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 $\textit{“The girl near the boys likes climbing''}$, the attractor ($\textit{boys}$) disagrees in grammatical number with the verb ($\textit{likes}$), 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

Assessing the influence of attractor-verb distance on grammatical agreement in humans and language models

Among primates, humans are special in their ability to create and manipulate highly elaborate structures of language, mathematics or music. We show that this sensitivity is present in a much simpler domain: the visual perception of geometric shapes. We asked human subjects to detect an intruder shape among six quadrilaterals. Although the intruder was defined by an identical amount of displacement of a single vertex, the results revealed a geometric regularity effect: detection was considerably easier with most regular shapes. This effect was replicated in several tasks and in both uneducated adults and preschoolers. Baboons, however, showed no such geometric regularity effect even after extensive training. Baboon behavior was captured by convolutional neural networks (CNNs) but a symbolic model was needed to fit human behavior. Our results indicate that the human propensity for symbolic abstraction permeates even elementary shape perception and they suggest a new putative signature of human singularity.

Sensitivity to geometric shape regularity in humans and baboons: A putative signature of human singularity

Linguistic Theories, Cognitive Modeling and Psycholinguistics

technical paper

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Poster Session 3 -  B: Animal Cognition and Communication

poster

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CogSci 2021

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

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Assessing the influence of attractor-verb distance on grammatical agreement in humans and language models

Sensitivity to geometric shape regularity in humans and baboons: A putative signature of human singularity

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