Decisions, decisions: infant language learning when multiple generalizations are possible

The developmental cognitive mechanism of learning algebraic rules from the dual-process theory perspective

Rule learning is an important ability that enables human beings to adapt to nature and develop civilizations. There have been many discussions on the mechanism and characteristics of algebraic rule learning, but there are still controversies due to the lack of theoretical guidance. Based on the dual-process theory, this study discussed the following arguments for algebraic rule learning across human and animal studies: whether algebraic rule learning is simply Type 1 processing, whether algebraic rule learning is a domain-general ability, whether algebraic rule learning is shared by humans and animals, and whether an algebraic rule is learned consciously. Moreover, we propose that algebraic rule learning is possibly a cognitive process that combines both Type 1 and Type 2 processing. Further exploration is required to establish the essence and neural basis of algebraic rule learning.

Chunk boundaries disrupt dependency processing in an AG: Reconciling incremental processing and discrete sampling

Language is rooted in our ability to compose: We link words together, fusing their meanings. Links are not limited to neighboring words but often span intervening words. The ability to process these non-adjacent dependencies (NADs) conflicts with the brain's sampling of speech: We consume speech in chunks that are limited in time, containing only a limited number of words. It is unknown how we link words together that belong to separate chunks. Here, we report that we cannot-at least not so well. In our electroencephalography (EEG) study, 37 human listeners learned chunks and dependencies from an artificial grammar (AG) composed of syllables. Multi-syllable chunks to be learned were equal-sized, allowing us to employ a frequency-tagging approach. On top of chunks, syllable streams contained NADs that were either confined to a single chunk or crossed a chunk boundary. Frequency analyses of the EEG revealed a spectral peak at the chunk rate, showing that participants learned the chunks. NADs that cross boundaries were associated with smaller electrophysiological responses than within-chunk NADs. This shows that NADs are processed readily when they are confined to the same chunk, but not as well when crossing a chunk boundary. Our findings help to reconcile the classical notion that language is processed incrementally with recent evidence for discrete perceptual sampling of speech. This has implications for language acquisition and processing as well as for the general view of syntax in human language.

Exploring Abstract Pattern Representation in The Brain and Non-symbolic Neural Networks

Human cognitive and linguistic generativity depends on the ability to identify abstract relationships between perceptually dissimilar items. Marcus et al. (1999) found that human infants can rapidly discover and generalize patterns of syllable repetition (reduplication) that depend on the abstract property of identity, but simple recurrent neural networks (SRNs) could not. They interpreted these results as evidence that purely associative neural network models provide an inadequate framework for characterizing the fundamental generativity of human cognition. Here, we present a series of deep long short-term memory (LSTM) models that identify abstract syllable repetition patterns and words based on training with cochleagrams that represent auditory stimuli. We demonstrate that models trained to identify individual syllable trigram words and models trained to identify reduplication patterns discover representations that support classification of abstract repetition patterns. Simulations examined the effects of training categories (words vs. patterns) and pretraining to identify syllables, on the development of hidden node representations that support repetition pattern discrimination. Representational similarity analyses (RSA) comparing patterns of regional brain activity based on MRI-constrained MEG/EEG data to patterns of hidden node activation elicited by the same stimuli showed a significant correlation between brain activity localized in primarily posterior temporal regions and representations discovered by the models. These results suggest that associative mechanisms operating over discoverable representations that capture abstract stimulus properties account for a critical example of human cognitive generativity.

Ignoring the alternatives: The N400 is sensitive to stimulus preactivation alone

The N400 component of the event-related brain potential is a neural signal of processing difficulty. In the language domain, it is widely believed to be sensitive to the degree to which a given word or its semantic features have been preactivated in the brain based on the preceding context. However, it has also been shown that the brain often preactivates many words in parallel. It is currently unknown whether the N400 is also affected by the preactivations of alternative words other than the stimulus that is actually presented. This leaves a weak link in the derivation chain-how can we use the N400 to understand the mechanisms of preactivation if we do not know what it indexes? This study directly addresses this gap. We estimate the extent to which all words in a lexicon are preactivated in a given context using the predictions of contemporary large language models. We then directly compare two competing possibilities: that the amplitude of the N400 is sensitive only to the extent to which the stimulus is preactivated, and that it is also sensitive to the preactivation states of the alternatives. We find evidence of the former. This result allows for better grounded inferences about the mechanisms underlying the N400, lexical preactivation in the brain, and language processing more generally.

The threshold of rule productivity in infants

Most learning theories agree that the productivity of a rule or a pattern relies on regular exemplars being dominant over exceptions; the threshold for productivity is, however, unclear; moreover, gradient productivity levels are assumed for different rules/patterns, regular or irregular. One theory by Yang, the Tolerance Principle (TP), specified a productivity threshold applicable to all rules, calculated by the numbers of total exemplars and exceptions of a rule; furthermore, rules are viewed as quantal, either productive or unproductive, with no gradient levels. We evaluated the threshold and gradience-quantalness questions by investigating infants' generalization. In an implicit learning task, 14-month-olds heard exemplars of an artificial word-order rule and exceptions; their distributions were set closed to the TP-threshold (5.77) on both sides: 11 regular exemplars vs. 5 exceptions in Condition 1 (productiveness predicted), and 10 regular exemplars vs. 6 exceptions in Condition 2 (unproductiveness predicted). These predictions were pitted against those of the statistical majority threshold (50%), a common assumption which would predict generalization in both conditions (68.75, 62.5%). Infants were tested on the trained rule with new exemplars. Results revealed generalization in Condition 1, but not in Condition 2, supporting the TP-threshold, not the statistical majority threshold. Gradience-quantalness was assessed by combined analyses of Conditions 1-2 and previous experiments by Koulaguina and Shi. The training across the conditions contained gradually decreasing regular exemplars (100, 80, 68.75, 62.5, 50%) relative to exceptions. Results of test trials showed evidence for quantalness in infants (productive: 100, 80, 68.75%; unproductive: 62.5, 50%), with no gradient levels of productivity.

Robustness of the rule-learning effect in 7-month-old infants: A close, multicenter replication of Marcus et al. (1999)

We conducted a close replication of the seminal work by Marcus and colleagues from 1999, which showed that after a brief auditory exposure phase, 7-month-old infants were able to learn and generalize a rule to novel syllables not previously present in the exposure phase. This work became the foundation for the theoretical framework by which we assume that infants are able to learn abstract representations and generalize linguistic rules. While some extensions on the original work have shown evidence of rule learning, the outcomes are mixed, and an exact replication of Marcus et al.'s study has thus far not been reported. A recent meta-analysis by Rabagliati and colleagues brings to light that the rule-learning effect depends on stimulus type (e.g., meaningfulness, speech vs. nonspeech) and is not as robust as often assumed. In light of the theoretical importance of the issue at stake, it is appropriate and necessary to assess the replicability and robustness of Marcus et al.'s findings. Here we have undertaken a replication across four labs with a large sample of 7-month-old infants (N = 96), using the same exposure patterns (ABA and ABB), methodology (Headturn Preference Paradigm), and original stimuli. As in the original study, we tested the hypothesis that infants are able to learn abstract "algebraic" rules and apply them to novel input. Our results did not replicate the original findings: infants showed no difference in looking time between test patterns consistent or inconsistent with the familiarization pattern they were exposed to.

Synthesizing theories of human language with Bayesian program induction

Automated, data-driven construction and evaluation of scientific models and theories is a long-standing challenge in artificial intelligence. We present a framework for algorithmically synthesizing models of a basic part of human language: morpho-phonology, the system that builds word forms from sounds. We integrate Bayesian inference with program synthesis and representations inspired by linguistic theory and cognitive models of learning and discovery. Across 70 datasets from 58 diverse languages, our system synthesizes human-interpretable models for core aspects of each language’s morpho-phonology, sometimes approaching models posited by human linguists. Joint inference across all 70 data sets automatically synthesizes a meta-model encoding interpretable cross-language typological tendencies. Finally, the same algorithm captures few-shot learning dynamics, acquiring new morphophonological rules from just one or a few examples. These results suggest routes to more powerful machine-enabled discovery of interpretable models in linguistics and other scientific domains.

Humans can infer rules for building words in a new language from a handful of examples, and linguists also can infer language patterns across related languages. Here, the authors provide an algorithm which models these grammatical abilities by synthesizing human-understandable programs for building words.

Statistical Learning in Vision

Vision and learning have long been considered to be two areas of research linked only distantly. However, recent developments in vision research have changed the conceptual definition of vision from a signal-evaluating process to a goal-oriented interpreting process, and this shift binds learning, together with the resulting internal representations, intimately to vision. In this review, we consider various types of learning (perceptual, statistical, and rule/abstract) associated with vision in the past decades and argue that they represent differently specialized versions of the fundamental learning process, which must be captured in its entirety when applied to complex visual processes. We show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision, what computational framework best accommodates this kind of statistical learning, and what plausible neural scheme could feasibly implement this framework. Finally, we list the challenges that the field of statistical learning faces in fulfilling the promise of being the right vehicle for advancing our understanding of vision in its entirety. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Jellybeans… or Jelly, Beans…? 5-6-year-olds can identify the prosody of compounds but not lists

Learning to use word versus phrase level prosody to identify compounds from lists is thought to be a protracted process, only acquired by 11 years (Vogel & Raimy, 2002). However, a recent study has shown that 5-year-olds can use prosodic cues other than stress for these two structures in production, at least for early-acquired noun-noun compounds (Yuen et al., 2021). This raises the question of whether children this age can also use naturally-produced prosody to identify noun-noun compounds from their list forms in comprehension. The results show that 5-6-year-olds (N = 28) can only identify compounds. Unlike adults, children as a group could not use boundary cues to identify lists and were significantly slower in their processing compared to adults. This suggests that the acquisition of word level prosody may precede the acquisition of phrase level prosody, i.e., some higher-level aspects of phrasal prosody may take longer to acquire.

Fast but Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction

The language abilities of young and adult learners range from memorizing specific items to finding statistical regularities between them (item-bound generalization) and generalizing rules to novel instances (category-based generalization). Both external factors, such as input variability, and internal factors, such as cognitive limitations, have been shown to drive these abilities. However, the exact dynamics between these factors and circumstances under which rule induction emerges remain largely underspecified. Here, we extend our information-theoretic model (Radulescu et al., 2019), based on Shannon’s noisy-channel coding theory, which adds into the “formula” for rule induction the crucial dimension of time: the rate of encoding information by a time-sensitive mechanism. The goal of this study is to test the channel capacity-based hypothesis of our model: if the input entropy per second is higher than the maximum rate of information transmission (bits/second), which is determined by the channel capacity, the encoding method moves gradually from item-bound generalization to a more efficient category-based generalization, so as to avoid exceeding the channel capacity. We ran two artificial grammar experiments with adults, in which we sped up the bit rate of information transmission, crucially not by an arbitrary amount but by a factor calculated using the channel capacity formula on previous data. We found that increased bit rate of information transmission in a repetition-based XXY grammar drove the tendency of learners toward category-based generalization, as predicted by our model. Conversely, we found that increased bit rate of information transmission in complex non-adjacent dependency aXb grammar impeded the item-bound generalization of the specific a_b frames, and led to poorer learning, at least judging by our accuracy assessment method. This finding could show that, since increasing the bit rate of information precipitates a change from item-bound to category-based generalization, it impedes the item-bound generalization of the specific a_b frames, and that it facilitates category-based generalization both for the intervening Xs and possibly for a/b categories. Thus, sped up bit rate does not mean that an unrestrainedly increasing bit rate drives rule induction in any context, or grammar. Rather, it is the specific dynamics between the input entropy and the maximum rate of information transmission.

When it's not appropriate to adapt: Toddlers' learning of novel speech patterns is affected by visual information

In adults, perceptual learning for speech is constrained, such that learning of novel pronunciations is less likely to occur if the (e.g., visual) context indicates that they are transient. However, adults have had a lifetime of experience with the types of cues that signal stable vs. transient speech variation. We ask whether visual context affects toddlers' learning of a novel speech pattern. Across conditions, 19-month-olds (N = 117) were exposed to familiar words either pronounced typically or in a novel, consonant-shifting accent. During exposure, some toddlers heard the accented pronunciations without a face present; others saw a video of the speaker producing the words with a lollipop against her cheek or in her mouth. Toddlers showed the weakest learning of the accent when the speaker had the lollipop in her mouth, suggesting that they treated the lollipop as the cause of the atypical pronunciations. These results demonstrate that toddlers' adaptation to a novel speech pattern is influenced by extra-linguistic context.

Infants differentially extract rules from language

Infants readily extract linguistic rules from speech. Here, we ask whether this advantage extends to linguistic stimuli that do not rely on the spoken modality. To address this question, we first examine whether infants can differentially learn rules from linguistic signs. We show that, despite having no previous experience with a sign language, six-month-old infants can extract the reduplicative rule (AA) from dynamic linguistic signs, and the neural response to reduplicative linguistic signs differs from reduplicative visual controls, matched for the dynamic spatiotemporal properties of signs. We next demonstrate that the brain response for reduplicative signs is similar to the response to reduplicative speech stimuli. Rule learning, then, apparently depends on the linguistic status of the stimulus, not its sensory modality. These results suggest that infants are language-ready. They possess a powerful rule system that is differentially engaged by all linguistic stimuli, speech or sign.

Learning a language from inconsistent input: Regularization in child and adult learners

When linguistic input contains inconsistent use of grammatical forms, children produce these forms more consistently, a process called 'regularization.' Deaf children learning American Sign Language from parents who are non-native users of the language regularize their parents' inconsistent usages (Singleton & Newport, 2004). In studies of artificial languages containing inconsistently used morphemes (Hudson Kam & Newport, 2005, 2009), children, but not adults, regularized these forms. However, little is known about the precise circumstances in which such regularization occurs. In three experiments we investigate how the type of input variation and the age of learners affects regularization. Overall our results suggest that while adults tend to reproduce the inconsistencies found in their input, young children introduce regularity: they learn varying forms whose occurrence is conditioned and systematic, but they alter inconsistent variation to be more regular. Older children perform more like adults, suggesting that regularization changes with maturation and cognitive capacities.

Theory and predictions for the development of morphology and syntax: A Universal Grammar + statistics approach

The key aim of this special issue is to make developmental theory proposals concrete enough to evaluate with empirical data. With this in mind, I discuss proposals from the "Universal Grammar + statistics" (UG+stats) perspective for learning several morphology and syntax phenomena. I briefly review why UG has traditionally been part of many developmental theories of language, as well as common statistical learning approaches that are part of UG+stats proposals. I then discuss each morphology or syntax phenomenon in turn, giving an overview of relevant UG+stats proposals for that phenomenon, specific predictions made by each proposal, and what we currently know about how those predictions hold up. I conclude by briefly discussing where we seem to be when it comes to how well UG+stats proposals help us understand the development of morphology and syntax knowledge.

Maternal stress predicts neural responses during auditory statistical learning in 26-month-old children: An event-related potential study

Exposure to high levels of early life stress have been associated with long-term difficulties in learning, behavior, and health, with particular impact evident in the language domain. While some have proposed that the increased stress of living in a low-income household mediates observed associations between socioeconomic status (SES) and child outcomes, considerable individual differences have been observed. The extent to which specific variables associated with socioeconomic status - in particular exposure to stressful life events - influence the neurocognitive mechanisms underlying language acquisition are not well understood. Auditory statistical learning, or the ability to segment a continuous auditory stream based on its statistical properties, develops during early infancy and is one mechanism thought to underlie language learning. The present study used an event-related potential (ERP) paradigm to test whether maternal stress, adjusting for socioeconomic variables (e.g., family income, maternal education) was associated with neurocognitive processes underlying statistical learning in a sample of 26-month-old children (n = 23) from predominantly low- to middle-income backgrounds. Event-related potentials were recorded while children listened to a continuous stream of tri-tone "words" in which tone elements varied in transitional probability. "Tone-words" were presented in random order, such that Tone 1 always predicted Tones 2 and 3 (transitional probability for Tone 3 = 1.0), but Tone 1 appeared randomly. A larger P2 amplitude was observed in response to Tone 3 compared to Tone 1, demonstrating that children implicitly tracked differences in transitional probabilities during passive listening. Maternal reports of stress at 26 months, adjusting for SES, were negatively associated with difference in P2 amplitude between Tones 1 and 3. These findings suggest that maternal stress, within a low-SES context, is associated with the manner in which children process statistical properties of auditory input.

Five-year-olds produce prosodic cues to distinguish compounds from lists in Australian English

Although previous research has indicated that five-year-olds can use acoustic cues to disambiguate compounds (N1 + N2) from lists (N1, N2) (e.g., 'ice-cream' vs. 'ice, cream') (Yoshida & Katz, 2004, 2006), their productions are not yet fully adult-like (Wells, Peppé & Goulandris, 2004). The goal of this study was to examine this issue in Australian English-speaking children, with a focus on their use of F0, word duration, and pauses. Twenty-four five-year-olds and 20 adults participated in an elicited production experiment. Like adults, children produced distinct F0 patterns for the two structures. They also used longer word durations and more pauses in lists compared to compounds, indicating the presence of a boundary in lists. However, unlike adults, they also inappropriately inserted more pauses within the compound, suggesting the presence of a boundary in compounds as well. The implications for understanding children's developing knowledge of how to map acoustic cues to prosodic structures are discussed.

Exploring the "anchor word" effect in infants: Segmentation and categorisation of speech with and without high frequency words

High frequency words play a key role in language acquisition, with recent work suggesting they may serve both speech segmentation and lexical categorisation. However, it is not yet known whether infants can detect novel high frequency words in continuous speech, nor whether they can use them to help learning for segmentation and categorisation at the same time. For instance, when hearing "you eat the biscuit", can children use the high-frequency words "you" and "the" to segment out "eat" and "biscuit", and determine their respective lexical categories? We tested this in two experiments. In Experiment 1, we familiarised 12-month-old infants with continuous artificial speech comprising repetitions of target words, which were preceded by high-frequency marker words that distinguished the targets into two distributional categories. In Experiment 2, we repeated the task using the same language but with additional phonological cues to word and category structure. In both studies, we measured learning with head-turn preference tests of segmentation and categorisation, and compared performance against a control group that heard the artificial speech without the marker words (i.e., just the targets). There was no evidence that high frequency words helped either speech segmentation or grammatical categorisation. However, segmentation was seen to improve when the distributional information was supplemented with phonological cues (Experiment 2). In both experiments, exploratory analysis indicated that infants' looking behaviour was related to their linguistic maturity (indexed by infants' vocabulary scores) with infants with high versus low vocabulary scores displaying novelty and familiarity preferences, respectively. We propose that high-frequency words must reach a critical threshold of familiarity before they can be of significant benefit to learning.

A Dynamic Network Approach to the Study of Syntax

Usage-based linguists and psychologists have produced a large body of empirical results suggesting that linguistic structure is derived from language use. However, while researchers agree that these results characterize grammar as an emergent phenomenon, there is no consensus among usage-based scholars as to how the various results can be explained and integrated into an explicit theory or model. Building on network theory, the current paper outlines a structured network approach to the study of grammar in which the core concepts of syntax are analyzed by a set of relations that specify associations between different aspects of a speaker's linguistic knowledge. These associations are shaped by domain-general processes that can give rise to new structures and meanings in language acquisition and language change. Combining research from linguistics and psychology, the paper proposes specific network analyses for the following phenomena: argument structure, word classes, constituent structure, constructions and construction families, and grammatical categories such as voice, case and number. The article builds on data and analyses presented in Diessel (2019; The Grammar Network. How Linguistic Structure is Shaped by Language Use) but approaches the topic from a different perspective.

Non-adjacent Dependency Learning in Humans and Other Animals

Learning and processing natural language requires the ability to track syntactic relationships between words and phrases in a sentence, which are often separated by intervening material. These nonadjacent dependencies can be studied using artificial grammar learning paradigms and structured sequence processing tasks. These approaches have been used to demonstrate that human adults, infants and some nonhuman animals are able to detect and learn dependencies between nonadjacent elements within a sequence. However, learning nonadjacent dependencies appears to be more cognitively demanding than detecting dependencies between adjacent elements, and only occurs in certain circumstances. In this review, we discuss different types of nonadjacent dependencies in language and in artificial grammar learning experiments, and how these differences might impact learning. We summarize different types of perceptual cues that facilitate learning, by highlighting the relationship between dependent elements bringing them closer together either physically, attentionally, or perceptually. Finally, we review artificial grammar learning experiments in human adults, infants, and nonhuman animals, and discuss how similarities and differences observed across these groups can provide insights into how language is learned across development and how these language-related abilities might have evolved.

Structured Sequence Learning: Animal Abilities, Cognitive Operations, and Language Evolution

Human language is a salient example of a neurocognitive system that is specialized to process complex dependencies between sensory events distributed in time, yet how this system evolved and specialized remains unclear. Artificial Grammar Learning (AGL) studies have generated a wealth of insights into how human adults and infants process different types of sequencing dependencies of varying complexity. The AGL paradigm has also been adopted to examine the sequence processing abilities of nonhuman animals. We critically evaluate this growing literature in species ranging from mammals (primates and rats) to birds (pigeons, songbirds, and parrots) considering also cross-species comparisons. The findings are contrasted with seminal studies in human infants that motivated the work in nonhuman animals. This synopsis identifies advances in knowledge and where uncertainty remains regarding the various strategies that nonhuman animals can adopt for processing sequencing dependencies. The paucity of evidence in the few species studied to date and the need for follow-up experiments indicate that we do not yet understand the limits of animal sequence processing capacities and thereby the evolutionary pattern. This vibrant, yet still budding, field of research carries substantial promise for advancing knowledge on animal abilities, cognitive substrates, and language evolution.

Incidental binding between predictive relations

Knowledge of predictive relations is a core aspect of learning. Beyond individual relations, we also represent intuitive theories of the world, which include interrelated sets of relations. We asked whether individual predictive relations learned incidentally in the same context become associatively bound and whether they spontaneously influence later learning. Participants performed a cover task while watching three sequences of events. Each sequence contained the same set of events, but differed in how the events related to each other. The first two sequences each had two strong predictive relations (R1 & R2, and R3 & R4). The third contained either a consistent pairing of relations (R1 & R2) or an inconsistent pairing (R1 & R3). We found that participants' learning of the individual relations in the third sequence was affected by pairing consistency, suggesting the mind associates relations to each other as part of the intrinsic way it learns about the world. This was despite participants' minimal ability to verbally describe most of the relations they had learned. Thus, participants spontaneously developed the expectation that pairs of relations should cohere, and this affected their ability to learn new evidence. Such associative binding of relational information may help us build intuitive theories.

Non-adjacent dependency learning in infancy, and its link to language development

To acquire language, infants must learn how to identify words and linguistic structure in speech. Statistical learning has been suggested to assist both of these tasks. However, infants' capacity to use statistics to discover words and structure together remains unclear. Further, it is not yet known how infants'  statistical learning ability relates to their language development. We trained 17-month-old infants on an artificial language comprising non-adjacent dependencies, and examined their looking times on tasks assessing sensitivity to words and structure using an eye-tracked head-turn-preference paradigm. We measured infants' vocabulary size using a Communicative Development Inventory (CDI) concurrently and at 19, 21, 24, 25, 27, and 30 months to relate performance to language development. Infants could segment the words from speech, demonstrated by a significant difference in looking times to words versus part-words. Infants' segmentation performance was significantly related to their vocabulary size (receptive and expressive) both currently, and over time (receptive until 24 months, expressive until 30 months), but was not related to the rate of vocabulary growth. The data also suggest infants may have developed sensitivity to generalised structure, indicating similar statistical learning mechanisms may contribute to the discovery of words and structure in speech, but this was not related to vocabulary size.

A review of computational models of basic rule learning: The neural-symbolic debate and beyond

We present a critical review of computational models of generalization of simple grammar-like rules, such as ABA and ABB. In particular, we focus on models attempting to account for the empirical results of Marcus et al. (Science, 283(5398), 77-80 1999). In that study, evidence is reported of generalization behavior by 7-month-old infants, using an Artificial Language Learning paradigm. The authors fail to replicate this behavior in neural network simulations, and claim that this failure reveals inherent limitations of a whole class of neural networks: those that do not incorporate symbolic operations. A great number of computational models were proposed in follow-up studies, fuelling a heated debate about what is required for a model to generalize. Twenty years later, this debate is still not settled. In this paper, we review a large number of the proposed models. We present a critical analysis of those models, in terms of how they contribute to answer the most relevant questions raised by the experiment. After identifying which aspects require further research, we propose a list of desiderata for advancing our understanding on generalization.

Experience with morphosyntactic paradigms allows toddlers to tacitly anticipate overregularized verb forms months before they produce them

When do children acquire abstract grammatical categories? Studies of 2- to 3-year-olds' productions of complete morphosyntactic paradigms (e.g., all legal determiners with all nouns) suggest relatively later category acquisition, while studies of infant discrimination of grammatical vs. ungrammatical sequences suggest earlier acquisition. However, few of the latter studies have probed category generalization by examining how learners treat gaps in their input, and none have found evidence that learners before the age of 2 years fill gaps in verb paradigms. Therefore, the three experiments presented here asked whether 16-month-olds tacitly expect to hear forms like breaked by presenting them with overregularized verbs vs. (1) nonce verbs + -ed, (2) real English nouns + -ed, and (3) the correct irregular counterparts. The pattern of listening preferences suggests that toddlers anticipate overregularized forms, suggesting that they have a general proto-category verb, to which they expect the complete set of verb inflections to apply.

The impact of foreign language caregiving on native language acquisition

There is increasing interest in the influence of language input during children's early years. Over the first 3 years of life, children are highly sensitive to the quantity and quality of language input they receive. The focus of this study was on whether learning a different language in the early years affects the acquisition of English over the longer term. In this study, we investigated effects of foreign language (Hokkien) caregiving on the eventual acquisition of English as well as on memory traces of Hokkien. We sampled individuals who received foreign language caregiving in Hokkien during their early years either predominantly or in addition to English. Our control group had lifetime primary exposure to English. We compared the Hokkien- and English-only reared groups on phonological, semantic, and grammatical knowledge in English. We also compared the groups on memories for Hokkien tonal phonology and vocabulary. Overall, there were no statistically significant differences in performance in English tasks between groups, yet the Hokkien-reared group demonstrated selective learning advantages in reacquiring Hokkien tonal contrasts. Findings are discussed with reference to the effects of timing and language input on later language proficiency.

No Evidence That Abstract Structure Learning Disrupts Novel-Event Learning in 8- to 11-Month-Olds

Although infants acquire specific information (e.g., motion of a specific toy) and abstract information (e.g., likelihood of events repeating), it is unclear whether extraction of abstract information interferes with specific learning. In the present study, 8- to 11-month-old infants were shown four audio-visual movies, either with a mixed or uniform presentation structure. Learning of abstract information was operationally defined as the looking time to changes in presentation structure of the movies (mixed vs. uniform blocks), and learning of specific information was defined as the looking time to changes in content in the four movies (object properties and identities). We found evidence of both specific and abstract learning, but did not find evidence that extraction of the presentation structure (i.e., abstract learning) impacts specific learning of the events. We discuss the implications of the costs and benefits of the interaction between abstract and specific learning for infants.

Infants' selective use of reliable cues in multidimensional language input

Learning always happens from input that contains multiple structures and multiple sources of variability. Though infants possess learning mechanisms to locate structure in the world, lab-based experiments have rarely probed how infants contend with input that contains many different structures and cues. Two experiments explored infants' use of two naturally occurring sources of variability-different sounds and different people-to detect regularities in language. Monolingual infants (9-10 months) heard a male and female talker produce two different speech streams, one of which followed a deterministic pattern (e.g., AAB, le-le-di) and one of which did not. For half of the infants, each speaker produced only one of the streams; for the other half of the infants, each speaker produced 50% of each stream. In Experiment 1, each stream consisted of distinct sounds, and infants successfully demonstrated learning regardless of the correspondence between speaker and stream. In Experiment 2, each stream consisted of the same sounds, and infants failed to show learning, even when speakers provided a perfect cue for separating each stream. Thus, monolingual infants can learn in the presence of multiple speech streams, but these experiments suggest that infants may rely more on sound-based rather than speaker-based distinctions when breaking into the structure of incoming information. This selective use of some cues over others highlights infants' ability to adaptively focus on distinctions that are most likely to be useful as they sort through their inherently multidimensional surroundings. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The profile of abstract rule learning in infancy: Meta-analytic and experimental evidence

Everyone agrees that infants possess general mechanisms for learning about the world, but the existence and operation of more specialized mechanisms is controversial. One mechanism-rule learning-has been proposed as potentially specific to speech, based on findings that 7-month-olds can learn abstract repetition rules from spoken syllables (e.g. ABB patterns: wo-fe-fe, ga-tu-tu…) but not from closely matched stimuli, such as tones. Subsequent work has shown that learning of abstract patterns is not simply specific to speech. However, we still lack a parsimonious explanation to tie together the diverse, messy, and occasionally contradictory findings in that literature. We took two routes to creating a new profile of rule learning: meta-analysis of 20 prior reports on infants' learning of abstract repetition rules (including 1,318 infants in 63 experiments total), and an experiment on learning of such rules from a natural, non-speech communicative signal. These complementary approaches revealed that infants were most likely to learn abstract patterns from meaningful stimuli. We argue that the ability to detect and generalize simple patterns supports learning across domains in infancy but chiefly when the signal is meaningfully relevant to infants' experience with sounds, objects, language, and people.

The roles of item repetition and position in infants' abstract rule learning

We asked whether 11- and 14- month-old infants' abstract rule learning, an early form of analogical reasoning, is susceptible to processing constraints imposed by limits in attention and memory for sequence position. We examined 11- and 14- month-old infants' learning and generalization of abstract repetition rules ("repetition anywhere," Experiment 1 or "medial repetition," Experiment 2) and ordering of specific items (edge positions, Experiment 3) in 4-item sequences. Infants were habituated to sequences containing repetition- and/or position-based structure and then tested with "familiar" vs. "novel" (random) sequences composed of new items. Eleven-month-olds (N = 40) failed to learn abstract repetition rules, but 14-month-olds (N = 40) learned rules under both conditions. In Experiment 3, 11-month-olds (N = 20) learned item edge positions in sequences identical to those in Experiment 2. We conclude that infant sequence learning is constrained by item position in similar ways as in adults.

Differences in relative frequency facilitate learning abstract rules

Humans learn the rules that govern how the elements of their language are organized over an input that is often not homogeneous (it might contain noise, or even include rules from different linguistic systems, as it might be the case for bilinguals). In the present study we explore the conditions under which participants can learn an abstract rule when it is presented in a heterogeneous context. Results from six experiments show that listeners can learn a token-independent rule even if it is presented together with some exemplars that implement a different regularity (Experiment 1a and 1b). In fact, learning rules from an input containing several patterns does not seem to differ from learning them from an input containing only one (Experiment 1c). More surprisingly, we observed that listeners can even learn an abstract rule that is only implemented over 10% of the exemplars that compose a familiarization stream (Experiments 2a and 2b). When the proportion of tokens implementing the target and the non-target rules is balanced, we did not observe any learning (Experiment 3). Our results suggest that listeners use differences in relative frequency to keep separate linguistic rules apart. This allows them to learn different abstract regularities from a non-homogeneous linguistic signal.

Learning of hierarchical serial patterns emerges in infancy

Recursive, hierarchically organized serial patterns provide the underlying structure in many cognitive and motor domains including speech, language, music, social interaction, and motor action. We investigated whether learning of hierarchical patterns emerges in infancy by habituating 204 infants to different hierarchical serial patterns and then testing for discrimination and generalization of such patterns. Results indicated that 8- to 10-month-old and 12- to 14-month-old infants exhibited sensitivity to the difference between hierarchical and non-hierarchical structure but that 4- to 6-month-old infants did not. These findings demonstrate that the ability to perceive, learn, and generalize recursive, hierarchical, pattern rules emerges in infancy and add to growing evidence that general-purpose pattern learning mechanisms emerge during the first year of life.

Assessing the uniqueness of language: Animal grammatical abilities take center stage

Questions related to the uniqueness of language can only be addressed properly by referring to sound knowledge of the relevant cognitive abilities of nonhuman animals. A key question concerns the nature and extent of animal rule-learning abilities. I discuss two approaches used to assess these abilities. One is comparing the structures of animal vocalizations to linguistic ones, and another is addressing the grammatical rule- and pattern-learning abilities of animals through experiments using artificial grammars. Neither of these approaches has so far provided unambiguous evidence of advanced animal abilities. However, when we consider how animal vocalizations are analyzed, the types of stimuli and tasks that are used in artificial grammar learning experiments, the limited number of species examined, and the groups to which these belong, I argue that the currently available evidence is insufficient to arrive at firm conclusions concerning the limitations of animal grammatical abilities. As a consequence, the gap between human linguistic rule-learning abilities and those of nonhuman animals may be smaller and less clear than is currently assumed. This means that it is still an open question whether a difference in the rule-learning and rule abstraction abilities between animals and humans played the key role in the evolution of language.

Seeing behind the surface: communicative demonstration boosts category disambiguation in 12-month-olds

In their first years, infants acquire an incredible amount of information regarding the objects present in their environment. While often it is not clear what specific information should be prioritized in encoding from the many characteristics of an object, different types of object representations facilitate different types of generalizations. We tested the hypotheses that 1-year-old infants distinctively represent familiar objects as exemplars of their kind, and that ostensive communication plays a role in determining kind membership for ambiguous objects. In the training phase of our experiment, infants were exposed to movies displaying an agent sorting objects from two categories (cups and plates) into two locations (left or right). Afterwards, different groups of infants saw either an ostensive or a non-ostensive demonstration performed by the agent, revealing that a new object that looked like a plate can be transformed into a cup. A third group of infants experienced no demonstration regarding the new object. During test, infants were presented with the ambiguous object in the plate format, and we measured generalization by coding anticipatory looks to the plate or the cup side. While infants looked equally often towards the two sides when the demonstration was non-ostensive, and more often to the plate side when there was no demonstration, they performed more anticipatory eye movements to the cup side when the demonstration was ostensive. Thus, ostensive demonstration likely highlighted the hidden dispositional properties of the target object as kind-relevant, guiding infants' categorization of the foldable cup as a cup, despite it looking like a plate. These results suggest that infants likely encode familiar objects as exemplars of their kind and that ostensive communication can play a crucial role in disambiguating what kind an object belongs to, even when this requires disregarding salient surface features.

Inflexible neurobiological signatures precede atypical development in infants at high risk for autism

Variability in neurobiological signatures is ubiquitous in early life but the link to adverse developmental milestones in humans is unknown. We examined how levels of signal and noise in movement signatures during the 1st year of life constrain early development in 71 healthy typically developing infants, either at High or Low familial Risk (HR or LR, respectively) for developing Autism Spectrum Disorders (ASD). Delays in early learning developmental trajectories in HR infants (validated in an analysis of 1,445 infants from representative infant-sibling studies) were predicted by worse stochastic patterns in their spontaneous head movements as early as 1-2 months after birth, relative to HR infants who showed more rapid developmental progress, as well as relative to all LR infants. While LR 1-2 mo-old infants' movements were significantly different during a language listening task compared to during sleep, HR infants' movements were more similar during both conditions, a striking lack of diversity that reveals context-inflexible experience of ambient information. Contrary to expectation, it is not the level of variability per se that is particularly detrimental in early life. Rather, inflexible sensorimotor systems and/or atypical transition between behavioral states may interfere with the establishment of capacity to extract structure and important cues from sensory input at birth, preceding and contributing to an atypical brain developmental trajectory in toddlerhood.

Infant Statistical Learning

Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant statistical learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent learning. In this selective review, we summarize findings that show that statistical learning is both a broad and flexible mechanism (supporting learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of learning). We suggest that statistical learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing learning environments, and differential developmental trajectories.

Learning abstract visual concepts via probabilistic program induction in a Language of Thought

The ability to learn abstract concepts is a powerful component of human cognition. It has been argued that variable binding is the key element enabling this ability, but the computational aspects of variable binding remain poorly understood. Here, we address this shortcoming by formalizing the Hierarchical Language of Thought (HLOT) model of rule learning. Given a set of data items, the model uses Bayesian inference to infer a probability distribution over stochastic programs that implement variable binding. Because the model makes use of symbolic variables as well as Bayesian inference and programs with stochastic primitives, it combines many of the advantages of both symbolic and statistical approaches to cognitive modeling. To evaluate the model, we conducted an experiment in which human subjects viewed training items and then judged which test items belong to the same concept as the training items. We found that the HLOT model provides a close match to human generalization patterns, significantly outperforming two variants of the Generalized Context Model, one variant based on string similarity and the other based on visual similarity using features from a deep convolutional neural network. Additional results suggest that variable binding happens automatically, implying that binding operations do not add complexity to peoples' hypothesized rules. Overall, this work demonstrates that a cognitive model combining symbolic variables with Bayesian inference and stochastic program primitives provides a new perspective for understanding people's patterns of generalization.

Broca's region: A causal role in implicit processing of grammars with crossed non-adjacent dependencies

Non-adjacent dependencies are challenging for the language learning machinery and are acquired later than adjacent dependencies. In this transcranial magnetic stimulation (TMS) study, we show that participants successfully discriminated between grammatical and non-grammatical sequences after having implicitly acquired an artificial language with crossed non-adjacent dependencies. Subsequent to transcranial magnetic stimulation of Broca's region, discrimination was impaired compared to when a language-irrelevant control region (vertex) was stimulated. These results support the view that Broca's region is engaged in structured sequence processing and extend previous functional neuroimaging results on artificial grammar learning (AGL) in two directions: first, the results establish that Broca's region is a causal component in the processing of non-adjacent dependencies, and second, they show that implicit processing of non-adjacent dependencies engages Broca's region. Since patients with lesions in Broca's region do not always show grammatical processing difficulties, the result that Broca's region is causally linked to processing of non-adjacent dependencies is a step towards clarification of the exact nature of syntactic deficits caused by lesions or perturbation to Broca's region. Our findings are consistent with previous results and support a role for Broca's region in general structured sequence processing, rather than a specific role for the processing of hierarchically organized sentence structure.

The infant's developmental path in phonological acquisition

Research on how language acquisition begins has been fragmented both in terms of scientific communities and in terms of the phenomena that are taken to characterize developmental progress. In her article, Marilyn Vihman argues for an integrative approach that takes the child's efforts at speech production as primary, and notes that infants' knowledge of how words sound may accrue over a protracted period developmentally. Here, I briefly discuss how reconceptualization of the process can help integrate perspectives previously at odds.

Sleep-Driven Computations in Speech Processing

Acquiring language requires segmenting speech into individual words, and abstracting over those words to discover grammatical structure. However, these tasks can be conflicting-on the one hand requiring memorisation of precise sequences that occur in speech, and on the other requiring a flexible reconstruction of these sequences to determine the grammar. Here, we examine whether speech segmentation and generalisation of grammar can occur simultaneously-with the conflicting requirements for these tasks being over-come by sleep-related consolidation. After exposure to an artificial language comprising words containing non-adjacent dependencies, participants underwent periods of consolidation involving either sleep or wake. Participants who slept before testing demonstrated a sustained boost to word learning and a short-term improvement to grammatical generalisation of the non-adjacencies, with improvements after sleep outweighing gains seen after an equal period of wake. Thus, we propose that sleep may facilitate processing for these conflicting tasks in language acquisition, but with enhanced benefits for speech segmentation.

Statistical learning: a powerful mechanism that operates by mere exposure

How do infants learn so rapidly and with little apparent effort? In 1996, Saffran, Aslin, and Newport reported that 8-month-old human infants could learn the underlying temporal structure of a stream of speech syllables after only 2 min of passive listening. This demonstration of what was called statistical learning, involving no instruction, reinforcement, or feedback, led to dozens of confirmations of this powerful mechanism of implicit learning in a variety of modalities, domains, and species. These findings reveal that infants are not nearly as dependent on explicit forms of instruction as we might have assumed from studies of learning in which children or adults are taught facts such as math or problem solving skills. Instead, at least in some domains, infants soak up the information around them by mere exposure. Learning and development in these domains thus appear to occur automatically and with little active involvement by an instructor (parent or teacher). The details of this statistical learning mechanism are discussed, including how exposure to specific types of information can, under some circumstances, generalize to never-before-observed information, thereby enabling transfer of learning. WIREs Cogn Sci 2017, 8:e1373. doi: 10.1002/wcs.1373 For further resources related to this article, please visit the WIREs website.

Learning multiple rules simultaneously: Affixes are more salient than reduplications

Language learners encounter numerous opportunities to learn regularities, but need to decide which of these regularities to learn, because some are not productive in their native language. Here, we present an account of rule learning based on perceptual and memory primitives (Endress, Dehaene-Lambertz, & Mehler, Cognition, 105(3), 577–614, 2007; Endress, Nespor, & Mehler, Trends in Cognitive Sciences, 13(8), 348–353, 2009), suggesting that learners preferentially learn regularities that are more salient to them, and that the pattern of salience reflects the frequency of language features across languages. We contrast this view with previous artificial grammar learning research, which suggests that infants “choose” the regularities they learn based on rational, Bayesian criteria (Frank & Tenenbaum, Cognition, 120(3), 360–371, 2013; Gerken, Cognition, 98(3)B67–B74, 2006, Cognition, 115(2), 362–366, 2010). In our experiments, adult participants listened to syllable strings starting with a syllable reduplication and always ending with the same “affix” syllable, or to syllable strings starting with this “affix” syllable and ending with the “reduplication”. Both affixation and reduplication are frequently used for morphological marking across languages. We find three crucial results. First, participants learned both regularities simultaneously. Second, affixation regularities seemed easier to learn than reduplication regularities. Third, regularities in sequence offsets were easier to learn than regularities at sequence onsets. We show that these results are inconsistent with previous Bayesian rule learning models, but mesh well with the perceptual or memory primitives view. Further, we show that the pattern of salience revealed in our experiments reflects the distribution of regularities across languages. Ease of acquisition might thus be one determinant of the frequency of regularities across languages.

Nexus to Lexis: Phonological Disorders in Children

Research on phonological disorders in children has conventionally emphasized the speech sound in search of causes, diagnoses, treatments, and prevention of the disorder. This article aims to shift the research focus to the word instead. The motivation comes from advances in psycholinguistics that demonstrate the word is central to the perception, production, and acquisition of phonological information. Three strands of potential study are outlined in evaluation of how words might initiate and boost, but perhaps also, interrupt learning for children with phonological disorders.

Learning in Complex Environments: The Effects of Background Speech on Early Word Learning

Although most studies of language learning take place in quiet laboratory settings, everyday language learning occurs under noisy conditions. The current research investigated the effects of background speech on word learning. Both younger (22- to 24-month-olds; n = 40) and older (28- to 30-month-olds; n = 40) toddlers successfully learned novel label-object pairings when target speech was 10 dB louder than background speech but not when the signal-to-noise ratio (SNR) was 5 dB. Toddlers (28- to 30-month-olds; n = 26) successfully learned novel words with a 5-dB SNR when they initially heard the labels embedded in fluent speech without background noise, before they were mapped to objects. The results point to both challenges and protective factors that may impact language learning in complex auditory environments.