning. Front Psychol 2021; 12:600694. [PMID: 33897523 PMCID: PMC8063043 DOI: 10.3389/fpsyg.2021.600694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 02/15/2021] [Indexed: 12/14/2022] Open
Abstract
PURPOSE This study investigated whether the ability to utilize statistical regularities from fluent speech and map potential words to meaning at 18-months predicts vocabulary at 18- and again at 24-months. METHOD Eighteen-month-olds (N = 47) were exposed to an artificial language with statistical regularities within the speech stream, then participated in an object-label learning task. Learning was measured using a modified looking-while-listening eye-tracking design. Parents completed vocabulary questionnaires when their child was 18-and 24-months old. RESULTS Ability to learn the object-label pairing for words after exposure to the artificial language predicted productive vocabulary at 24-months and amount of vocabulary change from 18- to 24 months, independent of non-verbal cognitive ability, socio-economic status (SES) and/or object-label association performance. CONCLUSION Eighteen-month-olds' ability to use statistical information derived from fluent speech to identify words within the stream of speech and then to map the "words" to meaning directly predicts vocabulary size at 24-months and vocabulary change from 18 to 24 months. The findings support the hypothesis that statistical word segmentation is one of the important aspects of word learning and vocabulary acquisition in toddlers.
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Affiliation(s)
- Erica M. Ellis
- Department of Communication Disorders, California State University, Los Angeles, Los Angeles, CA, United States
| | - Arielle Borovsky
- Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, IN, United States
| | - Jeffrey L. Elman
- Center for Research in Language, University of California, San Diego, La Jolla, CA, United States
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, United States
| | - Julia L. Evans
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
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When statistics collide: The use of transitional and phonotactic probability cues to word boundaries. Mem Cognit 2021; 49:1300-1310. [PMID: 33751490 DOI: 10.3758/s13421-021-01163-4] [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] [Accepted: 03/01/2021] [Indexed: 11/08/2022]
Abstract
Statistical regularities in linguistic input, such as transitional probability and phonotactic probability, have been shown to promote speech segmentation. It remains unclear, however, whether or how the combination of transitional probabilities and subtle phonotactic probabilities influence segmentation. The present study provides a fine-grained investigation of the effects of such combined statistics. Adults (N = 81) were tested in one of two conditions. In the Anchor condition, they heard a continuous stream of words with small differences in phonotactic probabilities. In the Uniform condition, all words had comparable phonotactic probabilities. In both conditions, transitional probability was stronger in words than in part-words. Only participants from the Anchor condition preferred words at test, indicating that the combination of transitional probabilities and subtle phonotactic probabilities may facilitate speech segmentation. We discuss the methodological implications of our findings, which demonstrate that even small phonotactic variations should be accounted for when investigating statistical speech segmentation.
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Transitional probabilities and expectation for word length impact verbal statistical learning. ACTA PSYCHOLOGICA SINICA 2021. [DOI: 10.3724/sp.j.1041.2021.00565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Endress AD, Slone LK, Johnson SP. Statistical learning and memory. Cognition 2020; 204:104346. [PMID: 32615468 DOI: 10.1016/j.cognition.2020.104346] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 05/12/2020] [Accepted: 05/30/2020] [Indexed: 01/01/2023]
Abstract
Learners often need to identify and remember recurring units in continuous sequences, but the underlying mechanisms are debated. A particularly prominent candidate mechanism relies on distributional statistics such as Transitional Probabilities (TPs). However, it is unclear what the outputs of statistical segmentation mechanisms are, and if learners store these outputs as discrete chunks in memory. We critically review the evidence for the possibility that statistically coherent items are stored in memory and outline difficulties in interpreting past research. We use Slone and Johnson's (2018) experiments as a case study to show that it is difficult to delineate the different mechanisms learners might use to solve a learning problem. Slone and Johnson (2018) reported that 8-month-old infants learned coherent chunks of shapes in visual sequences. Here, we describe an alternate interpretation of their findings based on a multiple-cue integration perspective. First, when multiple cues to statistical structure were available, infants' looking behavior seemed to track with the strength of the strongest one - backward TPs, suggesting that infants process multiple cues simultaneously and select the strongest one. Second, like adults, infants are exquisitely sensitive to chunks, but may require multiple cues to extract them. In Slone and Johnson's (2018) experiments, these cues were provided by immediate chunk repetitions during familiarization. Accordingly, infants showed strongest evidence of chunking following familiarization sequences in which immediate repetitions were more frequent. These interpretations provide a strong argument for infants' processing of multiple cues and the potential importance of multiple cues for chunk recognition in infancy.
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Affiliation(s)
- Ansgar D Endress
- Department of Psychology, City, University of London, United Kingdom.
| | - Lauren K Slone
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, United States; Department of Psychology, Hope College, Holland, United States
| | - Scott P Johnson
- Department of Psychology, University of California, Los Angeles, United States
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Lany J, Shoaib A. Individual differences in non-adjacent statistical dependency learning in infants. JOURNAL OF CHILD LANGUAGE 2020; 47:483-507. [PMID: 31190666 DOI: 10.1017/s0305000919000230] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
There is considerable controversy over the factors that shape infants' developing knowledge of grammar. Work with artificial languages suggests that infants' ability to track statistical regularities within the speech they hear could, in principle, support grammatical development. However, little work has tested whether infants' performance on laboratory tasks reflects factors that are relevant in real-world language learning. Here we tested whether the language that infants hear at home, and their receptive language skills, predict their performance on tasks assessing the ability to learn non-adjacent statistical dependencies (NADs) at 15 months, and whether that in turn predicts sensitivity to native-language NADs at 18 months. We found evidence for some (though not all) of these relations, and primarily for females. The results suggest that performance on the artificial language-learning task reveals something about the mechanisms of grammatical development, and that females and males may be learning NADs differently.
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Affiliation(s)
- Jill Lany
- Department of Psychology, University of Notre Dame, USA
| | - Amber Shoaib
- Department of Psychology, University of Notre Dame, USA
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