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Acta Psychologica Sinica    2014, Vol. 46 Issue (11) : 1649-1660     DOI: 10.3724/SP.J.1041.2014.01649
The Development of Graded Consciousness in Artificial Grammar Learning
ZHANG Runlai; LIU Dianzhi
(School of Education, Soochow University, Soochow, 215006, China)
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Consciousness has always been much concerned in cognition science and, with the discovery of implicit learning in artificial grammar learning (AGL) researches, influences of unconscious processes on human cognition have been unprecedentedly highlighted. Dozens of empirical investigations have distinguished two different types of learning, i.e. explicit learning and implicit learning which involve conscious and unconscious processing respectively. Hence their critical attributes and interactive patterns have undergone tentative explorations and several theoretical frameworks have been proposed to demonstrate the underlying mental mechanisms, most of which take the side of dualistic logic. Empirical data tend to indicate that, rather than stand in dichotomy, so often they co-exist only with a quantitative difference. The academic dilemma is now broken through by the graded consciousness hypothesis, as a result of which new perspective arises from the graded consciousness dimension to deeply investigate implicit learning. In the present research the dynamic mental evolution pattern has been explored in artificial grammar learning, concerning both consciousness and knowledge representation. The current research adopts hypothesis of distributive representation and representation rehearsal which indicates that the ever-optimizing distributive representation dominates the learning process. Thus graded consciousness comes into form due to the increasing contribution of conscious processing. A dual-task design was introduced in current study with reference to the PDP paradigm formerly adopted in implicit memory researches. The innovative paradigm adopted a ‘slow’ learning task and a ‘quick’ task and the contribution patterns of consciousness and unconsciousness are different in the two tasks, as a result of which the contributions of conscious and unconscious processes could be extracted dynamically along the learning course. This innovative paradigm also makes possible a direct investigation of graded consciousness during the learning phase. The results of current study show that, in the implicit phase of artificial grammar learning, the contribution of conscious processing exhibited a slow-first-fast-later growing pattern while a slow-first-stable-later pattern was found with unconscious processing. In the beginning unconscious contribution prevailed but was eventually exceeded by conscious process in the subsequent blocks. However, in the first half of learning phase, consciousness contribution suffered an undulating performance while unconsciousness more stable. While entering the second half of learning phase, unconsciousness contribution exhibited a stagnating pattern and the uneven mode faded with consciousness process. These findings, compatible with those empirical researches assuming dichotomy logic, not only define distinguishable features for implicit learning and explicit learning, but also demonstrate the graded consciousness in artificial grammar learning and make possible a learning continuum progressing throughout the learning phase of artificial grammar. In the current study which assumed graded consciousness perspective the structures of knowledge representations have also been discussed indirectly. Conclusions are that the developing of unconscious representations precedes that of conscious representations, considering the undulation in the first half phase for consciousness and the stagnation in the second half phase for unconsciousness, which indicates a possible synergic effect between conscious and unconscious processing.

Keywords artificial grammar learning      graded consciousness      implicit learning     
Corresponding Authors: LIU Dianzhi, E-mail:   
Issue Date: 25 November 2014
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ZHANG Runlai; LIU Dianzhi. The Development of Graded Consciousness in Artificial Grammar Learning[J]. Acta Psychologica Sinica,2014, 46(11): 1649-1660.
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