Linked Life Data

16716907

Source:http://linkedlifedata.com/resource/pubmed/id/16716907

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2006-5-23
pubmed:abstractText
An important component of language acquisition and cognitive learning is gaze imitation. Infants as young as one year of age can follow the gaze of an adult to determine the object the adult is focusing on. The ability to follow gaze is a precursor to shared attention, wherein two or more agents simultaneously focus their attention on a single object in the environment. Shared attention is a necessary skill for many complex, natural forms of learning, including learning based on imitation. This paper presents a probabilistic model of gaze imitation and shared attention that is inspired by Meltzoff and Moore's AIM model for imitation in infants. Our model combines a probabilistic algorithm for estimating gaze vectors with bottom-up saliency maps of visual scenes to produce maximum a posteriori (MAP) estimates of objects being looked at by an observed instructor. We test our model using a robotic system involving a pan-tilt camera head and show that combining saliency maps with gaze estimates leads to greater accuracy than using gaze alone. We additionally show that the system can learn instructor-specific probability distributions over objects, leading to increasing gaze accuracy over successive interactions with the instructor. Our results provide further support for probabilistic models of imitation and suggest new ways of implementing robotic systems that can interact with humans over an extended period of time.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0893-6080
pubmed:author
pubmed:issnType
Print
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
299-310
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
A probabilistic model of gaze imitation and shared attention.
pubmed:affiliation
Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA. mhoffman@cs.washington.edu
pubmed:publicationType
Journal Article, Comparative Study