Slaats Sophie, Martin Andrea E
Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands.
Departement de Neurosciences Fondamentales, Université de Genève, Chemin Des Mines 9, 1202 Geneva, Switzerland.
Comput Brain Behav. 2025;8(2):233-248. doi: 10.1007/s42113-025-00237-9. Epub 2025 Feb 21.
In the computational and experimental psycholinguistic literature, the mechanisms behind syntactic structure building (e.g., combining words into phrases and sentences) are the subject of considerable debate. Much experimental work has shown that surprisal is a good predictor of human behavioral and neural data. These findings have led some authors to model language comprehension in a purely probabilistic way. In this paper, we use simulation to exemplify why surprisal works so well to model human data and to illustrate why exclusive reliance on it can be problematic for the development of mechanistic theories of language comprehension, particularly those with emphasis on meaning composition. Rather than arguing for the importance of structural or probabilistic information to the exclusion or exhaustion of the other, we argue more emphasis should be placed on understanding how the brain leverages both types of information (viz., statistical and structured). We propose that probabilistic information is an important to the structure in the message, but is not a substitute for the structure itself-neither computationally, formally, nor conceptually. Surprisal and other probabilistic metrics must play a key role as theoretical objects in any explanatory mechanistic theory of language processing, but that role remains in the service of the brain's goal of constructing structured meaning from sensory input.
The online version contains supplementary material available at 10.1007/s42113-025-00237-9.
在计算和实验心理语言学文献中，句法结构构建背后的机制（例如，将单词组合成短语和句子）是一个备受争议的话题。大量实验工作表明，意外性是人类行为和神经数据的良好预测指标。这些发现导致一些作者以纯粹概率的方式对语言理解进行建模。在本文中，我们通过模拟来说明为什么意外性在模拟人类数据方面表现出色，并说明为什么单纯依赖它对于语言理解机制理论的发展可能存在问题，特别是那些强调意义组合的理论。我们并非主张结构或概率信息的重要性要排斥或耗尽另一方，而是主张应更加重视理解大脑如何利用这两种信息（即统计信息和结构化信息）。我们提出，概率信息是消息中结构的重要补充，但在计算、形式或概念上都不能替代结构本身。意外性和其他概率指标在任何语言处理的解释性机制理论中都必须作为理论对象发挥关键作用，但该作用仍然是为了大脑从感官输入构建结构化意义这一目标服务。
在线版本包含可在10.1007/s42113-025-00237-9获取的补充材料。
