Stress position in English words is well-known to be influenced by phonological, morphological, and (morpho)syntactic factors (esp. verbs vs. nouns). All these influences, however, are far from categorical, and very often stress varies from one word to another in a seemingly unpredictable manner. What is more, recent corpus-based work points to the relevance of opaque, i.e. etymological prefixes. It is, however, unclear why effects of opaque morphology should be productive synchronically. Moreover, there is an increasing body of research indicating that stress assignment works on the basis of lower-level information, i.e. analogy. In this paper we analyze the results of a pseudo-word experiment testing the production of trisyllabic verbs. Structural predictors tested in the analysis comprise syllable weight and prefixation (measuring the transparency of prefixes). To test analogy, we had a computational analogical model predict stress in the pseudo-words based on all verbs in the Cambridge Pronouncing Dictionary. Lexical support measures were then used as predictors alongside weight and prefixation in a mixed-effects logistic regression model. The results show that all three variables are significant predictors of main stress in the experimental data. Lexical support is strongly correlated with actual productions, but AML underpredicts some of participants’ preferences. These are, however, explained by syllable weight and transparency of the prefix in our regression model. We conclude that properties on different levels of representation are relevant for the prediction of stress position in English pseudo-verbs.

Guided by the evolutionary developmental perspective, this multi-method, longitudinal study seeks to examine “hyperparameters” that organize young children’s learning. In particular, we adopt computational modeling and a person-centered approach to characterize children’s exploration–exploitation profiles during a reward probability task. We also explored the family socioeconomic precursors and child psychological sequelae of the profiles. Participants were 243 families with a preschooler (Mean age = 4.60 years, 56% girls) from diverse racial and ethnic backgrounds (46% Black, 19% Latinx). Family socioeconomic risks were measured at wave one, and exploration–exploitation and socioemotional functioning were assessed two years later via a reward probability task and teacher report, respectively. Person-centered approach revealed three exploration–exploitation profiles, reflecting (a) a high-random exploration, (b) a high-directed exploration, and (c) a balanced exploration–exploitation profile. Greater family socioeconomic risk was linked to an elevated likelihood of membership in the high-random exploration profile (e.g., Z = −2.37). In turn, the high-random exploration profile exhibited the highest attention problems and greater externalizing problems. Findings have critical implications for understanding the processes through which children adapt to adverse family contexts.

Cholera is associated with devastating outbreaks among forcibly displaced people. Insights into the relative contributions of the public (extra-household) and domestic (intra-household) domains to cholera spread in camps, as well as the circumstances under which each may drive transmission, can support the design of response strategies. However, these have yet to be systematically investigated. We developed an agent-based model of cholera transmission in camps informed by a rapid appraisal conducted in Northeast Nigeria, an expert consultation, and humanitarian minimum standards. We simulated outbreaks in a stylized camp that meets water quantity standards and compared this with conditions where water supply is overwhelmed or compromised following floods and population influxes. We found that domestic transmission can exclusively drive cholera outbreaks. However, unless hygiene conditions are extremely poor and water is not adequately chlorinated, these outbreaks appear to be small. Following shocks, outbreaks can be large and progress rapidly. Although they are initially shaped by the public domain, domestic domain transmission can sustain or exacerbate them. We recommend directing humanitarian and development activities towards mitigating the consequences of extreme weather events and unplanned population influxes, as well as developing adaptive preparedness and response strategies that explicitly and comprehensively address them.

Assessing the pace of language change in historical settings can be impeded by a lack of contemporary documents. Loan word adaptation can provide an additional window on this problem. Given a series of formerly productive phonological processes and a well studied donor language, a search algorithm equipped with a simple phonotactic probability model can estimate how many loans entered before each phonological period ended. The population of loans during a period is a plausible proxy for the duration of the period, so periods with few loans could have been subject to rapid change. Our approach is illustrated with the Latin loans in Irish, shedding light on a period that pre-dates substantial written records. This period includes the development of rhythmic syncope in Irish, a process that has garnered interest due to its resistance to analysis in parallel but not serial models of phonology (McCarthy 2008, inter alia). Strikingly, our model finds that few loans entered during the rhythmic syncope phase of Irish, which is consistent with the process quickly becoming unproductive, as has been found for other languages (Bowers 2019, inter alia).

In acquiring a syntax, children must detect evidence for abstract structural dependencies that can be realized in variable ways in the surface forms of sentences. In What did David fix?, learners must identify a nonlocal relation between a fronted object of the verb (what) and the phonologically null ‘gap’ in canonical direct object position after the verb, where it is thematically interpreted. How do learners identify a nonadjacent dependency between an expression and something that has no overt phonological form? We propose that identifying abstract syntactic dependencies requires statistical inference over both overt linguistic material and unsatisfied grammatical expectations: noticing when a predicted argument for a verb is unexpectedly missing may serve as evidence for the gap of an argument movement dependency. We provide computational support for this hypothesis. We develop a learner that uses predicted but unexpectedly missing objects of verbs to identify possible gaps of object movement, and identifies which surface morphosyntactic properties of sentences are correlated with these possible movement gaps. We find that it is in principle possible for a learner using this mechanism to identify the majority of sentences with object movement in child-directed English, and that prior knowledge of which verbs require objects provides an important guide for identifying which surface distributions characterize object movement. This provides a computational account for why verb argument-structure knowledge developmentally precedes the acquisition of movement in a language like English. More broadly, these findings illustrate how statistical learning and learning from violated expectations can be combined to novel effect in the domain of language acquisition.

Social decision-making is a multifaceted process where decisions impact not only the individual but also the larger group. Acute stress may influence individual decision-making, potentially increasing reward-driven choices and affecting learning and adaptive adjustment. However, studies examining stress’s impact on social decision-making have presented inconsistencies, potentially arising from assessing decision-making as a singular dimension. This article aims to test, using computational modeling, the stress effect on social decision making and cognitive subprocesses involved during the Ultimatum Game (UG). Seventy-three healthy participants underwent the UG, with only half exposed before to the virtual Trier Social Stress Test (TSST-VR). In our data, prosocial behavior—as indexed by the number of accepted offers and sensitivity to unfair distributions—did not show immediate alterations 15 min following stressor onset. However, stressed participants exhibited a diminished capacity to learn and adapt during the task, alongside a more perseverative decision-making pattern. These results support the negative impact of stress on social decisions and underscore the importance of considering its effects in mitigating challenges related to social integration and cohesion.

Knowledge of the mechanisms of phonetically motivated sound changes is incomplete and based primarily on apparent—rather than real-time—evidence. How exactly does an innovative variant become the new norm within individual and population-level grammars? Here, eighteen L1 English speakers were recorded in 2014 and again in 2022. Acoustic analysis of sibilants in /str/ shows (i) coarticulatory /s/-retraction for all speakers in 2014 and (ii) a group-level increase in the degree of /s/-retraction in 2022. Perception data show that increased /s/-retraction was audible to listeners. Increased /s/-retraction also matched the outcome of simulated interaction between the speaker participants in an agent-based model. However, the effects of sound change are not predictable within idiolects: some speakers’ production targets shifted incrementally in the opposite direction of the group-level trend.

The English past tense contains pockets of variation, where regular and irregular forms compete (e.g. learned/learnt, weaved/wove). Individuals vary considerably in the degree to which they prefer irregular forms. This article examines the degree to which individuals may converge on their regularization patterns and preferences. We report on a novel experimental methodology, using a cooperative game involving nonce verbs. Analysis of participants' postgame responses indicates that their behavior shifted in response to an automated co-player's preferences, on two dimensions. First, players regularize more after playing with peers with high regularization rates, and less after playing with peers with low regularization rates. Second, players' overall patterns of regularization are also affected by the particular distribution of (ir)regular forms produced by the peer.

We model the effects of the exposure on participants' morphological preferences, using both a rule-based model and an instance-based analogical model (Nosofsky 1988, Albright & Hayes 2003). Both models contribute separately and significantly to explaining participants' pre-exposure regularization processes. However, only the instance-based model captures the shift in preferences that arises after exposure to the peer. We argue that the results suggest an account of morphological convergence in which new word forms are stored in memory, and on-line generalizations are formed over these instances.

Sequences such as [mb, kp, ts] pattern as complex segments in some languages but as clusters of simple consonants in others. What evidence is used to learn their language-specific status? We present an implemented computational model that starts with simple consonants and builds more complex representations by tracking statistical distributions of consonant sequences. This strategy succeeds in a wide range of cases, both in languages that supply clear phonotactic arguments for complex segments and in languages where the evidence is less clear. We then turn to the typological parallels between complex segments and consonant clusters: both tend to be limited in size and composition. We suggest that our approach allows the parallels to be reconciled. Finally, we compare our model with alternatives: learning complex segments from phonotactics and from phonetics.

To successfully learn language—and more specifically how to use verbs correctly—children must solve the linking problem: they must learn the mapping between the thematic roles specified by a verb's lexical semantics and the syntactic argument positions specified by a verb's syntactic frame. We use an empirically grounded and integrated quantitative framework involving corpus analysis, experimental meta-analysis, and computational modeling to implement minimally distinct versions of mapping approaches that (i) either are specified a priori or develop during language acquisition, and (ii) rely on either an absolute or a relative thematic role system. Using successful verb class learning as an evaluation metric, we embed each approach within a concrete model of the acquisition process and see which learning assumptions are able to match children's verb-learning behavior at three, four, and five years old. Our current results support a trajectory where children (i) may not have prior expectations about linking patterns between ages three and five, and (ii) begin with a relative thematic system, progressing toward optionality between a relative and an absolute system. We discuss implications of our results for both theories of syntactic representation and theories of how those representations are acquired. We also discuss the broader contribution of this study as a concrete modeling framework that can be updated with new linking theories, corpora, and experimental results.

Language learners are often faced with a scenario where the data allow multiple generalizations, even though only one is actually correct. One promising solution to this problem is that children are equipped with helpful learning strategies that guide the types of generalizations made from the data. Two successful approaches in recent work for identifying these strategies have involved (i) expanding the set of informative data to include INDIRECT POSITIVE EVIDENCE, and (ii) using observable behavior as a target state for learning. We apply both of these ideas to the case study of English anaphoric one, using computationally modeled learners that assume one’s antecedent is the same syntactic category as one and form their generalizations based on realistic data. We demonstrate that a learner that is biased to include indirect positive evidence coming from other pronouns in English can generate eighteen-month-old looking-preference behavior. Interestingly, we find that the knowledge state responsible for this target behavior is a context- dependent representation for anaphoric one, rather than the adult representation, but this immature representation can suffice in many communicative contexts involving anaphoric one. More generally, these results suggest that children may be leveraging broader sets of data to make the syntactic generalizations leading to their observed behavior, rather than selectively restricting their input. We additionally discuss the components of the learning strategies capable of producing the observed behavior, including their possible origin and whether they may be useful for making other linguistic generalizations.

Computational probabilistic modeling is increasingly popular in linguistics, but its relationship with linguistic theory is ambivalent. We argue here for the potential benefit of theory-driven statistical modeling, based on a case study situated at the semantics-pragmatics interface. Using data from a novel experiment, we employ Bayesian model comparison to evaluate the predictive adequacy of four models that differ in the extent to and manner in which grammatically generated candidate readings are taken into account in four probabilistic pragmatic models of utterance and interpretation choice. The data provide strong evidence for the idea that the full range of potential readings made available by recently popular grammatical approaches to scalar-implicature computation might be needed, and that classical Gricean reasoning may help manage the manifold ambiguity introduced by grammatical approaches to these. The case study thereby shows a way of bridging linguistic theory and empirical data with the help of probabilistic pragmatic modeling as a linking function.

Ambridge, Pine, and Lieven could have provided a stronger argument for their conclusion, which postulated that innate universal-grammar-specified knowledge does not simplify the language learning task, had they not paid so much attention to the Chomskyan paradigm. I argue that poverty-of-the-stimulus arguments do not take into account that children are opportunistic learners employing multiple strategies, that they do not accomplish individual tasks sequentially but acquire (partial) knowledge about multiple domains simultaneously, and that they do not acquire perfect knowledge of language. Furthermore, work in formal linguistics suggests that the Chomskyan paradigm is internally incoherent and that the formalism of the Chomskyan framework lacks mathematical precision, making it difficult to evaluate its predictions. Given that linguistics ought to provide crucial input for language acquisition research, more attention needs to be paid to non-Chomskyan work in linguistics.

I completely agree with Ambridge, Pine, and Lieven (AP&L) that anyone proposing a learning-strategy component needs to demonstrate precisely how that component helps solve the language acquisition task. To this end, I discuss how computational modeling is a tool well suited to doing exactly this, and that it has the added benefit of highlighting hidden assumptions underlying learning strategies. I also suggest general criteria relating to utility and usability that we can use to evaluate potential learning strategies. As a response to AP&L's request for Universal Grammar (UG) components that actually do work, I additionally provide a review of one potentially UG component that is part ofa successful learning strategy for syntactic islands, and that satisfies the evaluation criteria I propose.

Comprehending and producing words is a natural process for human speakers. In linguistic theory, investigating this process formally and computationally is often done by focusing on forms only. By moving beyond the world of forms, we show in this study that the discriminative lexicon (DL) model—operating with word comprehension as a mapping of form onto meaning, and word production as a mapping of meaning onto form—generates accurate predictions about what meanings listeners understand and what forms speakers produce. Furthermore, we show that measures derived from the computational model are predictive for human reaction times. Although mathematically very simple, the linear mappings between form and meaning posited by our model are powerful enough to capture the complexity and productivity of a Semitic language with a complex hybrid morphological system.

In this commentary, we focus on the linking problem Ambridge, Pine, and Lieven identify. Instead of taking a stance on the issue of universal grammar itself, we adopt an epistemological and methodological perspective on language acquisition research. We argue that the problem, linking the input to preexisting representations, constitutes just a small part of a larger methodological problem, namely how to link the input a learner receives, through a set of learning mechanisms and possibly innate representations, to the behavior the learner is producing, whether in spontaneous production or in laboratory experiments. Currently, none of the existing proposals provide an evaluated, or even a testable, account of the full process, that is, an input-output model of linguistic ontogeny. Although the focus on phenomena in isolation, probably an effect of the prevalence of the experimental method, allows the researcher some degree of control, it also distracts from the understanding of how the different mechanisms behave and interact. Our proposed solution is a more Holistic approach to the problem in which the learning mechanisms and their interaction are made fully Explicit, and in which the predicted behavior of the learner is (more) Globally evaluated. Computational modeling provides exactly the tools appropriate for this task, thereby furthering more precise and testable theories of the learning mechanisms involved.

This article reports on an experiment with miniature artificial languages that provides support for a synthesis of ideas from usage-based phonology (Bybee 1985, 2001, Nesset 2008) and harmonic grammar (Legendre et al. 1990, Smolensky & Legendre 2006). All miniature artificial languages presented to subjects feature velar palatalization (k → t∫) before a plural suffix, -i. I show that (i) examples of -i simply attaching to a [t∫]-final stem help palatalization (supporting t → t∫i over t → ti and p → t∫i over p → pi), a finding that provides specific support for product-oriented schemas like ‘plurals should end in [t∫i]’; (ii) learners tend to perseverate on the form they know, leveling stem changes, which provides support for paradigm-uniformity constraints in favor of retaining gestures composing the known form, for example, ‘keep labiality’; and (iii) the same plural schema helps untrained singular-plural mappings more than it helps trained ones. This result is accounted for by proposing that schemas and paradigm-uniformity constraints clamor for candidate plural forms that obey them. Given that competition is between candidate outputs, the same schema provides more help to candidates that violate strong paradigm-uniformity constraints and are therefore weak relative to competitor candidates. A computational model of schema extraction is proposed.