Generative AI technologies have the potential to be socially and politically transformative. In this paper, we focus on exploring the potential impacts that Generative AI could have on the functioning of our democracies and the nature of citizenship. We do so by drawing on accounts of deliberative democracy and the deliberative virtues associated with it, as well as the reciprocal impacts that social media and Generative AI will have on each other and the broader information landscape. Drawing on this background (...) theory, we outline some of the key positive and negative impacts that Generative AI is likely to have on democratic citizenship. The political significance of these impacts suggests the need for further regulation. (shrink)

Generative linguistics is widely claimed to produce theories at the level of computation in the sense outlined by David Marr. Marr even used generative grammar as an example of a computational level theory. At this level, a theory specifi es a function for mapping one kind of information into another. How this function is computed is then specified at the algorithmic level before an account of how this is algorithm is realised by some physical system is presented at the implementation (...) level. This paper will argue that generative linguistics does not fit anywhere within this framework. We will then look at several ways researchers have attempted to modify either the framework of generative theory to reconcile the two approaches. Finally, it presents and discusses an alternative position, anti-realism about generative grammar. While this position has attracted some recent support, it also runs into some of the problems that earlier modifications faced. (shrink)

This book argues that this powerful technique permits the social sciences to meet an explanation, in which one 'grows' the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors.

Social scientists associate agent-based simulation (ABS) models with three ideas about explanation: they provide generative explanations, they are models of mechanisms, and they implement methodological individualism. In light of a philosophical account of explanation, we show that these ideas are not necessarily related and offer an account of the explanatory import of ABS models. We also argue that their bottom-up research strategy should be distinguished from methodological individualism.

It has been proposed that intentional actions are supplied by a generative system of the sort described by Chomsky for language. In this paper I aim to provide a closer analysis of this claim for the sake of conceptual clarification. To this end, I will first clarify what is involved in the thesis of a structural analogy between language and action, and then I will consider what kind of evidence there seems to be in favour of the thesis of a (...) neurobiological identity. On this basis, I will subsequently focus on two definitional issues. The first is whether, as the claim of a generative system for intentional action suggests, humans may perform an infinite number of possible actions. The second is whether, as the claim of a generative system for intentional action suggests, what is at issue is conscious planning of action and therefore controlled processing. (shrink)

This Perspective traces the evolution of certain central notions in the theory of Generative Grammar (GG). The founding documents of the field suggested a relation between the grammar, construed as recursively enumerating an infinite set of sentences, and the idealized native speaker that was essentially equivalent to the relation between a formal language (a set of well-formed formulas) and an automaton that recognizes strings as belonging to the language or not. But this early view was later abandoned, when the focus (...) of the field shifted to the grammar's strong generative capacity as recursive generation of hierarchically structured objects as opposed to strings. The grammar is now no longer seen as specifying a set of well-formed expressions and in fact necessarily constructs expressions of any degree of intuitive “acceptability.” The field of GG, however, has not sufficiently acknowledged the significance of this shift in perspective, as evidenced by the fact that (informal and experimentally-controlled) observations about string acceptability continue to be treated as bona fide data and generalizations for the theory of GG. The focus on strong generative capacity, it is argued, requires a new discussion of what constitutes valid empirical evidence for GG beyond observations pertaining to weak generation. (shrink)

This paper explores the implications of the psychology of constructive memory for philosophical theories of the metaphysics of memory and for a central question in the epistemology of memory. I first develop a general interpretation of the psychology of constructive memory. I then argue, on the basis of this interpretation, for an updated version of Martin and Deutscher's influential causal theory of memory. I conclude by sketching the implications of this updated theory for the question of memory 's status as (...) a generative epistemic source. (shrink)

Social scientists associate agent-based simulation (ABS) models with three ideas about explanation: they provide generative explanations, they are models of mechanisms, and they implement methodological individualism. In light of a philosophical account of explanation, we show that these ideas are not necessarily related and offer an account of the explanatory import of ABS models. We also argue that their bottom-up research strategy should be distinguished from methodological individualism.

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. (shrink)

Although Hurricane Katrina precipitated considerable reflection across various media, a practice crucial to our capacities to apprehend and interpret the disaster has not yet been analyzed as such. I call this practice generative assembly. I don’t mean the events of emergency and political gathering that took place in response to the massive storm and fatal, preventable levee failures—although I will propose connections between different forms of assembly. Instead I mean a kind of documentary practice. That practice, which can be undertaken (...) individually or in collaboration, and sometimes at anonymous remove, involves the work of assembling records and signifiers related to the disaster into particular kinds of media artifacts. Those artifacts— which take shape in formats as varied as comics, photobooks, paintings, films, exhibitions, and multithousand-item online archives—characteristically maintain the active appearance and interactive potential of selection and arrangement. In exhibiting such qualities, or so I will argue, these assembly-based artifacts support various articulable and often highly effective kinds of generativity. And thus, I will further suggest, these artifacts can constitute especially powerful means of intervening in prevailing conditions of representation and remembrance around events of environmental and social violence. Crucially, those material legacies of generative assembly can also fall short, fail, and deceive, and this is especially true of those assemblies that take digital forms. It is my contention, however, that we would be misguided if we thus entirely dismissed such assemblies or if we refused their potential for dissident reuse or generative reassembly. (shrink)

Generative models have been proposed as a new type of non-representational scientific models recently. A generative model is characterized with the capacity of producing new models on the basis of the existing one. The current accounts do not explain sufficiently the mechanism of the generative capacity of a generative model. I attempt to accomplish this task in this paper. I outline two antecedent accounts of generative models. I point out that both types of generative models function to generate new homogenous (...) models in the sense that the latter is a straightforward derivative of the former, both of which share many similar features. Unfortunately, both accounts are implicit about the generative capacity of generative models. Using a case study, I articulate that a two-staged process of abstraction and idealization in modeling may contribute to the generative capacity of a scientific model. I also demonstrate that this two-staged process may go beyond the capacity of generating new homogenous models to generating new heterogeneous models. (shrink)

Discourses on Responsible Innovation and Responsible Research and Innovation, in short RI, have revolved around but not elaborated on the notion of critique. In this article, generative critique is introduced to RI as a practice that sits in-between adversarial armchair critique and co-opted, uncritical service. How to position oneself and be positioned on this spectrum has puzzled humanities scholars and social scientists who engage in interdisciplinary collaborations with scientists, engineers, and other professionals. Recently, generative critique has been presented as a (...) solution to the puzzle in interdisciplinary collaborations on neuroscientific experiments. Generative critique seeks to create connections across disciplines that help remake seemingly stable objects in moments when taken-for-granted ways of seeing and approaching objects are unsettled. In order to translate generative critique from the neurosciences to RI, socio-technical integration research is proposed as a practice of generative critique in interdisciplinary RI collaborations. These collaborations aim to account for societal aspects in research and technology development. For this purpose, a variety of approaches have been developed, including STIR and video-reflexive ethnography. STIR and VRE resemble each other but diverge on affective, collaborative, and temporal dimensions. Their juxtaposition serves to develop suggestions for how STIR could be modified on these dimensions to better enact generative critique in interdisciplinary RI collaborations. In this way, the article contributes to ongoing discussions in RI and in the engaged programme in science and technology studies more broadly on the dynamics of positioning in collaborative work. (shrink)

The generative entrenchment of an entity is a measure of how much of the generated structure or activity of a complex system depends upon the presence or activity of that entity. It is argued that entities with higher degrees of generative entrenchment are more conservative in evolutionary changes of such systems. A variety of models of complex structures incorporating the effects of generative entrenchment are presented and we demonstrate their relevance in analyzing and explaining a variety of developmental and evolutionary (...) phenomena, both on a macroscopic developmental and evolutionary scale, and using models and strategies pioneered by Kauffman, on the more microscopic scale appropriate to the analysis of the structure and behavior of gene control networks. The resulting picture suggests that generative entrenchment acts as a powerful and constructive developmental constraint on the course of evolutionary processes. Since virtually any system exhibits varying degrees of generative entrenchment among its parts and activities, these studies and results have in addition broad potential application for the analysis of generative structures in other areas. (shrink)

Generative Artificial Intelligence (AI) tools produce hallucinations exposing developers and users to a myriad of liabilities in courts. Given the absence of strict laws and regulations structuring how Generative AI content interact with potential allegations of defamation, libel, and slander, judges and attorneys are left with the semiotics of the fragmented articles and rules in each system attempting to settle such cases. The endless interpretations of written and non-verbal signs in the law across the world constitutes a new realm for (...) legal semiotics in the area of Generative AI and defamation. The present analysis examines existing civil liability articles in the Jordanian code to shed light on the litigation and defenses afforded in the defamation realm with respect to Generative AI content. The key finding is that like other countries, civil codes in Jordan are outdated concerning Generative AI content opening a plethora of liability scenarios in libel or slander cases. More importantly, the exercise of semiotic interpretation generates several defenses salvaging users and developers under strict sets of conditions. In sum, new amendments and updated legislative frameworks are needed to protect the healthy development of Generative AI while preserving citizens from damaging defamation, libel, and slander. (shrink)

Generative artificial intelligence (GenAI) promises to revolutionize data-driven fields (Milmo 2023). Building on decades of large language modeling (LLM) (Toner 2023), GenAI can collect, harmonize...

Generative AI programs can produce high-quality written and visual content that may be used for good or ill. We argue that a credit–blame asymmetry arises for assigning responsibility for these outputs and discuss urgent ethical and policy implications focused on large-scale language models.

The ever increasing use of generative artificial intelligence raises significant questions about authorship and related issues such as credit and accountability. In this paper, I consider whether works produced by means of users inputting natural language prompts into Generative Adversarial Networks are works of authorship. I argue that they are not. This is not due to concerns about randomness or machine-assistance compromising human labor or intellectual vision, but instead due to the syntactical and compositional limitations of existing AI systems in (...) handling natural language prompts. This, I argue, gives rise to ‘authorship gaps’. (shrink)

Peter Ludlow presents the first book on the philosophy of generative linguistics, including both Chomsky's government and binding theory and his minimalist ...

Jackendoff's Foundations of Language: Brain, Meaning, Grammar, Evolution attempts to reconnect generative linguistics to the rest of cognitive science. However, by minimally acknowledging decades of work in cognitive linguistics, treating dynamical systems approaches somewhat dismissively, and clinging to certain fundamental dogma while revising others, he clearly risks satisfying no one by almost pleasing everyone.

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as _computational phenomenology_ because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates (...) philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience. (shrink)

Generative Emergence provides insight into the non-linear dynamics that lead to organizational emergence through the use of complexity sciences. The book explores how the model of Generative Emergence could be applied to enact emergence within and across organizations.

Classical categorial grammars are the grammars introduced by Ajdukiewicz [1] and formalized by Bar-Hillel [2], Bar-Hillel et al. [3]. In [3] there is proved the weak equivalence of CCG’s and context-free grammars [6]. In this note we characterize the strong generative capacity of finite and rigid CCG’s, i.e. their capacity of structure generation. These results are more completely discussed in [4], [5].

Despite the many successes of generative AI, a number of fundamental questions have begun to arise around this technology. There is undoubtedly an interesting situation from a philosophical point of view. It can be carefully assumed that contemporary digital information processing technologies have arisen inside a circle of civilisation, one of the foundations of which is the classical account of truth. This account, even if seen as ideal and absolute, nevertheless seems to be a driving force in the field of (...) science. Is it possible that generative AI will result in the replacement of the pursuit of truth by a mere uncritical adherence to the results provided by AI systems, as well as the inhibition of lively creativity both scientific and artistic? (shrink)

Mesoudi et al.'s new synthesis for cultural evolution closely parallels the evolutionary synthesis of Neo-Darwinism. It too draws inspiration from population genetics, recruits other fields, and, unfortunately, also ignores development. Enculturation involves many serially acquired skills and dependencies that allow us to build a rich cumulative culture. The newer synthesis, evolutionary developmental biology, provides a key tool, generative entrenchment, to analyze them. (Published Online November 9 2006).

This paper will analyze how and to what extent terrorists and violent extremistshave interacted with generative artificial intelligence so far and identify potentialways in which they could misuse generative AI in the future. It is therefore necessaryto question assumptions that terrorist and violent extremist actors will quickly adoptgenerative AI solely based on an assessment of the capabilities it may offer. Althoughresearch has shown that technological capability and availability are key drivers of terroristinnovation, terrorist and violent extremist actors also assess any (...) new technologyon its compatibility (with both their modus operandi and ideology), relative complexity,cost, and the context in which they are operating. Understanding these factors, both inisolation and in relation to one another, will be a key aspect in monitoring the extent towhich terrorists and violent extremists adopt generative AI tools. In doing so, the paperwill seek to identify a likely trajectory for the abuse of this technology by terrorist actorsas well as conclude with some initial recommendations for policymakers. (shrink)

Mitte der fünfziger Jahre entsteht mit den Arbeiten der amerikanischen Linguisten Zellig S. Harris und Noam Chomsky die Theorie der generativen (Transformations-)Grammatiken a) Chomskys Grammatikmodell in den "Aspects" ... b) Entwicklung der Theorie ... nach 1965 ...

A probabilistic Chomsky–Schützenberger hierarchy of grammars is introduced and studied, with the aim of understanding the expressive power of generative models. We offer characterizations of the distributions definable at each level of the hierarchy, including probabilistic regular, context-free, (linear) indexed, context-sensitive, and unrestricted grammars, each corresponding to familiar probabilistic machine classes. Special attention is given to distributions on (unary notations for) positive integers. Unlike in the classical case where the "semi-linear" languages all collapse into the regular languages, using analytic tools (...) adapted from the classical setting we show there is no collapse in the probabilistic hierarchy: more distributions become definable at each level. We also address related issues such as closure under probabilistic conditioning. (shrink)

Humanistic management and mindfulness practices can potentiate one another to foster an inclusive society. By moving beyond a limited instrumental understanding of mindfulness practice to a generative mindfulness that incorporates a recognition of the rich nature of the human mind, awareness of cultural practices, and deeply rooted ethical foundations, managers can create organizational cultures that honor the sacred in every human being. A set of interviews with noted consultants and researchers on mindfulness and leadership suggests convergence on this perspective, as (...) does the experience of a university administrator in developing an anti-racist agenda for a large U.S. university. This article is based on both the personal experience and scholarly research of its authors and suggests ways that humanistic management education can contribute to creating inclusive workplaces through incorporating generative mindfulness in executive development as well as undergraduate programs. (shrink)

The new wave of ‘foundation models’—general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)—represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection mechanism for the (...) content it generates, as a condition of its public release. The detection mechanism should be made publicly available in a tool that allows users to query, for an arbitrary item of content, whether the item was generated (wholly or partly) by the model. In this paper, we argue that this requirement is technically feasible and would play an important role in reducing certain risks from new AI models in many domains. We also outline a number of options for the tool’s design, and summarize a number of points where further input from policymakers and researchers would be required. (shrink)

Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations: representational vehicles that represent their targets by being structurally similar to them. Here, I challenge this assumption, claiming that, at present, it lacks an adequate justification. I examine the only argument (...) offered to establish that generative models are structural representations, and argue that it does not substantiate the desired conclusion. Having so done, I consider a number of alternative arguments aimed at showing that the relevant structural similarity obtains, and argue that all these arguments are unconvincing for a variety of reasons. I then conclude the paper by briefly highlighting three themes that might be relevant for further investigation on the matter. (shrink)

Number concepts must support arithmetic inference. Using this principle, it can be argued that the integer concept of exactly ONE is a necessary part of the psychological foundations of number, as is the notion of the exact equality - that is, perfect substitutability. The inability to support reasoning involving exact equality is a shortcoming in current theories about the development of numerical reasoning. A simple innate basis for the natural number concepts can be proposed that embodies the arithmetic principle, supports (...) exact equality and also enables computational compatibility with real- or rational-valued mental magnitudes. (shrink)

We present a generative distributional model for the unsupervised induction of natural language syntax which explicitly models constituent yields and contexts. Parameter search with EM produces higher quality analyses than previously exhibited by unsupervised systems, giving the best published unsupervised parsing results on the ATIS corpus. Experiments on Penn treebank sentences of comparable length show an even higher F1 of 71% on nontrivial brackets. We compare distributionally induced and actual part-of-speech tags as input data, and examine extensions to the basic (...) model. We discuss errors made by the system, compare the system to previous models, and discuss upper bounds, lower bounds, and stability for this task. (shrink)

When cognitive scientists are looking for the neural basis of consciousness or the computational processes underlying vision, what are they looking to find? I argue for a new account of this explanatory project in cognitive science (and the special sciences more generally) on which it is best understood on close analogy with causal explanation in the special sciences. Causal explanations cite causal difference-makers: they explain how certain events causally depend on other events. Generative explanations cite generative difference-makers: they explain how (...) certain events obtain in virtue of (and so are generated by) other events. I show how that this generative difference-making account (GDM) extends well regarded developments in philosophical work on causal explanation, helps to usefully clarify and expands the hypothesis space of a key project in cognitive science – that of understanding how underlying nonmental processes give rise to the mental, including consciousness. In section 2, I develop GDM by close analogy with causal explanation, and locate both of them as species of a more general project in the special sciences: that of discovering what makes a difference to what. In section 3, I motivate GDM by contrasting it with other rival accounts, including correlationist, identity, and grounding accounts, as well as Carl Craver’s constitutive relevance account. In section 4, I tie all of the strands of the preceding discussion together to show how GDM provides a new and compelling response to the so-called hard problem of consciousness. (shrink)

This paper has two motivations. First, I want to delineate structurally the dimensions of phenomenological method: not merely the static and genetic methods, but along with them I want to introduce the new ideas of generativity and generative method (Section 2). Second, because these dimensions cannot merely be treated structurally, I want to examine their dynamic interrelation, that is, the system of motivations obtaining between them. I will do this by elaborating the phenomenological concept of "leading clue" (Section 3). Finally, (...) I will conclude by addressing the relation of phenomenology to generativity (Section 4). (shrink)

The advent of generative artificial intelligence and the widespread adoption of it in society engendered intensive debates about its ethical implications and risks. These risks often differ from those associated with traditional discriminative machine learning. To synthesize the recent discourse and map its normative concepts, we conducted a scoping review on the ethics of generative artificial intelligence, including especially large language models and text-to-image models. Our analysis provides a taxonomy of 378 normative issues in 19 topic areas and ranks them (...) according to their prevalence in the literature. The study offers a comprehensive overview for scholars, practitioners, or policymakers, condensing the ethical debates surrounding fairness, safety, harmful content, hallucinations, privacy, interaction risks, security, alignment, societal impacts, and others. We discuss the results, evaluate imbalances in the literature, and explore unsubstantiated risk scenarios. (shrink)

The revolution of artificial intelligence (AI), particularly generative AI, and its implications for human–robot interaction (HRI) opened up the debate on crucial regulatory, business, societal, and ethical considerations. This paper explores essential issues from the anthropomorphic perspective, examining the complex interplay between humans and AI models in societal and corporate contexts. We provided a comprehensive review of existing literature on HRI, with a special emphasis on the impact of generative models such as ChatGPT. The scientometric study posits that due to (...) their advanced linguistic capabilities and ability to mimic human-like behavior, generative AIs like ChatGPT will continue to grow in popularity in pair with human rational empathy, tendency for personification and their advanced linguistic capabilities and ability to mimic human-like behavior. As they blur the boundaries between humans and robots, these models introduce fresh moral and philosophical dilemmas. Our research aims to extrapolate key trends and unique factors in HRI and to elucidate the technical aspects of generative AI that enhance its effectiveness in this field compared to traditional rule-based AI systems. We further discuss the challenges and limitations of applying generative AI in HRI, providing a future research agenda for AI optimization in diverse applications, including education, entertainment, and healthcare. (shrink)

In The Birth of Sense, Don Beith proposes a new concept of generative passivity, the idea that our organic, psychological, and social activities take time to develop into sense. More than being a limit, passivity marks out the way in which organisms, persons, and interbodily systems take time in order to manifest a coherent sense. Beith situates his argument within contemporary debates about evolution, developmental biology, scientific causal explanations, psychology, postmodernism, social constructivism, and critical race theory. Drawing on empirical studies (...) and phenomenological reflections, Beith argues that in nature, novel meaning emerges prior to any type of constituting activity or deterministic plan. The Birth of Sense is an original phenomenological investigation in the style of Maurice Merleau-Ponty, and it demonstrates that the French philosopher's works cohere around the notion that life is radically expressive. While Merleau-Ponty's early works are widely interpreted as arguing for the primacy of human consciousness, Beith argues that a pivotal redefinition of passivity is already under way here, and extends throughout Merleau-Ponty's corpus. This work introduces new concepts in contemporary philosophy to interrogate how organic development involves spontaneous expression, how personhood emerges from this bodily growth, and how our interpersonal human life remains rooted in, and often thwarted by, domains of bodily expressivity. (shrink)

ABSTRACT How can imagination generate knowledge when its contents are voluntarily determined? Several philosophers have recently answered this question by pointing to the constraints that underpin imagination when it plays knowledge-generating roles. Nevertheless, little has been said about the nature of these constraints. In this paper, I argue that the constraints that underpin sensory imagination come from the structure of causal probabilistic generative models, a construct that has been highly influential in recent cognitive science and machine learning. I highlight several (...) attractions of this account, and I favourably contrast it with Peter Langland-Hassan’s account of sensory imagination in terms of the forward models exploited in sensorimotor control. (shrink)

The first part of this collection, “Emerging Life,” concentrates on the question of the origin in Husserlian phenomenology. The second part, “Generations,” is focused on the concreteness of time. The last part of the book, “Homes,” takes space rather than time as the most fundamental phenomenological concept.

The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and (...) shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models. (shrink)

The theory of language must predict the possible thought—signal (or meaning—sound or sign) pairings of a language. We argue for a Meaning First architecture of language where a thought structure is generated first. The thought structure is then realized using language to communicate the thought, to memorize it, or perhaps with another purpose. Our view contrasts with the T-model architecture of mainstream generative grammar, according to which distinct phrase-structural representations—Phonetic Form (PF) for articulation, Logical Form (LF) for interpretation—are generated within (...) the grammar. At the same time, our view differs from early transformational grammar and generative semantics: We view the relationship between the thought structure and the corresponding signal as one of compression. We specify a formal sketch of compression as a choice between multiple possible pronounciations balancing the desire to transmit information against the effort of pronounciation. The Meaning First architecture allows a greater degree of independence between thought structures and the linguistic signal. We present three arguments favoring this type of independence. First we argue that scopal properties can be better explained if we only compare thought structures independent of the their realization as a sentence. Secondly, we argue that Meaning First architecture allows contentful late insertion, an idea that has been argued for in Distributed Morphology already, but as we argue is also motivated by the division of the logical and socio-emotive meaning content of language. Finally, we show that only the Meaning First architecture provides a satisfying account of the mixing of multiple languages by multilingual speakers, especially for cases of simultaneous articulation across two modalities in bimodal speakers. Our view of the structure of grammar leads to a reassessment of priorities in linguistic analyses: while current mainstream work is often focused on establishing one-to-one relationships between concepts and morphemes, our view makes it plausible that primitive concepts are frequently marked indirectly or unpronounced entirely. Our view therefore assigns great value to the understanding of logical primitives and of compression. (shrink)