Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, Université Paris-Saclay, NeuroSpin center, Gif/Yvette, France.
Elife. 2023 May 2;12:e86430. doi: 10.7554/eLife.86430.
Successive auditory inputs are rarely independent, their relationships ranging from local transitions between elements to hierarchical and nested representations. In many situations, humans retrieve these dependencies even from limited datasets. However, this learning at multiple scale levels is poorly understood. Here, we used the formalism proposed by network science to study the representation of local and higher-order structures and their interaction in auditory sequences. We show that human adults exhibited biases in their perception of local transitions between elements, which made them sensitive to high-order network structures such as communities. This behavior is consistent with the creation of a parsimonious simplified model from the evidence they receive, achieved by pruning and completing relationships between network elements. This observation suggests that the brain does not rely on exact memories but on a parsimonious representation of the world. Moreover, this bias can be analytically modeled by a memory/efficiency trade-off. This model correctly accounts for previous findings, including local transition probabilities as well as high-order network structures, unifying sequence learning across scales. We finally propose putative brain implementations of such bias.
连续的听觉输入很少是独立的，它们之间的关系范围从元素之间的局部转换到层次化和嵌套的表示。在许多情况下，人类甚至可以从有限的数据集中学到这些依赖关系。然而，这种多尺度水平的学习还没有得到很好的理解。在这里，我们使用网络科学提出的形式主义来研究听觉序列中局部和高阶结构及其相互作用的表示。我们表明，成年人在感知元素之间的局部转换时存在偏见，这使他们对社区等高阶网络结构敏感。这种行为与他们从接收到的证据中创建一个简洁简化的模型是一致的，这是通过修剪和完成网络元素之间的关系来实现的。这一观察表明，大脑不是依赖于精确的记忆，而是依赖于对世界的简洁表示。此外，这种偏差可以通过记忆/效率权衡进行分析建模。该模型正确地解释了以前的发现，包括局部转换概率以及高阶网络结构，统一了跨尺度的序列学习。我们最后提出了这种偏差的潜在大脑实现。
