A Dual Simple Recurrent Network Model for Chunking and Abstract Processes in Sequence Learning

Frontiers in Psychology 12 (2021)
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Abstract

Although many studies have provided evidence that abstract knowledge can be acquired in artificial grammar learning, it remains unclear how abstract knowledge can be attained in sequence learning. To address this issue, we proposed a dual simple recurrent network model that includes a surface SRN encoding and predicting the surface properties of stimuli and an abstract SRN encoding and predicting the abstract properties of stimuli. The results of Simulations 1 and 2 showed that the DSRN model can account for learning effects in the serial reaction time task under different conditions, and the manipulation of the contribution weight of each SRN accounted for the contribution of conscious and unconscious processes in inclusion and exclusion tests in previous studies. The results of human performance in Simulation 3 provided further evidence that people can implicitly learn both chunking and abstract knowledge in sequence learning, and the results of Simulation 3 confirmed that the DSRN model can account for how people implicitly acquire the two types of knowledge in sequence learning. These findings extend the learning ability of the SRN model and help understand how different types of knowledge can be acquired implicitly in sequence learning.

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Implicit learning and tacit knowledge.Arthur S. Reber - 1989 - Journal of Experimental Psychology: General 118 (3):219-235.
Characteristics of dissociable human learning systems.David R. Shanks & Mark F. St John - 1994 - Behavioral and Brain Sciences 17 (3):367-447.
Characteristics of dissociable human learning systems.David R. Shanks & Mark F. St John - 1994 - Behavioral and Brain Sciences 17 (3):367-395.
Implicit learning: News from the front.Axel Cleeremans, Arnaud Destrebecqz & Maud Boyer - 1998 - Trends in Cognitive Sciences 2 (10):406-416.
Transfer of syntactic structure in synthetic languages.Arthur S. Reber - 1969 - Journal of Experimental Psychology 81 (1):115.

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