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

Following Mirman and Spivey’s investigation [12], 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 [13]. More RI was found for unstructured than for structured material, a result opposed to that of Mirman and Spivey [12]. 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 [13]. 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 [1, 2] 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.