A Neural Network Model of Lexical-Semantic Competition During Spoken Word Recognition

The effects of phonological and semantic similarity on early referent identification

Language processing encompasses a sophisticated interplay of phonological (sound-based) and semantic (meaning-based) processes. This intricate interaction develops progressively during early language acquisition. It involves not only the addition of new words to the child's vocabulary but also the evolving organization of lexico-semantic networks. The aim of the current research was to investigate how young children navigate the complexities of word relationships, with a focus on the interplay between phonological similarity and semantic relatedness. A preferential looking experiment was conducted involving 73 participants divided into two age groups, with 39 aged 18 months and 34 aged 24 months. The experimental design involved auditory presentation of target words together with pairs of target and distractor images that systematically varied the phonological and semantic similarity between the pairs. The results showed a significant developmental progression in young children's phonological and semantic processing abilities. At 18 months, children predominantly responded to phonological cues, displaying challenges in distinguishing between phonologically similar words regardless of semantic context. By 24 months, there was an evident sensitivity to both phonological and semantic cues. This age group especially showed processing difficulties with words that were both phonologically similar and semantically related, suggesting a more complex integration of lexico-semantic networks. These findings underscore the pivotal role of phonological processing in early language acquisition and point to the gradual integration of semantic relationships into children's lexicons.

A network model of referent identification by toddlers in a visual world task

We present a neural network model of referent identification in a visual world task. Inputs are visual representations of item pairs unfolding with sequences of phonemes identifying the target item. The model is trained to output the semantic representation of the target and to suppress the distractor. The training set uses a 200-word lexicon typically known by toddlers. The phonological, visual, and semantic representations are derived from real corpora. Successful performance requires correct association between labels and visual and semantic representations, as well as correct location identification. The model reproduces experimental evidence that phonological, perceptual, and categorical relationships modulate item preferences. The model provides an account of how language can drive visual attention in the inter-modal preferential looking task.

The Presence of Background Noise Extends the Competitor Space in Native and Non-Native Spoken-Word Recognition: Insights from Computational Modeling

Oral communication often takes place in noisy environments, which challenge spoken‐word recognition. Previous research has suggested that the presence of background noise extends the number of candidate words competing with the target word for recognition and that this extension affects the time course and accuracy of spoken‐word recognition. In this study, we further investigated the temporal dynamics of competition processes in the presence of background noise, and how these vary in listeners with different language proficiency (i.e., native and non‐native) using computational modeling. We developed ListenIN (Listen‐In‐Noise), a neural‐network model based on an autoencoder architecture, which learns to map phonological forms onto meanings in two languages and simulates native and non‐native spoken‐word comprehension. We also examined the model's activation states during online spoken‐word recognition. These analyses demonstrated that the presence of background noise increases the number of competitor words, which are engaged in phonological competition and that this happens in similar ways intra and interlinguistically and in native and non‐native listening. Taken together, our results support accounts positing a “many‐additional‐competitors scenario” for the effects of noise on spoken‐word recognition.