Abstract Artificial language philosophy (also called ‘ideal language philosophy’) is the position that philosophical problems are best solved or dissolved through a reform of language. Its underlying methodology—the development of languages for specific purposes—leads to a conventionalist view of language in general and of concepts in particular. I argue that many philosophical practices can be reinterpreted as applications of artificial language philosophy. In addition, many factually occurring interrelations between the sciences and philosophy of (...) science are justified and clarified by the assumption of an artificial language methodology. (shrink)

When one looks into the role of artificial languages in philosophy of language it seems appropriate to start with making a distinction between philosophy of language proper and formal semantics of natural language. Although the distinction between the two disciplines may not always be easy to make since there arguably exist substantial historical and systematic relationships between the two, it nevertheless pays to keep the two apart, at least initially, since the motivation commonly given for the (...) use of artificial languages in philosophy of language is often rather different from the one that drives the use of such languages in semantics. Of course, this difference in motivation should not blind us for the commonalities that exists between the two disciplines. Philosophy of language and formal semantics have a common history, and arguably also share some of their substance. Philosophy of language is by and large an outgrowth of work in the analytical tradition in philosophy in the first half of the twentieth century. Both ordinary language philosophy, with its emphasis on the description of actual language use, as well as the more logic-oriented and formally inclined school of logical positivism contributed to the definition of philosophy of language as a separate philosophical discipline, with its own set of problems and methods to solve them. Another major contributor to the establishment of philosophy of language as a distinct discipline has been modern linguistics, in particular generative grammar in the tradition of Chomsky that became the dominant paradigm in linguisticsin the fifties and sixties of the previous century. And as it happens, both the generative tradition of Chomsky and analytic philosophy in its formal and less formal guises have been important factors in the development of formal semantics as well. Thus, it should come as no surprise that the two have something in common. That the communalities go beyond a common ancestry, but are reflected in substance and methods as well, will be argued later on. However, be that as it may, it is still a good idea to keep philosophy of language and formal semantics separate, at least initially, since the role that is assigned to artificial languages and the ways in which these languages are employed in both does differ in a number of respects that are worth keeping in mind.. (shrink)

We investigated the emergence of sociolinguistic indexicality using an artificial-language-learning paradigm. Sociolinguistic indexicality involves the association of linguistic variants with nonlinguistic social or contextual features. Any linguistic variant can acquire “constellations” of such indexical meanings, though they also exhibit an ordering, with first-order indices associated with particular speaker groups and higher-order indices targeting stereotypical attributes of those speakers. Much natural-language research has been conducted on this phenomenon, but little experimental work has focused on how indexicality emerges. Here, (...) we present three miniature artificial-language experiments designed to break ground on this question. Results show ready formation of first-order indexicality based on co-occurrence alone, with higher-order indexicality emerging as a result of extension to new speaker groups, modulated by the perceived practical importance of the indexed social feature. (shrink)

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language‐learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners’ input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment (Culbertson, Smolensky, & Legendre, 2012) targeting the learning of word‐order patterns in the nominal domain. The model identifies (...) internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross‐linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners’ inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization—Greenberg's Universal 18—which bans a particular word‐order pattern relating nouns, adjectives, and numerals. (shrink)

Medieval Arabic algebra is a good example of an artificial language.Yet despite its abstract, formal structure, its utility was restricted to problem solving. Geometry was the branch of mathematics used for expressing theories. While algebra was an art concerned with finding specific unknown numbers, geometry dealtwith generalmagnitudes.Algebra did possess the generosity needed to raise it to a more theoretical level—in the ninth century Abū Kāmil reinterpreted the algebraic unknown “thing” to prove a general result. But mathematicians had no (...) motive to rework their theories in algebraic form. Because it offered no advantage over geometry, algebra remained a practical art in both the Islamic world and in Europe until the scientific uphevals of the 17th–18th centuries. (shrink)

Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult learning of an artificial language that contains dependencies between both adjacent and non-adjacent words. We found that learners rapidly acquired both types of regularities and that the strength (...) of the adjacent statistics influenced learning of both adjacent and non-adjacent dependencies. Additionally, though accuracy was similar for both types of structure, participants’ knowledge of the deterministic non-adjacent dependencies was more explicit than their knowledge of the probabilistic adjacent dependencies. The results are discussed in the context of current theories of statistical learning and language acquisition. (shrink)

The relationship is explored between formal derivations, which occur in artificial languages, and mathematical proof, which occurs in natural languages. The suggestion that ordinary mathematical proofs are abbreviations or sketches of formal derivations is presumed false. The alternative suggestion that the existence of appropriate derivations in formal logical languages is a norm for ordinary rigorous mathematical proof is explored and rejected.

Some empirical evidence in the artificial language acquisition literature has been taken to suggest that statistical learning mechanisms are insufficient for extracting structural information from an artificial language. According to the more than one mechanism (MOM) hypothesis, at least two mechanisms are required in order to acquire language from speech: (a) a statistical mechanism for speech segmentation; and (b) an additional rule-following mechanism in order to induce grammatical regularities. In this article, we present a set (...) of neural network studies demonstrating that a single statistical mechanism can mimic the apparent discovery of structural regularities, beyond the segmentation of speech. We argue that our results undermine one argument for the MOM hypothesis. (shrink)

Over half a century ago, George Zipf observed that more frequent words tend to be older. Corpus studies since then have confirmed this pattern, with more frequent words being replaced and regularized less often than less frequent words. Two main hypotheses have been proposed to explain this: that frequent words change less because selection against innovation is stronger at higher frequencies, or that they change less because stochastic drift is stronger at lower frequencies. Here, we report the first experimental test (...) of these hypotheses. Participants were tasked with learning a miniature language consisting of two nouns and two plural markers. Nouns occurred at different frequencies and were subjected to treatments that varied drift and selection. Using a model that accounts for participant heterogeneity, we measured the rate of noun regularization, the strength of selection, and the strength of drift in participant responses. Results suggest that drift alone is sufficient to generate the elevated rate of regularization we observed in low‐frequency nouns, adding to a growing body of evidence that drift may be a major driver of language change. (shrink)

This study investigates the joint influences of three factors on the discovery of new word‐like units in a continuous artificial speech stream: the statistical structure of the ongoing input, the initial word‐likeness of parts of the speech flow, and the contextual information provided by the earlier emergence of other word‐like units. Results of an experiment conducted with adult participants show that these sources of information have strong and interactive influences on word discovery. The authors then examine the ability of (...) different models of word segmentation to account for these results. PARSER (Perruchet & Vinter, 1998) is compared to the view that word segmentation relies on the exploitation of transitional probabilities between successive syllables, and with the models based on the Minimum Description Length principle, such as INCDROP. The authors submit arguments suggesting that PARSER has the advantage of accounting for the whole pattern of data without ad‐hoc modifications, while relying exclusively on general‐purpose learning principles. This study strengthens the growing notion that nonspecific cognitive processes, mainly based on associative learning and memory principles, are able to account for a larger part of early language acquisition than previously assumed. (shrink)

Although philosophising about given languages had been going on ever since the time of Plato's Kratylos, the idea of an artificial philosophical language or system of signs began to take shape in the seventeenth century. Both Descartes and Mersenne explored the ground for the foundations of a system of expressions which could meet all the requirements of logical thought; but the merit of presenting the first elaborate plans goes to the British authors George Dalgarno and John Wilkins. 1 (...) Leibniz followed soon after with his characteristica universalis. (shrink)

Although philosophising about given languages had been going on ever since the time of Plato's Kratylos , the idea of an artificial philosophical language or system of signs began to take shape in the seventeenth century. Both Descartes and Mersenne explored the ground for the foundations of a system of expressions which could meet all the requirements of logical thought; but the merit of presenting the first elaborate plans goes to the British authors George Dalgarno and John Wilkins. (...) 1 Leibniz followed soon after with his characteristica universalis. (shrink)

I try to show that there is much sap (synthetic a priori) knowledge although one may not find many, or even any, sap true statements in most natural languages. Reasons are given for the difficulty of expressing sap truths in natural languages, but it is argued that these are not necessary features of language as such. There are, then, sap true statements in some possible languages.Admission of the sap gives one a way of distinguishing logical from metaphysical possiblity. Something (...) is metaphysically impossible just in case it is a sap truth that it is impossible. I argue that the realm of logically possible entities is vastly larger than the realm of metaphysically possible entities.My.defence of the sap begins with a partial defence of the analytic/synthetic distinction. In particular, I attack the intentionality circle argument,Quine's indeterminacy thesis and some applications of the Kripke/Putnam theory of meaning for natural kind terms. Without being a conclusive defence of the analytic/synthetic distinction, this section makes the defence of the sap more secure. Arguments are then mounted in direct defence of the sap by showing that the purported reduction of sap truths to analytic truths is mistaken. Examples are adduced to show that, even where expressions of natural language fail, one can stipulate usage in ways that allow expression of sap truths. I then consider and reply to various objections, giving an example of the sort of meaning theory which illegitimately legislates the analyticity of any purported sap true statement. (shrink)

Artificial language learning experiments have become an important tool in exploring principles of language and language learning. A persistent question in all of this work, however, is whether ALL engages the linguistic system and whether ALL studies are ecologically valid assessments of natural language ability. In the present study, we considered these questions by examining the relationship between performance in an ALL task and second language learning ability. Participants enrolled in a Spanish language (...) class were evaluated using a number of different measures of Spanish ability and classroom performance, which was compared to IQ and a number of different measures of ALL performance. The results show that success in ALL experiments, particularly more complex artificial languages, correlates positively with indices of L2 learning even after controlling for IQ. These findings provide a key link between studies involving ALL and our understanding of second language learning in the classroom. (shrink)

Wittgenstein’s Private Language Argument holds that language requires rule-following, rule following requires the possibility of error, error is precluded in pure introspection, and inner mental life is known only by pure introspection, thus language cannot exist entirely within inner mental life. Fodor defends his Language of Thought program against the Private Language Argument with a dilemma: either privacy is so narrow that internal mental life can be known outside of introspection, or so broad that computer (...) language serves as a counter-example. I suggest that the developing field of artificial intelligence (deep learning neural networks) tends to vitiate Fodor’s defense and hence vindicate the Private Language Argument. The first horn of Fodor’s dilemma requires language to encompass genuinely internal mental life, i.e. non-projected intentional states, which are not exhibited in classical machine learning but only by deep learning neural networks (artificial intelligence). Such networks act as black boxes, however, whose state cannot be understood by tracking the changes in their supervenience bases without shared context, and that shared context introduces the possibility of error. The language of artificial intelligence is not private. (shrink)

This series will include monographs and collections of studies devoted to the investigation and exploration of knowledge, information and data-processing systems of all kinds, no matter whether human, (other) animal or machine. Its scope is intended to span the full range of interests from classical problems in the philosophy of mind and phi losophical psychology through issues in cognitive psychology and socio biology (concerning the mental capabilities of other species) to ideas related to artificial intelligence and computer science. While (...) primary emphasis will be placed upon theoretical, conceptual and epistemologi cal aspects of these problems and domains, empirical, experimental and methodological studies will also appear from time to time. Among the most challenging and difficult projects within the scope of artificial intelligence is the development and implementation of com puter programs suitable for processing natural language. Our purpose in compiling the present volume has been to contribute to the foundations of this enterprise by bringing together classic papers devoted to crucial problems involved in understanding natural language, which range from issues of formal syntax and logical form to those of possible-worlds and situation semantics. The book begins with a comprehensive introduc tion composed by Jack Kulas, the senior editor of this work, which pro vides a systematic orientation to this complex field, and ends with a selected bibliography intended to promote further research. If our efforts assist others in dealing with these problems, they will have been worthwhile. J. H. F. (shrink)

This paper studies the relationship of Artificial Intelligence to the study of language and the representation of the underlying knowledge which supports the comprehension process. It develops the view that intelligence is based on the ability to use large amounts of diverse kinds of knowledge in procedural ways, rather than on the possession of a few general and uniform principles. The paper also provides a unifying thread to a variety of recent approaches to natural language comprehension. We (...) conclude with a brief discussion of how Artificial Intelligence may have a radical impact on education if the principles which it utilizes to explore the representation and use of knowledge are made available to the student to use in his own learning experiences. (shrink)

The foundations of AI design discourse are worth analyzing. Here, attention is paid to the nature of theory languages used in designing new AI technologies because the limits of these languages can clarify some fundamental questions in the development of AI. We discuss three types of theory language used in designing AI products: formal, computational, and natural. Formal languages, such as mathematics, logic, and programming languages, have fixed meanings and no actual-world semantics. They are context- and practically content-free. Computational (...) languages use terms referring to the actual world, i.e., to entities, events, and thoughts. Thus, computational languages have actual-world references and semantics. They are thus no longer context- or content-free. However, computational languages always have fixed meanings and, for this reason, limited domains of reference. Finally, unlike formal and computational languages, natural languages are creative, dynamic, and productive. Consequently, they can refer to an unlimited number of objects and their attributes in an unlimited number of domains. The differences between the three theory languages enable us to reflect on the traditional problems of strong and weak AI. (shrink)

Large-scale language technologies are increasingly used in various forms of communication with humans across different contexts. One particular use case for these technologies is conversational agents, which output natural language text in response to prompts and queries. This mode of engagement raises a number of social and ethical questions. For example, what does it mean to align conversational agents with human norms or values? Which norms or values should they be aligned with? And how can this be accomplished? (...) In this paper, we propose a number of steps that help answer these questions. We start by developing a philosophical analysis of the building blocks of linguistic communication between conversational agents and human interlocutors. We then use this analysis to identify and formulate ideal norms of conversation that can govern successful linguistic communication between humans and conversational agents. Furthermore, we explore how these norms can be used to align conversational agents with human values across a range of different discursive domains. We conclude by discussing the practical implications of our proposal for the design of conversational agents that are aligned with these norms and values. (shrink)

How did human vocalizations come to acquire meaning in the evolution of our species? Charles Darwin proposed that language and music originated from a common emotional signal system based on the imitation and modification of sounds in nature. This protolanguage is thought to have diverged into two separate systems, with speech prioritizing referential functionality and music prioritizing emotional functionality. However, there has never been an attempt to empirically evaluate the hypothesis that a single communication system can split into two (...) functionally distinct systems that are characterized by music- and languagelike properties. Here, we demonstrate that when referential and emotional functions are introduced into an artificial communication system, that system will diverge into vocalization forms with speech- and music-like properties, respectively. Participants heard novel vocalizations as part of a learning task. Half referred to physical entities and half functioned to communicate emotional states. Participants then reproduced each sound with the defined communicative intention in mind. Each recorded vocalization was used as the input for another participant in a serial reproduction paradigm, and this procedure was iterated to create 15 chains of five participants each. Referential vocalizations were rated as more speech-like, whereas emotional vocalizations were rated as more music-like, and this association was observed cross-culturally. In addition, a stable separation of the acoustic profiles of referential and emotional vocalizations emerged, with some attributes diverging immediately and others diverging gradually across iterations. The findings align with Darwin’s hypothesis and provide insight into the roles of biological and cultural evolution in the divergence of language and music. (shrink)

Artificial intelligence, conceived either as an attempt to provide models of human cognition or as the development of programs able to perform intelligent tasks, is primarily interested in theuses of language. It should be concerned, therefore, withpragmatics. But its concern with pragmatics should not be restricted to the narrow, traditional conception of pragmatics as the theory of communication (or of the social uses of language). In addition to that, AI should take into account also the mental uses (...) of language (in reasoning, for example) and the existential dimensions of language as a determiner of the world we (and our computers) live in. In this paper, the relevance of these three branches of pragmatics-sociopragmatics, psychopragmatics, and ontopragmatics-for AI are explored. (shrink)

This paper explores the challenges posed by the rapid advancement of artificial intelligence specifically Large Language Models (LLMs). I show that traditional linguistic theories and corpus studies are being outpaced by LLMs’ computational sophistication and low perplexity levels. In order to address these challenges, I suggest a focus on language as a cognitive tool shaped by embodied-environmental imperatives in the context of _Agentive Cognitive Construction Grammar_. To that end, I introduce an _Embodied Human Language_ Model (EHLM), inspired (...) by Active Inference research, as a promising alternative that integrates sensory input, embodied representations, and adaptive strategies for contextualized analysis and conceptual utility maximization. By incorporating Active Inference, which sees perception as inferring the world's state from sensory data, the findings reveal that the characterization of the English modal _be able to_, as a triadic construction encoding biological intelligent agency, introduces a more plausible theoretical basis for the positing of linguistic constructions. This emphasizes the crucial role of embodied human language models in the comprehension of how humans construct preferred futures through language. (shrink)