In a recent article, Jamieson and Mewhort (2009) proposed a novel account of artificial grammar learning (AGL), which is based on a multitrace model of episodic memory, the Minerva 2 model. According to this account, test performance in AGL is based on an assessment of global similarity of the test strings to the memory traces of the training strings. In this article, simulation studies are presented, showing for three different AGL experiments that the predictions of the Minerva 2 model strikingly deviate from participants’ performance. It is argued that participants’ test performance is not generally based on general similarity.
BoucherL.DienesZ. (2003). Two ways of learning associations.Cognitive Science27, 807–842.
2.
DienesZ. (1992). Connectionist and memory array models of artificial grammar learning.Cognitive Science16, 41–79.
3.
DienesZ.AltmannG. T. M.GaoS. (1999). Mapping across domains without feedback: A neural network model of transfer of implicit knowledge.Cognitive Science23, 53–82.
4.
HintzmanD. L. (1986). “Schema abstraction” in a multiple-trace memory model.Psychological Review93, 411–428.
5.
JamiesonR. K.MewhortD. J. K. (2009). Applying an exemplar model to the artificial-grammar task: Inferring grammaticality from similarity.Quarterly Journal of Experimental Psychology62, 550–575.
6.
JohnstoneT.ShanksD. R. (1999). Two mechanisms in implicit artificial grammar learning? Comment on Meulemans and Van der Linden (1997).Journal of Experimental Psychology: Learning, Memory, & Cognition25, 524–531.
7.
JohnstoneT.ShanksD. R. (2001). Abstractionist and processing accounts of implicit learning.Cognitive Psychology42, 61–112.
8.
KinderA. (2000). The knowledge acquired during artificial grammar learning: Testing the predictions of two connectionist models.Psychological Research63, 95–105.
9.
KinderA.LotzA. (2009). Connectionist models of artificial grammar learning: What type of knowledge is acquired?.Psychological Research73, 659–673.
10.
KinderA.ShanksD. R. (2001). Amnesia and the declarative/nondeclarative distinction: A recurrent network model of classification, recognition, and repetition priming.Journal of Cognitive Neuroscience13, 648–669.
11.
KnowltonB. J.SquireL. R. (1996). Artificial grammar learning depends on implicit acquisition of both abstract and exemplar-specific information.Journal of Experimental Psychology: Learning, Memory, and Cognition22, 169–181.
12.
LotzA.KinderA. (2006). Transfer in artificial grammar learning: The role of repetition information.Journal of Experimental Psychology: Learning, Memory, and Cognition32, 707–715.
13.
MeulemansT.Van der LindenM. (1997). Associative chunk strength in artificial grammar learning.Journal of Experimental Psychology: Learning, Memory, and Cognition23, 1007–1028.
14.
PerruchetP.PacteauC. (1990). Synthetic grammar learning: Implicit rule abstraction or explicit fragmentary knowledge?.Journal of Experimental Psychology: General119, 264–275.
15.
PerruchetP.VinterA. (1998). PARSER: A model for word segmentation.Journal of Memory and Language39, 246–263.
16.
Servan-SchreiberE.AndersonJ. R. (1990). Learning artificial grammars with competitive chunking.Journal of Experimental Psychology: Learning, Memory and Cognition16, 592–608.
