Haptic and hand experience predict AoA of word learning in Chinese and visual and interceptive predict AoA of word learning in English: A corpus and computational integrative approach

Haptic and hand experience predict AoA of word learning in Chinese and visual and interceptive predict AoA of word learning in English: A corpus and computational integrative approach

First Author: Qun Guan -- University of Science and Technology Beijing & Florida State University
Additional authors/chairs: 
Louise Connell; Wanjin Meng
Keywords: (Chinese) characters, Word Learning, Bayesian modeling, Cross-linguistic, Sensorimotor Perceptual Strength
Abstract / Summary: 

Objective: Age of acquisition (AoA) is one of the most fundamental variables in word recognition as well as one of significant language achievement hallmarks. Traditional approach suggested that AoA correlated strongly with some explicit factors, such as word frequency, word prevalence, and concreteness and imageability. Recent research trends suggest both explicit and implicit psycholinguistic factors can predict AoA of word learning (Connell and Lynott. 2012). The goal of this study is to conduct the corpus and computational integrative analyses to conduct Bayesian modeling AoA using embodied sensorimotor indicators (11 dimensions of sensorimotor factors) in word learning of both Chinese and English.
Methods: We combined the norms of (1) Prevelance (Brysbaert, Mandera, McCormick, &Keuleers, 2019), two norms of AOA (Kuperman et al., 2012; and Brysbaert & Biemiller, 2017), and sensorimotor strength a concreteness/ imageability (Connell & Lynott, 2012; Connell, Lynott, Bank, 2008). We then conducted Bayesian analysis to consider AOA as DVs, and basic psycholinguistic factors at Step1IVs, and Prevalence, Concreteness and Imageability at Step 2, and all the sensorimotor strength predictors at Step 3. Within Step 3, we used Perceptual Strength at first, and then the Introceptive factor at last. There are 8 dimensions of Perceptual strength indicators (gustatory, haptic, hand-related, leg-related, olfactory, torso, etc.)
Results: Bayesian modeling suggested “haptic and hand-related PS” is the best model above beyond the basic explicit model containing word frequency, familiarity, and prevalence of Chinese basic 3000 characters. Whereas the visual and interceptive related indicators predicts AoA of word learning in English.
Conclusion: Getting exposed to novel words through haptic and hand-related experience contributes to early acquisition of vocabulary in Chinese, and early visual and interceptive experiences with objects contributes to early acquisition of word lea