Shane Steinert-Threlkeld

How did the very first meaning components arise in animals? We argue that answers interact in interesting ways with data on current and ancestral animal communication systems. Using standard notions of evolutionary stability in biology, we develop a simple framework to analyze the emergence of three meaning components: individual signals, nontrivial combinations, and pragmatic principles of competition among signals. We show that for elementary signals to arise, they should have null cost, or be understood from the start. While this conclusion dovetails with the traditional idea that signals often originate in cues, i.e., informative byproducts of non-communicative processes, the two scenarios (null cost vs. understanding from the start) can be distinguished in case studies involving ancestral meaning reconstruction. For nontrivial combinations of the form CC’ (such as pyow-hack sequences in putty-nosed monkeys and ABC-D sequences in Japanese tits), we show that their emergence is heavily constrained because they should initially give rise to some miscommunication, as CC’ could also be understood as the (trivial) combination of separate utterances C and C’. Finally, we investigate the evolution of two pragmatic principles that were posited in recent animal linguistics: the Informativity Principle and the Urgency Principle. We argue that both have a clear evolutionary path, especially if they start appearing in production, and then in comprehension. Overall, recent work in animal linguistics can be fruitfully combined with simple principles of evolutionary stability and with ancestral signal reconstruction to address in a precise fashion questions about the very first meaning operations in nature.

While recent “animal linguistics” treats call form as arbitrary, various results suggest that some animals use a biological code to understand the calls of unrelated/unfamiliar species. To clarify matters, we distinguish among three degrees of interspecies comprehension. In the first (“Understand thy neighbor”), a species understands the calls of a neighboring species through exposure. In the second (“call convergence”), it understands the calls of an unrelated/unfamiliar species through evolutionary convergence and resemblance to familiar calls. In the third degree (“featural interpretation”), it uses a rule associating a meaning to a specific acoustic feature—hence a new road to (featural) compositionality.

According to logical theories of meaning, a meaning of an expression can be formalized and encoded in truth conditions. Vagueness of the language and individual differences between people are a challenge to incorporate into the meaning representations. In this paper, we propose a new approach to study truth-conditional representations of vague concepts. For a case study, we selected two natural language quantifiers most and more than half. We conducted two online experiments, each with 90 native English speakers. In the first experiment, we tested between-subjects variability in meaning representations. In the second experiment, we tested the stability of meaning representations over time by testing the same group of participants in two experimental sessions. In both experiments, participants performed the verification task. They verified a sentence with a quantifier (e.g., “Most of the gleerbs are feezda.”) based on the numerical information provided in the second sentence, (e.g., “60% of the gleerbs are feezda”). To investigate between-subject and within-subject differences in meaning representations, we proposed an extended version of the Diffusion Decision Model with two parameters capturing truth conditions and vagueness. We fit the model to responses and reaction times data. In the first experiment, we found substantial between-subject differences in representations of most as reflected by the variability in the truth conditions. Moreover, we found that the verification of most is proportion-dependent as reflected in the reaction time effect and model parameter. In the second experiment, we showed that quantifier representations are stable over time as reflected in stable model parameters across two experimental sessions. These findings challenge semantic theories that assume the truth-conditional equivalence of most and more than half and contribute to the representational theory of vague concepts. The current study presents a promising approach to study semantic representations, which can have a wide application in experimental linguistics.

Philosophy of emotions has become an established sub-discipline of philosophy, and emotions are no longer exclusively seen as disturbances that threaten our rational faculties. Philosophers now take seriously the multi-facetted relation between emotion, knowledge, and reason. Laura Candiotto's edited volume on emotions and their role in epistemic practice brings together texts that look at this relation from different angles and from different traditions. The volume includes texts that zoom in on a wide variety of themes, like agency, emotion regulation, group emotions and social epistemology, cognitive science, and pragmatist accounts of emotions. The book will be interesting not only for those engaging with philosophy of emotions but...

Natural languages exhibit manysemantic universals, that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents.

In recent years, the methods of formal semantics and pragmatics have been fruitfully applied to the analysis of primate communication systems. Most analyses therein appeal to a division of labor between semantics and pragmatics which has the following three features: calls are given referential meanings, some calls have a general meaning, and the meanings of calls in context are enriched by competition with more informative calls, along the lines of scalar implicatures. In this paper, we develop highly simplified models to independently assess the conditions under which such features would emerge. After identifying a sufficient condition for, we find a range of conditions under which and are not evolutionarily stable, and discuss the consequences for both modeling and empirical work.

Expressivists about epistemic modals deny that ‘Jane might be late’ canonically serves to express the speaker’s acceptance of a certain propositional content. Instead, they hold that it expresses a lack of acceptance. Prominent expressivists embrace pragmatic expressivism: the doxastic property expressed by a declarative is not helpfully identified with that sentence’s compositional semantic value. Against this, we defend semantic expressivism about epistemic modals: the semantic value of a declarative from this domain is the property of doxastic attitudes it canonically serves to express. In support, we synthesize data from the critical literature on expressivism—largely reflecting interactions between modals and disjunctions—and present a semantic expressivism that readily predicts the data. This contrasts with salient competitors, including: pragmatic expressivism based on domain semantics or dynamic semantics; semantic expressivism à la Moss [2015]; and the bounded relational semantics of Mandelkern [2019].

The semantic automata framework, developed originally in the 1980s, provides computational interpretations of generalized quantifiers. While recent experimental results have associated structural features of these automata with neuroanatomical demands in processing sentences with quantifiers, the theoretical framework has remained largely unexplored. In this paper, after presenting some classic results on semantic automata in a modern style, we present the first application of semantic automata to polyadic quantification, exhibiting automata for iterated quantifiers. We also discuss the role of semantic automata in linguistic theory and offer new empirical predictions for sentence processing with embedded quantifiers.

A semantic universal, which we here dub the Veridical Uniformity Universal, has recently been argued to hold of responsive verbs (those that take both declarative and interrogative complements). This paper offers a preliminary explanation of this universal: verbs satisfying it are easier to learn than those that do not. This claim is supported by a computational experiment using artificial neural networks, mirroring a recent proposal for explaining semantic universals of quantifiers. This preliminary study opens up many avenues for future work on explaining semantic universals more generally, which are discussed in the conclusion.

One of the great successes of the application of generalized quantifiers to natural language has been the ability to formulate robust semantic universals. When such a universal is attested, the question arises as to the source of the universal. In this paper, we explore the hypothesis that many semantic universals arise because expressions satisfying the universal are easier to learn than those that do not. While the idea that learnability explains universals is not new, explicit accounts of learning that can make good on this hypothesis are few and far between. We propose a model of learning — back-propagation through a recurrent neural network — which can make good on this promise. In particular, we discuss the universals of monotonicity, quantity, and conservativity and perform computational experiments of training such a network to learn to verify quantifiers. Our results are able to explain monotonicity and quantity quite well. We suggest that conservativity may have a different source than the other universals.

Natural language expressions fall into two categories: content and function words. While function words are essential to compositional semantics, surprisingly little has been said about their emergence. In this paper, I will show that most extant approaches to the emergence of compositional signaling fail to account for the emergence of functional vocabulary. After providing a result that explains why this is so,, I will present a model and simulation results exhibiting conditions under which such vocabulary can emerge from simple learning dynamics. This model captures the intuition that function words help aid communication with a limited vocabulary in the presence of contextual variability.

A special kind of substitution on languages called iteration is presented and studied. These languages arise in the application of semantic automata to iterations of generalized quantifiers. We show that each of the star-free, regular, and deterministic context-free languages are closed under iteration and that it is decidable whether a given regular or determinstic context-free language is an iteration of two such languages. This result can be read as saying that the van Benthem/Keenan ‘Frege Boundary’ is decidable for large subclasses of natural language quantifiers. We also determine the state complexity of iteration of regular languages.

We investigate, in a logical setting, the expressivist proposal that assertion primarily functions to express and coordinate doxastic states and that ‘might’ fundamentally expresses lack of belief. We provide a formal model of an agent’s doxastic state and novel assertability conditions for an associated formal language. We thereby prove that an arbitrary assertion always succeeds in expressing a well-defined doxastic state, and propose a fully general and intuitive update operation as a model of an agent coming to accept an arbitrary assertion. Leaving a comprehensive philosophical and linguistic defense for elsewhere, we explore technical aspects of our framework, providing, for instance, a complete logic of assertability and reduction axioms for the novel update operations related to our proposal. Finally, we contrast our work with related proposals in the logic literature.

Natural languages are compositional in that the meaning of complex expressions depends on those of the parts and how they are put together. Here, I ask the following question: why are languages compositional? I answer this question by extending Lewis–Skyrms signaling games with a rudimentary form of compositional signaling and exploring simple reinforcement learning therein. As it turns out: in complex worlds, having compositional signaling helps simple agents learn to communicate. I am also able to show that learning the meaning of a function word, once meanings of atomic words are known, presents no difficulty.

The semantic automata framework, developed originally in the 1980s, provides computational interpretations of generalized quantifiers. While recent experimental results have associated structural features of these automata with neuroanatomical demands in processing sentences with quantifiers, the theoretical framework has remained largely unexplored. In this paper, after presenting some classic results on semantic automata in a modern style, we present the first application of semantic automata to polyadic quantification, exhibiting automata for iterated quantifiers. We also discuss the role of semantic automata in linguistic theory and offer new empirical predictions for sentence processing with embedded quantifiers