Publications

Abstract

In the word-learning domain, both adults and young children are able to find the correct referent of a word from highly ambiguous contexts that involve many words and objects by computing distributional statistics across the co-occurrences of words and referents at multiple naming moments (Yu & Smith, 2007; Smith & Yu, 2008). However, there is still debate regarding how learners accumulate distributional information to learn object labels in natural learning environments, and what underlying learning mechanism learners are most likely to adopt. Using the Human Simulation Paradigm (Gillette, Gleitman, Gleitman & Lederer, 1999), we found that participants’ learning performance gradually improved and that their ability to remember and carry over partial knowledge from past learning instances facilitated subsequent learning. These results support the statistical learning model that word learning is a continuous process.

Abstract

In informal interaction, speakers rarely thank a person who has complied with a request. Examining data from British English, German, Italian, Polish, and Telugu, we ask when speakers do thank after compliance. The results show that thanking treats the other’s assistance as going beyond what could be taken for granted in the circumstances. Coupled with the rareness of thanking after requests, this suggests that cooperation is to a great extent governed by expectations of helpfulness, which can be long-standing, or built over the course of a particular interaction. The higher frequency of thanking in some languages (such as English or Italian) suggests that cultures differ in the importance they place on recognizing the other’s agency in doing as requested.