Three‐ to Four‐Year‐Old Children Rapidly Adapt Their Predictions and Use Them to Learn Novel Word Meanings

Havron, Naomi; Carvalho, Alex de; Fiévet, Anne‐Caroline; Christophe, Anne

doi:10.1111/cdev.13113

Cited by 46 publications

(87 citation statements)

References 36 publications

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“…This perspective receives support from numerous studies showing that prediction effects during language comprehension are absent or less pronounced for populations with reduced executive resources, such as children, older individuals, and non-native speakers (e.g., Federmeier et al, 2002;Federmeier & Kutas, 2005;Dagerman, et al, 2006;Federmeier et al, 2010;Mani & Huettig, 2012;Wlotko & Federmeier, 2012;Martin et al, 2013;Kaan, 2014;Mitsugi & Macwhinney, 2016;Gambi et al, 2018;Payne & Federmeier, 2018;cf. Dave et al, 2018;Havron et al, 2019). Furthermore, several neuroimaging studies have reported sensitivity to linguistic manipulations in what appear to be cortical regions thought to support domain-general executive function (e.g., Kaan & Swaab;Kuperberg et al, 2003;Novick et al, 2005;Rodd et al, 2005;Novais-Santos, 2007;January et al, 2009;Peelle et al, 2010;Rogalsky & Hickock, 2011;Nieuwland et al, 2012;Wild et al, 2012;McMillan et al, 2012McMillan et al, , 2013, suggesting that such regions may also be implicated in language processing, including perhaps prediction.…”

Section: Introductionmentioning

confidence: 99%

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Shain

Blank

Schijndel

et al. 2019

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Much research in cognitive neuroscience supports prediction as a canonical computation of cognition in many domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brain's response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed deconvolutional time series regression technique that supports data-driven hemodynamic response function discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found we found effects of prediction measures in the language network but not in the domain-general, multiple-demand network. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms. 4 2016; Lopopolo et al., 2017; see Table 1 for summary), a well-established predictor of behavioral measures in naturalistic language comprehension (These previous naturalistic studies of linguistic prediction effects in the brainusing estimates of prediction effort such as surprisal, the negative log probability of a word given its context, or entropy, an information-theoretic measure of the degree of constraint placed by the context on upcoming words (Hale, 2001)have yielded mixed results on the existence, type, and functional location of such effects. For example, of the lexicalized and unlexicalized (part-of-speech) bigram and trigram models of word surprisal explored in Brennan et al. (2016), only part-of-speech bigrams positively modulated neural responses in most regions of the functionally localized language network. Lexicalized bi-and trigrams and part-of-speech trigrams yielded generally null or negative results (16 out of 18 comparisons). By contrast, Willems et al. (2015) found lexicalized trigram effects in regions typically associated with language processing (e.g., anterior and posterior temporal lobe). In addition, Willems et al. (2015) and Lopopolo et al. (2017) found prediction effects in regions that are unlikely to be specialized for language processing, including (aggregating across both studies) the brain stem, amygdala, putamen, and hippocampus, as well as in superior frontal areas more typically associated with domain-general executive functions like...

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Section: Introductionmentioning

confidence: 99%

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Shain

Blank

Schijndel

et al. 2019

Preprint

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“…For example, in a recent study, familiar words were used to induce children to expect either a noun or a verb following a specific syntactic context (that could be followed by either nouns or verbs). Thus, after hearing four familiar verbs in this context, children were more likely to infer that a novel word appearing in this context referred to an action, than children who heard four familiar nouns in this context (Havron, de Carvalho, Fiévet, & Christophe, 2019).…”

Section: Introductionmentioning

confidence: 89%

“…Appendix for the items), because no distractor is available. No feedback is given to the participants to validate their interpretation after their choice, in any item, induction or test, as was the case in other similar previous studies(Havron et al, 2019;Yurovsky et al, 2016 andDautriche et al, 2018).…”

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confidence: 99%

4-5-year-old children adapt to the reliability of conflicting sources of information to learn novel words

Beretti¹,

Havron²,

Christophe³

2020

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A central challenge in language acquisition is the integration of multiple sources of information, potentially in conflict, to acquire new knowledge and adjust current linguistic representations. One way to accomplish this is to assign more weight to more reliable sources of information in context. We test the hypothesis that children adjust the weight of different sources of information during learning, considering two specific sources of information: their knowledge of the meaning of familiar words (semantics) and their familiarity with syntax. We varied the reliability of these sources of information through an induction phase (reliable-syntax or reliable-semantics). At test, French 4-5-year-old children and adults listened to sentences where information provided by these two cues conflicted, and were asked to choose between two videos that illustrate the sentence. One video presented the reasonable choice if the sentence is assumed to be syntactically correct, but familiar words refer to novel things (e.g., une mange - "an eats" describes a novel object). The other video was the reasonable choice if the sentence is assumed to be syntactically incorrect and familiar words’ meaning is preserved (e.g., "an eats" describes a girl eating, and should have actually been "she eats"). As predicted, the proportion of syntactic choices (e.g., interpreting mange - "eats", as a novel noun) was found to be higher in the reliable-syntax condition than in the reliable-semantics condition, showing that children and adults can adapt their expectations to the reliability of sources of information.

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“…First, researchers might choose to perform the analysis over the duration of the whole trial (orange line in Figure 3, panel a; panel c). Alternatively, they might choose an analysis such as the nonparametric permutation test (Maris & Oostenveldt, 2007; see Havron, de Carvalho, Fiévet, & Christophe, 2019, for an implementation in a visual world task with young children), which will determine the time windows of statistically significant differences in a bottom‐up fashion without inflating false‐positive rates (the time window in which the permutation analysis shows a significant effect is illustrated by the red shaded area in Figure 3, panel a). These choices have the advantage of not being tied to any specific predetermined time window.…”

Section: Best Practices In Preregistration For Infant Studiesmentioning

confidence: 99%

Preregistration in infant research—A primer

Havron¹,

Bergmann

Tsuji

2020

Infancy

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Preregistration, the act of specifying a research plan in advance, is becoming more common in scientific research. Infant researchers contend with unique problems that might make preregistration particularly challenging. Infants are a hard‐to‐reach population, usually yielding small sample sizes, they can only complete a limited number of trials, and they can be excluded based on hard‐to‐predict complications (e.g., parental interference, fussiness). In addition, as effects themselves potentially change with age and population, it is hard to calculate an a priori effect size. At the same time, these very factors make preregistration in infant studies a valuable tool. A priori examination of the planned study, including the hypotheses, sample size, and resulting statistical power, increases the credibility of single studies and adds value to the field. Preregistration might also improve explicit decision making to create better studies. We present an in‐depth discussion of the issues uniquely relevant to infant researchers, and ways to contend with them in preregistration and study planning. We provide recommendations to researchers interested in following current best practices.

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Three‐ to Four‐Year‐Old Children Rapidly Adapt Their Predictions and Use Them to Learn Novel Word Meanings

Cited by 46 publications

References 36 publications

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

4-5-year-old children adapt to the reliability of conflicting sources of information to learn novel words

Preregistration in infant research—A primer

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