The pattern of implicatures of modified numeral `more than n' depends on the roundness of n. Cummins, Sauerland, and Solt (2012) present experimental evidence for the relation between roundness and implicature patterns, and propose a pragmatic account of the phenomenon. More recently, Hesse and Benz (2020) present more extensive evidence showing that implicatures also depend on the magnitude of n and propose a novel explanation based on the Approximate Number System (Dehaene 1999). Despite the wealth of experimental data, no formal account has yet been proposed to characterize the full posterior distribution over numbers of a listener after hearing `more than n'. We develop one such account within the Rational Speech Act framework, quantitatively reconstructing the pragmatic reasoning of a rational listener. We show that our pragmatic account correctly predicts various features of the experimental data.

A foundational hypothesis in cognitive science is that some of human thinking happens in a language of thought (LoT), which is universal across humans (Fodor, 1975). According to this hypothesis, words in different natural languages are labels for primitive concepts or their combinations in LoT. What are LoT's primitives? This is a major challenge because LoT is not directly observable, and thus needs to be inferred or 'reverse-engineered'. We put forward a novel approach to reverse-engineering LoT, capitalizing on the existing knowledge about the optimization of the trade-off between complexity and informativeness in natural languages.

We present a natural logic for reasoning with quanti-fiers that can predict human performance in appro-priate reasoning tasks. The model is an extension ofthat in (Geurts, 2003) but allows for better fit withdata on syllogistic reasoning and is extended to ac-count for reasoning with iterated quantifiers. Weassign weights to inference rules and operationalizethe complexity of a reasoning pattern as weightedlength of proof in our logic – this results in a measureof complexity that outperforms other models in theirpredictive capacity and allows for the derivation ofempirically testable hypotheses.

We extend universal moral grammar theory (UMGT) with nonmonotonic logic. Our experiment shows that such revision is necessary as it allows to account for the effects of alleviations and aggravations in moral reasoning. Our new theory updates UMGT from classical to non-monotonic logic, which reflects the incompleteness of information and uncertainty in actual human reasoning. In addition, it provides an explanation of the paradoxical findings in the moral dilemma of the Trolley problem and the Knobe effect

The vocabulary of human languages has been argued to support efficient communication by optimizing the trade-offbetween complexity and informativeness (Kemp and Regier, 2012). The argument has been based on cross-linguisticanalyses of vocabulary in semantic domains of content words such as kinship, color, and number terms. The present workextends this analysis to a category of function words: indefinite pronouns (e.g. someone, anyone, no-one, cf. Haspelmath,2001). We establish the meaning space and feature-based representations for indefinite pronouns, and show that indefinitepronoun systems across languages optimize the complexity/informativeness trade-off. This demonstrates that pressuresfor efficient communication shape both content and function word categories, thus tying in with the conclusions of recentwork on quantifiers (Steinert-Threlkeld, 2019). Furthermore, we argue that the trade-off may explain some of the universalproperties of indefinite pronouns, thus reducing the explanatory load for linguistic theories.

The Interface Transparency Thesis (ITT) proposes that people tend to use a canonical interpretation of linguistic expressions, even when this interpretation is sub-optimal for the task at hand. The current paper sought to investigate this claim further by adding a time-pressure manipulation to a quantified sentence verification task and analyzing the results through a computational model of decision-making. The results indicate that time pressure -while effectively changing behavioral responses- does not alter cognitive processes associated with quantifier verification, thus supporting the ITT.