Russin Jacob, Zolfaghar Maryam, Park Seongmin A, Boorman Erie, O'Reilly Randall C
Dept. of Psychology, UC Davis.
Center for Neuroscience, UC Davis.
Cogsci. 2022 Jul;44:1064-1071.
Neural networks struggle in continual learning settings from catastrophic forgetting: when trials are blocked, new learning can overwrite the learning from previous blocks. Humans learn effectively in these settings, in some cases even showing an advantage of blocking, suggesting the brain contains mechanisms to overcome this problem. Here, we build on previous work and show that neural networks equipped with a mechanism for cognitive control do not exhibit catastrophic forgetting when trials are blocked. We further show an advantage of blocking over interleaving when there is a bias for active maintenance in the control signal, implying a tradeoff between maintenance and the strength of control. Analyses of map-like representations learned by the networks provided additional insights into these mechanisms. Our work highlights the potential of cognitive control to aid continual learning in neural networks, and offers an explanation for the advantage of blocking that has been observed in humans.
当试验被分块时，新的学习会覆盖之前块中的学习内容。人类在这些环境中能有效地学习，在某些情况下甚至表现出分块的优势，这表明大脑包含克服此问题的机制。在此，我们基于之前的工作表明，配备认知控制机制的神经网络在试验被分块时不会表现出灾难性遗忘。我们进一步表明，当控制信号中存在主动维持的偏差时，分块比交错排列具有优势，这意味着在维持和控制强度之间存在权衡。对网络学习的类似地图的表征进行分析，为这些机制提供了更多见解。我们的工作突出了认知控制在帮助神经网络进行持续学习方面的潜力，并为在人类中观察到的分块优势提供了解释。
