ATTENTION TO SHARED PERCEPTUAL FEATURES INFLUENCES EARLY NOUN-CONCEPT PROCESSING Ryan Peters 10.25394/PGS.8986370.v1 https://hammer.purdue.edu/articles/thesis/ATTENTION_TO_SHARED_PERCEPTUAL_FEATURES_INFLUENCES_EARLY_NOUN-CONCEPT_PROCESSING/8986370 Recent modeling work shows that patterns of shared perceptual features relate to the group-level order of acquisition of early-learned words (Peters & Borovsky, 2019). Here we present results for two eye-tracked word recognition studies showing patterns of shared perceptual features likewise influence processing of known and novel noun-concepts in individual 24- to 30-month-old toddlers. In the first study (Chapter 2, N=54), we explored the influence of perceptual connectivity on both initial attentional biases to known objects and subsequent label processing. In the second study (Chapter 3, N=49), we investigated whether perceptual connectivity influences patterns of attention during learning opportunities for novel object-features and object-labels, subsequent pre-labeling attentional biases, and object-label learning outcomes. Results across studies revealed four main findings. First, patterns of shared (visual-motion and visual-form and surface) perceptual features do relate to differences in early noun-concept processing at the individual level. Second, such influences are tentatively at play from the outset of novel noun-concept learning. Third, connectivity driven attentional biases to both recently learned and well-known objects follow a similar timecourse and show similar patterns of individual differences. Fourth, initial, pre-labeling attentional biases to objects relate to subsequent label processing, but do not linearly explain effects of connectivity. Finally, we consider whether these findings provide support for shared-feature-guided selective attention to object features as a mechanism underlying early lexico-semantic development. 2019-08-15 18:09:25 Word Learning Semantic representation Semantic Networks. Attentional biases perceptual features Linguistic Processes (incl. Speech Production and Comprehension) Developmental and Educational Psychology Knowledge Representation and Machine Learning