AIMC Topic: Language Development
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Quantifying the roles of visual, linguistic, and visual-linguistic complexity in noun and verb acquisition.
PloS one
May 23, 2025
Children often learn the meanings of nouns before they grasp the meanings of verbs. This discrepancy could arise from differences in the complexity of visual characteristics for categories that language describes, the inherent structure of language, ...
A developmental model of audio-visual attention (MAVA) for bimodal language learning in infants and robots.
Scientific reports
Sep 3, 2024
A social individual needs to effectively manage the amount of complex information in his or her environment relative to his or her own purpose to obtain relevant information. This paper presents a neural architecture aiming to reproduce attention mec...
Predicting autism traits from baby wellness records: A machine learning approach.
Autism : the international journal of research and practice
May 29, 2024
Timely identification of autism spectrum conditions is a necessity to enable children to receive the most benefit from early interventions. Emerging technological advancements provide avenues for detecting subtle, early indicators of autism from rout...
Grounded language acquisition through the eyes and ears of a single child.
Science (New York, N.Y.)
Feb 1, 2024
Starting around 6 to 9 months of age, children begin acquiring their first words, linking spoken words to their visual counterparts. How much of this knowledge is learnable from sensory input with relatively generic learning mechanisms, and how much ...
Transmission Versus Truth, Imitation Versus Innovation: What Children Can Do That Large Language and Language-and-Vision Models Cannot (Yet).
Perspectives on psychological science : a journal of the Association for Psychological Science
Oct 26, 2023
Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents. We present an alternative perspective. First, we argue that these artificial intelligence (AI) models are cultural ...
Dialogue with a conversational agent promotes children's story comprehension via enhancing engagement.
Child development
Nov 8, 2021
Dialogic reading, when children are read a storybook and engaged in relevant conversation, is a powerful strategy for fostering language development. With the development of artificial intelligence, conversational agents can engage children in elemen...
A little labeling goes a long way: Semi-supervised learning in infancy.
Developmental science
Sep 18, 2018
There is considerable evidence that labeling supports infants' object categorization. Yet in daily life, most of the category exemplars that infants encounter will remain unlabeled. Inspired by recent evidence from machine learning, we propose that i...
Too Much of a Good Thing: How Novelty Biases and Vocabulary Influence Known and Novel Referent Selection in 18-Month-Old Children and Associative Learning Models.
Cognitive science
Apr 6, 2018
Identifying the referent of novel words is a complex process that young children do with relative ease. When given multiple objects along with a novel word, children select the most novel item, sometimes retaining the word-referent link. Prior work i...
Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner.
Cognition
Jan 8, 2018
Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which 'reverse engineering' language development, i.e...
Using a social robot to teach gestural recognition and production in children with autism spectrum disorders.
Disability and rehabilitation. Assistive technology
Jul 4, 2017
While it has been argued that children with autism spectrum disorders are responsive to robot-like toys, very little research has examined the impact of robot-based intervention on gesture use. These children have delayed gestural development. We use...
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