Balancing Effort and Information Transmission During Language Acquisition: Evidence From Word Order and Case Marking

Across languages of the world, some grammatical patterns have been argued to be more common than expected by chance. These are sometimes referred to as (statistical) language universals. One such universal is the correlation between constituent order freedom and the presence of a case system in a language. Here, we explore whether this correlation can be explained by a bias to balance production effort and informativity of cues to grammatical function. Two groups of learners were presented with miniature artificial languages containing optional case marking and either flexible or fixed constituent order. Learners of the flexible order language used case marking significantly more often. This result parallels the typological correlation between constituent order flexibility and the presence of case marking in a language and provides a possible explanation for the historical development of Old English to Modern English, from flexible constituent order with case marking to relatively fixed order without case marking. In addition, learners of the flexible order language conditioned case marking on constituent order, using more case marking with the cross‐linguistically less frequent order, again mirroring typological data. These results suggest that some cross‐linguistic generalizations originate in functionally motivated biases operating during language learning.     

Learning biases predict a word order universal

How recurrent typological patterns, or universals, emerge from the extensive diversity found across the world's languages constitutes a central question for linguistics and cognitive science. Recent challenges to a fundamental assumption of generative linguistics-that universal properties of the human language acquisition faculty constrain the types of grammatical systems which can occur-suggest the need for new types of empirical evidence connecting typology to biases of learners. Using an artificial language learning paradigm in which adult subjects are exposed to a mix of grammatical systems (similar to a period of linguistic change), we show that learners' biases mirror a word-order universal, first proposed by Joseph Greenberg, which constrains typological patterns of adjective, numeral, and noun ordering. We briefly summarize the results of a probabilistic model of the hypothesized biases and their effect on learning, and discuss the broader implications of the results for current theories of the origins of cross-linguistic word-order preferences.     

A probabilistic computational model of cross-situational word learning

Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations.     

All words are not created equal: expectations about word length guide infant statistical learning

Infants have been described as ‘statistical learners’ capable of extracting structure (such as words) from patterned input (such as language). Here, we investigated whether prior knowledge influences how infants track transitional probabilities in word segmentation tasks. Are infants biased by prior experience when engaging in sequential statistical learning? In a laboratory simulation of learning across time, we exposed 9- and 10-month-old infants to a list of either disyllabic or trisyllabic nonsense words, followed by a pause-free speech stream composed of a different set of disyllabic or trisyllabic nonsense words. Listening times revealed successful segmentation of words from fluent speech only when words were uniformly disyllabic or trisyllabic throughout both phases of the experiment. Hearing trisyllabic words during the pre-exposure phase derailed infants’ abilities to segment speech into disyllabic words, and vice versa. We conclude that prior knowledge about word length equips infants with perceptual expectations that facilitate efficient processing of subsequent language input.     

The evolution of frequency distributions: Relating regularization to inductive biases through iterated learning

The regularization of linguistic structures by learners has played a key role in arguments for strong innate constraints on language acquisition, and has important implications for language evolution. However, relating the inductive biases of learners to regularization behavior in laboratory tasks can be challenging without a formal model. In this paper we explore how regular linguistic structures can emerge from language evolution by iterated learning, in which one person’s linguistic output is used to generate the linguistic input provided to the next person. We use a model of iterated learning with Bayesian agents to show that this process can result in regularization when learners have the appropriate inductive biases. We then present three experiments demonstrating that simulating the process of language evolution in the laboratory can reveal biases towards regularization that might not otherwise be obvious, allowing weak biases to have strong effects. The results of these experiments suggest that people tend to regularize inconsistent word-meaning mappings, and that even a weak bias towards regularization can allow regular languages to be produced via language evolution by iterated learning.     

Competitive Processes in Cross‐Situational Word Learning

Cross‐situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross‐situational word learning provides information at multiple scales—both within and across trials/situations—learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross‐situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is considered from the perspective of a process‐level understanding of cross‐situational learning.     

Language Evolution Can Be Shaped by the Structure of the World

Human languages vary in many ways but also show striking cross‐linguistic universals. Why do these universals exist? Recent theoretical results demonstrate that Bayesian learners transmitting language to each other through iterated learning will converge on a distribution of languages that depends only on their prior biases about language and the quantity of data transmitted at each point; the structure of the world being communicated about plays no role (Griffiths & Kalish, 2005 , 2007 ). We revisit these findings and show that when certain assumptions about the relationship between language and the world are abandoned, learners will converge to languages that depend on the structure of the world as well as their prior biases. These theoretical results are supported with a series of experiments showing that when human learners acquire language through iterated learning, the ultimate structure of those languages is shaped by the structure of the meanings to be communicated.     

(Non)words, (non)words, (non)words: evidence for a protolexicon during the first year of life

Previous research with artificial language learning paradigms has shown that infants are sensitive to statistical cues to word boundaries (Saffran, Aslin & Newport, 1996) and that they can use these cues to extract word‐like units (Saffran, 2001). However, it is unknown whether infants use statistical information to construct a receptive lexicon when acquiring their native language. In order to investigate this issue, we rely on the fact that besides real words a statistical algorithm extracts sound sequences that are highly frequent in infant‐directed speech but constitute nonwords. In three experiments, we use a preferential listening paradigm to test French‐learning 11‐month‐old infants' recognition of highly frequent disyllabic sequences from their native language. In Experiments 1 and 2, we use nonword stimuli and find that infants listen longer to high‐frequency than to low‐frequency sequences. In Experiment 3, we compare high‐frequency nonwords to real words in the same frequency range, and find that infants show no preference. Thus, at 11 months, French‐learning infants recognize highly frequent sound sequences from their native language and fail to differentiate between words and nonwords among these sequences. These results are evidence that they have used statistical information to extract word candidates from their input and stored them in a ‘protolexicon’, containing both words and nonwords.     

Testing the limits of statistical learning for word segmentation

Past research has demonstrated that infants can rapidly extract syllable distribution information from an artificial language and use this knowledge to infer likely word boundaries in speech. However, artificial languages are extremely simplified with respect to natural language. In this study, we ask whether infants’ ability to track transitional probabilities between syllables in an artificial language can scale up to the challenge of natural language. We do so by testing both 5.5‐ and 8‐month‐olds’ ability to segment an artificial language containing four words of uniform length (all CVCV) or four words of varying length (two CVCV, two CVCVCV). The transitional probability cues to word boundaries were held equal across the two languages. Both age groups segmented the language containing words of uniform length, demonstrating that even 5.5‐month‐olds are extremely sensitive to the conditional probabilities in their environment. However, neither age group succeeded in segmenting the language containing words of varying length, despite the fact that the transitional probability cues defining word boundaries were equally strong in the two languages. We conclude that infants’ statistical learning abilities may not be as robust as earlier studies have suggested.     

Infants track word forms in early word–object associations

A central component of language development is word learning. One characterization of this process is that language learners discover objects and then look for word forms to associate with these objects (Mcnamara, 1984 ; Smith, 2000 ). Another possibility is that word forms themselves are also important, such that once learned, hearing a familiar word form will lead young word learners to look for an object to associate with it (Juscyzk, 1997 ). This research investigates the relative weighing of word forms and objects in early word–object associations using the anticipatory eye‐movement paradigm (AEM; McMurray & Aslin, 2004 ). Eighteen‐month‐old infants and adults were taught novel word–object associations and then tested on ambiguous stimuli that pitted word forms and objects against each other. Results revealed a change in weighing of these components across development. For 18‐month‐old infants, word forms weighed more in early word–object associative learning, while for adults, objects were more salient. Our results suggest that infants preferentially use word forms to guide the process of word–object association.     

The Role of Simple Semantics in the Process of Artificial Grammar Learning

This study investigated the effect of semantic information on artificial grammar learning (AGL). Recursive grammars of different complexity levels (regular language, mirror language, copy language) were investigated in a series of AGL experiments. In the with-semantics condition, participants acquired semantic information prior to the AGL experiment; in the without-semantics control condition, participants did not receive semantic information. It was hypothesized that semantics would generally facilitate grammar acquisition and that the learning benefit in the with-semantics conditions would increase with increasing grammar complexity. Experiment 1 showed learning effects for all grammars but no performance difference between conditions. Experiment 2 replicated the absence of a semantic benefit for all grammars even though semantic information was more prominent during grammar acquisition as compared to Experiment 1. Thus, we did not find evidence for the idea that semantics facilitates grammar acquisition, which seems to support the view of an independent syntactic processing component.     

Quantitative Standards for Absolute Linguistic Universals

Absolute linguistic universals are often justified by cross‐linguistic analysis: If all observed languages exhibit a property, the property is taken to be a likely universal, perhaps specified in the cognitive or linguistic systems of language learners and users. In many cases, these patterns are then taken to motivate linguistic theory. Here, we show that cross‐linguistic analysis will very rarely be able to statistically justify absolute, inviolable patterns in language. We formalize two statistical methods—frequentist and Bayesian—and show that in both it is possible to find strict linguistic universals, but that the numbers of independent languages necessary to do so is generally unachievable. This suggests that methods other than typological statistics are necessary to establish absolute properties of human language, and thus that many of the purported universals in linguistics have not received sufficient empirical justification.     

Adaptation to novel accents by toddlers

Word recognition is a balancing act: listeners must be sensitive to phonetic detail to avoid confusing similar words, yet, at the same time, be flexible enough to adapt to phonetically variable pronunciations, such as those produced by speakers of different dialects or by non‐native speakers. Recent work has demonstrated that young toddlers are sensitive to phonetic detail during word recognition; pronunciations that deviate from the typical phonological form lead to a disruption of processing. However, it is not known whether young word learners show the flexibility that is characteristic of adult word recognition. The present study explores whether toddlers can adapt to artificial accents in which there is a vowel category shift with respect to the native language. Nineteen‐month‐olds heard mispronunciations of familiar words (e.g. vowels were shifted from [a] to [æ]: ‘dog’ pronounced as ‘dag’). In test, toddlers were tolerant of mispronunciations if they had recently been exposed to the same vowel shift, but not if they had been exposed to standard pronunciations or other vowel shifts. The effects extended beyond particular items heard in exposure to words sharing the same vowels. These results indicate that, like adults, toddlers show flexibility in their interpretation of phonological detail. Moreover, they suggest that effects of top‐down knowledge on the reinterpretation of phonological detail generalize across the phono‐lexical system.     

Processing typology and why psychologists need to know about it

This paper illustrates an interdisciplinary research program based on cross-linguistic comparison that is of relevance for psychologists working on language processing, so-called “processing typology” [Hawkins, J. A. (1994). A performance theory of order and constituency. Cambridge: Cambridge University Press; (2004). Efficiency and complexity in grammars. Oxford: Oxford University Press]. Its most original feature is the hypothesis that patterns and preferences found in performance in languages with several structures of a given type (e.g. preferences among alternative word orders) are the same patterns and preferences one finds across languages in the fixed conventions of grammars that permit less variation (i.e. in fixed word orders). Data supporting this “performance-grammar correspondence hypothesis” are summarized. One of its consequences is that principles of performance can be used to make predictions for patterns of grammatical variation, while preferences in grammars become relevant for the testing of psycholinguistic ideas. Two proposed principles of ordering in performance, in terms of “end weight” and “memory cost”, are criticized on the basis of cross-linguistic data. Both predict an asymmetry in ordering, whereby some category A precedes B. But end weight is not a valid cross-linguistic asymmetry, and memory cost cannot explain certain asymmetries for which it has been invoked when different language types are considered. The paper argues for greater mutual awareness between processing theorists and language typologists, for more consideration of non-European grammars and language types in psycholinguistics, and for a greater appeal to processing in the explanation of typological variation.     

All Together Now: Concurrent Learning of Multiple Structures in an Artificial Language

Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult learning of an artificial language that contains dependencies between both adjacent and non‐adjacent words. We found that learners rapidly acquired both types of regularities and that the strength of the adjacent statistics influenced learning of both adjacent and non‐adjacent dependencies. Additionally, though accuracy was similar for both types of structure, participants’ knowledge of the deterministic non‐adjacent dependencies was more explicit than their knowledge of the probabilistic adjacent dependencies. The results are discussed in the context of current theories of statistical learning and language acquisition.     

Influences of vowel and tone variation on emergent word knowledge: a cross‐linguistic investigation

To learn words, infants must be sensitive to native phonological contrast. While lexical tone predominates as a source of phonemic contrast in human languages, there has been little investigation of the influences of lexical tone on word learning. The present study investigates infants’ sensitivity to tone mispronunciations in two groups of infants. For one group (Chinese learners), tone is phonemic in their native language, and for the second group (English learners), tone is non‐phonemic and constituted suprasegmental variation. In Experiment 1, English learners were trained on novel word–object pairings and tested on their recognition of correct pronunciations, tone and vowel mispronunciations of these words at 18 and 24 months. In Experiment 2a, bilingual English‐Chinese learners were tested on a similar task translated into Chinese at the same age intervals. Results demonstrate that non‐tonal learners treated tonal and vowel substitutions alike as mispronunciations at 18 months but only treated vowel substitutions as mispronunciations at 24 months. Tonal learners treated both tonal and vowel substitutions as mispronunciations at both ages. In Experiment 2b, bilingual non‐tone language learners were tested on the same set of tasks replicating a similar set of results as monolingual non‐tone language learners (Experiment 1). Findings point to an early predisposition to treat tone as a defining characteristic of words regardless of its lexical relevance at 18 months. Between 18 and 24 months, learners appear to ascribe lexical relevance to tone in a language‐specific manner. The current study identifies the influences of tone variation on memories for newly learned words and the time period during which lexical tone – a highly frequent constituent of human languages – actually becomes lexical for early learners. Findings are contextualized with prevailing models of the developing lexicon.     

The moment‐to‐moment pitch dynamics of child‐directed speech shape toddlers’ attention and learning

Young children have an overall preference for child‐directed speech (CDS) over adult‐directed speech (ADS), and its structural features are thought to facilitate language learning. Many studies have supported these findings, but less is known about processing of CDS at short, sub‐second timescales. How do the moment‐to‐moment dynamics of CDS influence young children's attention and learning? In Study 1, we used hierarchical clustering to characterize patterns of pitch variability in a natural CDS corpus, which uncovered four main word‐level contour shapes: ‘fall’, ‘rise’, ‘hill’, and ‘valley’. In Study 2, we adapted a measure from adult attention research—pupil size synchrony—to quantify real‐time attention to speech across participants, and found that toddlers showed higher synchrony to the dynamics of CDS than to ADS. Importantly, there were consistent differences in toddlers’ attention when listening to the four word‐level contour types. In Study 3, we found that pupil size synchrony during exposure to novel words predicted toddlers’ learning at test. This suggests that the dynamics of pitch in CDS not only shape toddlers’ attention but guide their learning of new words. By revealing a physiological response to the real‐time dynamics of CDS, this investigation yields a new sub‐second framework for understanding young children's engagement with one of the most important signals in their environment.     

Language experience changes subsequent learning

What are the effects of experience on subsequent learning? We explored the effects of language-specific word order knowledge on the acquisition of sequential conditional information. Korean and English adults were engaged in a sequence learning task involving three different sets of stimuli: auditory linguistic (nonsense syllables), visual non-linguistic (nonsense shapes), and auditory non-linguistic (pure tones). The forward and backward probabilities between adjacent elements generated two equally probable and orthogonal perceptual parses of the elements, such that any significant preference at test must be due to either general cognitive biases, or prior language-induced biases. We found that language modulated parsing preferences with the linguistic stimuli only. Intriguingly, these preferences are congruent with the dominant word order patterns of each language, as corroborated by corpus analyses, and are driven by probabilistic preferences. Furthermore, although the Korean individuals had received extensive formal explicit training in English and lived in an English-speaking environment, they exhibited statistical learning biases congruent with their native language. Our findings suggest that mechanisms of statistical sequential learning are implicated in language across the lifespan, and experience with language may affect cognitive processes and later learning.     

语言经验对概率词切分的影响

概率词切分指个体利用音节间的转换概率切分语流、发现词语边界的过程。经典的概率词切分研究多采用“学习-测试”范式, 首先要求被试切分一段无意义人工语言, 随后对切分效果进行测试。近年来, 研究者逐渐关注语言经验对概率词切分的影响, 具体包括语音经验和被试掌握的语言知识两方面。今后的研究, 一方面可以更多地关注普通话母语者的语言经验如何作用于概率词切分过程; 另一方面还可以在语言经验的分类上进行拓展, 细分群体语言经验和个体语言经验的影响。     

When Cars Hit Trucks and Girls Hug Boys: The Effect of Animacy on Word Order in Gestural Language Creation

A well‐known typological observation is the dominance of subject‐initial word orders, SOV and SVO, across the world's languages. Recent findings from gestural language creation paradigms offer possible explanations for the prevalence of SOV. When asked to gesture transitive events with an animate agent and inanimate patient, gesturers tend to produce SOV order, regardless of their native language biases. Interestingly, when the patient is animate, gesturers shift away from SOV to use of other orders, like SVO and OSV. Two competing hypotheses have been proposed for this switch: the noisy channel account (Gibson et al., 2013) and the role conflict account (Hall, Mayberry, & Ferreira, 2013). We set out to distinguish between these two hypotheses, disentangling event reversibility and patient animacy, by looking at gestural sequences for events with two inanimate participants (inanimate‐inanimate, reversible). We replicated the previous findings of a preference for SOV order when describing animate‐inanimate, irreversible events as well as a decrease in the use of SOV when presented with animate‐animate, reversible events. Accompanying the drop in SOV, in a novel condition we observed an increase in the use of SVO and OSV orders when describing events involving two animate entities. In sum, we find that the observed avoidance of SOV order in gestural language creation paradigms when the event includes an animate agent and patient is driven by the animacy of the participants rather than the reversibility of the event. We suggest that findings from gestural creation paradigms are not automatically linkable to spoken language typology.