Unveiling Cognitive Constraints in Language Production: Extracting and Validating the Active Ego Network of Words

TL;DR

This paper introduces a methodology to extract and validate the active ego network of words, revealing cognitive constraints in language production and demonstrating its robustness across datasets and domains.

Contribution

It defines and validates a novel method for extracting the active part of the ego network of words, emphasizing its importance for understanding cognitive effort in language use.

Findings

Active ego network extraction improves estimation of cognitive effort.

Method is robust to data size and temporal variations.

Model generalizes across different datasets and domains.

Abstract

The "ego network of words" model captures structural properties in language production associated with cognitive constraints. While previous research focused on the layer-based structure and its semantic properties, this paper argues that an essential element, the concept of an active network, is missing. The active part of the ego network of words only includes words that are regularly used by individuals, akin to the ego networks in the social domain, where the active part includes relationships regularly nurtured by individuals and hence demanding cognitive effort. In this work, we define a methodology for extracting the active part of the ego network of words and validate it using interview transcripts and tweets. The robustness of our method to varying input data sizes and temporal stability is demonstrated. We also demonstrate that without the active network concept (and a tool for properly extracting the active network from data), the "ego network of words" model is not able to properly estimate the cognitive effort involved and it becomes vulnerable to the amount of data considered (leading to the disappearance of the layered structure in large datasets). Our results are well-aligned with prior analyses of the ego network of words, where the limitation of the data collected led automatically (and implicitly) to approximately consider the active part of the network only. Moreover, the validation on the transcripts dataset (MediaSum) highlights the generalizability of the model across diverse domains and the ingrained cognitive constraints in language usage.