A Neural Network Model of Lexical Competition during Infant Spoken Word Recognition
This addresses a specific problem in cognitive science for understanding infant spoken word recognition mechanisms, but it is incremental as it builds on existing visual world studies and hypotheses.
The paper tackled the problem of explaining why infants and adults initially look at phonologically related items when hearing a word, before shifting to semantic or visual ones, by developing a neural network model that processes dynamic phonological inputs to simulate this effect. The result showed that incremental word unfolding alone can account for the early phonological preference without needing top-down feedback.
Visual world studies show that upon hearing a word in a target-absent visual context containing related and unrelated items, toddlers and adults briefly direct their gaze towards phonologically related items, before shifting towards semantically and visually related ones. We present a neural network model that processes dynamic unfolding phonological representations and maps them to static internal semantic and visual representations. The model, trained on representations derived from real corpora, simulates this early phonological over semantic/visual preference. Our results support the hypothesis that incremental unfolding of a spoken word is in itself sufficient to account for the transient preference for phonological competitors over both unrelated and semantically and visually related ones. Phonological representations mapped dynamically in a bottom-up fashion to semantic-visual representations capture the early phonological preference effects reported in a visual world task. The semantic-visual preference observed later in such a trial does not require top-down feedback from a semantic or visual system.