Predictability and Variation in Language Are Differentially Affected by Learning and Production.

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.

Evaluating the Relative Importance of Wordhood Cues Using Statistical Learning.

Identifying wordlike units in language is typically done by applying a battery of criteria, though how to weight these criteria with respect to one another is currently unknown. We address this question by investigating whether certain criteria are also used as cues for learning an artificial language-if they are, then perhaps they can be relied on more as trustworthy top-down diagnostics. The two criteria for grammatical wordhood that we consider are a unit's free mobility and its internal immutability. These criteria also map to two cognitive mechanisms that could underlie successful statistical learning: learners might orient themselves around the low transitional probabilities at unit boundaries, or they might seek chunks with high internal transitional probabilities. We find that each criterion has its own facilitatory effect, and learning is best where they both align. This supports the battery-of-criteria approach to diagnosing wordhood, and also suggests that the mechanism behind statistical learning may not be a question of either/or; perhaps the two mechanisms do not compete, but mutually reinforce one another.

A Universal Cognitive Bias in Word Order: Evidence From Speakers Whose Language Goes Against It.

There is a long-standing debate in cognitive science surrounding the source of commonalities among languages of the world. Indeed, there are many potential explanations for such commonalities-accidents of history, common processes of language change, memory limitations, constraints on linguistic representations, and so on. Recent research has used psycholinguistic experiments to provide empirical evidence linking common linguistic patterns to specific features of human cognition, but these experiments tend to use English speakers, who in many cases have direct experience with the common patterns of interest. Here we highlight the importance of testing populations whose languages go against cross-linguistic trends. We investigate whether adult monolingual speakers of Kîîtharaka, which has an unusual way of ordering words, mirror the word-order preferences of English speakers. We find that they do, supporting the hypothesis that universal cognitive representations play a role in shaping word order.

IRAK4 degrader in hidradenitis suppurativa and atopic dermatitis: a phase 1 trial.

Toll-like receptor-driven and interleukin-1 (IL-1) receptor-driven inflammation mediated by IL-1 receptor-associated kinase 4 (IRAK4) is involved in the pathophysiology of hidradenitis suppurativa (HS) and atopic dermatitis (AD). KT-474 (SAR444656), an IRAK4 degrader, was studied in a randomized, double-blind, placebo-controlled phase 1 trial where the primary objective was safety and tolerability. Secondary objectives included pharmacokinetics, pharmacodynamics and clinical activity in patients with moderate to severe HS and in patients with moderate to severe AD. KT-474 was administered as a single dose and then daily for 14 d in 105 healthy volunteers (HVs), followed by dosing for 28 d in an open-label cohort of 21 patients. Degradation of IRAK4 was observed in HV blood, with mean reductions after a single dose of ≥93% at 600-1,600 mg and after 14 daily doses of ≥95% at 50-200 mg. In patients, similar IRAK4 degradation was achieved in blood, and IRAK4 was normalized in skin lesions where it was overexpressed relative to HVs. Reduction of disease-relevant inflammatory biomarkers was demonstrated in the blood and skin of patients with HS and patients with AD and was associated with improvement in skin lesions and symptoms. There were no drug-related infections. These results, from what, to our knowledge, is the first published clinical trial using a heterobifunctional degrader, provide initial proof of concept for KT-474 in HS and AD to be further confirmed in larger trials. ClinicalTrials.gov identifier: NCT04772885 .

The Impact of Information Structure on the Emergence of Differential Object Marking: An Experimental Study.

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.

Nobody Doesn't Like Negative Concord.

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).

Cue reliability, salience and early comprehension of agreement: Evidence from Greek.

Previous studies across languages (English, Spanish, French) have argued that perceptual salience and cue reliability can explain cross-linguistic differences in early comprehension of verbal agreement. Here we tested this hypothesis further by investigating early comprehension in Greek, where markers have high salience and reliability (compared to Spanish and English) predicting early comprehension, as in French. We investigated two and three-year-old Greek-speaking children's ability to distinguish third person singular and plural agreement in a picture-selection task. We also examined the frequency of these morphemes in child-directed speech to address input effects. Results showed that three-year-olds are sensitive to both singular and plural agreement, earlier than children acquiring English and Spanish, but later than French, and despite singular agreement being more frequent than plural agreement in the child corpus. These findings provide further support for the role of salience and reliability during early acquisition, while highlighting a potential effect of morpheme position.

Revisiting the Suffixing Preference: Native-Language Affixation Patterns Influence Perception of Sequences.

Similarities among the world's languages may be driven by universal features of human cognition or perception. For example, in many languages, complex words are formed by adding suffixes to the ends of simpler words, but adding prefixes is much less common: Why might this be? Previous research suggests this is due to a domain-general perceptual bias: Sequences differing at their ends are perceived as more similar to each other than sequences differing at their beginnings. However, as is typical in psycholinguistic research, the evidence comes exclusively from one population-English speakers-who have extensive experience with suffixing. Here, we provided a much stronger test of this claim by investigating perceptual-similarity judgments in speakers of Kîîtharaka, a heavily prefixing Bantu language spoken in rural Kenya. We found that Kîîtharaka speakers (N = 72) showed the opposite judgments to English speakers (N = 51), which calls into question whether a universal bias in human perception can explain the suffixing preference in the world's languages.

A learning bias for word order harmony: Evidence from speakers of non-harmonic languages.

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.

Simplicity and informativeness in semantic category systems.

Recent research has shown that semantic category systems, such as color and kinship terms, find an optimal balance between simplicity and informativeness. We argue that this situation arises through pressure for simplicity from learning and pressure for informativeness from communicative interaction, two distinct pressures that often (but not always) pull in opposite directions. Another account argues that learning might also act as a pressure for informativeness, that learners might be biased toward inferring informative systems. This results in two competing hypotheses about the human inductive bias. We formalize these competing hypotheses in a Bayesian iterated learning model in order to simulate what kinds of languages are expected to emerge under each. We then test this model experimentally to investigate whether learners' biases, isolated from any communicative task, are better characterized as favoring simplicity or informativeness. We find strong evidence to support the simplicity account. Furthermore, we show how the application of a simplicity principle in learning can give the impression of a bias for informativeness, even when no such bias is present. Our findings suggest that semantic categories are learned through domain-general principles, negating the need to posit a domain-specific mechanism.

Evolving artificial sign languages in the lab: From improvised gesture to systematic sign.

Recent work on emerging sign languages provides evidence for how key properties of linguistic systems are created. Here we use laboratory experiments to investigate the contribution of two specific mechanisms-interaction and transmission-to the emergence of a manual communication system in silent gesturers. We show that the combined effects of these mechanisms, rather than either alone, maintain communicative efficiency, and lead to a gradual increase of regularity and systematic structure. The gestures initially produced by participants are unsystematic and resemble pantomime, but come to develop key language-like properties similar to those documented in newly emerging sign systems.

Zipf's Law of Abbreviation and the Principle of Least Effort: Language users optimise a miniature lexicon for efficient communication.

The linguist George Kingsley Zipf made a now classic observation about the relationship between a word's length and its frequency; the more frequent a word is, the shorter it tends to be. He claimed that this "Law of Abbreviation" is a universal structural property of language. The Law of Abbreviation has since been documented in a wide range of human languages, and extended to animal communication systems and even computer programming languages. Zipf hypothesised that this universal design feature arises as a result of individuals optimising form-meaning mappings under competing pressures to communicate accurately but also efficiently-his famous Principle of Least Effort. In this study, we use a miniature artificial language learning paradigm to provide direct experimental evidence for this explanatory hypothesis. We show that language users optimise form-meaning mappings only when pressures for accuracy and efficiency both operate during a communicative task, supporting Zipf's conjecture that the Principle of Least Effort can explain this universal feature of word length distributions.

Innovation of Word Order Harmony Across Development.

The tendency for languages to use harmonic word order patterns-orders that place heads in a consistent position with respect to modifiers or other dependents-has been noted since the 1960s. As with many other statistical typological tendencies, there has been debate regarding whether harmony reflects properties of human cognition or forces external to it. Recent research using laboratory language learning has shown that children and adults find harmonic patterns easier to learn than nonharmonic patterns (Culbertson & Newport, 2015; Culbertson, Smolensky, & Legendre, 2012). This supports a link between learning and typological frequency: if harmonic patterns are easier to learn, while nonharmonic patterns are more likely to be targets of change, then, all things equal, harmonic patterns will be more frequent in the world's languages. However, these previous studies relied on variation in the input as a mechanism for change in the lab; learners were exposed to variable word order, allowing them to shift the frequencies of different orders so that harmonic patterns became more frequent. Here we teach adult and child learners languages that are consistently nonharmonic, with no variation. While adults perfectly maintain these consistently nonharmonic patterns, young child learners innovate novel orders, changing nonharmonic patterns into harmonic ones.

Developing knowledge of nonadjacent dependencies.

Characterizing the nature of linguistic representations and how they emerge during early development is a central goal in the cognitive science of language. One area in which this development plays out is in the acquisition of dependencies-relationships between co-occurring elements in a word, phrase, or sentence. These dependencies often involve multiple levels of representation and abstraction, built up as infants gain experience with their native language. The authors used the Headturn Preference Procedure to systematically investigate the early acquisition of 1 such dependency, the agreement between a subject and verb in French, at 6 different ages between 14 and 24 months. The results reveal a complex developmental trajectory that provides the first evidence that infants might indeed progress through distinct stages in the acquisition of this nonadjacent dependency. The authors discuss how changes in general cognition and representational knowledge (from reflecting surface statistics to higher-level morphological features) might account for their findings. These findings highlight the importance of studying language acquisition at close time intervals over a substantial age range. (PsycINFO Database Record

Collecting psycholinguistic response time data using Amazon mechanical Turk.

Researchers in linguistics and related fields have recently begun exploiting online crowd-sourcing tools, like Amazon Mechanical Turk (AMT), to gather behavioral data. While this method has been successfully validated for various offline measures--grammaticality judgment or other forced-choice tasks--its use for mainstream psycholinguistic research remains limited. This is because psycholinguistic effects are often dependent on relatively small differences in response times, and there remains some doubt as to whether precise timing measurements can be gathered over the web. Here we show that three classic psycholinguistic effects can in fact be replicated using AMT in combination with open-source software for gathering response times client-side. Specifically, we find reliable effects of subject definiteness, filler-gap dependency processing, and agreement attraction in self-paced reading tasks using approximately the same numbers of participants and/or trials as similar laboratory studies. Our results suggest that psycholinguists can and should be taking advantage of AMT and similar online crowd-sourcing marketplaces as a fast, low-resource alternative to traditional laboratory research.

Harmonic biases in child learners: in support of language universals.

A fundamental question for cognitive science concerns the ways in which languages are shaped by the biases of language learners. Recent research using laboratory language learning paradigms, primarily with adults, has shown that structures or rules that are common in the languages of the world are learned or processed more easily than patterns that are rare or unattested. Here we target child learners, investigating a set of biases for word order learning in the noun phrase studied by Culbertson, Smolensky, and Legendre (2012) in college-age adults. We provide the first evidence that child learners exhibit a preference for typologically common harmonic word order patterns-those which preserve the order of the head with respect to its complements-validating the psychological reality of a principle formalized in many different linguistic theories. We also discuss important differences between child and adult learners in terms of both the strength and content of the biases at play during language learning. In particular, the bias favoring harmonic patterns is markedly stronger in children than adults, and children (unlike adults) acquire adjective ordering more readily than numeral ordering. The results point to the importance of investigating learning biases across development in order to understand how these biases may shape the history and structure of natural languages.

Simplicity and Specificity in Language: Domain-General Biases Have Domain-Specific Effects.

The extent to which the linguistic system-its architecture, the representations it operates on, the constraints it is subject to-is specific to language has broad implications for cognitive science and its relation to evolutionary biology. Importantly, a given property of the linguistic system can be "specific" to the domain of language in several ways. For example, if the property evolved by natural selection under the pressure of the linguistic function it serves then the property is domain-specific in the sense that its design is tailored for language. Equally though, if that property evolved to serve a different function or if that property is domain-general, it may nevertheless interact with the linguistic system in a way that is unique. This gives a second sense in which a property can be thought of as specific to language. An evolutionary approach to the language faculty might at first blush appear to favor domain-specificity in the first sense, with individual properties of the language faculty being specifically linguistic adaptations. However, we argue that interactions between learning, culture, and biological evolution mean any domain-specific adaptations that evolve will take the form of weak biases rather than hard constraints. Turning to the latter sense of domain-specificity, we highlight a very general bias, simplicity, which operates widely in cognition and yet interacts with linguistic representations in domain-specific ways.

Language learners privilege structured meaning over surface frequency.

Although it is widely agreed that learning the syntax of natural languages involves acquiring structure-dependent rules, recent work on acquisition has nevertheless attempted to characterize the outcome of learning primarily in terms of statistical generalizations about surface distributional information. In this paper we investigate whether surface statistical knowledge or structural knowledge of English is used to infer properties of a novel language under conditions of impoverished input. We expose learners to artificial-language patterns that are equally consistent with two possible underlying grammars--one more similar to English in terms of the linear ordering of words, the other more similar on abstract structural grounds. We show that learners' grammatical inferences overwhelmingly favor structural similarity over preservation of superficial order. Importantly, the relevant shared structure can be characterized in terms of a universal preference for isomorphism in the mapping from meanings to utterances. Whereas previous empirical support for this universal has been based entirely on data from cross-linguistic language samples, our results suggest it may reflect a deep property of the human cognitive system--a property that, together with other structure-sensitive principles, constrains the acquisition of linguistic knowledge.

Cognitive biases, linguistic universals, and constraint-based grammar learning.

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.

A Bayesian model of biases in artificial language learning: the case of a word-order universal.

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.