Publications

Abstract

Hierarchical centre-embedded structures pose a large difficulty for language learners due to their complexity. A recent artificial grammar learning study (Lai & Poletiek, 2011) demonstrated a starting-small (SS) effect, i.e., staged-input and sufficient exposure to 0-level-of-embedding exemplars were the critical conditions in learning AnBn structures. The current study aims to test: (1) a more sophisticated type of SS (a gradually rather than discretely growing input), and (2) the frequency distribution of the input. The results indicate that SS optimally works under other conditional cues, such as a skewed frequency distribution with simple stimuli being more numerous than complex ones.

Abstract

Previous research suggests that lie detection can be improved by asking the interviewee unexpected questions. The present experiment investigates the effect of two types of unexpected questions: background questions and detail questions, on detecting lies about topics with which the interviewee is (a) familiar or (b) unfamiliar. In this experiment, 66 participants read interviews in which interviewees answered background or detail questions, either truthfully or deceptively. Those who answered deceptively could be lying about a topic they were familiar with or about a topic they were unfamiliar with. The participants were asked to judge whether the interviewees were lying. The results revealed that background questions distinguished truths from both types of lies, while the detail questions distinguished truths from unfamiliar lies, but not from familiar lies. The implications of these findings are discussed.

Abstract

A theoretical debate in artificial grammar learning (AGL) regards the learnability of hierarchical structures. Recent studies using an AnBn grammar draw conflicting conclusions (Bahlmann and Friederici, 2006, De Vries et al., 2008). We argue that 2 conditions crucially affect learning AnBn structures: sufficient exposure to zero-level-of-embedding (0-LoE) exemplars and a staged-input. In 2 AGL experiments, learning was observed only when the training set was staged and contained 0-LoE exemplars. Our results might help understanding how natural complex structures are learned from exemplars.