Language models (LMs) call for a theoretical rethinking grounded within linguistics itself. Rather than signalling the “end of linguistics” or merely encouraging interdisciplinarity, LMs function as an empirical testing ground for formal linguistic concepts. They prompt a renewed examination of form–meaning mapping, theoretical autonomy, and the conditions under which computational systems can genuinely inform linguistic explanation.

Norm, the formal theoretical linguist, and Claudette, the computational language scientist, have a lovely time discussing whether modern language models can inform important questions in the language sciences. Just as they are about to part ways until they meet again, 25 of their closest friends show up – from linguistics, neuroscience, cognitive science, psychology, philosophy, and computer science. We use this discussion to highlight what we see as some common underlying issues: the String Statistics Strawman (the mistaken idea that LMs can’t be linguistically competent or interesting because they, like their Markov model predecessors, are statistical models that learn from strings) and the As Good As It Gets Assumption (the idea that LM research as it stands in 2026 is the limit of what it can tell us about linguistics). We clarify the role of LM-based work for scientific insights into human language and advocate for a more expansive research program for the language sciences in the AI age, one that takes on the commentators’ concerns in order to produce a better and more robust science of both human language and of LMs.

Language models excel at finding patterns in linguistic data, and can therefore prove insightful for statistical approaches to linguistics in that they provide further evidence for the strong reliance of natural languages on recurrent, fixed patterns. Nevertheless, regarding actual usage-based language processing, their implications are severely limited as they lack a crucial aspect of language use: interaction.

Futrell and Mahowald claim language models (LMs) “serve as model systems,” but an assessment at each of Marr’s three levels suggests the claim is clearly not true at the implementation level, poorly motivated at the algorithmic-representational level, and problematic at the computational theory level. LMs are good candidates as tools; calling them cognitive models overstates the case and unnecessarily feeds large language model hype.

This article addresses a central challenge in political science: how to choose between competing conceptions and structures of concepts. Existing approaches to concept validity offer useful criteria—such as resonance, consistency, differentiation, causal utility, and operationalization—but tend to omit criteria for evaluating the normative considerations that often underpin conceptual choices. As a result, conceptualization may face a fundamental indeterminacy when multiple conceptions appear equally well grounded. To address this lacuna, I introduce a “concern” criterion, which evaluates concepts according to the extent to which they capture what is most worrisome in the political world. Building on the semantic–pragmatic approach to conceptualization, I argue that normative considerations can be disciplined, rather than avoided. The argument is illustrated through the case of political polarization, where a shift from issue-based to affect-based conceptions reflects changing concerns about political division. I also examine the implications of the concern criterion for structuring multidimensional concepts and address objections concerning objectivity, stability, and legitimacy. A concern criterion, although not sufficient on its own, provides a valuable complement to existing criteria and helps ground conceptual choices in a normatively informed yet methodologically disciplined manner.

Large Language Models (LLMs) can serve as tools for understanding how probabilistic constraints interact during language acquisition. To motivate such use cases of LLMs, we discuss several examples from allied fields, including neurobiology and animal behavior, of how soft constraints shape learning and development in cognitive systems. We end by outlining four challenges that LLM cognitive modeling should address in the coming decade.

Under the lens of Marr’s (levels of analysis, we critique and extend the authors’ two points about language models (LMs) and language processing: first, predicting upcoming linguistic information based on context is key to language processing, and second, that many advances in psycholinguistics would be impossible without LLMs. We also outline directions combining LLMs’ strengths with psycholinguistic models.

Evidence-based nutrition guidance for female athletes remains limited relative to that available for males; in part, this has contributed to widespread reliance on social media for dietary information. Whilst social media can enhance health communication, it also facilitates the rapid dissemination of unverified, commercially driven nutrition claims. This narrative review critically synthesises the current scientific literature underpinning four prevalent claims targeting nutrition close to exercise for active females; (1) fasted training is harmful for all females, (2) menstrual cycle-related hormonal fluctuations require sex-specific hydration strategies, (3) carbohydrate differences by sex and menstrual cycle phase and (4) precise protein timing is essential for optimal adaptation in females. Despite social media and ‘influencer’ claims of no evidence in humans for many of the claims, there is some, albeit limited, evidence. This review evaluates the available research and the evidence supporting these claims to provide practical advice for active females. Collectively, this review demonstrates that many widely circulated nutrition claims directed at active females lack robust scientific support. The findings emphasise the importance of individual context, including training load, energy availability, environmental conditions and total dietary intake, over rigid, sex-specific nutrition rules. Improved translation of female-specific sports nutrition research into accurate, accessible public messaging is urgently needed to counter persistent misinformation in digital media.

This paper examines virtual reality gaming as a form of embodied interaction at the intersection of digital mediation, improvisation, and agency. In VR environments, players act through avatars, and their actions are shaped in real time by shifting relations among embodiment, disembodiment, subjectivity, and intersubjectivity. The analysis brings together Gilles Deleuze and Félix Guattari’s concept of the Body without Organs, Charles Goodwin and Marjorie Harness Goodwin’s work on cooperation and multimodal interaction, and Alessandro Duranti’s account of improvisation. Focusing on Population: One and Richie’s Plank Experience, I argue that improvisation emerges through the unstable relation between the biophysical body and the digital body. Glitches, misalignments, and other breakdowns create moments in which participants must adjust ongoing action spontaneously, thereby destabilizing established physical and linguistic categories. These moments reveal a continuing process of deterritorialization and reterritorialization through which bodies, joint action, and agency are continuously reconfigured.

This special issue pursues a social semiotic study of improvisation. The approach considers the phenomenon both as a constitutive dimension of action and as a socially recognizable achievement. Contributions share a common focus on interaction, which is analyzed across multiple modalities including virtual reality, heavy machinery, paint and canvas, rock, theater, war, and the ethical relation between self and other.