Manenti Giorgio L, Dizaji Aslan S, Schwiedrzik Caspar M
Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen, A Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Grisebachstraße 5, 37077 Göttingen, Germany; Perception and Plasticity Group, German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany; Systems Neuroscience Program, Graduate School for Neurosciences, Biophysics and Molecular Biosciences (GGNB), 37077 Göttingen, Germany.
Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen, A Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Grisebachstraße 5, 37077 Göttingen, Germany.
Curr Biol. 2023 Mar 13;33(5):817-826.e3. doi: 10.1016/j.cub.2023.01.011. Epub 2023 Jan 31.
Stimulus and location specificity are long considered hallmarks of visual perceptual learning. This renders visual perceptual learning distinct from other forms of learning, where generalization can be more easily attained, and therefore unsuitable for practical applications, where generalization is key. Based on the hypotheses derived from the structure of the visual system, we test here whether stimulus variability can unlock generalization in perceptual learning. We train subjects in orientation discrimination, while we vary the amount of variability in a task-irrelevant feature, spatial frequency. We find that, independently of task difficulty, this manipulation enables generalization of learning to new stimuli and locations, while not negatively affecting the overall amount of learning on the task. We then use deep neural networks to investigate how variability unlocks generalization. We find that networks develop invariance to the task-irrelevant feature when trained with variable inputs. The degree of learned invariance strongly predicts generalization. A reliance on invariant representations can explain variability-induced generalization in visual perceptual learning. This suggests new targets for understanding the neural basis of perceptual learning in the higher-order visual cortex and presents an easy-to-implement modification of common training paradigms that may benefit practical applications.
刺激和位置特异性长期以来被视为视觉感知学习的标志。这使得视觉感知学习有别于其他形式的学习，在其他学习形式中更容易实现泛化，因此不适用于以泛化为关键的实际应用。基于从视觉系统结构得出的假设，我们在此测试刺激变异性是否能在感知学习中开启泛化。我们让受试者进行方向辨别训练，同时改变一个与任务无关的特征——空间频率的变异性。我们发现，无论任务难度如何，这种操作都能使学习泛化到新的刺激和位置，同时不会对任务上的总体学习量产生负面影响。然后我们使用深度神经网络来研究变异性如何开启泛化。我们发现，当用可变输入进行训练时，网络会对与任务无关的特征产生不变性。所学不变性的程度能有力地预测泛化情况。对不变表征的依赖可以解释视觉感知学习中由变异性引发的泛化。这为理解高阶视觉皮层中感知学习的神经基础指明了新的目标，并提出了一种易于实施的对常见训练范式的修改方法，这可能会惠及实际应用。
