Statistical Language Learning in Infancy

Saffran, Jenny R.

doi:10.1111/cdep.12355

Cited by 62 publications

(65 citation statements)

References 43 publications

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“…Nevertheless, by observing the multiple predictive patterns in the surface structure of numbers (Yuan, Xiang, Crandall, & Smith, 2020), preschoolers learn to map spoken multidigit number words to their written forms (Mix, Prather, Smith, & Stockton, 2014;Yuan, Prather, Mix, & Smith, 2019). In sum, children learn from complex, irregular input (i.e., language, symbolic numbers) without supervised instruction, in order to make predictions about their environment (Saffran, 2020). Emotion concepts are possibly learned in a similar fashion, allowing infants to make predictions and inferences about their emotional environments beyond simple affective dimensions (i.e., valence, arousal).…”

Section: Katie Hoemannmentioning

confidence: 99%

Comment: A role of Language in Infant Emotion Concept Acquisition

2020

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Ruba and Repacholi (2019) review an important debate in the emotion development literature: whether infants can perceive and understand facial configurations as instances of discrete emotion categories. Consistent with a psychological constructionist account (Lindquist & Gendron, 2013; Shablack & Lindquist, 2019), they conclude that infants can perceive valence on faces, but argue the evidence is far from clear that infants perceive and understand discrete emotions. Ruba and Repacholi outline a novel developmental trajectory of emotion perception and understanding in which early emotion concept learning may be language-independent. In this comment, we argue that language may play a role in emotion concept acquisition even prior to children’s ability to produce emotion labels. We look forward to future research addressing this hypothesis.

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Section: Katie Hoemannmentioning

confidence: 99%

Comment: A role of Language in Infant Emotion Concept Acquisition

2020

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“…Statistical learning is a form of implicit learning in which individuals detect and use regularities within complex input to improve their knowledge and skills, primarily by providing evidence to make predications and reduce uncertainty [27]. It has been observed across language domains in typically functioning infants, children, and adults [28][29][30].…”

Section: Statistical Learning and Shape Biasmentioning

confidence: 99%

Teaching shape bias to increase the expressive vocabularies of late talkers

Niese¹,

Brackenbury²

2020

Clin Arch Commun Disord

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Shape bias is a lexical principle that suggests shape is the primary feature by which concrete noun labels apply to other objects. It helps children generalize the labels of newly encountered words to other referents. Shape bias typically develops around two years of age and has been linked with increases in expressive vocabulary. Smith et al. [1], for example, found significant increases in 17-month-olds' noun vocabularies following an intervention that taught shape bias. Methods: The present investigation, a non-randomized pilot study, applied an eight-week shape bias-based intervention to Late Talkers who were between the ages of 23 and 34 months. The eight participants were exposed to novel objects, associated nonsense spoken labels, and objects that did and did not match their shape. Pre-and post-treatment expressive vocabularies were measured using the MacArthur-Bates Communicative Development Inventory [2]. Results: The participants increased their expressive vocabularies by 24 to 119 words, adding more new nouns than other word types. Conclusions: These results, along with lessons learned about implementing the treatment, support larger-scale research on teaching shape bias to positively influence the expressive vocabularies of Late Talkers.

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“…Here, we examine whether learning that is based upon the distributional properties of perceptual input also applies to vocal emotion cues. This type of learning has already been implicated in a range of developmental processes that includes children’s category learning in language (Saffran, 2020 ), faces (Dotsch et al, 2017 ), color, and action sequences (see Frost et al, 2019 for review). For instance, distributional statistics can aid children’s language learning by allowing them to detect phoneme categories (Maye et al, 2002 ) and can influence adults’ color perceptions based on the amounts of each color in their current environment (Levari et al, 2018 ).…”

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confidence: 99%

Categorization of Vocal Emotion Cues Depends on Distributions of Input

et al. 2021

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Learners use the distributional properties of stimuli to identify environmentally relevant categories in a range of perceptual domains, including words, shapes, faces, and colors. We examined whether similar processes may also operate on affective information conveyed through the voice. In Experiment 1, we tested how adults (18–22-year-olds) and children (8–10-year-olds) categorized affective states communicated by vocalizations varying continuously from “calm” to “upset.” We found that the threshold for categorizing both verbal (i.e., spoken word) and nonverbal (i.e., a yell) vocalizations as “upset” depended on the statistical distribution of the stimuli participants encountered. In Experiment 2, we replicated and extended these findings in adults using vocalizations that conveyed multiple negative affect states. These results suggest perceivers’ flexibly and rapidly update their interpretation of affective vocal cues based upon context. Supplementary Information The online version contains supplementary material available at 10.1007/s42761-021-00038-w.

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Statistical Language Learning in Infancy

Cited by 62 publications

References 43 publications

Comment: A role of Language in Infant Emotion Concept Acquisition

Comment: A role of Language in Infant Emotion Concept Acquisition

Teaching shape bias to increase the expressive vocabularies of late talkers

Categorization of Vocal Emotion Cues Depends on Distributions of Input

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