Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Syntactic transfer in artificial grammar learning
AU - Beesley, T.
AU - Wills, A.J.
AU - le Pelley, M.E.
N1 - cited By 3
PY - 2010/2
Y1 - 2010/2
N2 - In an artificial grammar learning (AGL) experiment, participants were trained with instances of one grammatical structure before completing a test phase in which they were required to discriminate grammatical from randomly created strings. Importantly, the underlying structure used to generate test strings was different from that used to generate the training strings. Despite the fact that grammatical training strings were more similar to nongrammatical test strings than they were to grammatical test strings, this manipulation resulted in a positive transfer effect, as compared with controls trained with nongrammatical strings. It is suggested that training with grammatical strings leads to an appreciation of set variance that aids the detection of grammatical test strings in AGL tasks. The analysis presented demonstrates that it is useful to conceptualize test performance in AGL as a form of unsupervised category learning.
AB - In an artificial grammar learning (AGL) experiment, participants were trained with instances of one grammatical structure before completing a test phase in which they were required to discriminate grammatical from randomly created strings. Importantly, the underlying structure used to generate test strings was different from that used to generate the training strings. Despite the fact that grammatical training strings were more similar to nongrammatical test strings than they were to grammatical test strings, this manipulation resulted in a positive transfer effect, as compared with controls trained with nongrammatical strings. It is suggested that training with grammatical strings leads to an appreciation of set variance that aids the detection of grammatical test strings in AGL tasks. The analysis presented demonstrates that it is useful to conceptualize test performance in AGL as a form of unsupervised category learning.
U2 - 10.3758/PBR.17.1.122
DO - 10.3758/PBR.17.1.122
M3 - Journal article
VL - 17
SP - 122
EP - 128
JO - Psychonomic Bulletin and Review
JF - Psychonomic Bulletin and Review
SN - 1069-9384
IS - 1
ER -