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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]

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]

In language learning, learners engage with their environment, incorporating cues from different sources. However, in lab‐based experiments, using artificial languages, many of the cues and features that are part of real‐world language learning are stripped away. In three experiments, we investigated the role of positive, negative, and mixed feedback on the gradual learning of language‐like statistical regularities within an active guessing game paradigm. In Experiment 1, participants received deterministic feedback (100%), whereas probabilistic feedback (i.e., 75% or 50%) was introduced in Experiment 2. Finally, Experiment 3 explored the impact of mixed probabilistic feedback (33% positive, 33% negative, 33% no feedback). The results showed that cross‐situational learning of words was observed without feedback, but participants were able to learn structural regularities of the miniature language only when feedback was provided. Interestingly, the presence of positive feedback was particularly helpful for the learner, promoting more in‐depth learning of the artificial language. [ABSTRACT FROM AUTHOR]

Over the last two decades, the question of to which linguistic cues learners pay attention when they decode a new language has been subject to controversy in the field of third language (L3) acquisition. In this article, we present an artificial language learning experiment that investigated how lexical and syntactic similarities between an artificial L3 and preexisting grammars impact crosslinguistic influence at the very beginning of the acquisition process. We exposed four groups of 30 Norwegian–English bilinguals each to one of four L3s and gave them training in that L3. The participants gave forced‐choice acceptability judgments on pairs of nonsubject‐initial declarative clauses that differed in word order, one grammatical in English, the other grammatical in Norwegian. The participants had not been exposed to nonsubject‐initial declaratives during the exposure and training phases to avoid confounds with learning. The results showed that both lexical and syntactic similarities affect crosslinguistic influence. We discuss this result considering contemporary accounts of L3 acquisition. A one‐page Accessible Summary of this article in non‐technical language is freely available in the Supporting Information online and at https://oasis‐database.org [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]

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]

Many languages exhibit differential object marking (DOM), where only certain types of grammatical objects are marked with morphological cases. Traditionally, it has been claimed that DOM arises as a way to prevent ambiguity by marking objects that might otherwise be mistaken for subjects (e.g., animate objects). While some recent experimental work supports this account, research on language typology suggests at least one alternative hypothesis. In particular, DOM may instead arise as a way of marking objects that are atypical from the point of view of information structure. According to this account, rather than being marked to avoid ambiguity, objects are marked when they are given (already familiar in the discourse) rather than new. Here, we experimentally investigate this hypothesis using two artificial language learning experiments. We find that information structure impacts participants' object marking, but in an indirect way: atypical information structure leads to a change in word order, which then triggers increased object marking. Interestingly, this staged process of change is compatible with documented cases of DOM emergence. We argue that this process is driven by two cognitive tendencies. First, a tendency to place discourse given information before new information, and second, a tendency to mark noncanonical word order. Taken together, our findings provide corroborating evidence for the role of information structure in the emergence of DOM systems. [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]

Many studies suggest that detecting statistical regularities in linguistic input plays a key role in language acquisition. Although statistical learning is not necessarily implicit in nature, it is often defined as learning that happens without awareness. This article investigates whether statistical learning in young children is indeed implicit, as often assumed. We trained 63 kindergarteners on a miniature language and assessed learning using a picture‐matching task. We used an opt‐out task to measure whether the kindergarteners possessed awareness of an acquired meaningful grammatical marker. In the opt‐out paradigm, participants demonstrate awareness by expressing uncertainty through a nonverbal response: opting out. Our results are compatible with an earlier study of which the present study is a partial replication, suggesting that kindergarteners can acquire this marker from distributional properties in the input. Furthermore, although none of the children could verbalize knowledge of the structure during exit interviews, their behavior during the opt‐out task indicated that they developed awareness of it. [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]

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]

According to classical arguments, language learning is both facilitated and constrained by cognitive biases. These biases are reflected in linguistic typology-the distribution of linguistic patterns across the world's languages-and can be probed with artificial grammar experiments on child and adult learners. Beginning with a widely successful approach to typology (Optimality Theory), and adapting techniques from computational approaches to statistical learning, we develop a Bayesian model of cognitive biases and show that it accounts for the detailed pattern of results of artificial grammar experiments on noun-phrase word order (Culbertson, Smolensky, & Legendre, 2012). Our proposal has several novel properties that distinguish it from prior work in the domains of linguistic theory, computational cognitive science, and machine learning. This study illustrates how ideas from these domains can be synthesized into a model of language learning in which biases range in strength from hard (absolute) to soft (statistical), and in which language-specific and domain-general biases combine to account for data from the macro-level scale of typological distribution to the micro-level scale of learning by individuals. [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]