A well-trod debate at the nexus of cognitive science and linguistics, the so-called past tense debate, has examined how rules and exceptions are individually acquired. However, this debate focuses primarily on individual mechanisms in learning, saying little about how rules and exceptions function from a sociolinguistic perspective. To remedy this, we use agent-based models to examine how rules and exceptions function across populations. We expand on earlier work by considering how repeated interaction and cultural transmission across speakers affects the dynamics (...) of rules and exceptions in language, measuring linguistic outcomes within a social system rather than focusing individual learning outcomes. We consider how population turnover and growth effect linguistic rule dynamics in large and small populations, showing that this method has considerable potential particularly in probing the mechanisms underlying the linguistic niche hypothesis. (shrink)

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. (shrink)

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. (shrink)

If human language must be squeezed through a narrow cognitive bottleneck, what are the implications for language processing, acquisition, change, and structure? In our target article, we suggested that the implications are far-reaching and form the basis of an integrated account of many apparently unconnected aspects of language and language processing, as well as suggesting revision of many existing theoretical accounts. With some exceptions, commentators were generally supportive both of the existence of the bottleneck and its potential implications. Many commentators (...) suggested additional theoretical and linguistic nuances and extensions, links with prior work, and relevant computational and neuroscientific considerations; some argued for related but distinct viewpoints; a few, though, felt traditional perspectives were being abandoned too readily. Our response attempts to build on the many suggestions raised by the commentators and to engage constructively with challenges to our approach. (shrink)

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. (shrink)

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. (shrink)

Words refer to objects in the world, but this correspondence is not one-to-one: Each word has a range of referents that share features on some dimensions but differ on others. This property of language is called underspecification. Parts of the lexicon have characteristic patterns of underspecification; for example, artifact nouns tend to specify shape, but not color, whereas substance nouns specify material but not shape. These regularities in the lexicon enable learners to generalize new words appropriately. How does the lexicon (...) come to have these helpful regularities? We test the hypothesis that systematic backgrounding of some dimensions during learning and use causes language to gradually change, over repeated episodes of transmission, to produce a lexicon with strong patterns of underspecification across these less salient dimensions. This offers a cultural evolutionary mechanism linking individual word learning and generalization to the origin of regularities in the lexicon that help learners generalize words appropriately. (shrink)

Natural languages make prolific use of conventional constituent‐ordering patterns to indicate “who did what to whom,” yet the mechanisms through which these regularities arise are not well understood. A series of recent experiments demonstrates that, when prompted to express meanings through silent gesture, people bypass native language conventions, revealing apparent biases underpinning word order usage, based on the semantic properties of the information to be conveyed. We extend the scope of these studies by focusing, experimentally and computationally, on the interpretation (...) of silent gesture. We show cross‐linguistic experimental evidence that people use variability in constituent order as a cue to obtain different interpretations. To illuminate the computational principles that govern interpretation of non‐conventional communication, we derive a Bayesian model of interpretation via biased inductive inference and estimate these biases from the experimental data. Our analyses suggest people's interpretations balance the ambiguity that is characteristic of emerging language systems, with ordering preferences that are skewed and asymmetric, but defeasible. (shrink)

Natural languages make prolific use of conventional constituent‐ordering patterns to indicate “who did what to whom,” yet the mechanisms through which these regularities arise are not well understood. A series of recent experiments demonstrates that, when prompted to express meanings through silent gesture, people bypass native language conventions, revealing apparent biases underpinning word order usage, based on the semantic properties of the information to be conveyed. We extend the scope of these studies by focusing, experimentally and computationally, on the interpretation (...) of silent gesture. We show cross‐linguistic experimental evidence that people use variability in constituent order as a cue to obtain different interpretations. To illuminate the computational principles that govern interpretation of non‐conventional communication, we derive a Bayesian model of interpretation via biased inductive inference and estimate these biases from the experimental data. Our analyses suggest people's interpretations balance the ambiguity that is characteristic of emerging language systems, with ordering preferences that are skewed and asymmetric, but defeasible. (shrink)

Words are processed more easily when they have canonical phonotactic shapes, i.e., shapes that are frequent both in the lexicon and in usage. We explore whether this cognitively grounded constraint or preference implies testable predictions about the implementation of sound change. Specifically, we hypothesise that words with canonical shapes favour, or ‘select for’, sound changes that produce words with the same shapes. To test this, we investigate a Middle English sound change known as Open Syllable Lengthening. OSL lengthened vowels in (...) disyllables such as ME /ma.kə/ make, but more or less only when they became monosyllabic and when their vowels were non-high. We predict that word shapes produced by this implementation pattern should correspond to the shapes that were most common among morphologically simple monosyllables and disyllables at the time when OSL occurred. We test this prediction against Early Middle English corpus data. Our results largely confirm our prediction: monosyllables produced by OSL indeed conformed to the shapes that were most frequent among already existing monosyllables. At the same time, the failure of OSL to affect disyllables prevented them from assuming shapes that were far more typical of morphologically complex word forms than of simple ones. This suggests that the actuation and implementation of sound changes may be even more sensitive to lexical probabilities than hitherto suspected. Also, it demonstrates how diachronic data can be used to test hypotheses about constraints on word recognition and processing. (shrink)

Cognitive Science, Volume 46, Issue 1, January 2022.

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, , ). 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. (shrink)

Studies of language evolution in the lab have used the iterated learning paradigm to show how linguistic structure emerges through cultural transmission—repeated cycles of learning and use across generations of speakers. However, agent-based simulations suggest that prior biases crucially impact the outcome of cultural transmission. Here, we explored this notion through an iterated learning study of English-French bilingual adults (mostly sequential bilinguals dominant in English). Each participant learned two unstructured artificial languages in a counterbalanced fashion, one resembling English, another resembling (...) French at the phono-orthographic level. The output of each participant was passed down to the next participant, forming diffusion chains of 10 generations per language. We hypothesized that artificial languages would become easier to learn and exhibit greater structure when they were aligned with participants’ bilingual experience (i.e., English languages being easier to learn overall), or as a function of practice (i.e., languages learned second being easier to learn overall). Instead, we found that English-like languages became more structured over generations, but only when they were learned first. In contrast, French-like languages became more structured regardless of the order of learning, suggesting the presence of an asymmetric switch cost during artificial language learning. Moreover, individual differences in language usage modulated the amount of structure produced by the participants. Overall, these data suggest that bilingual experience impacts how novel languages are learned at an individual level, which can then scale up to cultural transmission of novel language at a group level. (shrink)

In a great variety of cultures oaths, ordeals, or lie detectors are used to adjudicate in trials, even though they do not reliably discern liars from truth tellers. I suggest that these practices owe their cultural success to the triggering of cognitive mechanisms that make them more culturally attractive. Informal oaths would trigger mechanisms related to commitment in communication. Oaths used in judicial contexts, by invoking supernatural punishments, would trigger intuitions of immanent justice, linking misfortunes following an oath with perjury. (...) These intuitions would justify the infliction of costs on oath takers in a way that appears morally justified. Ordeals reflect the same logic. Intuitions about immanent justice link a worse outcome following the ordeal with a guilty verdict. This link justifies the application of the ordeal, and the fixed costs involved. Lie detectors also rely on the creation of a link between a specified outcome and a guilty verdict. However, they do not rely on intuitions about immanent justice, but on a variety of intuitions ranging from the plausibly universal to the culturally idiosyncratic. As a result, lie detectors involve lower fixed costs than ordeals, and are less cross-culturally successful than oaths or ordeals. (shrink)

How did human vocalizations come to acquire meaning in the evolution of our species? Charles Darwin proposed that language and music originated from a common emotional signal system based on the imitation and modification of sounds in nature. This protolanguage is thought to have diverged into two separate systems, with speech prioritizing referential functionality and music prioritizing emotional functionality. However, there has never been an attempt to empirically evaluate the hypothesis that a single communication system can split into two functionally (...) distinct systems that are characterized by music- and languagelike properties. Here, we demonstrate that when referential and emotional functions are introduced into an artificial communication system, that system will diverge into vocalization forms with speech- and music-like properties, respectively. Participants heard novel vocalizations as part of a learning task. Half referred to physical entities and half functioned to communicate emotional states. Participants then reproduced each sound with the defined communicative intention in mind. Each recorded vocalization was used as the input for another participant in a serial reproduction paradigm, and this procedure was iterated to create 15 chains of five participants each. Referential vocalizations were rated as more speech-like, whereas emotional vocalizations were rated as more music-like, and this association was observed cross-culturally. In addition, a stable separation of the acoustic profiles of referential and emotional vocalizations emerged, with some attributes diverging immediately and others diverging gradually across iterations. The findings align with Darwin’s hypothesis and provide insight into the roles of biological and cultural evolution in the divergence of language and music. (shrink)

Learning in natural environments is often characterized by a degree of inconsistency from an input. These inconsistencies occur, for example, when learning from more than one source, or when the presence of environmental noise distorts incoming information; as a result, the task faced by the learner becomes ambiguous. In this study, we investigate how learners handle such situations. We focus on the setting where a learner receives and processes a sequence of utterances to master associations between objects and their labels, (...) where the source is inconsistent by design: It uses both “correct” and “incorrect” object‐label pairings. We hypothesize that depending on the order of presentation, the result of the learning may be different. To this end, we consider two types of symbolic learning procedures: the Object‐Label (OL) and the Label‐Object (LO) process. In the OL process, the learner is first exposed to the object, and then the label. In the LO process, this order is reversed. We perform experiments with human subjects, and also construct a computational model that is based on a nonlinear stochastic reinforcement learning algorithm. It is observed experimentally that OL learners are generally better at processing inconsistent input compared to LO learners. We show that the patterns observed in the learning experiments can be reproduced in the simulations if the model includes (a) an ability to regularize the input (and also to do the opposite, i.e., undermatch) and (b) an ability to take account of implicit negative evidence (i.e., interactions among different objects/labels). The model suggests that while both types of learners utilize implicit negative evidence in a similar way, there is a difference in regularization patterns: OL learners regularize the input, whereas LO learners undermatch. As a result, OL learners are able to form a more consistent system of image‐utterance associations, despite the ambiguous learning task. (shrink)

Cognitive Science, Volume 46, Issue 2, February 2022.

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. (shrink)

Cognitive Science, Volume 46, Issue 4, April 2022.

Information changes as it is passed from person to person, with this process of cultural transmission allowing the minds of individuals to shape the information that they transmit. We present mathematical models of cultural transmission which predict that the amount of information passed from person to person should affect the rate at which that information changes. We tested this prediction using a function-learning task, in which people learn a functional relationship between two variables by observing the values of those variables. (...) We varied the total number of observations and the number of those observations that take unique values. We found an effect of the number of observations, with functions transmitted using fewer observations changing form more quickly. We did not find an effect of the number of unique observations, suggesting that noise in perception or memory may have affected learning. (shrink)

Processes governing the creation, perception and production of spoken words are sensitive to the patterns of speech sounds in the language user’s lexicon. Generative linguistic theory suggests that listeners infer constraints on possible sound patterning from the lexicon and apply these constraints to all aspects of word use. In contrast, emergentist accounts suggest that these phonotactic constraints are a product of interactive associative mapping with items in the lexicon. To determine the degree to which phonotactic constraints are lexically mediated, we (...) observed the effects of learning new words that violate English phonotactic constraints on phonotactic perceptual repair processes in nonword consonant-consonant-vowel stimuli. Subjects who learned such words were less likely to “repair” illegal onset clusters and report them as legal ones. Effective connectivity analyses of MRI-constrained reconstructions of simultaneously collected magnetoencephalography and EEG data showed that these behavioral shifts were accompanied by changes in the strength of influences of lexical areas on acoustic-phonetic areas. These results strengthen the interpretation of previous results suggesting that phonotactic constraints on perception are produced by top-down lexical influences on speech processing. (shrink)

Artificial language learning 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. (shrink)