@article{LotzKinderLachnit2009,
  author    = {Lotz, Anja and Kinder, Annette and Lachnit, Harald},
  title     = {Multiple regression analyses in artificial-grammar learning : the importance of control groups},
  issn      = {1747-0218},
  doi       = {10.1080/17470210802103739},
  year      = {2009},
  abstract  = {In artificial-grammar learning, it is crucial to ensure that above-chance performance in the test stage is due to learning in the training stage but not due to judgemental biases. Here we argue that multiple regression analysis call be successfully combined with the use of control groups to assess whether participants were able to transfer knowledge acquired during training where making judgements about test stimuli. We compared the regression weights of judgements in a transfer condition (training and test strings were constructed by the same grammar but with different letters) with those in a control condition. Predictors were identical in both conditions-judgements of control participants were treated as if they were based oil knowledge gained in a standard training stage. The results of this experiment as well as reanalyses of a former study support the usefulness of our approach.},
  language  = {en}
}
@article{LotzKinder2006,
  author    = {Lotz, Anja and Kinder, Annette},
  title     = {Transfer in artificial grammar learning : the role of repetition information},
  issn      = {0278-7393},
  doi       = {10.1037/0278-7393.32.4.707},
  year      = {2006},
  abstract  = {In this article, the authors report 2 experiments that investigated the sources of information used in transfer and nontransfer tasks in artificial grammar learning. Multiple regression analyses indicated that 2 types of information about repeating elements were crucial for performance in both tasks: information about the repetition of adjacent elements and information about repetition of elements in the whole item. Similarity of test items to specific training items and chunk information influenced participants' judgments only in nontransfer tasks},
  language  = {en}
}
@article{KinderLotz2009,
  author    = {Kinder, Annette and Lotz, Anja},
  title     = {Connectionist models of artificial grammar learning : what type of knowledge is acquired?},
  issn      = {0340-0727},
  doi       = {10.1007/s00426-008-0177-z},
  year      = {2009},
  abstract  = {Two experiments are presented that test the predictions of two associative learning models of Artificial Grammar Learning. The two models are the simple recurrent network (SRN) and the competitive chunking (CC) model. The two experiments investigate acquisition of different types of knowledge in this task: knowledge of frequency and novelty of stimulus fragments (Experiment 1) and knowledge of letter positions, of small fragments, and of large fragments up to entire strings (Experiment 2). The results show that participants acquired all types of knowledge. Simulation studies demonstrate that the CC model explains the acquisition of all types of fragment knowledge but fails to account for the acquisition of positional knowledge. The SRN model, by contrast, accounts for the entire pattern of results found in the two experiments.},
  language  = {en}
}