47,286 research outputs found
The ITALK project : A developmental robotics approach to the study of individual, social, and linguistic learning
This is the peer reviewed version of the following article: Frank Broz et al, âThe ITALK Project: A Developmental Robotics Approach to the Study of Individual, Social, and Linguistic Learningâ, Topics in Cognitive Science, Vol 6(3): 534-544, June 2014, which has been published in final form at doi: http://dx.doi.org/10.1111/tops.12099 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." Copyright © 2014 Cognitive Science Society, Inc.This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.Peer reviewe
Modeling Human Visual Search Performance on Realistic Webpages Using Analytical and Deep Learning Methods
Modeling visual search not only offers an opportunity to predict the
usability of an interface before actually testing it on real users, but also
advances scientific understanding about human behavior. In this work, we first
conduct a set of analyses on a large-scale dataset of visual search tasks on
realistic webpages. We then present a deep neural network that learns to
predict the scannability of webpage content, i.e., how easy it is for a user to
find a specific target. Our model leverages both heuristic-based features such
as target size and unstructured features such as raw image pixels. This
approach allows us to model complex interactions that might be involved in a
realistic visual search task, which can not be easily achieved by traditional
analytical models. We analyze the model behavior to offer our insights into how
the salience map learned by the model aligns with human intuition and how the
learned semantic representation of each target type relates to its visual
search performance.Comment: the 2020 CHI Conference on Human Factors in Computing System
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