Information density as a predictor of communication dynamics.

In a recent paper, Aceves and Evans computed information and semantic density measures for hundreds of languages, and showed that these measures predict the pace and breadth of ideas in communication. Here, we summarize their key findings and situate them in a broader debate about the adaptive nature of language.

Large Language Models Demonstrate the Potential of Statistical Learning in Language.

To what degree can language be acquired from linguistic input alone? This question has vexed scholars for millennia and is still a major focus of debate in the cognitive science of language. The complexity of human language has hampered progress because studies of language-especially those involving computational modeling-have only been able to deal with small fragments of our linguistic skills. We suggest that the most recent generation of Large Language Models (LLMs) might finally provide the computational tools to determine empirically how much of the human language ability can be acquired from linguistic experience.

Models of Language and Multiword Expressions.

Traditional accounts of language postulate two basic components: words stored in a lexicon, and rules that govern how they can be combined into meaningful sentences, a grammar. But, although this words-and-rules framework has proven itself to be useful in natural language processing and cognitive science, it has also shown important shortcomings when faced with actual language use. In this article, we review evidence from language acquisition, sentence processing, and computational modeling that shows how multiword expressions such as idioms, collocations, and other meaningful and common units that comprise more than one word play a key role in the organization of our linguistic knowledge.

Correction to 'Open science and modified funding lotteries can impede the natural selection of bad science'.

[This corrects the article DOI: 10.1098/rsos.190194.

Open science and modified funding lotteries can impede the natural selection of bad science.

Assessing scientists using exploitable metrics can lead to the degradation of research methods even without any strategic behaviour on the part of individuals, via 'the natural selection of bad science.' Institutional incentives to maximize metrics like publication quantity and impact drive this dynamic. Removing these incentives is necessary, but institutional change is slow.

Interacting Timescales in Perspective-Taking.

Through theoretical discussion, literature review, and a computational model, this paper poses a challenge to the notion that perspective-taking involves a fixed architecture in which particular processes have priority. For example, some research suggests that egocentric perspectives can arise more quickly, with other perspectives (such as of task partners) emerging only secondarily. This theoretical dichotomy-between fast egocentric and slow other-centric processes-is challenged here.