Decisions over the timing of actions are critical in severalsafety, security and healthcare scenarios. These decisions, sim-ilar to discrete decisions, can be influenced by biases and in-dividual traits. In this paper, a bias of impatience is studiedin an experiment with 626 participants, with a focus on gen-der differences. Impatience was moderated with a manipula-tion of a variable-speed countdown. Men and women differedin how they expressed impatience. While men systematicallyand irrationally act earlier when become impatient followingthe slower countdowns, women react by irrationally request-ing earlier information about the outcome of each trial, andimpulsively pressing an inactive key.

Decisions over the timing of actions are critical in severalsafety, security and healthcare scenarios. These decisions, sim-ilar to discrete decisions, can be influenced by biases and in-dividual traits. In this paper, a bias of impatience is studiedin an experiment with 626 participants, with a focus on gen-der differences. Impatience was moderated with a manipula-tion of a variable-speed countdown. Men and women differedin how they expressed impatience. While men systematicallyand irrationally act earlier when become impatient followingthe slower countdowns, women react by irrationally request-ing earlier information about the outcome of each trial, andimpulsively pressing an inactive key.

We investigated how the language background (L1) of bilinguals influences the representation and use of the second language (L2) through computational models. With the essays part from The International Corpus Network of Asian Learners of English (ICNALE), we compared variables indicating syntactic complexity in their L2 production to predict L1. We then trained neural language models based on BERT to predict the L1 of these English learners. Results showed the systematic influence of L1 syntax properties on English learners' L2 production, which further confirmed integrations of syntactic knowledge across languages in bilingual speakers. Results also found neural models can learn to represent and detect such L1 impacts, while multilingually trained models have no advantage in doing so.

We propose an augmented word embedding model that better incorporates subword information with additional parametersthat characterize the semantic weights of characters in composing words. Our model can reveal some interesting patternsof long-term change in Chinese language, which provides novel evidence and methodology that enriches existing theoriesin evolutionary linguistics. The resulting word vectors also has decent performance in NLP-related tasks.

Native speakers can judge whether a sentence is an acceptableinstance of their language. Acceptability provides a means ofevaluating whether computational language models are pro-cessing language in a human-like manner. We test the abilityof language models, simple language features, and word em-beddings to predict native speakers’ judgments of acceptabil-ity on English essays written by non-native speakers. We findthat much sentence acceptability variance can be captured by acombination of misspellings, word order, and word similarity(r = 0.494). While predictive neural models fit acceptabilityjudgments well (r = 0.527), we find that a 4-gram model isjust as good (r = 0.528). Thanks to incorporating misspellings,our 4-gram model surpasses both the previous unsupervisedstate-of-the art (r = 0.472), and the average native speaker(r = 0.46), demonstrating that acceptability is well capturedby n-gram statistics and simple language features.