Modes of knowledge acquisition and retrieval in artificial grammar learning
Autor: | Joseph Tzelgov, Yael Poznanski |
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Rok vydání: | 2010 |
Předmět: |
Signal Detection
Psychological Artificial grammar learning Universities Physiology Experimental and Cognitive Psychology Neuropsychological Tests computer.software_genre Task (project management) Fluency Automation Artificial Intelligence Physiology (medical) Confidence Intervals Humans Detection theory Dimension (data warehouse) Students General Psychology Communication business.industry Mode (statistics) Linguistics General Medicine Verbal Learning Knowledge acquisition Neuropsychology and Physiological Psychology Mental Recall Sequence learning Artificial intelligence Psychology business computer Natural language processing |
Zdroj: | Quarterly journal of experimental psychology (2006). 63(8) |
ISSN: | 1747-0226 |
Popis: | The aim of this study was to conceptualize artificial grammar learning (AGL) in terms of two orthogonal dimensions—the mode of knowledge acquisition and the mode of knowledge retrieval—as was done by Perlman and Tzelgov (2006) for sequence learning. Experiment 1 was carried out to validate our experimental task; Experiments 2–4 tested, respectively, performance in the intentional, incidental, and automatic retrieval modes, for each of the three modes of acquisition. Furthermore, signal detection theory (SDT) was used as an analytic tool, consistent with our assumption that the processing of legality-relevant information involves decisions along a continuous dimension of fluency. The results presented support the analysis of AGL in terms of the proposed dimensions. They also indicate that knowledge acquired during training may include many aspects of the presented stimuli (whole strings, relations among elements, etc.). The contribution of the various components to performance depends on both the specific instruction in the acquisition phase and the requirements of the retrieval task. |
Databáze: | OpenAIRE |
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