EFFECT OF THE LEARNING MATERIAL STRUCTURE ON RETROACTIVE AND PROACTIVE INTERFERENCE IN HUMANS: WHEN THE SELF-REFRESHING NEURAL NETWORK MECHANISM PROVIDES NEW INSIGHTS

摘要

Following Mirman and Spivey's investigation, Musca, Rousset and Ans conducted a study on the influence of the nature of the to-be-learned material on retroactive interference (RI) in humans. More RI was found for unstructured than for structured material, a result opposed to that of Mirman and Spivey. This paper first presents two simulations. The first, using a three-layer backpropagation hetero-associator produced a pattern of RI results that mirrored qualitatively the structure effect on RI found in humans. However the level of RI was high. In the second simulation the Dual Reverberant memory Self-Refreshing neural network model (DRSR) of Ans and Rousset was used. As expected, the global level of RI was reduced and the structure effect on RI was still present. We further investigated the functioning of DRSR in this situation. A proactive interference (PI) was observed, and also a structure effect on PI. Furthermore, the structure effect on RI and the structure effect on PI were negatively correlated. This trade-off between structure effect on RI and structure effect on PI found in simulation points to an interesting potential phenomenon to be investigated in humans.