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Mastering how to convey meanings using language is perhaps the main challenge facing any language learner. However, satisfactory accounts of how this is achieved, and even of what it is for a linguistic item to have meaning, are hard to come by. Nick Chater was one of the pioneers involved in the early development of one of the most successful methodologies within the cognitive science of language for discovering meaning: distributional semantics. In this article, we review this approach and discuss its successes and shortcomings in capturing semantic phenomena. In particular, we discuss what we dub the "distributional paradox:" how can models that do not implement essential dimensions of human semantic processing, such as sensorimotor grounding, capture so many meaning-related phenomena? We conclude by providing a preliminary answer, arguing that distributional models capture the statistical scaffolding of human language acquisition that allows for communication, which, in line with Nick Chater's more recent ideas, has been shaped by the features of human cognition on the timescale of cultural evolution., (© 2024 Cognitive Science Society LLC.)

Advances in research on language processing have originally come from group-level comparisons, but there is now a growing interest in individual differences. To investigate individual differences, tasks that have shown robust group-level differences are often used with the implicit assumption that they will also be reliable when used as an individual differences measure. Here, we examined whether one of the primary tasks used in psycholinguistic research on language processing, the self-paced reading task, can reliably measure individual differences in relative clause processing. We replicated the well-established effects of relative clauses at the group level, with object relative clauses being more difficult to process than subject relative clauses. However, when using difference scores, the reliability of the size of the relative clause effect was close to zero because the self-paced reading times for the different relative clause types were highly correlated within individuals. Nonetheless, we found that the self-paced reading task can be used to reliably capture individual differences in overall reading speed as well as key sentence regions when the two types of relative clause sentences are considered separately. Our results indicate that both the reliability and validity of different sentence regions need to be assessed to determine whether and when self-paced reading can be used to examine individual differences in language processing., (© 2024. The Psychonomic Society, Inc.)

Literacy is in decline in many parts of the world, accompanied by drops in associated cognitive skills (including IQ) and an increasing susceptibility to fake news. It is possible that the recent explosive growth and widespread deployment of Large Language Models (LLMs) might exacerbate this trend, but there is also a chance that LLMs can help turn things around. We argue that cognitive science is ideally suited to help steer future literacy development in the right direction by challenging and informing current educational practices and policy. Cognitive scientists have the right interdisciplinary skills to study, analyze, evaluate, and change LLMs to facilitate their critical use, to encourage turn-taking that promotes rather than hinders literacy, to support literacy acquisition in diverse and equitable ways, and to scaffold potential future changes in what it means to be literate. We urge cognitive scientists to take up this mantle-the future impact of LLMs on human literacy skills is too important to be left to the large, predominately U.S.-based tech companies., (© 2024 Cognitive Science Society LLC.)

Background: Several studies have been conducted with the 1.0 version of the Hospital Survey on Patient Safety Culture (HSOPSC) in Norway and globally. The 2.0 version has not been translated and tested in Norwegian hospital settings. This study aims to 1) assess the psychometrics of the Norwegian version (N-HSOPSC 2.0), and 2) assess the criterion validity of the N-HSOPSC 2.0, adding two more outcomes, namely 'pleasure of work' and 'turnover intention'., Methods: The HSOPSC 2.0 was translated using a sequential translation process. A convenience sample was used, inviting hospital staff from two hospitals (N = 1002) to participate in a cross-sectional questionnaire study. Data were analyzed using Mplus. The construct validity was tested with confirmatory factor analysis (CFA). Convergent validity was tested using Average Variance Explained (AVE), and internal consistency was tested with composite reliability (CR) and Cronbach's alpha. Criterion related validity was tested with multiple linear regression., Results: The overall statistical results using the N-HSOPSC 2.0 indicate that the model fit based on CFA was acceptable. Five of the N-HSOPSC 2.0 dimensions had AVE scores below the 0.5 criterium. The CR criterium was meet on all dimensions except Teamwork (0.61). However, Teamwork was one of the most important and significant predictors of the outcomes. Regression models explained most variance related to patient safety rating (adjusted R 2  = 0.38), followed by 'turnover intention' (adjusted R 2  = 0.22), 'pleasure at work' (adjusted R 2  = 0.14), and lastly, 'number of reported events' (adjusted R 2= 0.06)., Conclusion: The N-HSOPSC 2.0 had acceptable construct validity and internal consistency when translated to Norwegian and tested among Norwegian staff in two hospitals. Hence, the instrument is appropriate for use in Norwegian hospital settings. The ten dimensions predicted most variance related to 'overall patient safety', and less related to 'number of reported events'. In addition, the safety culture dimensions predicted 'pleasure at work' and 'turnover intention', which is not part of the original instrument., (© 2024. The Author(s).)

In language learning, learners engage with their environment, incorporating cues from different sources. However, in lab-based experiments, using artificial languages, many of the cues and features that are part of real-world language learning are stripped away. In three experiments, we investigated the role of positive, negative, and mixed feedback on the gradual learning of language-like statistical regularities within an active guessing game paradigm. In Experiment 1, participants received deterministic feedback (100%), whereas probabilistic feedback (i.e., 75% or 50%) was introduced in Experiment 2. Finally, Experiment 3 explored the impact of mixed probabilistic feedback (33% positive, 33% negative, 33% no feedback). The results showed that cross-situational learning of words was observed without feedback, but participants were able to learn structural regularities of the miniature language only when feedback was provided. Interestingly, the presence of positive feedback was particularly helpful for the learner, promoting more in-depth learning of the artificial language., (© 2024 Cognitive Science Society LLC.)

This study compared the acoustic properties of 26 (100% female, 100% monolingual) Danish caregivers' spontaneous speech addressed to their 11- to 24-month-old infants (infant-directed speech, IDS) and an adult experimenter (adult-directed speech, ADS). The data were collected between 2016 and 2018 in Aarhus, Denmark. Prosodic properties of Danish IDS conformed to cross-linguistic patterns, with a higher pitch, greater pitch variability, and slower articulation rate than ADS. However, an acoustic analysis of vocalic properties revealed that Danish IDS had a reduced or similar vowel space, higher within-vowel variability, raised formants, and lower degree of vowel discriminability compared to ADS. None of the measures, except articulation rate, showed age-related differences. These results push for future research to conduct theory-driven comparisons across languages with distinct phonological systems., (© 2023 The Authors. Child Development published by Wiley Periodicals LLC on behalf of Society for Research in Child Development.)

Establishing and maintaining mutual understanding in everyday conversations is crucial. To do so, people employ a variety of conversational devices, such as backchannels, repair, and linguistic entrainment. Here, we explore whether the use of conversational devices might be influenced by cross-linguistic differences in the speakers' native language, comparing two matched languages-Danish and Norwegian-differing primarily in their sound structure, with Danish being more opaque, that is, less acoustically distinguished. Across systematically manipulated conversational contexts, we find that processes supporting mutual understanding in conversations vary with external constraints: across different contexts and, crucially, across languages. In accord with our predictions, linguistic entrainment was overall higher in Danish than in Norwegian, while backchannels and repairs presented a more nuanced pattern. These findings are compatible with the hypothesis that native speakers of Danish may compensate for its opaque sound structure by adopting a top-down strategy of building more conversational redundancy through entrainment, which also might reduce the need for repairs. These results suggest that linguistic differences might be met by systematic changes in language processing and use. This paves the way for further cross-linguistic investigations and critical assessment of the interplay between cultural and linguistic factors on the one hand and conversational dynamics on the other., (© 2023 Cognitive Science Society LLC.)

Rapid individual cognitive phenotyping holds the potential to revolutionize domains as wide-ranging as personalized learning, employment practices, and precision psychiatry. Going beyond limitations imposed by traditional lab-based experiments, new efforts have been underway toward greater ecological validity and participant diversity to capture the full range of individual differences in cognitive abilities and behaviors across the general population. Building on this, we developed Skill Lab, a novel game-based tool that simultaneously assesses a broad suite of cognitive abilities while providing an engaging narrative. Skill Lab consists of six mini-games as well as 14 established cognitive ability tasks. Using a popular citizen science platform (N = 10,725), we conducted a comprehensive validation in the wild of a game-based cognitive assessment suite. Based on the game and validation task data, we constructed reliable models to simultaneously predict eight cognitive abilities based on the users' in-game behavior. Follow-up validation tests revealed that the models can discriminate nuances contained within each separate cognitive ability as well as capture a shared main factor of generalized cognitive ability. Our game-based measures are five times faster to complete than the equivalent task-based measures and replicate previous findings on the decline of certain cognitive abilities with age in our large cross-sectional population sample (N = 6369). Taken together, our results demonstrate the feasibility of rapid in-the-wild systematic assessment of cognitive abilities as a promising first step toward population-scale benchmarking and individualized mental health diagnostics., (© 2023 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).)

In conversation, individuals work together to achieve communicative goals, complementing and aligning language and body with each other. An important emerging question is whether interlocutors entrain with one another equally across linguistic levels (e.g., lexical, syntactic, and semantic) and modalities (i.e., speech and gesture), or whether there are complementary patterns of behaviors, with some levels or modalities diverging and others converging in coordinated fashions. This study assesses how kinematic and linguistic entrainment interact with one another across levels of measurement, and according to communicative context. We analyzed data from two matched corpora of dyadic interaction between-respectively-Danish and Norwegian native speakers engaged in affiliative conversations and task-oriented conversations. We assessed linguistic entrainment at the lexical, syntactic, and semantic level, and kinetic alignment of the head and hands using video-based motion tracking and dynamic time warping. We tested whether-across the two languages-linguistic alignment correlates with kinetic alignment, and whether these kinetic-linguistic associations are modulated either by the type of conversation or by the language spoken. We found that kinetic entrainment was positively associated with low-level linguistic (i.e., lexical) entrainment, while negatively associated with high-level linguistic (i.e., semantic) entrainment, in a cross-linguistically robust way. Our findings suggest that conversation makes use of a dynamic coordination of similarity and complementarity both between individuals as well as between different communicative modalities, and provides evidence for a multimodal, interpersonal synergy account of interaction., (© 2023 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).)

To what degree can language be acquired from linguistic input alone? This question has vexed scholars for millennia and is still a major focus of debate in the cognitive science of language. The complexity of human language has hampered progress because studies of language-especially those involving computational modeling-have only been able to deal with small fragments of our linguistic skills. We suggest that the most recent generation of Large Language Models (LLMs) might finally provide the computational tools to determine empirically how much of the human language ability can be acquired from linguistic experience. LLMs are sophisticated deep learning architectures trained on vast amounts of natural language data, enabling them to perform an impressive range of linguistic tasks. We argue that, despite their clear semantic and pragmatic limitations, LLMs have already demonstrated that human-like grammatical language can be acquired without the need for a built-in grammar. Thus, while there is still much to learn about how humans acquire and use language, LLMs provide full-fledged computational models for cognitive scientists to empirically evaluate just how far statistical learning might take us in explaining the full complexity of human language., (© 2023 Cognitive Science Society LLC.)

Humans readily engage in idle chat and heated discussions and negotiate tough joint decisions without ever having to think twice about how to keep the conversation grounded in mutual understanding. However, current attempts at identifying and assessing the conversational devices that make this possible are fragmented across disciplines and investigate single devices within single contexts. We present a comprehensive conceptual framework to investigate conversational devices, their relations, and how they adjust to contextual demands. In two corpus studies, we systematically test the role of three conversational devices: backchannels, repair, and linguistic entrainment. Contrasting affiliative and task-oriented conversations within participants, we find that conversational devices adaptively adjust to the increased need for precision in the latter: We show that low-precision devices such as backchannels are more frequent in affiliative conversations, whereas more costly but higher-precision mechanisms, such as specific repairs, are more frequent in task-oriented conversations. Further, task-oriented conversations involve higher complementarity of contributions in terms of the content and perspective: lower semantic entrainment and less frequent (but richer) lexical and syntactic entrainment. Finally, we show that the observed variations in the use of conversational devices are potentially adaptive: pairs of interlocutors that show stronger linguistic complementarity perform better across the two tasks. By combining motivated comparisons of several conversational contexts and theoretically informed computational analyses of empirical data the present work lays the foundations for a comprehensive conceptual framework for understanding the use of conversational devices in dialogue. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

We agree with Heintz & Scott-Phillips that pragmatics does not supplement, but is prior to and underpins, language. Indeed, human non-linguistic communication is astonishingly rich, flexible, and subtle, as we illustrate through the game of charades, where people improvise communicative signals when linguistic channels are blocked. The route from non-linguistic charade-like communication to combinatorial language involves (1) local processes of conventionalization and grammaticalization and (2) spontaneous order arising from mutual constraints between different communicative signals.

Background: Cervical cancer screening participation is suboptimal in most settings. We assessed whether human papillomavirus (HPV) self-sampling may increase screening participation among long-term non-attenders in Norway., Methods: A pragmatic randomised controlled trial with participation as the primary outcome was initiated in the national cervical screening programme in March 2019. A random sample of 6000 women aged 35-69 years who had not attended screening for at least 10 years were randomised 1:1:1 to receive either (i) a reminder to attend regular screening (control), (ii) an offer to order a self-sampling kit (opt-in) for HPV testing or (iii) a self-sampling kit unsolicited (send-to-all) for HPV testing., Results: Total participation was 4.8%, 17.0% and 27.7% among control, opt-in and send-to-all (P < 0.0001; participation difference (%) send-to-all vs. control: 22.9 (95%CI: 20.7, 25.2); opt-in vs. control: 12.3 (95%CI: 10.3, 14.2); send-to-all vs. opt-in: 10.7 (95% CI: 8.0, 13.3)). High-risk HPV was detected in 11.5% of self-samples and 9.2% of clinician-collected samples (P = 0.40). Most women (92.5%) who returned a positive self-sample attended the clinic for triage testing. Of the 933 women screened, 33 (3.5%) had CIN2 + (1.1%, 3.7%, 3.8% among control, opt-in, and send-to-all, respectively), and 11 (1.2%) had cervical cancer (0%, 1.2%, 1.3% among control, opt-in, send-to-all, respectively)., Conclusion: Opt-in and send-to-all self-sampling increased screening participation among long-term, higher-risk non-attenders., Clinical Trial Registration: ClinicalTrials.gov NCT03873376., (© 2022. The Author(s).)

How individuals learn complex regularities in the environment and generalize them to new instances is a key question in cognitive science. Although previous investigations have advocated the idea that learning and generalizing depend upon separate processes, the same basic learning mechanisms may account for both. In language learning experiments, these mechanisms have typically been studied in isolation of broader cognitive phenomena such as memory, perception, and attention. Here, we show how learning and generalization in language is embedded in these broader theories by testing learners on their ability to chunk nonadjacent dependencies-a key structure in language but a challenge to theories that posit learning through the memorization of structure. In two studies, adult participants were trained and tested on an artificial language containing nonadjacent syllable dependencies, using a novel chunking-based serial recall task involving verbal repetition of target sequences (formed from learned strings) and scrambled foils. Participants recalled significantly more syllables, bigrams, trigrams, and nonadjacent dependencies from sequences conforming to the language's statistics (both learned and generalized sequences). They also encoded and generalized specific nonadjacent chunk information. These results suggest that participants chunk remote dependencies and rapidly generalize this information to novel structures. The results thus provide further support for learning-based approaches to language acquisition, and link statistical learning to broader cognitive mechanisms of memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Statistical learning is a key concept in our understanding of language acquisition. Ample work has highlighted its role in numerous linguistic functions-yet statistical learning is not a unitary construct, and its consistency across different language properties remains unclear. In a meta-analysis of auditory-linguistic statistical learning research spanning the last 25 years, we evaluated how learning varies across different language properties in infants, children, and adults and surveyed the methodological trends in the literature. We found robust learning across stimuli (syllables, words, etc.) in infants, and across stimuli and structures (adjacent dependencies, non-adjacent dependencies, etc.) in adults, with larger effect sizes when multiple cues were present. However, the analysis also showed significant publication bias and revealed a tendency toward using a narrow range of simplified language properties, including in the strength of the transitional probabilities used during training. Bayes factor analyses revealed prevalent data insensitivity of moderators commonly hypothesized to impact learning, such as the amount of exposure and transitional probability strength, which contradict core theoretical assumptions in the field. Methodological factors, such as the tasks used at test, also significantly impacted effect sizes in adults and children, suggesting that choice of task may critically constrain current theories of how statistical learning operates. Collectively, our results suggest that auditory-linguistic statistical learning has the kind of robustness needed to play a foundational role in language acquisition, but that more research is warranted to reveal its full potential., (© 2022 Cognitive Science Society LLC.)

Statistical learning (SL) is considered a cornerstone of cognition. While decades of research have unveiled the remarkable breadth of structures that participants can learn from statistical patterns in experimental contexts, how this ability interfaces with real-world cognitive phenomena remains inconclusive. These mixed results may arise from the fact that SL is often treated as a general ability that operates uniformly across all domains, typically assuming that sensitivity to one kind of regularity implies equal sensitivity to others. In a preregistered study, we sought to clarify the link between SL and language by aligning the type of structure being processed in each task. We focused on the learning of trigram patterns using artificial and natural language statistics, to evaluate whether SL predicts sensitivity to comparable structures in natural speech. Adults were trained and tested on an artificial language incorporating statistically-defined syllable trigrams. We then evaluated their sensitivity to similar statistical structures in natural language using a multiword chunking task, which examines serial recall of high-frequency word trigrams-one of the building blocks of language. Participants' aptitude in learning artificial syllable trigrams positively correlated with their sensitivity to high-frequency word trigrams in natural language, suggesting that similar computations span learning across both tasks. Short-term SL taps into key aspects of long-term language acquisition when the statistical structures-and the computations used to process them-are comparable. Better aligning the specific statistical patterning across tasks may therefore provide an important steppingstone toward elucidating the relationship between SL and cognition at large., (Copyright © 2022. Published by Elsevier B.V.)

Recent publications have lamented the dominance of psychology in cognitive science. However, this relies on a limited definition of collaboration between fields. We call for a renewed conception of interdisciplinarity as a "mixture of expertise." We describe an information-theoretic measure of interdisciplinarity and apply it to multiauthored published articles. Results suggest that cognitive science journals mix expertise more than topically related journals. We suggest that perceptions of diminishing interdisciplinarity may in part be due to the emergence of different theoretical perspectives and use a semantic model to illustrate this argument. We conclude by describing some benefits of this broader conception., (© 2022 Cognitive Science Society LLC.)

Traditional accounts of language postulate two basic components: words stored in a lexicon, and rules that govern how they can be combined into meaningful sentences, a grammar. But, although this words-and-rules framework has proven itself to be useful in natural language processing and cognitive science, it has also shown important shortcomings when faced with actual language use. In this article, we review evidence from language acquisition, sentence processing, and computational modeling that shows how multiword expressions such as idioms, collocations, and other meaningful and common units that comprise more than one word play a key role in the organization of our linguistic knowledge. Importantly, multiword expressions straddle the line between lexicon and grammar, calling into question how useful this distinction is as a foundation for our understanding of language. Nonetheless, finding a replacement for the foundational role the words-and-rules approach has played in our theories is not straightforward. Thus, the second part of our article reviews and synthesizes the diverse approaches that have attempted to account for the central role of multiword expressions in language representation, acquisition, and processing., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Contreras Kallens and Christiansen.)

A growing body of research investigates individual differences in the learning of statistical structure, tying them to variability in cognitive (dis)abilities. This approach views statistical learning (SL) as a general individual ability that underlies performance across a range of cognitive domains. But is there a general SL capacity that can sort individuals from 'bad' to 'good' statistical learners? Explicating the suppositions underlying this approach, we suggest that current evidence supporting it is meager. We outline an alternative perspective that considers the variability of statistical environments within different cognitive domains. Once we focus on learning that is tuned to the statistics of real-world sensory inputs, an alternative view of SL computations emerges with a radically different outlook for SL research., Competing Interests: Declaration of interests No interests are declared., (Copyright © 2021 Elsevier Ltd. All rights reserved.)