This paper brings new evidence for prosodic correspondence, where prosodic units (e.g. main-stressed nuclei and prominent syllables) of morphologically related forms are compared. Since prosodic correspondence was formalized in Crosswhite's (1998) analysis of Chamorro, it has received almost no empirical discussion. I argue that Tgdaya Seediq (Austronesian, Atayalic) has vowel alternations that should be analyzed using prosodic correspondence. In Seediq, unsuffixed and suffixed forms tend to share the same stressed syllable nucleus. This vowel matching pattern cannot be explained as surface harmony, but it can be explained as the result of a constraint enforcing vowel identity of main-stressed nuclei in morphologically related forms. Unlike the categorical alternations analyzed by Crosswhite (1998), Seediq vowel matching is gradient and only emerges on a statistical level. Nevertheless, prosodic correspondence appears to be active in the synchronic grammar of Seediq; in a production experiment, speakers applied vowel matching to novel forms and even over-generalized it to environments not predicted by lexical statistics. Vowel matching is modeled in Maximum Entropy Harmonic Grammar (Goldwater & Johnson 2003), a stochastic variant of OT. I use prosodic correspondence to enforce vowel matching, and Zuraw's (2000, 2010) dual listing approach to capture the discrepancy between lexical and experimental results.

The goal of this article is to provide a balanced assessment of the significance autism has for the scientific study of language. While linguistic profiles in autism vary greatly, spanning from a total absence of functional language to verbal levels within the typical range, the entire autism spectrum is robustly characterized by lifelong disabilities in intersubjective communication and persistent difficulties in adopting the perspective of other people. In that sense, autism constitutes a unique profile in which linguistic competence is dissociated from communication skills. Somewhat paradoxically, autism is often mentioned to underscore the importance of mind reading for language use and of intersubjective communication for the emergence of language. Yet experimental studies on pragmatics in autism indicate that many pragmatic processes unfold without adopting one's conversational partner's perspective. Moreover, the patterns of language acquisition and learning in autism represent a strong challenge to the central role constructionist theories assign to socio-communicative skills. Data on autism thus force a reconsideration of the a priori conceptual boundaries on language learnability that shape the foundational debates between constructionist and nativist linguistic theories.

This article is an analysis of the claim that a universal ban on certain (‘anti-markedness’) grammars is necessary in order to explain their nonoccurrence in the languages of the world. Such a claim is based on the following assumptions: that phonological typology shows a highly asymmetric distribution, and that such a distribution cannot possibly arise ‘naturally’—that is, without a universal grammar-based restriction of the learner’s hypothesis space. Attempting to test this claim reveals a number of open issues in linguistic theory. In the first place, there exist critical aspects of synchronic theory that are not specified explicitly enough to implement computationally. Second, there remain many aspects of linguistic competence, language acquisition, sound change, and even typology that are still unknown. It is not currently possible, therefore, to reach a definitive conclusion about the necessity, or lack thereof, of an innate substantive grammar module. This article thus serves two main functions: acting both as a pointer to the areas of phonological theory that require further development, especially at the overlap between traditionally separate subdomains, and as a template for the type of argumentation required to defend or attack claims about phonological universals.

Non-native languages tend to be acquired through a combination of explicit and implicit learning, where implicit learning requires coordination of language information with referents in the environment. In this study, we examined how learners use both language input and environmental cues to acquire vocabulary and morphology in a novel language and how their language background influences this process. We trained 105 adults with native languages (L1s) varying in morphological richness (English, German, Mandarin) on an artificial language comprising nouns and verbs with morphological features (number, tense, and subject-verb [SV] agreement) appearing alongside referential visual scenes. Participants were able to learn both word stems and morphological features from cross-situational statistical correspondences between language and the environment, without any instruction. German-speakers learned SV agreement worse than other morphological features, which were acquired equally effectively by English or Mandarin speakers, indicating the subtle and varied influence of L1 morphological richness on implicit non-native language learning.

Language models can produce fluent, grammatical text. Nonetheless, some maintain that language models don’t really learn language and also that, even if they did, that would not be informative for the study of human learning and processing. On the other side, there have been claims that the success of LMs obviates the need for studying linguistic theory and structure. We argue that both extremes are wrong. LMs can contribute to fundamental questions about linguistic structure, language processing, and learning. They force us to rethink arguments and ways of thinking that have been foundational in linguistics. While they do not replace linguistic structure and theory, they serve as model systems and working proofs of concept for gradient, usage-based approaches to language. We offer an optimistic take on the relationship between language models and linguistics.

Recent evidence from cross-situational learning (CSL) studies have shown that adult learners can acquire words and grammar simultaneously when sentences of the novel language co-occur with dynamic scenes to which they refer. Syntactic bootstrapping accounts suggest that grammatical knowledge may help scaffold vocabulary acquisition by constraining possible meanings, thus, for children, words and grammar may be acquired at different rates. Twenty children (ages 8 to 9) were exposed in a CSSL study to an artificial language comprising nouns, verbs, and case markers occurring within a verb-final grammatical structure. Children acquired syntax (i.e., word order) effectively, but we found no evidence of vocabulary learning, whereas previous adult studies showed learning of both from similar input. Grammatical information may thus be available early for children, to help constrain and support later vocabulary learning. We propose that gradual maturation of declarative memory systems may result in more effective vocabulary learning in adults.

Studies investigating phonological processing indicate that words with high regularity/consistency in pronunciation or high frequency positively impact reading speed and accuracy. Such effects of consistency and frequency have been demonstrated in Japanese kanji words and are known as consistency and frequency effects. Using a mixed-effects model analysis, this study reexamines the two effects in Chinese–Japanese second-language (L2) learners with two different L2 proficiency levels. The two effects are robustly replicated in oral reading tasks; in particular, the performance of intermediate learners is similar to that of Japanese semantic dementia patients, whose reading accuracy is affected by sensitivity to the statistical properties of words (i.e., reading consistency and lexical frequency). These results are explained by the interaction between semantic memory and word statistical properties. Moreover, the interaction highlights the important consequences of statistical learning underlying L2 phonological processing.

In today’s insurance market, numerous cyber insurance products provide bundled coverage for losses resulting from different cyber events, including data breaches and ransomware attacks. Every category of incident has its own specific coverage limit and deductible. Although this gives prospective cyber insurance buyers more flexibility in customizing the coverage and better manages the risk exposures of sellers, it complicates the decision-making process in determining the optimal amount of risks to retain and transfer for both parties. This article aims to build an economic foundation for these incident-specific cyber insurance products with a focus on how incident-specific indemnities should be designed for achieving Pareto optimality for both the insurance seller and the buyer. Real data on cyber incidents are used to illustrate the feasibility of this approach. Several implementation improvement methods for practicality are also discussed.

The suffixing bias (the tendency to exploit suffixes more often than prefixes to express grammatical meanings) in languages was identified a century ago, yet we still lack a clear account for why it emerged, namely, whether the bias emerged because general cognitive mechanisms shape languages to be more easily processed by available cognitive machinery, or if the bias is speech-specific and is determined by domain-specific mechanisms. We used statistical learning (SL) experiments to compare processing of suffixed and prefixed sequences on linguistic and non-linguistic material. SL is not speech-specific, and we observed the suffixing preference only on linguistic material, suggesting its language-specific origin. Moreover, morphological properties of native languages (existence of grammatical prefixes) modulate suffixing preferences in SL experiments only on linguistic material, suggesting limited cross-domain transfer.

The present study examined whether length of bilingual experience and language ability contributed to cross-situational word learning (XSWL) in Spanish-English bilingual school-aged children. We contrasted performance in a high variability condition, where children were exposed to multiple speakers and exemplars simultaneously, to performance in a condition where children were exposed to no variability in either speakers or exemplars. Results revealed graded effects of bilingualism and language ability on XSWL under conditions of increased variability. Specifically, bilingualism bolstered learning when variability was present in the input but not when variability was absent in the input. Similarly, robust language abilities supported learning in the high variability condition. In contrast, children with weaker language skills learned more word-object associations in the no variability condition than in the high variability condition. Together, the results suggest that variation in the learner and variation in the input interact and modulate mechanisms of lexical learning in children.

Prediction and classification are two very active areas in modern data analysis. In this paper, prediction with nonlinear optimal scaling transformations of the variables is reviewed, and extended to the use of multiple additive components, much in the spirit of statistical learning techniques that are currently popular, among other areas, in data mining. Also, a classification/clustering method is described that is particularly suitable for analyzing attribute-value data from systems biology (genomics, proteomics, and metabolomics), and which is able to detect groups of objects that have similar values on small subsets of the attributes.