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.

Abstract

In the contextual cueing paradigm, Endo and Takeda (in Percept Psychophys 66:293–302, 2004) provided evidence that implicit learning involves selection of the aspect of a structure that is most useful to one’s task. The present study attempted to replicate this finding in artificial grammar learning to investigate whether or not implicit learning commonly involves such a selection. Participants in Experiment 1 were presented with an induction task that could be facilitated by several characteristics of the exemplars. For some participants, those characteristics included a perfectly predictive feature. The results suggested that the aspect of the structure that was most useful to the induction task was selected and learned implicitly. Experiment 2 provided evidence that, although salience affected participants’ awareness of the perfectly predictive feature, selection for implicit learning was mainly based on usefulness.