Role of prior knowledge in implicit and explicit learning of artificial grammars |
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| Affiliation: | 1. Department of Psychology, University of Ioannina, Greece;2. Department of Psychology, City University London, UK;3. Sackler Centre for Consciousness Science and School of Psychology, University of Sussex, Brighton, UK;1. Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, United States;2. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States;3. Department of Neurosurgery, Massachusetts General Hospital/Harvard Medical School, Boston, MA, United States;4. Program in Neuroscience, Harvard Medical School, Boston, MA, United States;5. Department of Psychology, UCSD, San Diego, CA, United States;1. Department of Psychology, City University London, London, EC1V 0HB, UK;2. Department of Cognitive Sciences, 3151 Social Sciences Plaza, University of California, Irvine, CA 92697-5100, USA;1. Purdue University, United States;2. Rensselaer Polytechnic Institute, United States |
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| Abstract: | Artificial grammar learning (AGL) performance reflects both implicit and explicit processes and has typically been modeled without incorporating any influence from general world knowledge. Our research provides a systematic investigation of the implicit vs. explicit nature of general knowledge and its interaction with knowledge types investigated by past AGL research (i.e., rule- and similarity-based knowledge). In an AGL experiment, a general knowledge manipulation involved expectations being either congruent or incongruent with training stimulus structure. Inconsistent observations paradoxically led to an advantage in structural knowledge and in the use of general world knowledge in both explicit (conscious) and implicit (unconscious) cases (as assessed by subjective measures). The above findings were obtained under conditions of reduced processing time and impaired executive resources. Key findings from our work are that implicit AGL can clearly be affected by general knowledge, and implicit learning can be enhanced by the violation of expectations. |
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| Keywords: | AGL Implicit learning Explicit learning General knowledge Similarity Rules |
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