摘要
近几年来,研究者们借助人工神经网络模型的方法对内隐学习研究中突出的争论性问题进行了新的探索。针对内隐学习的无意识问题,模拟研究发现确实存在一种无意识的内隐学习,然而这种无意识加工的发生与否要取决于规则的难易程度;针对内隐学习的抽象性问题,人工神经网络模型所主张的分布式概率表征能较好地加以解释。可见,神经网络模型的原理和模拟研究可能为真正地解决内隐学习的无意识性和抽象性等目前争论较多的领域提供一个全新的视角和研究方向。
Many researchers have explored some controversial issues about implicit learning by neural network modeling approach in resent years. Is implicit learning a kind of unconscious processing？ Can we acquire abstract rules during implicit learning？ When a neural network is used to model the way by which people conduct implicit learning task, the simulation results give a sound explanation about these questions. It reveals that implicit learning is an unconscious processing and whether it happens or not relies on the difficulty of rules which are needed to learn. It also reveals that what subjects acquire during implicit learning is not abstract rules or fragment knowledge, but a distributed representation. Maybe, in the future, neural network modeling can be used to provide more and more information about implicit learning.
出处
《心理科学》
CSSCI
CSCD
北大核心
2006年第2期480-484,共5页
Journal of Psychological Science
基金
教育部新世纪优秀人才支持计划项目(批准号为41193002)
教育部高等学校全国优秀博士学位论文作者专项资金资助项目(批准号为200309)支持
