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

Adults and children acquire knowledge of the structure of their environment on the basis of repeated exposure to samples of structured stimuli. In the study of inductive learning, a straightforward issue is how much sample information is needed to learn the structure. The present study distinguishes between two measures for the amount of information in the sample: set size and the extent to which the set of exemplars statistically covers the underlying structure. In an artificial grammar learning experiment, learning was affected by the sample’s statistical coverage of the grammar, but not by its mere size. Our result suggests an alternative explanation of the set size effects on learning found in previous studies (McAndrews & Moscovitch, 1985; Meulemans & Van der Linden, 1997), because, as we argue, set size was confounded with statistical coverage in these studies. nt]mis|This research was supported by a grant from the Netherlands Organization for Scientific Research. We thank Jarry Porsius for his help with the data analyses.

Abstract

Learning local regularities in sequentially structured materials is typically assumed to be based on encoding of the frequencies of these regularities. We explore the view that transitional probabilities between elements of chunks, rather than frequencies of chunks, may be the primary factor in artificial grammar learning (AGL). The transitional probability model (TPM) that we propose is argued to provide an adaptive and parsimonious strategy for encoding local regularities in order to induce sequential structure from an input set of exemplars of the grammar. In a variant of the AGL procedure, in which participants estimated the frequencies of bigrams occurring in a set of exemplars they had been exposed to previously, participants were shown to be more sensitive to local transitional probability information than to mere pattern frequencies.