Subject-verb agreement in the presence of an attractor noun located between the main noun and the verb elicits complex behavior: judgments of grammaticality are modulated by the grammatical features of the attractor. For example, in the sentence "The girl near the boys likes climbing", the attractor (boys) disagrees in grammatical number with the verb (likes), creating a locally implausible transition probability. Here, we parametrically modulate the distance between the attractor and the verb while keeping the length of the sentence equal. We evaluate the performance of both humans and two artificial neural network models: both make more mistakes when the attractor is closer to the verb, but neural networks get close to the chance level while humans are mostly able to overcome the attractor interference. Additionally, we report a linear effect of attractor distance on reaction times. We hypothesize that a possible reason for the proximity effect is the calculation of transition probabilities between adjacent words. Nevertheless, classical models of attraction such as the cue-based model might suffice to explain this phenomenon, thus paving the way for new research. Data and analyses available at https://osf.io/d4g6k

翻译：在主名词和动词之间存在吸引名词的情况下，主谓一致引发复杂行为：对语法正确性的判断受到吸引词语法特征的调节。例如，在句子"The girl near the boys likes climbing"中，吸引词（boys）与动词（likes）在语法数上不一致，产生了局部不合理的转换概率。本研究在保持句子长度不变的前提下，参数化地调节吸引词与动词之间的距离。我们评估了人类和两种人工神经网络模型的性能：当吸引词更接近动词时，两者都犯更多错误，但神经网络接近随机水平，而人类大多能克服吸引词干扰。此外，我们报告了吸引词距离对反应时间的线性效应。我们假设邻近效应的一个可能原因是相邻词之间转换概率的计算。尽管如此，诸如基于线索模型之类的经典吸引模型可能足以解释这一现象，从而为后续研究铺平道路。数据和分析见https://osf.io/d4g6k