Monotone Quantifiers Emerge via Iterated Learning

Carcassi, Fausto; Steinert‐Threlkeld, Shane; Szymanik, Jakub

doi:10.1111/cogs.13027

Cited by 8 publications

(7 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, we define a graded measure of monotonicity, which we can check for correlation with optimality. (See References [57,58], which show that this measure also increases over time during iterated learning. )…”

Section: Monotonicitymentioning

confidence: 99%

“…To see how this measure tracks intuitions, References [57,58] reports results for the previously mentioned quantifiers "some", "between 3 and 5", and "an even number of" on all models of a fixed size. "Some" gets monotonicity 1.0 because knowing whether a structure has a substructure that verifies "some" eliminates all uncertainty about the truth of the structure.…”

Section: Monotonicitymentioning

confidence: 99%

See 1 more Smart Citation

Quantifiers in Natural Language: Efficient Communication and Degrees of Semantic Universals

Steinert‐Threlkeld

2021

Entropy

View full text Add to dashboard Cite

While the languages of the world vary greatly, they exhibit systematic patterns, as well. Semantic universals are restrictions on the variation in meaning exhibit cross-linguistically (e.g., that, in all languages, expressions of a certain type can only denote meanings with a certain special property). This paper pursues an efficient communication analysis to explain the presence of semantic universals in a domain of function words: quantifiers. Two experiments measure how well languages do in optimally trading off between competing pressures of simplicity and informativeness. First, we show that artificial languages which more closely resemble natural languages are more optimal. Then, we introduce information-theoretic measures of degrees of semantic universals and show that these are not correlated with optimality in a random sample of artificial languages. These results suggest both that efficient communication shapes semantic typology in both content and function word domains, as well as that semantic universals may not stand in need of independent explanation.

show abstract

Section: Monotonicitymentioning

confidence: 99%

Section: Monotonicitymentioning

confidence: 99%

Quantifiers in Natural Language: Efficient Communication and Degrees of Semantic Universals

Steinert‐Threlkeld

2021

Entropy

View full text Add to dashboard Cite

show abstract

“…Also, they concluded that monotone quantifiers consistently evolved in the iterated learning model, suggesting that the propensity for monotonicity in natural language quantifiers could be explained by the iterated learning process. Moreover, they found that these evolved quantifiers often did not rely on the identity of specific individuals, aligning with another semantic universal, the universal of quantity [14]. Therefore, this outcome supports the hypothesis that certain properties of natural language quantifiers, like monotonicity and quantity, may arise from the cognitive biases of learners and the dynamics of cultural transmission.…”

Section: Monotonicity Universal Of Quantifiersmentioning

confidence: 54%

Challenging the Monotonicity Universal Theory of Quantifiers: A Cross-Linguistic Analysis of Monomorphemic Quantifiers in English, Japanese, and French

Zhang

2024

CHR

View full text Add to dashboard Cite

The paper aims to criticize the monotonicity universal theory of quantifiers to question the universal applicability of the monotonicity theory. A qualitative analysis method was employed, focusing on counterexamples of monomorphemic quantifiers from English, Japanese, and French to critically evaluate the monotonicity universal theory by analyzing these quantifiers behavior and examining the implications of findings on the theorys validity. The significant finding was that not all monomorphemic quantifiers, such as few in English, peu in French, and in Japanese, are monotonic. Therefore, this result contradicted the existing claim that all monomorphemic natural language quantifiers are monotonic (upward monotonic, downward monotonic, or non-monotonic), challenging the universal of the monotonicity theory of quantifiers. The research concludes that the monotonicity universal theory of quantifiers does not universally apply to all monomorphemic natural language quantifiers and underscores the theorys one-sidedness and imperfections. Hence, this work highlights the need for a broader approach to understanding semantic universal and quantifiers behavior in natural languages.

show abstract

“…The iterated learning paradigm is one way to study the transmission of any type of cultural knowledge, which makes it an ideal tool to experimentally study language evolution and the emergence of structure. In this paradigm, agents (computational, e.g., Navarro, Perfors, Kary, Brown, & Donkin, 2018; Carcassi, Steinert‐Threlkeld, & Szymanik, 2021; or human, e.g., Raviv & Arnon, 2018) are taught an artificial language, which they are then asked to produce. The artificial language begins with a generation of completely random (i.e., linguistically unstructured) input.…”

Section: Introductionmentioning

confidence: 99%

From One Bilingual to the Next: An Iterated Learning Study on Language Evolution in Bilingual Societies

et al. 2023

View full text Add to dashboard Cite

Studies of language evolution in the lab have used the iterated learning paradigm to show how linguistic structure emerges through cultural transmission—repeated cycles of learning and use across generations of speakers . However, agent‐based simulations suggest that prior biases crucially impact the outcome of cultural transmission. Here, we explored this notion through an iterated learning study of English‐French bilingual adults (mostly sequential bilinguals dominant in English). Each participant learned two unstructured artificial languages in a counterbalanced fashion, one resembling English, another resembling French at the phono‐orthographic level. The output of each participant was passed down to the next participant, forming diffusion chains of 10 generations per language. We hypothesized that artificial languages would become easier to learn and exhibit greater structure when they were aligned with participants’ bilingual experience (i.e., English languages being easier to learn overall), or as a function of practice (i.e., languages learned second being easier to learn overall). Instead, we found that English‐like languages became more structured over generations, but only when they were learned first. In contrast, French‐like languages became more structured regardless of the order of learning, suggesting the presence of an asymmetric switch cost during artificial language learning. Moreover, individual differences in language usage modulated the amount of structure produced by the participants. Overall, these data suggest that bilingual experience impacts how novel languages are learned at an individual level, which can then scale up to cultural transmission of novel language at a group level.

show abstract

Monotone Quantifiers Emerge via Iterated Learning

Cited by 8 publications

References 43 publications

Quantifiers in Natural Language: Efficient Communication and Degrees of Semantic Universals

Quantifiers in Natural Language: Efficient Communication and Degrees of Semantic Universals

Challenging the Monotonicity Universal Theory of Quantifiers: A Cross-Linguistic Analysis of Monomorphemic Quantifiers in English, Japanese, and French

From One Bilingual to the Next: An Iterated Learning Study on Language Evolution in Bilingual Societies

Contact Info

Product

Resources

About