Surprisal from language models can predict ERPs in processing predicate-argument structures only if enriched by an Agent Preference principle

Language models based on artificial neural networks increasingly capture key aspects of how humans process sentences. Most notably, model-based surprisals predict event-related potentials such as N400 amplitudes during parsing. Assuming that these models represent realistic estimates of human linguistic experience, their success in modelling language processing raises the possibility that the human processing system relies on no other principles than the general architecture of language models and on sufficient linguistic input. Here, we test this hypothesis on N400 effects observed during the processing of verb-final sentences in German, Basque, and Hindi. By stacking Bayesian generalised additive models, we show that, in each language, N400 amplitudes and topographies in the region of the verb are best predicted when model-based surprisals are complemented by an Agent Preference principle that transiently interprets initial role-ambiguous NPs as agents, leading to reanalysis when this interpretation fails. Our findings demonstrate the need for this principle independently of usage frequencies and structural differences between languages. The principle has an unequal force, however. Compared to surprisal, its effect is weakest in German, stronger in Hindi, and still stronger in Basque. This gradient is correlated with the extent to which grammars allow unmarked NPs to be patients, a structural feature that boosts reanalysis effects. We conclude that language models gain more neurobiological plausibility by incorporating an Agent Preference. Conversely, theories of human processing profit from incorporating surprisal estimates in addition to principles like the Agent Preference, which arguably have distinct evolutionary roots.

DOI: https://doi.org/10.1162/nol_a_00121

基于人工神经网络的语料模型日益捕捉到人类处理句子的关键方面。尤为显著的是，基于模型的惊喜度可以预测解析过程中的事件相关电位，如N400振幅。假定这些模型代表了人类语言经验的现实估计，它们在模拟语言处理方面的成功引发了一种可能性，即人类处理系统仅依赖于语言模型的一般架构以及充足的语料输入。在本研究中，我们对德语、巴斯克语和印地语中动词结尾句子的处理过程中观察到的N400效应进行了测试。通过堆叠贝叶斯广义加性模型，我们表明，在每种语言中，动词区域的N400振幅和地形最好地由基于模型的惊喜度与一种暂时将初始角色歧义性NP解释为行为体的Agent Preference原则相补充来预测，当这种解释失败时，会导致重新分析。我们的发现证明了这一原则的必要性，无论在语言的使用频率和结构差异方面。然而，这一原则的力量并不均衡。与惊喜度相比，它在德语中的效果最弱，在印地语中较强，在巴斯克语中则更为显著。这种梯度与语法允许未标记NP作为受事的结构特征的程度相关，这是一种增强重新分析效应的结构特征。我们得出结论，通过纳入Agent Preference，语言模型获得了更多的神经生物学合理性。相反，人类处理理论通过纳入惊喜度估计以及诸如Agent Preference等原则，这些原则据称具有不同的进化根源，从而受益匪浅。