This study investigated the role of cross-linguistic influence in L2 learners’ integration of a verb and a construction during online English sentence processing. In a self-paced reading task, L1-English speakers and Chinese-L1 learners of English read the English double-object and prepositional dative constructions with verbs whose Chinese translation equivalents are either compatible or incompatible with each dative form. When including only a subset of trials for which participants provided expected translations for the target sentences (i.e., translating the English prepositional dative (...) construction into a Chinese prepositional dative sentence and translating the English double-object construction into a Chinese double-object sentence), the effect of cross-linguistic influence emerged only in a certain type of verbs. When including all trials in the analysis, we found the effect of cross-linguistic influence for all verb types. These results provide some evidence that the cross-linguistic activation of verbs can influence verb-construction integration in L2 processing. The study highlights how bilingual co-activation of verbs extends beyond the lexical and structural levels to influence the integration of multiple sources of information. (shrink)

Natural language involves both speaking and listening. Recent models claim that production and comprehension share aspects of processing and are linked within individuals (Dell & Chang, 2014; MacDonald, 2013; Pickering & Garrod, 2004; 2013a). Evidence for this claim has come from studies of cross-modality structural priming, mainly examining processing in the direction of comprehension to production. The current study replicated these comprehension to production findings and developed a novel cross-modal structural priming paradigm from production to comprehension using a temporally-sensitive online (...) measure of comprehension, Event-Related Potentials. For Comprehension-to-Production priming, participants first listened to active or passive sentences and then described target pictures using either structure. In Production-to-Comprehension priming, participants first described a picture using either structure and then listened to target passive sentences while EEG was recorded. Comprehension-to-Production priming showed the expected passive sentence priming for syntactic choice, but not response time or average syllable duration. In Production-to-Comprehension priming, primed, versus unprimed, passive sentences elicited a reduced N400. These effects support the notion that production and comprehension share aspects of processing and are linked within the individual. Moreover, this paradigm can be used for the exploration priming at different linguistic levels as well as the influence of extra-linguistic factors on natural language use. (shrink)

Selectional preferences have a long history in both generative and computational linguistics. However, since the publication of Resnik's dissertation in 1993, a new approach has surfaced in the computational linguistics community. This new line of research combines knowledge represented in a pre‐defined semantic class hierarchy with statistical tools including information theory, statistical modeling, and Bayesian inference. These tools are used to learn selectional preferences from examples in a corpus. Instead of simple sets of semantic classes, selectional preferences are viewed as (...) probability distributions over various entities. We survey research that extends Resnik's initial work, discuss the strengths and weaknesses of each approach, and show how they together form a cohesive line of research. (shrink)

Epstein et al. take as given that, (1) a hypothetical Universal Grammar (UG) exists that allows children effortlessly to acquire their first language; they then argue (2) that critical or sensitive periods do not block the UG from second language acquisition. Therefore, why can't we all effortlessly “acquire” Tibetan in six months or so? Data concerning the neural bases of language are also noted.

Segregated neural circuits that effect particular domain-specific behaviors can be differentiated from neuroanatomical structures implicated in many different aspects of behavior. The basal ganglionic components of circuits regulating nonlinguistic motor behavior, speech, and syntax all function in a similar manner. Hence, it is unlikely that special properties and evolutionary mechanisms are associated with the neural bases of human language.

L2 learners may access UG principles because these are realized in all natural languages. Since this is not the case for the parametrized aspects of language, parameter setting may be progressively replaced by restructuring mechanisms which are specifically related to the process of representational redescription because L2 learners are not sensitive to the [±] features which define parameters.

We propose a framework for including information‐processing bounds in rational analyses. It is an application of bounded optimality (Russell & Subramanian, 1995) to the challenges of developing theories of mechanism and behavior. The framework is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into the definition of (...) rational action. Theories are specified as optimal program problems, defined by an adaptation environment, a bounded machine, and a utility function. Such theories yield different classes of explanation, depending on the extent to which they emphasize adaptation to bounds, and adaptation to some ecology that differs from the immediate local environment. We illustrate this variation with examples from three domains: visual attention in a linguistic task, manual response ordering, and reasoning. We explore the relation of this framework to existing “levels” approaches to explanation, and to other optimality‐based modeling approaches. (shrink)

The aim of this work is to identify and analyze a set of challenges that are likely to be encountered when one embarks on fieldwork in linguistic communities that feature small, young, and/or non-standard languages with a goal to elicit big sets of rich data. For each challenge, we (i) explain its nature and implications, (ii) offer one or more examples of how it is manifested in actual linguistic communities, and (iii) where possible, offer recommendations for addressing it effectively. Our (...) list of challenges involves static characteristics (e.g., absence of orthographic conventions and how it affects data collection), dynamic processes (e.g., speed of language change in small languages and how it affects longitudinal collection of big amounts of data), and interactive relations between non-dynamic features that are nevertheless subject to potentially rapid change (e.g., absence of standardized assessment tools or estimates for psycholinguistic variables). The identified challenges represent the domains of data collection and handling, participant recruitment, and experimental design. Among other issues, we discuss population limits and degree of power, inter- and intraspeaker variation, absence of metalanguage and its implications for the process of eliciting acceptability judgments, and challenges that arise from absence of local funding, conflicting regulations in relation to privacy issues, and exporting large samples of data across countries. Finally, the ten experimental challenges presented cover a broad typological spectrum, encompassing both spoken and sign, extant and nearly extinct languages. (shrink)

A search for the terms ‘acceptability judgment tasks’ & ‘language’ and ‘grammaticality judgment tasks’ & ‘language’ produces results which report findings that are based on the exact same elicitation technique. Although certain scholars have argued that acceptability and grammaticality are two separable notions that refer to different concepts, there are contexts in which the two terms are used interchangeably. The present work shows that these two notions and their scales do not coincide: there are sentences that are acceptable, even though (...) they are ungrammatical, and sentences that are unacceptable, despite being grammatical. First we adduce a number of examples for both cases, including grammatical illusions, violations of Identity Avoidance, and sentences that involve a level of processing complexity that overloads the cognitive parser and tricks it into (un)acceptability. We then discuss whether the acceptability of grammatically ill-formed sentences entails that we actually assign a meaning to them. Last, it is shown that there are n ways of unacceptability, and two ways of ungrammaticality, in the absolute and the relative sense. Since the use of the terms ‘acceptable’ and ‘grammatical’ is often found in experiments that constitute the core of the evidential base of linguistics, disentangling their various uses is likely to aid the field reach a better level of terminological clarity. (shrink)

Je défends la classification carnapienne des disciplines sémiotiques en montrant qu'elle permet de caractériser adéquatement la nature de la pragmatique. J'indique, en particulier, comment une notion de système pragmatique pourrait être développée par analogie avec celles de système syntaxique et de système sémantique.I defend Carnap's classification of semiotic disciplines by showing that it leads to an adequate characterization of the nature of pragmatics. In particular, I indicate how a notion of pregmatic system could be constructed on the analogy with those (...) of syntactic system and semantic system. (shrink)

As AI systems become increasingly complex it may become unclear, even to the designer of a system, why exactly a system does what it does. This leads to a lack of trust in AI systems. To solve this, the field of explainable AI has been working on ways to produce explanations of these systems’ behaviors. Many methods in explainable AI, such as LIME, offer only a statistical argument for the validity of their explanations. However, some methods instead study the internal (...) structure of the system and try to find components which can be assigned an interpretation. I believe that these methods provide more valuable explanations than those statistical in nature. I will try to identify which explanations can be considered internal to the system using the Chomskyan notion of tacit knowledge. I argue that each explanation expresses a rule, and through the localization of this rule in the system internals, we can take a system to have tacit knowledge of the rule. I conclude that the only methods which are able to sufficiently establish this tacit knowledge are those along the lines of Olah : 4901–4911, 2017), and therefore they provide explanations with unique strengths. (shrink)