000			01537nam a22002537a 4500
003			ZW-GwMSU
005			20221207140403.0
008			221207b |||||||| |||| 00| 0 eng d
040			_aMSU _cMSU _erda
100			_aLai, Jun _eauthor
245			_aHow “small” is “starting small” for learning hierarchical centre-embedded structures? _ccreated by Jun Lai, Fenna H. Poletiek
264			_aNetherlands : _bTaylor & Francis; _c2013
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_vVolume , number ,
520			_aHierarchical 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.
650			_aArtificial grammar learning
650			_aCentre-embedding
650			_aFrequency distribution
700			_aPoletiek, Fenna H. _eauthor
856			_uhttps://doi.org/10.1080/20445911.2013.779247
942			_2lcc _cJA
999			_c160717 _d160717