  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.
