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Although there is a broad consensus that both the procedural and declarative memory systems play a crucial role in language learning, use, and knowledge, the mapping between linguistic types and memory structures remains underspecified: by default, a dual-route mapping of language systems to memory systems is assumed, with declarative memory handling idiosyncratic lexical knowledge and procedural memory handling rule-governed knowledge of grammar. We experimentally contrast the processing of morphology (case and aspect), syntax (subordination), and lexical semantics (collocations) in a healthy L1 population of Polish, a language rich in form distinctions. We study the processing of these four types under two conditions: a single task condition in which the grammaticality of stimuli was judged and a concurrent task condition in which grammaticality judgments were combined with a digit span task. Dividing attention impedes access to declarative memory while leaving procedural memory unaffected and hence constitutes a test that dissociates which types of linguistic information each long-term memory construct subserves. Our findings confirm the existence of a distinction between lexicon and grammar as a generative, dual-route model would predict, but the distinction is graded, as usage-based models assume: the hypothesized grammar-lexicon opposition appears as a continuum on which grammatical phenomena can be placed as being more or less "ruly" or "idiosyncratic." However, usage-based models, too, need adjusting as not all types of linguistic knowledge are proceduralized to the same extent. This move away from a simple dichotomy fundamentally changes how we think about memory for language, and hence how we design and interpret behavioral and neuroimaging studies that probe into the nature of language cognition.

Full-fledged grammatical article systems as attested in Germanic and Romance languages are rather uncommon from a typological perspective. The frequency with which articles occur in these languages, together with the difficulty encountered in detecting them and the lack of a water-tight account of article use, make article errors one of the most frequent errors in language produced by L2 learners whose L1 does not feature an article system of similar complexity, all the while appearing unproblematic for L1 users. We present a conceptually and methodologically interdisciplinary approach to the grammatical category of articles in English and combine a usage-based, cognitive linguistic account of the function and use of articles that respects its discourse-based nature with a computational exploration of the challenges the system poses from the perspective of learning. Running a statistical classifier on a large sample of spoken and written discourse chunks extracted from the BNC and annotated for the five main determinants of article use reveals that Hearer Knowledge is the driver of a hierarchical system. Once Hearer Knowledge is acknowledged as the motivating principle of the category, article use becomes eminently predictable and restrictions are in line with the forms from which the articles have developed historically, with the and a acting as category defaults and zero acting as default override. Simulations with a computational model anchored in the psychology of learning shed light on whether and how human cognition would handle the proposed relations detected in the data. We find that different articles have different learnability profiles that, again, are in line with their historical development: while the can be learned from one strong indicator, the relationships for the zero article are less exclusive. On the basis of these findings, we argue that the article category appears as a referent tracking system that grammaticalizes the principles of “audience design”: it forces a speaker to track and mark reference from the vantage point of the memory of the hearer, thereby reducing the processing effort required from the hearer. This particular mindset inverses the typologically dominant situation in which this information is not explicitly marked by the speaker but implicitly retrieved from context by the hearer.

The current study investigated the contributions of working memory, vocabulary size, education and age to letter fluency performance in healthy adults (N = 50, mean age = 26.5). We administered a letter fluency task using three stimulus letters (F, A, S). Using Bayesian generalized linear mixed model analysis, we established independent contributions of each predictor to letter fluency performance. While our results demonstrate that age, vocabulary size, education level and, to a lesser extent, working memory capacity contribute to performance on the letter fluency task, they also indicate a complex interplay of these factors. The results highlight the importance of separately assessing various verbal and executive skills in combination with demographic factors when using fluency tasks as indicators of adults' general verbal skill., Author(s): Srdan Medimorec [sup.1] , Petar Milin [sup.2] , Dagmar Divjak [sup.2] [sup.3] Author Affiliations: (1) grid.26597.3f, 0000 0001 2325 1783, Department of Psychology, Teesside University, , Tees Valley, TS1 [...]

While the effects of pattern learning on language processing are well known, the way in which pattern learning shapes exploratory behavior has long gone unnoticed. We report on the way in which individual differences in statistical pattern learning affect performance in the domain of language along multiple dimensions. Analyzing data from healthy monolingual adults' performance on a serial reaction time task and a self-paced reading task, we show how individual differences in statistical pattern learning are reflected in readers' knowledge of linguistic co-occurrence patterns and in their exploration and exploitation of content-specific and task-general information. First, we investigated the extent to which an individual's pattern learning correlates with his or her sensitivity to systematic morphological and syntactic co-occurrences, as evidenced while reading authentic sentences. We found that the stream of morphological and syntactic information has a more pronounced effect on the reading speed of, as we will label them, content-sensitive learners in that the more probable the co-occurrence pattern, the faster their reading of that pattern will be. Next, we investigated how differences in pattern learning are reflected in the ways in which individuals approach the reading task itself and adapt to it. Casting this relation in terms of exploration/exploitation strategies, known from Reinforcement Learning, we conclude that content-sensitive learners are also more likely to initially probe (explore) a wider range of directly relevant patterns, which they can later use (exploit) to optimize their reading performance further. By affecting exploratory behavior, pattern learning influences the information that is gathered and becomes available for exploitation, thereby increasing the effect pattern learning has on language cognition.

Since its first adoption as a computational model for language learning, evidence has accumulated that Rescorla–Wagner error‐correction learning (Rescorla & Wagner, 1972) captures several aspects of language processing. Whereas previous studies have provided general support for the Rescorla–Wagner rule by using it to explain the behavior of participants across a range of tasks, we focus on testing predictions generated by the model in a controlled natural language learning task and model the data at the level of the individual learner. By adjusting the parameters of the model to fit the trial‐by‐trial behavioral choices of participants, rather than fitting a one‐for‐all model using a single set of default parameters, we show that the model accurately captures participants' choices, time latencies, and levels of response agreement. We also show that gender and working memory capacity affect the extent to which the Rescorla–Wagner model captures language learning. A one‐page Accessible Summary of this article in non‐technical language is freely available in the Supporting Information online and at https://oasis‐database.org [ABSTRACT FROM AUTHOR]

The goal of the present study is to understand the role orthographic and semantic information play in the behavior of skilled readers. Reading latencies from a self-paced sentence reading experiment in which Russian near-synonymous verbs were manipulated appear well-predicted by a combination of bottom-up sublexical letter triplets (trigraphs) and top-down semantic generalizations, modeled using the Naive Discrimination Learner. The results reveal a complex interplay of bottom-up and top-down support from orthography and semantics to the target verbs, whereby activations from orthography only are modulated by individual differences. Using performance on a serial reaction time (SRT) task for a novel operationalization of the mental speed hypothesis, we explain the observed individual differences in reading behavior in terms of the exploration/exploitation hypothesis from reinforcement learning, where initially slower and more variable behavior leads to better performance overall.

A number of studies report that frequency is a poor predictor of acceptability, in particular at the lower end of the frequency spectrum. Because acceptability judgments provide a substantial part of the empirical foundation of dominant linguistic traditions, understanding how acceptability relates to frequency, one of the most robust predictors of human performance, is crucial. The relation between low frequency and acceptability is investigated using corpus- and behavioral data on the distribution of infinitival and finite "that"-complements in Polish. Polish verbs exhibit substantial subordination variation and for the majority of verbs taking an infinitival complement, the "that"-complement occurs with low frequency (<0.66 ipm). These low-frequency "that"-clauses, in turn, exhibit large differences in how acceptable they are to native speakers. It is argued that acceptability judgments are based on configurations of internally structured exemplars, the acceptability of which cannot reliably be assessed until sufficient evidence about the core component has accumulated.

In cognitive linguistics, grammatical structure is known to be representative of meaning. This is also true of English articles. In this paper, we argue that the choice of article, when the grammar allows it, is dependent on the wider discourse context and most importantly on how the speaker construes this context. Using survey data from 181 native speakers of English, we show that the choice of article depends on the activation of semantic frames and how speakers may choose to highlight different elements of a frame to construe the situation differently. We rely on Entropy to measure the restrictiveness of a context and to identify particular contexts in which choice is allowed or inhibited. We find that some contextual features such as the specificity of the referent are more restrictive while Hearer Knowledge is more open to construal. [ABSTRACT FROM AUTHOR]

In this study, we propose an operationalization of the concept of emergence which plays a crucial role in usage-based theories of language. The abstractions linguists operate with are assumed to emerge through a process of generalization over the data language users are exposed to. Here, we use two types of computational learning algorithms that differ in how they formalize and execute generalization and, consequently, abstraction, to probe whether a type of language knowledge that resembles linguistic abstractions could emerge from exposure to raw data only. More specifically, we investigated whether a phone, undisputedly the simplest of all linguistic abstractions, could emerge from exposure to speech sounds using two computational learning processes: memory-based learning and error-correction learning (ECL). Both models were presented with a significant amount of pre-processed speech produced by one speaker. We assessed (1) the consistency or stability of what these simple models learn and (2) their ability to approximate abstract categories. Both types of models fare differently regarding these tests. We show that only ECL models can learn abstractions and that at least part of the phone inventory and its grouping into traditional types can be reliably identified from the input. [ABSTRACT FROM AUTHOR]