One cue that may facilitate children's word learning is iconicity, or the correspondence between a word's form and meaning. Some have even proposed that iconicity in the early lexicon may serve to help children learn how to learn words, supporting the acquisition of even noniconic, or arbitrary, word–referent associations. However, this proposal remains untested. Here, we investigate the iconicity of caregivers’ speech to young children during a naturalistic free‐play session with novel stimuli and ask whether the iconicity of caregivers’ speech (...) facilitates children's learning of the noniconic novel names of those stimuli. Thirty‐four 1.5‐2‐year‐olds (19 girls; half monolingual English learners and half bilingual English‐Spanish learners) participated in a naturalistic free‐play task with their caregivers followed by a test of word‐referent retention. We found that caregivers’ use of iconicity, particularly in utterances in which they named the novel stimuli, was associated with the likelihood that children learned that novel name. This result held even when controlling for other factors associated with word learning, such as the concreteness and frequency of words in caregiver speech. Together, the results demonstrate that iconicity not only can serve to help children identify the referent of novel words (as in previous research) but can also support their ability to retain even noniconic word‐referent mappings. (shrink)

How does the human brain link relational concepts to perceptual experience? For example, a speaker may say “the cup to the left of the computer” to direct the listener's attention to one of two cups on a desk. We provide a neural dynamic account for both perceptual grounding, in which relational concepts enable the attentional selection of objects in the visual array, and for the generation of descriptions of the visual array using relational concepts. In the model, activation in neural (...) populations evolves dynamically under the influence of both inputs and strong interaction as formalized in dynamic field theory. Relational concepts are modeled as patterns of connectivity to perceptual representations. These generalize across the visual array through active coordinate transforms that center the representation of target objects in potential reference objects. How the model perceptually grounds or generates relational descriptions is probed in 104 simulations that systematically vary the spatial and movement relations employed, the number of feature dimensions used, and the number of matching and nonmatching objects. We explain how sequences of decisions emerge from the time‐ and state‐continuous neural dynamics, how relational hypotheses are generated and either accepted or rejected, followed by the selection of new objects or the generation of new relational hypotheses. Its neural realism distinguishes the model from information processing accounts, its capacity to autonomously generate sequences of processing steps distinguishes it from deep neural network accounts. The model points toward a neural dynamic theory of higher cognition. (shrink)

Variability is prevalent in early language acquisition, but, whether it supports or hinders learning is unclear; while target variability has been shown to facilitate word learning, variability in competitor items has been shown to make the task harder. Here, we tested whether background variability could boost learning in a referent selection task. Two groups of 2-year-old children saw arrays of one novel and two known objects on a screen, and they heard a novel or known label. Stimuli were identical across (...) conditions, with the exception that in the constant color condition objects appeared on a uniform white background, and in the variable color condition backgrounds were different, uniform colors. At test, only children in the variable condition showed evidence of retaining label-object associations. These data support findings from the adult memory literature, which suggest that variability supports learning by decontextualizing representations. We argue that these data are consistent with dynamic systems accounts of learning in which low-level entropy adds sufficient noise to the developmental system to precipitate a change in behavior. (shrink)