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Ji Won Bang, Sumire Sato, Takeo Watanabe, Yuka Sasaki; Reconsolidation in visual perceptual learning. Journal of Vision 2013;13(9):910. doi: https://doi.org/10.1167/13.9.910.
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Visual perceptual learning (VPL) is defined as long-term improvement in a visual task after visual experiences. After training is over, VPL is strengthened, or consolidated, by sleep so that VPL is not interfered with by training on a new task. In some other types of learning and memory, even after they are consolidated, they are rendered transiently malleable shortly after they are reactivated. In that case, the reactivated learning and memory becomes once again fragile to interference by training on a new task unless they are reconsolidated. Does such a reactivation effect also occur in VPL? To address this question, we used a texture discrimination task (TDT), since interference occurs between two trainings of TDTs with different background orientations with a short interval. The complete experiment consisted of 3 consecutive days. A different group of subjects participated in one of Short- and Long-interval conditions. On the first day, subjects in both conditions were trained on a TDT with one background orientation. On the following day, a brief re-test of a TDT with the trained background orientation was followed by a new training of a TDT with a new background orientation with immediate interval in the Short-interval condition and with a 6-hour interval in the Long-interval condition. On the third day, a brief re-test was conducted for the previously trained 2 TDTs with the 2 different background orientations. The results showed that the firstly learned TDT was deteriorated on the third day for the Short-interval condition, but improved for the Long-interval condition. These results indicate that a brief rehearsal of already consolidated VPL makes it vulnerable to interference, and that further reconsolidation of VPL is necessary after its reactivation to avoid it from being interfered with by a new training.
Meeting abstract presented at VSS 2013
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