Stability and change in young children's linguistic experience during the COVID-19 pandemic: insight from a citizen-science sample in the United States.

Early in the COVID-19 pandemic, extensive lockdowns interrupted daily routines, including childcare. We asked whether these interruptions, and the inevitable changes in the people with whom children spent their waking hours, caused changes in the languages that children heard. We retrospectively queried parents of young children (0-4 years) in the US about childcare arrangements and exposure to English and non-English languages at four timepoints from February 2020 to September 2021. Despite discontinuity in childcare arrangements, we found that children's exposure to English versus other languages remained relatively stable. We also identified demographic variables (child age at pandemic onset, parental proficiency in a non-English language) that consistently predicted exposure to non-English languages. Thus, multilingually-exposed children in this population did not appear to significantly gain or lose the opportunity to hear non-English languages overall. These results provide insight into the experiences of this unique cohort and inform our understanding of how language development can be shaped by complex environmental systems.

Talker variability is not always the right noise: 14 month olds struggle to learn dissimilar word-object pairs under talker variability conditions.

Seminal work by Stager & Werker (1997) finds that 14-month-olds can rapidly learn two word-object pairings if the words are distinct (e.g. "neem" and "lif") but not similar (e.g. the minimal pair "bih" and "dih"). More recently, studies have found that adding talker variability during exposure to new word-object pairs lets 14-month-olds succeed on the more challenging minimal pair task, presumably due to talker variability highlighting the "relevant" consistencies between the similar words (Rost & McMurray, 2009; Galle et al., 2015; Hohle et al., 2020). It remains an open question, however, whether talker variability would be similarly useful for learning new word-object pairings when the words themselves are already distinct, or whether instead this extra variability may extinguish learning due to increased task demands. We find evidence for the latter. Namely, in our sample of 54 English-learning 14-month-olds, training infants on two word-object pairings (e.g. "neem" with a dog toy and "lof" with a kitchen tool) only led them to notice when the words and objects were switched if they were trained with single-speaker identical word tokens. When the training featured talker variability (from one or multiple talkers) infants failed to learn the pairings. We suggest that when talker variability is not necessary to highlight the invariant differences between similar words, it may actually increase task difficulty, making it harder for infants to determine what to attend to in the earliest phases of word learning.

Talker variability shapes early word representations in English-learning 8-month-olds.

Infants must form appropriately specific representations of how words sound and what they mean. Previous research suggests that while 8-month-olds are learning words, they struggle with recognizing different-sounding instances of words (e.g., from new talkers) and with rejecting incorrect pronunciations. We asked how adding talker variability during learning may change infants' ability to learn and recognize words. Monolingual English-learning 7- to 9-month-olds heard a single novel word paired with an object in either a "no variability," "within-talker variability," or "between-talker variability" habituation. We then tested whether infants formed appropriately specific representations by changing the talker (Experiment 1a) or mispronouncing the word (Experiment 2) and by changing the trained word or object altogether (both experiments). Talker variability influenced learning. Infants trained with no-talker variability learned the word-object link, but failed to recognize the word trained by a new talker, and were insensitive to the mispronunciation. Infants trained with talker variability dishabituated only to the new object, exhibiting difficulty forming the word-object link. Neither pattern is adult-like. Results are reported for both in-lab and Zoom participants. Implications for the role of talker variability in early word learning are discussed.

Quantifying Talker Variability in North-American Infants' Daily Input.

Words sound slightly different each time they are said, both by the same talker and across talkers. Rather than hurting learning, lab studies suggest that talker variability helps infants learn similar sounding words. However, very little is known about how much variability infants hear within a single talker or across talkers in naturalistic input. Here, we quantified these types of talker variability for highly frequent words spoken to 44 infants, from naturalistic recordings sampled longitudinally over a year of life (from 6 to 17 months). We used non-contrastive acoustic measurements (e.g., mean pitch, duration, harmonics-to-noise ratio) and holistic measures of sound similarity (normalized acoustic distance) to quantify acoustic variability. We find three key results. First, pitch-based variability was generally lower for infants' top talkers than across their other talkers, but overall acoustic distance is higher for tokens from the top talker versus the others. Second, the amount of acoustic variability infants heard could not be predicted from, and thus was not redundant with, other properties of the input such as the number of talkers or tokens, or proportion of speech from particular sources (e.g., women, children, electronics). Finally, we find that patterns of pitch-based acoustic variability heard in naturalistic input were similar to those found with in-lab stimuli that facilitated word learning. This large-scale quantification of talker variability in infants' everyday input sets the stage for linking naturally occurring variability "in the wild" to early word learning.

Cross-situational statistical learning in younger and older adults.

Research investigating statistical learning, the process of tracking regularities in the environment, in older adults has been limited; with existing studies suggesting there are age-related declines. We aim to further understand older adults' statistical learning abilities using a cross-situational statistical learning paradigm in which learners map novel words to novel objects. In Experiment 1, we manipulated task difficulty and found an overall age deficit but no interaction between age and difficulty. In Experiment 2, after extended practice with a first set of object-word mappings, learners could remap a subset of previously learned words to novel objects. Based on hyper-binding, older adults might be more willing to remap previously learned words to novel objects. However, despite overall poorer learning, older adults were actually less likely to remap. Even though older adults may have an associative memory deficit, learned associations are not more weakly bound for older relative to younger adults.

Desirable Difficulties in Language Learning? How Talker Variability Impacts Artificial Grammar Learning.

Contending with talker variability has been found to lead to processing costs but also benefits by focusing learners on invariant properties of the signal, indicating that talker variability acts as a desirable difficulty. That is, talker variability may lead to initial costs followed by long-term benefits for retention and generalization. Adult participants learned an artificial grammar affording learning of multiple components in two experiments varying in difficulty. They learned from one, two, or eight talkers and were tested at three time points. The eight-talker condition did not impact learning. The two-talker condition negatively impacted some aspects of learning, but only under more difficult conditions. Generalization of the grammatical dependency was difficult. Thus, we discovered that high and limited talker variability can differentially impact artificial grammar learning. However, talker variability did not act as a desirable difficulty in the current paradigm as the few evidenced costs were not related to long-term benefits.

Is Language Production Planning Emergent From Action Planning? A Preliminary Investigation.

The nature of syntactic planning for language production may reflect language-specific processes, but an alternative is that syntactic planning is an example of more domain-general action planning processes. If so, language and non-linguistic action planning should have identifiable commonalities, consistent with an underlying shared system. Action and language research have had little contact, however, and such comparisons are therefore lacking. Here, we address this gap by taking advantage of a striking similarity between two phenomena in language and action production. One is known as syntactic priming-the tendency to re-use a recently produced sentence structure-and the second is hysteresis-the tendency to re-use a previously executed abstract action plan, such as a limb movement. We examined syntactic priming/hysteresis in parallel language and action tasks intermixed in a single experimental session. Our goals were to establish the feasibility of investigating language and action planning within the same participants and to inform debates on the language-specific vs. domain-general nature of planning systems. In both action and language tasks, target trials afforded two alternative orders of subcomponents in the participant's response: in the language task, a picture could be described with two different word orders, and in the action task, locations on a touch screen could be touched in two different orders. Prime trials preceding the target trial promoted one of two plans in the respective domain. Manipulations yielded higher rates of primed behavior in both tasks. In an exploratory cross-domain analysis, there was some evidence for stronger priming effects in some combinations of action and language priming conditions than others. These results establish a method for investigating the degree to which language planning is part of a domain-general action planning system.

Accuracy of the Language Environment Analysis System Segmentation and Metrics: A Systematic Review.

Purpose The Language Environment Analysis (LENA) system provides automated measures facilitating clinical and nonclinical research and interventions on language development, but there are only a few, scattered independent reports of these measures' validity. The objectives of the current systematic review were to (a) discover studies comparing LENA output with manual annotation, namely, accuracy of talker labels, as well as involving adult word counts (AWCs), conversational turn counts (CTCs), and child vocalization counts (CVCs); (b) describe them qualitatively; (c) quantitatively integrate them to assess central tendencies; and (d) quantitatively integrate them to assess potential moderators. Method Searches on Google Scholar, PubMed, Scopus, and PsycInfo were combined with expert knowledge, and interarticle citations resulting in 238 records screened and 73 records whose full text was inspected. To be included, studies must target children under the age of 18 years and report on accuracy of LENA labels (e.g., precision and/or recall) and/or AWC, CTC, or CVC (correlations and/or error metrics). Results A total of 33 studies, in 28 articles, were discovered. A qualitative review revealed most validation studies had not been peer reviewed as such and failed to report key methodology and results. Quantitative integration of the results was possible for a broad definition of recall and precision (M = 59% and 68%, respectively; N = 12-13), for AWC (mean r = .79, N = 13), CVC (mean r = .77, N = 5), and CTC (mean r = .36, N = 6). Publication bias and moderators could not be assessed meta-analytically. Conclusion Further research and improved reporting are needed in studies evaluating LENA segmentation and quantification accuracy, with work investigating CTC being particularly urgent. Supplemental Material https://osf.io/4nhms/.

Look who's talking: A comparison of automated and human-generated speaker tags in naturalistic day-long recordings.

The LENA system has revolutionized research on language acquisition, providing both a wearable device to collect day-long recordings of children's environments, and a set of automated outputs that process, identify, and classify speech using proprietary algorithms. This output includes information about input sources (e.g., adult male, electronics). While this system has been tested across a variety of settings, here we delve deeper into validating the accuracy and reliability of LENA's automated diarization, i.e., tags of who is talking. Specifically, we compare LENA's output with a gold standard set of manually generated talker tags from a dataset of 88 day-long recordings, taken from 44 infants at 6 and 7 months, which includes 57,983 utterances. We compare accuracy across a range of classifications from the original Lena Technical Report, alongside a set of analyses examining classification accuracy by utterance type (e.g., declarative, singing). Consistent with previous validations, we find overall high agreement between the human and LENA-generated speaker tags for adult speech in particular, with poorer performance identifying child, overlap, noise, and electronic speech (accuracy range across all measures: 0-92%). We discuss several clear benefits of using this automated system alongside potential caveats based on the error patterns we observe, concluding with implications for research using LENA-generated speaker tags.

Statistical learning of multiple speech streams: A challenge for monolingual infants.

Language acquisition depends on the ability to detect and track the distributional properties of speech. Successful acquisition also necessitates detecting changes in those properties, which can occur when the learner encounters different speakers, topics, dialects, or languages. When encountering multiple speech streams with different underlying statistics but overlapping features, how do infants keep track of the properties of each speech stream separately? In four experiments, we tested whether 8-month-old monolingual infants (N = 144) can track the underlying statistics of two artificial speech streams that share a portion of their syllables. We first presented each stream individually. We then presented the two speech streams in sequence, without contextual cues signaling the different speech streams, and subsequently added pitch and accent cues to help learners track each stream separately. The results reveal that monolingual infants experience difficulty tracking the statistical regularities in two speech streams presented sequentially, even when provided with contextual cues intended to facilitate separation of the speech streams. We discuss the implications of our findings for understanding how infants learn and separate the input when confronted with multiple statistical structures.

Stacking the evidence: Parents' use of acoustic packaging with preschoolers.

Segmenting continuous events into discrete actions is critical for understanding the world. As infants may lack top-down knowledge of event structure, caregivers provide audiovisual cues to guide the process, aligning action descriptions with event boundaries to increase their salience. This acoustic packaging may be specific to infant-directed speech, but little is known about when and why the use of this cue wanes. We explore whether acoustic packaging persists in parents' teaching of 2.5-5.5-year-old children about various toys. Parents produced a smaller percentage of action speech relative to studies with infants. However, action speech largely remained more aligned to action boundaries relative to non-action speech. Further, for the more challenging novel toys, parents modulated their use of acoustic packaging, providing it more for those children with lower vocabularies. Our findings suggest that acoustic packaging persists beyond interactions with infants, underscoring the utility of multimodal cues for learning, particularly for less knowledgeable learners in challenging learning environments.

Multi-Pattern Visual Statistical Learning in Monolinguals and Bilinguals.

To date, the impact of bilingualism on statistical learning remains unclear. Here we test a novel visual statistical learning task that affords simultaneous learning of two types of regularities: co-occurrence regularities between pairs of elements and the co-occurrence of visual features that could define categories. We compared performance by English monolinguals, Spanish-Catalan bilinguals and Spanish-English bilinguals, as previous studies have suggested that bilinguals might be more open than monolinguals to the presence of multiple regularities, though no previous studies have tested the learning of multiple patterns within a single task. We demonstrated that both monolingual and bilingual participants could learn the co-occurrence probabilities and the features that define categories. To the best of our knowledge, this study is the first to demonstrate that learners can extract co-occurrence regularities along two dimensions in the visual modality. However, we did not detect significant differences in performance across groups. We close by discussing the implications for the growing literature on bilingualism and statistical learning.

Statistical Learning and Bilingualism.

Purpose: Over the last 2 decades, research on statistical learning has demonstrated its importance in supporting language development. Notably, most of the research to date has focused on monolingual populations (or has not reported the language background of participants). Several recent studies, however, have begun to focus on the impact of bilingualism on statistical learning. To date, the results have been quite mixed, with a handful of studies finding differences between monolinguals and bilinguals and several other studies reporting no differences. Thus, the purpose of this manuscript is to review the literature to date on how bilingualism impacts statistical learning abilities. Method: We review the contemporary literature, organized by the age of participants and by task when relevant. Conclusions: We note that there are many discrepant findings within this nascent field, although some trends have emerged. For instance, differences in performance may be attributed to factors such as age of acquisition. However, we note that the state of the field does not yet permit firm clinical recommendations.

Sampling over Nonuniform Distributions: A Neural Efficiency Account of the Primacy Effect in Statistical Learning.

Successful knowledge acquisition requires a cognitive system that is both sensitive to statistical information and able to distinguish among multiple structures (i.e., to detect pattern shifts and form distinct representations). Extensive behavioral evidence has highlighted the importance of cues to structural change, demonstrating how, without them, learners fail to detect pattern shifts and are biased in favor of early experience. Here, we seek a neural account of the mechanism underpinning this primacy effect in learning. During fMRI scanning, adult participants were presented with two artificial languages: a familiar language (L1) on which they had been pretrained followed by a novel language (L2). The languages were composed of the same syllable inventory organized according to unique statistical structures. In the absence of cues to the transition between languages, posttest familiarity judgments revealed that learners on average more accurately segmented words from the familiar language compared with the novel one. Univariate activation and functional connectivity analyses showed that participants with the strongest learning of L1 had decreased recruitment of fronto-subcortical and posterior parietal regions, in addition to a dissociation between downstream regions and early auditory cortex. Participants with a strong new language learning capacity (i.e., higher L2 scores) showed the opposite trend. Thus, we suggest that a bias toward neural efficiency, particularly as manifested by decreased sampling from the environment, accounts for the primacy effect in learning. Potential implications of this hypothesis are discussed, including the possibility that "inefficient" learning systems may be more sensitive to structural changes in a dynamic environment.

Anchors aweigh: The impact of overlearning on entrenchment effects in statistical learning.

Previous research has revealed that when learners encounter multiple artificial languages in succession only the first is learned, unless there are contextual cues correlating with the change in structure or if exposure to the second language is protracted. These experiments provided a fixed amount of exposure irrespective of when learning occurred. Here, the authors presented learners with 2 consecutive artificial languages testing learning after each minute of familiarization. In Experiment 1, learners received fixed input, and the authors replicated the primacy effect. In Experiment 2, learners advanced to the second language immediately following robust learning of the first language (thereby limiting additional exposure past the point of learning). Remarkably, learners tended to acquire and retain both languages, although contextual cues did not boost performance further. Notably, there was no correlation between performance on this task and a flanker task that measured inhibitory control. Overall, the findings suggest that anchoring effects in statistical learning may be because of overlearning. We speculate that learners may reduce their attention to the input once they achieve a low level of estimation uncertainty. (PsycINFO Database Record