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There is ample evidence that individual‐level cognitive mechanisms active during language learning and use can contribute to the evolution of language. For example, experimental work suggests that learners will reduce case marking in a language where grammatical roles are reliably indicated by fixed word order, a correlation found robustly in the languages of the world. However, such research often assumes homogeneity among language learners and users, or at least does not dig into individual differences in behavior. Yet, it is increasingly clear that language users vary in a large number of ways: in culture, in demographics, and—critically for present purposes—in terms of cognitive diversity. Here, we explore how neurodiversity impacts behavior in an experimental task similar to the one summarized above, and how this behavior interacts with social pressures. We find both similarities and differences between autistic and nonautistic English‐speaking individuals, suggesting that neurodiversity can impact language change in the lab. This, in turn, highlights the potential for future research on the role of neurodivergent populations in language evolution more generally. [ABSTRACT FROM AUTHOR]

A foundational goal of linguistics has been to understand why languages look the way they do. A range of possible explanations exist – from domain-specific representations to cognition-external factors like history and grammaticalization – and all of these undoubtedly play some role. But determining exactly how these link with specific features of language remains challenging, and the role of domain-specific mechanisms has been particularly contentious. In this paper, I highlight a growing new approach, which uses artificial language experiments to link individual-level biases to cross-linguistic trends in language structure. Using word and morpheme order as case studies, I will show how a range of different paradigms and learner populations allow us to make progress on this crucial issue in linguistics. I will focus on typological trends in word and morpheme order. For some ordering trends, experimental evidence points to variation across populations, suggesting that the best explanation for these patterns likely has its root in language history and grammaticalization. In other cases, the evidence points to the role of universal but domain-general cognitive biases, like transparency and simplicity. These domain-general biases interact with linguistic representations in important ways. Taken together these studies help adjudicate between alternative explanations for a number of specific ordering patterns and suggest a new sense of domain-specificity in the evolution of language. [ABSTRACT FROM AUTHOR]

Grammatical redundancy is a widespread feature across languages. Although redundant cues can be seen to increase the complexity and processing burden of structures, it has been suggested that they can assist language acquisition. Here, we explored if this learning benefit can be observed from the very initial stages of second language (L2) acquisition and whether the effect of redundancy is modulated by the perceptual salience of the redundant linguistic cues. Across two experiments, three groups of adult native speakers of English were incidentally exposed to three different artificial languages; one that had a fixed word order, Verb-Object-Subject, and two in which thematic role assignment was additionally determined by a low-salient (Experiment 1) or a high-salient (Experiment 2) redundant case marker. While all groups managed to learn the novel language, our results pointed towards a hindering role of redundancy, with participants in the non-redundant condition achieving greater learning outcomes compared to those in both redundant conditions. Results also revealed that this impeding effect of redundancy on L2 learners can be attenuated by the salience of the redundant cue (Experiment 2). In conjunction with earlier findings, the present results suggest that the effect of redundancy on L2 acquisition can be differentially manifested depending on the stage of L2 development, learners' first language biases and age. [ABSTRACT FROM AUTHOR]

General principles of human cognition can help to explain why languages are more likely to have certain characteristics than others: structures that are difficult to process or produce will tend to be lost over time. One aspect of cognition that is implicated in language use is working memory--the component of short-term memory used for temporary storage and manipulation of information. In this study, we consider the relationship between working memory and regularization of linguistic variation. Regularization is a well-documented process whereby languages become less variable (on some dimension) over time. This process has been argued to be driven by the behavior of individual language users, but the specific mechanism is not agreed upon. Here, we use an artificial language learning experiment to investigate whether limitations in working memory during either language learning or language production drive regularization behavior. We find that taxing working memory during production results in the loss of all types of variation, but the process by which random variation becomes more predictable is better explained by learning biases. A computational model offers a potential explanation for the production effect using a simple self-priming mechanism. [ABSTRACT FROM AUTHOR]

We investigated the emergence of sociolinguistic indexicality using an artificial‐language‐learning paradigm. Sociolinguistic indexicality involves the association of linguistic variants with nonlinguistic social or contextual features. Any linguistic variant can acquire "constellations" of such indexical meanings, though they also exhibit an ordering, with first‐order indices associated with particular speaker groups and higher‐order indices targeting stereotypical attributes of those speakers. Much natural‐language research has been conducted on this phenomenon, but little experimental work has focused on how indexicality emerges. Here, we present three miniature artificial‐language experiments designed to break ground on this question. Results show ready formation of first‐order indexicality based on co‐occurrence alone, with higher‐order indexicality emerging as a result of extension to new speaker groups, modulated by the perceived practical importance of the indexed social feature. [ABSTRACT FROM AUTHOR]

In traditional, generative phonology, sound patterns are represented in terms of abstract features, typically based on the articulatory properties of the sounds. The present study makes use of an artificial language learning experiment to explore when and how learners extend a novel phonological pattern to novel segments. Adult, English-speaking learners were exposed to a spirantization pattern in which a stop became a fricative between two vowels (e.g., /bib/ + /o/ ➔ [bivo]). Participants were trained on spirantization for two of four possible stop-fricative pairs, and were tested on their generalization to the held-out segments. Two groups of participants were trained on items based on voicing (e.g., the Voiced condition was trained on /b/ ➔ [v], and /d/ ➔ [z], and tested on /p/ ➔ [f], and /t/ ➔ [s]), and two groups of participants were trained on items based on place of articulation (e.g., the Labial condition was trained on /b/ ➔ [v], and /p/ ➔ [f] and tested on /t/ ➔ [s], and /d/ ➔ [z]). Participants in both Place and Voice conditions were successful at learning and generalizing the spirantization pattern to novel segments, but rates of generalization were higher in the Voice conditions. These results support a similarity-based approach to generalization, particularly one that takes into account articulatorily-based features and natural classes. Implications for phonological theory are discussed. [ABSTRACT FROM AUTHOR]

Over half a century ago, George Zipf observed that more frequent words tend to be older. Corpus studies since then have confirmed this pattern, with more frequent words being replaced and regularized less often than less frequent words. Two main hypotheses have been proposed to explain this: that frequent words change less because selection against innovation is stronger at higher frequencies, or that they change less because stochastic drift is stronger at lower frequencies. Here, we report the first experimental test of these hypotheses. Participants were tasked with learning a miniature language consisting of two nouns and two plural markers. Nouns occurred at different frequencies and were subjected to treatments that varied drift and selection. Using a model that accounts for participant heterogeneity, we measured the rate of noun regularization, the strength of selection, and the strength of drift in participant responses. Results suggest that drift alone is sufficient to generate the elevated rate of regularization we observed in low‐frequency nouns, adding to a growing body of evidence that drift may be a major driver of language change. [ABSTRACT FROM AUTHOR]

Person systems convey the roles entities play in the context of speech (e.g., speaker, addressee). As with other linguistic category systems, not all ways of partitioning the person space are equally likely crosslinguistically. Different theories have been proposed to constrain the set of possible person partitions that humans can represent, explaining their typological distribution. This article introduces an artificial language learning methodology to investigate the existence of universal constraints on person systems. We report the results of three experiments that inform these theoretical approaches by generating behavioral evidence for the impact of constraints on the learnability of different person partitions. Our findings constitute the first experimental evidence for learnability differences in this domain. [ABSTRACT FROM AUTHOR]

Inflectional affixes expressing the same grammatical category (e.g., subject agreement) tend to appear in the same morphological position in the word. We hypothesize that this cross‐linguistic tendency toward category clustering is at least partly the result of a learning bias, which facilitates the transmission of morphology from one generation to the next if each inflectional category has a consistent morphological position. We test this in an online artificial language experiment, teaching adult English speakers a miniature language consisting of noun stems representing shapes and suffixes representing the color and number features of each shape. In one experimental condition, each suffix category has a fixed position, with color in the first position and number in the second position. In a second condition, each specific combination of suffixes has a fixed order, but some combinations have color in the first position, and some have number in the first position. In a third condition, suffixes are randomly ordered on each presentation. While the language in the first condition is consistent with the category clustering principle, those in the other conditions are not. Our results indicate that category clustering of inflectional affixes facilitates morphological learning, at least in adult English speakers. Moreover, we found that languages that violate category clustering but still follow fixed affix ordering patterns are more learnable than languages with random ordering. Altogether, our results provide evidence for individual biases toward category clustering; we suggest that this bias may play a causal role in shaping the typological regularities in affix order we find in natural language. [ABSTRACT FROM AUTHOR]

Languages vary with respect to whether sentences with two negative elements give rise to double negation or negative concord meanings. We explore an influential hypothesis about what governs this variation: namely, that whether a language exhibits double negation or negative concord is partly determined by the phonological and syntactic nature of its negative marker (Zeijlstra 2004; Jespersen 1917). For example, one version of this hypothesis argues that languages with affixal negation must be negative concord (Zeijlstra 2008). We use an artificial language learning experiment to investigate whether English speakers are sensitive to the status of the negative marker when learning double negation and negative concord languages. Our findings fail to provide evidence supporting this hypothesised connection. Instead, our results suggest that learners find it easier to learn negative concord languages compared to double negation languages independently of whether the negative marker is an adverb or an affix. This is in line with evidence from natural language acquisition (Thornton et al. 2016). [ABSTRACT FROM AUTHOR]

We investigated L1 and L2 frequency effects in the sharing of syntax across languages (reflected in cross-linguistic structural priming) using an artificial language (AL) paradigm. Ninety-six Dutch speakers learned an AL with either a prepositional-object (PO) dative bias (PO datives appeared three times as often as double-object [DO] datives) or a DO dative bias (DOs appeared three times as often as POs). Priming was assessed from the AL to Dutch (a strongly PO-biased language). There was weak immediate priming for DOs, but not for POs in both bias conditions. This suggests that L1, but not AL, frequency influenced immediate priming. Furthermore, the DO bias group produced 10% more DOs in Dutch than the PO bias group, showing that cumulative priming was influenced by AL frequency. We discuss the different effects of L1 and AL frequency on cross-linguistic structural priming in terms of lexicalist and implicit learning accounts. [ABSTRACT FROM AUTHOR]

What is the connection between the cultural evolution of a language and the rapid processing response to that language in the brains of individual learners? In an iterated communication study that was conducted previously, participants were asked to communicate temporal concepts such as "tomorrow," "day after," "year," and "past" using vertical movements recorded on a touch screen. Over time, participants developed simple artificial 'languages' that used space metaphorically to communicate in nuanced ways about time. Some conventions appeared rapidly and universally (e.g., using larger vertical movements to convey greater temporal durations). Other conventions required extensive social interaction and exhibited idiosyncratic variation (e.g., using vertical location to convey past or future). Here we investigate whether the brain's response during acquisition of such a language reflects the process by which the language's conventions originally evolved. We recorded participants' EEG as they learned one of these artificial space-time languages. Overall, the brain response to this artificial communication system was language-like, with, for instance, violations to the system's conventions eliciting an N400-like component. Over the course of learning, participants' brain responses developed in ways that paralleled the process by which the language had originally evolved, with early neural sensitivity to violations of a rapidly-evolving universal convention, and slowly developing neural sensitivity to an idiosyncratic convention that required slow social negotiation to emerge. This study opens up exciting avenues of future work to disentangle how neural biases influence learning and transmission in the emergence of structure in language. [ABSTRACT FROM AUTHOR]

General vowel harmony and disharmony rules have comparable formal complexity but differ dramatically in typological frequency and phonetic motivation. Previous studies found no difference in learning between vowel harmony and disharmony; this putative equivalence has been used to discount the view that learners are influenced by substantive learning biases. In the current study, we use a more nuanced test to show that there is a clear difference in learning between vowel harmony and disharmony: learners readily infer a vowel harmony pattern, but not a disharmony pattern. The findings suggest that vowel disharmony is in fact strongly disfavored during learning. [ABSTRACT FROM AUTHOR]

Phonological alternations often happen to conform to phonotactic regularities, from which a single mechanism for phonotactics and alternations has been claimed. We note, however, that empirical evidence supporting the link between phonotactics and alternations comes only from English native speakers whose first language (L1) does exhibit phonotactically motivated alternation patterns. This article examines whether the link between phonotactics and alternations is universally available. To do so, we test learning of phonotactics and alternations with Cantonese native speakers, whose L1 provides no evidence for or against the link. We address learning of a vowel harmony pattern through the use of three artificial languages; one with a harmony pattern both within and across stems, another with a harmony pattern only across stems; and the other with a disharmony pattern within stems but harmony across stems. Learners successfully acquired harmony phonotactics according to input patterns, but they showed no difference in learning alternation patterns across the three languages. Our results suggest that the link between phonotactics and alternations might be language-specific: Only upon receiving L1 evidence, learners can use a unified mechanism to encode phonotactics and alternations. [ABSTRACT FROM AUTHOR]

Over the last decade, iterated learning studies have provided compelling evidence for the claim that linguistic structure can emerge from non‐structured input, through the process of transmission. However, it is unclear whether individuals differ in their tendency to add structure, an issue with implications for understanding who are the agents of change. Here, we identify and test two contrasting predictions: The first sees learning as a pre‐requisite for structure addition, and predicts a positive correlation between learning accuracy and structure addition, whereas the second maintains that it is those learners who struggle with learning and reproducing their input who add structure to it. This prediction is hard to test in standard iterated learning paradigms since each learner is exposed to a different input, and since structure and accuracy are computed using the same test items. Here, we test these contrasting predictions in two experiments using a one‐generation artificial language learning paradigm designed to provide independent measures of learning accuracy and structure addition. Adults (N = 48 in each study) were exposed to a semi‐regular language (with probabilistic structure) and had to learn it: Learning was assessed using seen items, whereas structure addition was calculated over unseen items. In both studies, we find a strong positive correlation between individuals' ability to learn the language and their tendency to add structure to it: Better learners also produced more structured languages. These findings suggest a strong link between learning and generalization. We discuss the implications of these findings for iterated language models and theories of language change more generally. [ABSTRACT FROM AUTHOR]

There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques—even simple ones that are straightforward to use—can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space. [ABSTRACT FROM AUTHOR]

Several studies found cross‐linguistic structural priming with various language combinations. Here, we investigated the role of two important domains of language variation: case marking and word order, for transitive and ditransitive structures. We varied these features in an artificial language learning paradigm, using three different artificial language versions in a between‐subjects design. Priming was assessed between Dutch (no overt case marking, SVO word order) and a) an SVO order version, b) a case marking version, and c) an SOV order version. Similar within‐language and cross‐linguistic priming was found in all versions for transitives, indicating that cross‐linguistic structural priming was not hindered. In contrast, for ditransitives we found similar within‐language priming for all versions, but no cross‐linguistic priming. The finding that cross‐linguistic priming is possible between languages that vary in morphological marking or word order, is compatible with studies showing cross‐linguistic priming between natural languages that differ on these dimensions. [ABSTRACT FROM AUTHOR]

Statistical learning (SL) involving sensitivity to distributional regularities in the environment has been suggested to be an important factor in many aspects of cognition, including language. However, the degree to which statistically-learned information is retained over time is not well understood. To establish whether or not learners are able to preserve such regularities over time, we examined performance on an artificial second language learning task both immediately after training and also at a follow-up session 2 weeks later. Participants were exposed to an artificial language (Brocanto2), half of them receiving simplified training items in which only 20% of sequences contained complex structures, whereas the other half were exposed to a training set in which 80% of the items were composed of complex sequences. Overall, participants showed signs of learning at the first session and retention at the second, but the degree of learning was affected by the nature of the training they received. Participants exposed to the simplified input outperformed those in the more complex training condition. A GLMM was used to model the relationship between stimulus properties and participants' endorsement strategies across both sessions. The results indicate that participants in the complex training condition relied more on an item's chunk strength than those in the simple training condition. Taken together, this set of findings shows that statistically learned regularities are retained over the course of 2 weeks. The results also demonstrate that training on input featuring simple items leads to improved learning and retention of grammatical regularities. [ABSTRACT FROM AUTHOR]

Previous research on the acquisition of noun classification systems (e.g. grammatical gender) has found that child learners rely disproportionately on phonological cues to determine the class of a new noun, even when competing semantic cues are more reliable in their language. Culbertson, gagliardi, and Smith (2017) use artificial language learning experiments with adults to argue that this likely results from the early availability of phonological information during acquisition. Learners base their initial representations on formal features of nouns, only later integrating semantic cues from noun meanings. Here, we use these same methods to show that early availability affects cue use in children (six- to seven-year-olds) as well. However, we also find evidence of developmental changes in sensitivity to semantics; when both cue types are simultaneously available, children are more likely to rely on phonology than adults are. our results suggest that both early availability and a bias favoring phonological cues contribute to children's overreliance on phonology in natural language acquisition. [ABSTRACT FROM AUTHOR]

We investigated whether learning an artificial language at 17 months was predictive of children's natural language vocabulary and grammar skills at 54 months. Children at 17 months listened to an artificial language containing non-adjacent dependencies, and were then tested on their learning to segment and to generalise the structure of the language. At 54 months, children were then tested on a range of standardised natural language tasks that assessed receptive and expressive vocabulary and grammar. A structural equation model demonstrated that learning the artificial language generalisation at 17 months predicted language abilities – a composite of vocabulary and grammar skills – at 54 months, whereas artificial language segmentation at 17 months did not predict language abilities at this age. Artificial language learning tasks – especially those that probe grammar learning – provide a valuable tool for uncovering the mechanisms driving children's early language development. [ABSTRACT FROM AUTHOR]

Noun phrase word order varies cross-linguistically, however, two distributional asymmetries have attracted substantial attention. First, the most common orders place adjectives closest to the noun, then numerals, then demonstratives (e.g., N-Adj-Num-Dem). Second, exceptions to this are restricted to post-nominal position (e.g., N-Dem-Num-Adj, but not, for instance, Adj-Num-Dem-N). These observations have been argued to reflect constraints on cognition. Here we report on two experiments, providing support for this claim. We taught English- and Thai-speaking participants artificial languages in which the position of modifiers relative to the noun differed from their native order (post-nominal position in English, pre-nominal in Thai). We trained participants on single-modifier phrases, and asked them to extrapolate to multiple modifier phrases. We found that both populations infer relative orders of modifiers that conform to the tendency for closest proximity of adjectives, then numerals, then demonstratives. Further, we show that Thai participants, learning pre-nominal modifiers, exhibit a stronger such preference. These results track the typology closely and are consistent with the claim that noun phrase word order reflects properties of human cognition. We discuss future research needed to rule out alternative explanations for our findings, including prior language experience. [ABSTRACT FROM AUTHOR]

Language learners often spend more time comprehending than producing a new language. However, memory research suggests reasons to suspect that production practice might provide a stronger learning experience than comprehension practice. We tested the benefits of production during language learning and the degree to which this learning transfers to comprehension skill. We taught participants an artificial language containing multiple linguistic dependencies. Participants were randomly assigned to either a production- or a comprehension-learning condition, with conditions designed to balance attention demands and other known production--comprehension differences. After training, production-learning participants outperformed comprehension-learning participants on vocabulary comprehension and on comprehension tests of grammatical dependencies, even when we controlled for individual differences in vocabulary learning. This result shows that producing a language during learning can improve subsequent comprehension, which has implications for theories of memory and learning, language representations, and educational practices. [ABSTRACT FROM AUTHOR]

According to the competitive exclusion principle (Gause, 1934), competition for the same niche must eventually lead one competitor to extinction or the occupation of a new niche. This principle applies in both biology and the cultural evolution of language, where different words and structures compete for the same function or meaning (Aronoff, 2016). Across languages, for example, word order trades off with case marking as a means of indicating who did what to whom in a sentence. Previous experimental work has shed light on how such trade-offs come about as languages adapt to human biases through learning and production, with biases becoming amplified through iterated learning over generations. At the same time, a large body of work has documented the impact of social biases on language change. However, little work has investigated how social biases interact with learning and production biases. In particular, the social dimension of language may provide alternative niches for otherwise redundant forms, preventing or slowing their extinction. We tested this hypothesis in an iterated-learning experiment in which participants were exposed to a language with two dialects, both of which had fixed word order, but differed in whether they employed case markers. In one condition, we biased participants socially towards speakers of the dialect that employed case; in other conditions we provided no bias, or biased participants for or against the dialect without case. As expected under our hypothesis, the use of case markers declined over time in all conditions, but the social bias in favor of case-dialect speakers slowed the decline. [ABSTRACT FROM AUTHOR]

While variation occurs in both phonology and morphosyntax, phonological variation also includes phonetic variation motivated by articulatory or perceptual factors. While learning in both domains is subject to cognitive biases, phonological learning may also be biased by physical factors, which may enhance learnability of phonetically motivated alternations. This study aims to identify whether learning differs when children are exposed to phonological or morphosyntactic patterns with equal complexity. Cantonese-speaking children learned an artificial language involving rounding harmony, where unround [e] or round [o] harmonizes with the following noun, or gender agreement, with feminine and masculine allomorphs lo ~ le. Patterns applied variably in 67% of training items, or categorically. Children were tested on generalization to novel stems. In the categorical learning conditions, participants showed comparable rates of harmony/agreement. In the variable phonological learning conditions, application of harmony exceeded the rate of exposure in training, suggesting the influence of a bias toward phonetically grounded rounding harmony. In the variable morphosyntactic condition, participants applied agreement below the rate of exposure. This finding points to a qualitative difference between learning in the two domains, with phonological (but not morphosyntactic) learning influenced by substantive grounding. [ABSTRACT FROM AUTHOR]

Languages exhibit sociolinguistic variation, such that adult native speakers condition the usage of linguistic variants on social context, gender, and ethnicity, among other cues. While the existence of this kind of socially conditioned variation is well-established, less is known about how it is acquired. Studies of naturalistic language use by children provide various examples where children’s production of sociolinguistic variants appears to be conditioned on similar factors to adults’ production, but it is difficult to determine whether this reflects knowledge of sociolinguistic conditioning or systematic differences in the input to children from different social groups. Furthermore, artificial language learning experiments have shown that children have a tendency to eliminate variation, a process which could potentially work against their acquisition of sociolinguistic variation. The current study used a semi-artificial language learning paradigm to investigate learning of the sociolinguistic cue of speaker identity in 6-year-olds and adults. Participants were trained and tested on an artificial language where nouns were obligatorily followed by one of two meaningless particles and were produced by one of two speakers (one male, one female). Particle usage was conditioned deterministically on speaker identity (Experiment 1), probabilistically (Experiment 2), or not at all (Experiment 3). Participants were given tests of production and comprehension. In Experiments 1 and 2, both children and adults successfully acquired the speaker identity cue, although the effect was stronger for adults and in Experiment 1. In addition, in all three experiments, there was evidence of regularization in participants’ productions, although the type of regularization differed with age: children showed regularization by boosting the frequency of one particle at the expense of the other, while adults regularized by conditioning particle usage on lexical items. Overall, results demonstrate that children and adults are sensitive to speaker identity cues, an ability which is fundamental to tracking sociolinguistic variation, and that children’s well-established tendency to regularize does not prevent them from learning sociolinguistically conditioned variation. [ABSTRACT FROM AUTHOR]

Linguists from across sub-disciplines have noted that congruence (i.e., form-function mapping) across languages in contact seems to affect acquisition and play a role in language emergence (e.g. Creole genesis). However, because congruence is often confounded with other variables (e.g., frequency, language type, speakers' proficiency levels, perceptual salience, semantic transparency), it remains unclear whether congruence per se benefits learners. In this paper, we provide an experimental test of the effects of congruence on acquisition through an artificial language-learning experiment involving English (L1) and two artificial languages (Flugerdu and Zamperese). English-speakers who identified as "native" (i.e., first-language) speakers (N = 163) were randomly assigned to one of four conditions, varying which of the three languages expressed negation with congruent forms: all three languages; only Flugerdu and Zamperese; only English and Flugerdu; or none. Our findings show that participants better acquired the negation morpheme when the form was congruent with negation in English but not when the two artificial languages alone shared a congruent form. We likewise found unanticipated spillover effects in which participants better acquired the vocabulary and grammar of the artificial languages when all three languages had congruent negation forms. These findings provide insight into the effects of congruence on language acquisition in multilingual environments and Creole language formation. [ABSTRACT FROM AUTHOR]

We investigate the learning of contextual meaning by adults in an artificial language. Contextual meaning here refers to the non-denotative contextual information that speakers attach to a linguistic construction. Through a series of short games, played online, we test how well adults can learn different contextual meanings for a word-formation pattern in an artificial language. We look at whether learning contextual meanings depends on the social salience of the context, whether our players interpret these contexts generally, and whether the learned meaning is generalized to new words. Our results show that adults are capable of learning contextual meaning if the context is socially salient, coherent, and interpretable. Once a contextual meaning is recognized, it is readily generalized to related forms and contexts. [ABSTRACT FROM AUTHOR]

Psychological experiments have revealed that in normal visual perception of humans, color cues are more salient than shape cues, which are more salient than textural patterns.We carried out an artificial language learning experiment to study whether such perceptual saliency hierarchy (color > shape > texture) influences the learning of orders regulating adjectives of involved visual features in a manner either congruent (expressing a salient feature in a salient part of the form) or incongruent (expressing a salient feature in a less salient part of the form) with that hierarchy. Results showed that within a few rounds of learning participants could learn the compositional segments encoding the visual features and the order between them, generalize the learned knowledge to unseen instances with the same or different orders, and show learning biases for orders that are congruent with the perceptual saliency hierarchy. Although the learning performances for both the biased and unbiased orders became similar given more learning trials, our study confirms that this type of individual perceptual constraint could contribute to the structural configuration of language, and points out that such constraint, as well as other factors, could collectively affect the structural diversity in languages. [ABSTRACT FROM AUTHOR]

Artificial language learning ( ALL) experiments have become an important tool in exploring principles of language and language learning. A persistent question in all of this work, however, is whether ALL engages the linguistic system and whether ALL studies are ecologically valid assessments of natural language ability. In the present study, we considered these questions by examining the relationship between performance in an ALL task and second language learning ability. Participants enrolled in a Spanish language class were evaluated using a number of different measures of Spanish ability and classroom performance, which was compared to IQ and a number of different measures of ALL performance. The results show that success in ALL experiments, particularly more complex artificial languages, correlates positively with indices of L2 learning even after controlling for IQ. These findings provide a key link between studies involving ALL and our understanding of second language learning in the classroom. [ABSTRACT FROM AUTHOR]

Many languages restrict their lexicons by OCP- Place , a phonotactic constraint against co-occurrences of consonants with shared [place] (e.g., McCarthy, 1986 ). While many previous studies have suggested that listeners have knowledge of OCP- Place and use this for speech processing, it is less clear whether they make reference to an abstract representation of this constraint. In Dutch, OCP- Place gradiently restricts non-adjacent consonant co-occurrences in the lexicon. Focusing on labial-vowel-labial co-occurrences, we found that there are, however, exceptions from the general effect of OCP- Labial : (A) co-occurrences of identical labials are systematically less restricted than co-occurrences of homorganic labials, and (B) some specific pairs (e.g., /pVp/, /bVv/) occur more often than expected. Setting out to study whether exceptions such as (A) and (B) had an effect on processing, the current study presents an artificial language learning experiment and a reanalysis of Boll-Avetisyan and Kager's (2014) speech segmentation data. Results indicate that Dutch listeners can use both knowledge of phonotactic detail and an abstract constraint OCP- Labial as a cue for speech segmentation. We suggest that whether detailed or abstract representations are drawn on depends on the complexity of processing demands. [ABSTRACT FROM AUTHOR]

We present the results from a three-day artificial language learning study on adults. The study examined whether sentence-parsing limitations, in particular, difficulties revising initial syntactic/semantic commitments during comprehension, shape learners’ ability to acquire a language. Findings show that both comprehension and production of morphology pertaining to sentence argument structure are delayed when this morphology consistently appears at the end, rather than at the beginning, of sentences in otherwise identical grammatical systems. This suggests that real-time processing constraints impact acquisition; morphological cues that tend to guide linguistic analyses are easier to learn than cues that revise these analyses. Parallel performance in production and comprehension indicates that parsing constraints affect grammatical acquisition, not just real-time commitments. Properties of the linguistic system (e.g., ordering of cues within a sentence) interact with the properties of the cognitive system (cognitive control and conflict-resolution abilities) and together affect language acquisition. [ABSTRACT FROM AUTHOR]

The prevalence of redundancy in the world languages has long puzzled language researchers. It is especially surprising in light of the growing evidence on speakers' tendency to avoid redundant elements in production (omitting or reducing more predictable elements). Here, we propose that redundancy can be functional for learning. In particular, we argue that redundant cues can facilitate learning, even when they make the language system more complicated. This prediction is further motivated by the Linguistic Niche Hypothesis (Lupyan & Dale, 2010), which suggests that morphological complexity can arise due to the advantage redundancy might confer for child learners. We test these hypotheses in an artificial language learning study with children and adults, where either word order alone or both word order and case marking serve as cues for thematic assignment in a novel construction. We predict, and find, that children learning the redundant language learn to produce it, and show better comprehension of the novel thematic assignment than children learning the non-redundant language, despite having to learn an additional morpheme. Children in both conditions were similarly accurate in producing the novel word order, suggesting redundancy might have a differential effect on comprehension and production. Adults did not show better learning in the redundant condition, most likely because they were at ceiling in both conditions. We discuss implications for theories of language learning and language change. [ABSTRACT FROM AUTHOR]

Several theoretical proposals for the evolution of language have sparked a renewed search for comparative data on human and non-human animal computational capacities. However, conceptual confusions still hinder the field, leading to experimental evidence that fails to test for comparable human competences. Here we focus on two conceptual and methodological challenges that affect the field generally: 1) properly characterizing the computational features of the faculty of language in the narrow sense; 2) defining and probing for human language-like computations via artificial language learning experiments in non-human animals. Our intent is to be critical in the service of clarity, in what we agree is an important approach to understanding how language evolved. [ABSTRACT FROM AUTHOR]

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment (Culbertson, Smolensky, & Legendre, 2012) targeting the learning of word-order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross-linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners' inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization-Greenberg's Universal 18-which bans a particular word-order pattern relating nouns, adjectives, and numerals. [ABSTRACT FROM AUTHOR]

Abstract: Children learning the inflections of their native language show the ability to generalize beyond the perceptual particulars of the examples they are exposed to. The phenomenon of “rule learning”—quick learning of abstract regularities from exposure to a limited set of stimuli—has become an important model system for understanding generalization in infancy. Experiments with adults and children have revealed differences in performance across domains and types of rules. To understand the representational and inferential assumptions necessary to capture this broad set of results, we introduce three ideal observer models for rule learning. Each model builds on the next, allowing us to test the consequences of individual assumptions. Model 1 learns a single rule, Model 2 learns a single rule from noisy input, and Model 3 learns multiple rules from noisy input. These models capture a wide range of experimental results—including several that have been used to argue for domain-specificity or limits on the kinds of generalizations learners can make—suggesting that these ideal observers may be a useful baseline for future work on rule learning. [Copyright &y& Elsevier]

The article presents a study on the acquisition of an artificial language that is made according to vowel harmony (VH) rules. The researchers observed 36 undergraduate students from Hacettepe University in Ankara, Turkey, all of which were native speakers of Turkish. They found that vowel harmony is a pattern that was easy to master for Turkish speakers.

Abstract: A study combining artificial grammar and sentence comprehension methods investigated the learning and online use of probabilistic, nonadjacent combinatorial constraints. Participants learned a small artificial language describing cartoon monsters acting on objects. Self-paced reading of sentences in the artificial language revealed comprehenders’ sensitivity to nonadjacent combinatorial constraints, without explicit awareness of the probabilities embedded in the language. These results show that even newly-learned constraints have an identifiable effect on online sentence processing. The rapidity of learning in this paradigm relative to others has implications for theories of implicit learning and its role in language acquisition. [Copyright &y& Elsevier]

Abstract: This study combined artificial language learning (ALL) with conventional experimental techniques to test whether statistical speech segmentation outputs are integrated into adult listeners’ mental lexicon. Lexicalization was assessed through inhibitory effects of novel neighbors (created by the parsing process) on auditory lexical decisions to real words. Both immediately after familiarization and post-one week, ALL outputs were lexicalized only when the cues available during familiarization (transitional probabilities and wordlikeness) suggested the same parsing (Experiments 1 and 3). No lexicalization effect occurred with incongruent cues (Experiments 2 and 4). Yet, ALL differed from chance, suggesting a dissociation between item knowledge and lexicalization. Similarly contrasted results were found when frequency of occurrence of the stimuli was equated during familiarization (Experiments 3 and 4). Our findings thus indicate that ALL outputs may be lexicalized as far as the segmentation cues are congruent, and that this process cannot be accounted for by raw frequency. [Copyright &y& Elsevier]

Peña, Bonatti, Nespor, and Mehler (2002) investigated an artificial language where the structure of words was determined by nonadjacent dependencies between syllables. They found that segmentation of continuous speech could proceed on the basis of these dependencies. However, Peña et al.'s artificial language contained a confound in terms of phonology, in that the dependent syllables began with plosives and the intervening syllables began with continuants. We consider three hypotheses concerning the role of phonology in speech segmentation in this task: (1) participants may recruit probabilistic phono tactic information from their native language to the artificial language learning task; (2) phonetic properties of the stimuli, such as the gaps that precede unvoiced plosives, can influences segmentation; and (3) grouping by phonological similarity between dependent syllables contributes to learning the dependency. In a series of experiments controlling the phonological and statistical structure of the language, we found that segmentation performance is influenced by the three factors in different degrees. Learning of nonadjacent dependencies did not occur when (3) is eliminated. We suggest that phonological processing provides a fundamental contribution to distributional analysis. [ABSTRACT FROM AUTHOR]

Language learning involves exposure to inconsistent systems - that is, systems where multiple patterns or methods exist to mark some meaning. Inconsistent systems often change to be more regular over time - they become systematized. However, some recent studies have reported that learners tend to reproduce inconsistency in the input, leading to models in which the language learning mechanism is basically preservatory. We ran an artificial language learning experiment using a novel paradigm to extend our understanding of systematizing versus preservatory mechanisms in language learning. Participants were taught two number marking systems, either completely consistently (the probability P of the system is 1.00) or inconsistently (with P = 0.875 for one system and P = 0.125 for the other, and so on for P = 0.75 and P = 0.625). One marking system was a plural-marking system. The other was a typologically rare singulative-marking system. When generalizing to novel items, participants produced more regular output patterns overall for more consistent input conditions than for less consistent ones, and more for the plural-marking conditions than for the singulative-marking conditions. For the singulative-marking conditions, the inter-participant variation was much greater than for the plural-marking ones; some individuals systematized towards the more familiar pattern, some systematized towards the less familiar pattern and some were not significantly different from probability-matching. We analyze the variation in relation to current statistical learning models, showing that preservatory learning models, as well as all models with a single free parameter, fail to capture our results. We show how a model with two free parameters in which individuals can vary in their propensity to systematize in any given situation is more successful. We also discuss implications for the theory of language change. [ABSTRACT FROM AUTHOR]

Word order harmony describes the tendency, found across the world's languages, to consistently order syntactic heads relative to dependents. It is one of the most well-known and well-studied typological universals. Almost since it was first noted by Greenberg (1963), there has been disagreement about what role, if any, the cognitive system plays in driving harmony. Recently, a series of studies using artificial language learning experiments reported that harmonic noun phrase word orders were preferred over non-harmonic orders by English-speaking adults and children (Culbertson et al., 2012; Culbertson & Newport, 2015, 2017). However, this evidence is potentially confounded by the fact that English is itself a harmonic language (Goldberg, 2013). Here we sought to extend the results from these studies by exploring whether learners who have substantial experience with a non-harmonic language still showed a bias for harmonic patterns during learning. We found that monolingual French- and Hebrew-speaking children, whose language has a non-harmonic noun phrase order (N Adj, Num N) nevertheless preferred harmonic patterns when learning an artificial language. We also found evidence for a harmony bias across several populations of adult learners, although this interacted in complex ways with their L2 experience. Our results suggest that transfer from the L1 cannot explain the preference for harmony found in previous studies. Moreover, they provide the strongest evidence yet that a cognitive bias for harmony is a plausible candidate for shaping linguistic typology. [ABSTRACT FROM AUTHOR]

Non-adjacent dependencies are ubiquitous in language, but difficult to learn in artificial language experiments in the lab. Previous research suggests that non-adjacent dependencies are more learnable given structural support in the input - for instance, in the presence of high variability between dependent items. However, not all non-adjacent dependencies occur in supportive contexts. How are such regularities learned? One possibility is that learning one set of non-adjacent dependencies can highlight similar structures in subsequent input, facilitating the acquisition of new non-adjacent dependencies that are otherwise difficult to learn. In three experiments, we show that prior exposure to learnable non-adjacent dependencies - i.e., dependencies presented in a learning context that has been shown to facilitate discovery - improves learning of novel non-adjacent regularities that are typically not detected. These findings demonstrate how the discovery of complex linguistic structures can build on past learning in supportive contexts. [ABSTRACT FROM AUTHOR]

It is often assumed that cross-linguistically more prevalent distinctions are easier to learn (Typological Prevalence Hypothesis; TPH). Prior work supports this idea in phonology, morphology and syntax but has not addressed semantics. Using Artificial Language Learning experiments with adults, we test predictions made by the TPH about the relative learnability of semantic distinctions in the domain of evidentiality, i.e., the linguistic encoding of information source. As the TPH predicted, when exposed to miniature evidential morphological systems, adult speakers of English whose language does not encode evidentiality grammatically learned the typologically most prevalent system (marking indirect, reportative information) better compared to less-attested systems (Experiments 1-2). Similar patterns were observed when non-linguistic symbols were used to encode evidential distinctions (Experiment 3). Our data support the conjecture that some semantic distinctions are marked preferentially and acquired more easily compared to others in both language and other symbolic systems. [ABSTRACT FROM AUTHOR]

Studies of artificial language learning provide insight into how learning biases and iterated learning may shape natural languages. Prior work has looked at how learners deal with unpredictable variation and how a language changes across multiple generations of learners. The present study combines these features, exploring how word order variation is preserved or regularized over generations. We investigate how these processes are affected by (1) learning biases, (2) the size of the language community, and (3) the amount of input provided. Our results show that when the input comes from a single speaker, adult learners frequency match, reproducing the variability in the input across three generations. However, when the same amount of input is distributed across multiple speakers, frequency matching breaks down. When regularization occurs, there is a strong bias for SOV word order (relative to OSV and VSO). Finally, when the amount of input provided by multiple speakers is increased, learners are able to frequency match. These results demonstrate that both population size and the amount of input per speaker each play a role in language convergence. [ABSTRACT FROM AUTHOR]