What can computational models learn from human selective attention? A review from an audiovisual crossmodal perspective

09/05/2019
by   Di Fu, et al.
10

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.

READ FULL TEXT
research
09/18/2023

Survey of Consciousness Theory from Computational Perspective

Human consciousness has been a long-lasting mystery for centuries, while...
research
11/11/2017

Building machines that adapt and compute like brains

Building machines that learn and think like humans is essential not only...
research
03/25/2019

Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning a...
research
06/29/2006

May We Have Your Attention: Analysis of a Selective Attention Task

In this paper we present a deeper analysis than has previously been carr...
research
09/05/2018

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...
research
02/28/2018

Computational Theories of Curiosity-Driven Learning

What are the functions of curiosity? What are the mechanisms of curiosit...
research
07/31/2013

A Prototyping Environment for Integrated Artificial Attention Systems

Artificial visual attention systems aim to support technical systems in ...

Please sign up or login with your details

Forgot password? Click here to reset