A Computational Swinging Lexical Network: Cohort Concentration Predicts Semantic Interference in a Large-Scale Picture–Word Interference Dataset

Abstract

The Swinging Lexical Network (SLN) model explains semantic context effects in word production through the lexical cohort that a target and distractor co-activate: whether naming is facilitated or slowed depends on the balance between semantic priming and lexical competition within this cohort. As a verbal theory the SLN leaves open what counts as a competitor: any co-activated word, or only words whose activation stands out. We implemented the SLN computationally, letting activation spread through a 30,000-word distributional semantic network. From this process we derived two measures. Cohort concentration is the degree to which co-activation falls on a few words instead of being spread thinly across many. The swing is how much the activation pattern still changes over later spreading steps. In a harmonized picture--word interference dataset of 392,156 trials from 86 English and German experiments, cohort concentration was the only semantic predictor with a reliable effect in both languages: naming was slower when a few words stood out as strongly activated competitors and faster when activation was diffuse. Competitors are thus the words whose activation stands out from the background, not every word that receives some activation. Adding the swing improved fit, but its effect could not be separated from cohort concentration, with which it was strongly correlated. Because spreading converges on the same network-wide state for every word pair, later steps largely re-express the initial cohort. In line with the SLN, our computational implementation shows that semantic interference depends on how co-activation is distributed across the cohort.