In this study, we use temporally aligned word embeddings and a large diachronic corpus of English to quantify language change in a data-driven, scalable way, which is grounded in language use. We show a unique and reliable relation between measures of language change and age of acquisition ("AoA") while controlling for frequency,…

Expertise in Chinese character recognition is marked by reduced holistic processing (HP), which depends mainly on writing rather than reading experience. Here we show that, while simplified and traditional Chinese readers demonstrated a similar level of HP when processing characters shared between the simplified and traditional scripts, simplified…

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…

It has been recently argued that some machine learning techniques known as Kernel methods could be relevant for capturing cognitive and neural mechanisms (Jakel, Scholkopf, & Wichmann, 2009). We point out that "String kernels," initially designed for protein function prediction and spam detection, are virtually identical to one contending proposal…

Through computational modeling, here we examine whether visual and task characteristics of writing systems alone can account for lateralization differences in visual word recognition between different languages without assuming influence from left hemisphere (LH) lateralized language processes. We apply a hemispheric processing model of face…