Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands.
Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands.
Neural Netw. 2020 Jan;121:339-355. doi: 10.1016/j.neunet.2019.09.027. Epub 2019 Sep 30.
Previous research has shown that performance of a novice skill can be easily interfered with by subsequent training of another skill. We address the open questions whether extensively trained skills show the same vulnerability to interference as novice skills and which memory mechanism regulates interference between expert skills. We developed a recurrent neural network model of V1 able to learn from feedback experienced over the course of a long-term orientation discrimination experiment. After first exposing the model to one discrimination task for 3480 consecutive trials, we assessed how its performance was affected by subsequent training in a second, similar task. Training the second task strongly interfered with the first (highly trained) discrimination skill. The magnitude of interference depended on the relative amounts of training devoted to the different tasks. We used these and other model outcomes as predictions for a perceptual learning experiment in which human participants underwent the same training protocol as our model. Specifically, over the course of three months participants underwent baseline training in one orientation discrimination task for 15 sessions before being trained for 15 sessions on a similar task and finally undergoing another 15 sessions of training on the first task (to assess interference). Across all conditions, the pattern of interference observed empirically closely matched model predictions. According to our model, behavioral interference can be explained by antagonistic changes in neuronal tuning induced by the two tasks. Remarkably, this did not stem from erasing connections due to earlier learning but rather from a reweighting of lateral inhibition.
先前的研究表明，新手技能的表现很容易受到后续训练的干扰。我们探讨了一个开放性问题，即经过广泛训练的技能是否像新手技能一样容易受到干扰，以及哪种记忆机制调节专家技能之间的干扰。我们开发了一个 V1 的递归神经网络模型，该模型能够从长期定向辨别实验过程中的反馈中进行学习。在首先让模型暴露于一个辨别任务中进行 3480 次连续试验后，我们评估了其在第二个类似任务中的后续训练对其表现的影响。训练第二个任务强烈干扰了第一个（高度训练的）辨别技能。干扰的程度取决于不同任务的相对训练量。我们使用这些和其他模型结果作为对人类参与者进行相同训练方案的感知学习实验的预测。具体来说，在三个月的时间里，参与者在一个定向辨别任务的基线训练中进行了 15 次训练，然后在类似的任务中进行了 15 次训练，最后在第一个任务上进行了另外 15 次训练（以评估干扰）。在所有条件下，经验观察到的干扰模式与模型预测非常吻合。根据我们的模型，行为干扰可以用两个任务引起的神经元调谐的拮抗变化来解释。值得注意的是，这不是由于早期学习导致的连接消除，而是由于侧向抑制的重新加权。
