Ryan Mark Nefdt

In her article, Stanton (2025) argues that the real pattern analysis of language proffered by Nefdt (2023) fails as a demarcation of language versus non-language. She also makes the case that structuralism about linguistics does not characterise certain important metasemantic aspects of the broader field. In this article, I offer a rejoinder to these points by emphasising the role the linguistic community plays in determining real patterns as well as the theoretical benefits the view that grammars are multilayered compressions algorithms has for semantics and other linguistic disciplines.

This article is not about machine consciousness. It's about our understanding of new technology. An emerging contemporary trend treats large language models (LLMs) as cognitive beings partly because they display high-level linguistic abilities. This is in part because we struggle to conceive of a purely linguistic agent. I flesh out this new typological possibility while suggesting that there are interesting features attributable to LLMs based on their architectures and the kinds of information they can process that help us to see the world through their nodes.

This article addresses the relationship between philosophy and cognitive neuroscience, focusing on possible sources of experimental ideas when theoretical constructs are absent, weak, or only partly developed. We argue that philosophical thought experiments and cases provide one such source, and we examine their role and nature as conduits to actual laboratory experiments. We demonstrate that the structure of thought experiments, in ethics and in the philosophies of language and mind, may lend itself naturally to translations into cognitive neuroscience research. Such translations into experimental tasks involve structural preservation as well as modification of the original cases, with implications for the mutual relevance of the newly obtained results and the original philosophical analyses. Given philosophy’s long-held place within the cognitive sciences, this analysis sheds additional light on the issue of cognitive neuroscience itself as a disciplinary enterprise.

In this paper, I address the issue of scientific modelling in contemporary linguistics, focusing on the generative tradition. In so doing, I identify two common varieties of linguistic idealisation, which I call determination and isolation respectively. I argue that these distinct types of idealisation can both be described within the remit of Weisberg’s :639–659, 2007) minimalist idealisation strategy in the sciences. Following a line set by Blutner :27–35, 2011), I propose this minimalist idealisation analysis for a broad construal of the generative linguistic programme and thus cite examples from a wide range of linguistic frameworks including early generative syntax, Minimalism, the parallel architecture and optimality theory. Lastly, I claim that from a modelling perspective, the dynamic turn in syntax can be explained as a continuation, as opposed to a marked shift, of the generative modelling paradigm. Seen in this light, my proposal is an even broader construal of the generative tradition, along scientific modelling lines. Thus, I offer a lens through which to appreciate the scientific contribution of generative grammar, amid an increased resistance to some of its core theoretical posits, in terms of a brand of structural realism in the philosophy of science and specifically scientific modelling.

The concept of linguistic relativity (or Whorfianism) has its roots in the linguistic anthropology of Edward Sapir and his student Benjamin Whorf in the early twentieth century. However, questions over the relationship between natural language and human cognition go much further and deeper. Unfortunately, linguistic relativity has about as many misinterpretations as it does labels (linguistic relativity, linguistic relativism, linguistic determinism, Whorfianism, Sapir-Whorf hypothesis - weak and strong). The idea that language determines thought through an environmentally constrained feedback system is at the heart of most concepts associated with linguistic relativity. The real philosophical questions, however, only seem to present themselves at a level beyond the trivial truism that linguistic structure has an effect on thought, i.e. different languages might encode environmental information differently resulting in variation in things like processing times, measured in psycholinguistic experiments. These questions are important for a number of related disciplines, yet the concept itself is one of the most misunderstood in modern anthropology, sociology, philosophy of language, linguistics, and cognitive science. This book contributes much needed clarity to a theoretical landscape at the centre of insights into what makes us human, both linguistically and cognitively.

As large language models (LLMs) such as ChatGPT, Claude, Gemini, and Perplexity become increasingly ubiquitous as both tools and objects of scientific study, in addition to their established roles as chatbots, text generators and translators, questions about their identity conditions become scientifically as well as philosophically and socially important. This paper is about how to count language models. We argue that much of the emerging literature on these systems presupposes an answer to the question of identity for these AIs but lacks a framework for counting or individuating them. We propose four levels of analysis, inspired by an analogy with software as well as insights from social epistemology. This account produces practical consequences for both research on language models, the ethics of AI, and our everyday talk about them.

In this paper, I discuss the emerging field of conceptual engineering from the perspective of the philosophy of mind and cognitive science. I explore the role the latter disciplines can and do play by specifically focusing on how different notions of what a concept is affects the general conceptual engineering framework, both positively and negatively, before considering a particular account that eschews talk of concepts altogether. I call this project ‘Cappelen's challenge’ as it draws from suggestive remarks in his work (Cappelen, H. 2018. Fixing Language: Conceptual Engineering and the Limits of Revision. Oxford: Oxford University Press). I argue that by appreciating the range of options that each theory of concepts brings with it, the field as a whole can come to better grips with its foundations.

In this paper, I critique a recent claim made by Stokhof and van Lambalgen (2011) (hereafter S&vL) that linguistics and science are at odds as to the models and constructions they employ. I argue that their distinction between abstractions and idealisations, the former belonging to the methodology of science and the latter to linguistics, is not a real one. I show that the majority of their arguments are flawed and evidence they cite misleading. Contrary to this distinction, I argue that linguistics, like some variants of the scientific enterprise, uses a minimalist method of idealisation (Weisberg, 2007b), one which includes abstractions (as defined by S&vL) and other idealisations not uncommon to scientific model-building. Finally, I offer an alternative account of the problems cited by S&vL as a direct result of the modelling choices of linguists as opposed to the methods they use to define such models. I do so through the use of the specific example of the treatment of tense and aspect in the mainstream literature.

My aim in this chapter is to extend the Realist account of the foundations of linguistics offered by Postal, Katz and others. I first argue against the idea that naive Platonism can capture the necessary requirements on what I call a ‘mixed realist’ view of linguistics, which takes aspects of Platonism, Nominalism and Mentalism into consideration. I then advocate three desiderata for an appropriate ‘mixed realist’ account of linguistic ontology and foundations, namely (1) linguistic creativity and infinity, (2) linguistics as a theory of types (and not tokens) and (3) independence but structural respect between language and the linguistic competence thereof. My own brand of mixed realism, what I call ante rem realism, is defended along the lines of an ante rem or non-eliminative structuralism, the likes of which has been offered for mathematics by Resnik (1997) and Shapiro (1997). In other words, grammars describe a mind-independent (but not necessarily unconnected) linguistic reality in terms of linguistic patterns or structures also known as natural languages. I further amend this picture to allow for the possibility of a naturalistic account of language acquisition and evolution by arguing against a particular view of the type-token distinction.

What is a language? What do scientific grammars tell us about the structure of individual languages and human language in general? What kind of science is linguistics? These and other questions are the subject of Ryan M. Nefdt's Language, Science, and Structure. Linguistics presents a unique and challenging subject matter for the philosophy of science. As a special science, its formalisation and naturalisation inspired what many consider to be a scientific revolution in the study of mind and language. Yet radical internal theory change, multiple competing frameworks, and issues of modelling and realism have largely gone unaddressed in the field. Nefdt develops a structural realist perspective on the philosophy of linguistics which aims to confront the aforementioned topics in new ways while expanding the outlook toward new scientific connections and novel philosophical insights. On this view, languages are real patterns which emerge from complex biological systems. Nefdt's exploration of this novel view will be especially valuable to those working in formal and computational linguistics, cognitive science, and the philosophies of science, mathematics, and language.

The Covid-19 global pandemic had a profound effect on scientific practice. During this time, officials crucially relied on the work done by modellers. This raises novel questions for the philosophy of science. Here, I investigate the possibility of ‘natural models’ in predicting the virus’ trajectory for epidemiological purposes. I argue that to the extent that these can be consideredscientific models, they support the possibility of a continuum from scientific models to natural models differing in artifactual commitment. In making my case, I draw from work on model organisms and natural experiments as well as recent work in epidemiology.

This volume brings together a diverse range of scholars to address important philosophical and interdisciplinary questions in the study of language. Linguistics throughout history has been a conduit to the study of the mind, brain, societal structure, literature and history itself. The epistemic and methodological transfer between the sciences and humanities in regards to linguistics has often been documented, but the underlying philosophical issues have not always been adequately addressed. With 15 original and interdisciplinary chapters, this volume therefore tackles vital questions relating to the philosophy, history, and theoretical interplay between the study of language and fields as varied as logic, physics, biology, classical philology and neuroscience. With a four part structure, questions of the mathematical foundations of linguistics, links to the natural sciences, cognitive implications and historical connections, take centre stage throughout the volume. The final chapters present research related to the linguistic connections between history, philosophy and the humanities more broadly. Advancing new avenues of research, this volume is exemplary in its treatment of diachronic and cross-disciplinary interaction, and will be of interest to all scholars interested in the study of language.

In their recent book, Ladyman and Wiesner (What is a complex system?, Yale University Press, 2020) delineate the bounds of the exciting interdisciplinary field of complexity science. In this work, they provide examples of generally accepted complex systems and common features which these possess to varying degrees. In this paper, I plan to extend their list to include the formal study of natural language, i.e. linguistics. In fact, I will argue that language exhibits many of the hallmarks of a complex system, specifically a complex biological system. Thus, my aim is to advocate contra the the ‘Minimalist Program’ (Chomsky, The minimalist program, MIT Press, Cambridge, 1995), which motivates simple underlying mechanisms (i.e. Merge) in their idealisations, that biolinguistics should embrace a ‘Maximalist Program’ in which multiple subfields contribute component explanations to an emerging whole.

In recent years, dependency grammars have established themselves as valuable tools in theoretical and computational linguistics. To many linguists, dependency grammars and the more standard constituency-based formalisms are notational variants. We argue that, beyond considerations of formal equivalence, cognition may also serve as a background for a genuine comparison between these different views of syntax. In this paper, we review and evaluate some of the most common arguments and evidence employed to advocate for the cognitive or neural reality of dependency grammars in linguistics, psycholinguistics, or neurolinguistics. We then raise the possibility that the abilities to represent and track, alternatively or in parallel, constituency and dependency structures co-exist in human cognition and are constitutive of syntactic competence.

This paper discusses the logical space of alternative conceptual engineering projects, with a specific focus on (1) the processes, (2) the targets and goals, and (3) the methods of such projects. We present an overview of how these three aspects interact in the contemporary literature and discuss those alternative projects that have yet to be explored based on our suggested typology. We show how choices about each element in a conceptual engineering project constrain the possibilities for the others, thereby giving rise to distinct groupings of possible projects under the banner of conceptual engineering. We conclude with a critical reflection on the potential ethical issues that arise as a result of effectively putting conceptual engineering into practice.

Political philosophy has traditionally been defined as a normative discipline with a distinctively ideal component, largely informed by moral philosophy. In this paper, I investigate a prominent critique of ideal theory specifically with the goal of resituating the debate within a larger framework in the philosophy of science. I then mount a novel case for how ideal theory should be viewed in terms of scientific modelling. I close with a discussion of how this view can dissolve apparent paradoxes and provide distinct advantages to the political theorist.

Linguistics as a science has rapidly changed during the course of a relatively short period. The mathematical foundations of the science, however, present a different story below the surface. In this paper, I argue that due to the former, the seismic shifts in theory over the past 80 years opens linguistics up to the problem of pessimistic meta-induction or radical theory change. I further argue that, due to the latter, one current solution to this problem in the philosophy of science, namely structural realism :403–424, 1998; French in Proc Aristot Soc 106:167–185, 2006), should be viewed as especially enticing for linguists, as their field is a largely structural enterprise. I discuss particular historical instances of theory change in generative syntax before investigating two views on the nature of structural properties and eventually proposing an approach in terms of invariance :162–186, 2015) as a grounding for structural realism in the history and philosophy of linguistics.

Linguistics as a science has rapidly changed during the course of a relatively short period. The mathematical foundations of the science, however, present a different story below the surface. In this paper, I argue that due to the former, the seismic shifts in theory over the past 80 years opens linguistics up to the problem of pessimistic meta-induction or radical theory change. I further argue that, due to the latter, one current solution to this problem in the philosophy of science, namely structural realism, should be viewed as especially enticing for linguists, as their field is a largely structural enterprise. I discuss particular historical instances of theory change in generative syntax before investigating two views on the nature of structural properties and eventually proposing an approach in terms of invariance as a grounding for structural realism in the history and philosophy of linguistics.

This paper attempts to describe and address a specific puzzle related to compositionality in artificial networks such as Deep Neural Networks and machine learning in general. The puzzle identified here touches on a larger debate in Artificial Intelligence related to epistemic opacity but specifically focuses on computational applications of human level linguistic abilities or properties and a special difficulty with relation to these. Thus, the resulting issue is both general and unique. A partial solution is suggested.

This article surveys the philosophical literature on theoretical linguistics. The focus of the paper is centred around the major debates in the philosophy of linguistics, past and present, with specific relation to how they connect to the philosophy of science. Specific issues such as scientific realism in linguistics, the scientific status of grammars, the methodological underpinnings of formal semantics, and the integration of linguistics into the larger cognitive sciences form the crux of the discussion.

In this paper, I utilise the growing literature on scientific modelling to investigate the nature of formal semantics from the perspective of the philosophy of science. Specifically, I incorporate the inferential framework proposed by Bueno and Colyvan : 345–374, 2011) in the philosophy of applied mathematics to offer an account of how formal semantics explains and models its data. This view produces a picture of formal semantic models as involving an embedded process of inference and representation applying indirectly to linguistic phenomena. The final aim of the paper is directed at proposing a novel account of the syntax–semantics interface while shedding light on empty categories, semantically null forms, underspecified content and compositionality as a whole.