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Building machines that adapt and compute like brains
Building machines that learn and think like humans is essential not only...
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May We Have Your Attention: Analysis of a Selective Attention Task
In this paper we present a deeper analysis than has previously been carr...
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Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning a...
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Hierarchical Selective Recruitment in Linear-Threshold Brain Networks - Part I: Intra-Layer Dynamics and Selective Inhibition
Goal-driven selective attention (GDSA) refers to the brain's function of...
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Refounding legitimacy towards Aethogenesis
The fusion of humans and technology takes us into an unknown world descr...
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Understanding Attention: In Minds and Machines
Attention is a complex and broad concept, studied across multiple discip...
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Computational Theories of Curiosity-Driven Learning
What are the functions of curiosity? What are the mechanisms of curiosit...
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What can computational models learn from human selective attention? A review from an audiovisual crossmodal perspective
Selective attention plays an essential role in information acquisition and utilization from the environment. In the past 50 years, research on selective attention has been a central topic in cognitive science. Compared with unimodal studies, crossmodal studies are more complex but necessary to solve real-world challenges in both human experiments and computational modeling. Although an increasing number of findings on crossmodal selective attention have shed light on humans' behavioral patterns and neural underpinnings, a much better understanding is still necessary to yield the same benefit for computational intelligent agents. This article reviews studies of selective attention in unimodal visual and auditory and crossmodal audiovisual setups from the multidisciplinary perspectives of psychology and cognitive neuroscience, and evaluates different ways to simulate analogous mechanisms in computational models and robotics. We discuss the gaps between these fields in this interdisciplinary review and provide insights about how to use psychological findings and theories in artificial intelligence from different perspectives.
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