Abstract
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 intelligent computational 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.
Highlights
The real world is complex, uncertain and rich in dynamic ambiguous stimuli
Song et al (2017) conducted a mice experiment by using a task with audiovisual conflicts, where audition was required to dominate vision. They found that when the conflict occurred, the co-activation of the primary visual and auditory cortices suppressed the response evoked by vision but maintained the response evoked by audition in the posterior parietal cortex (PPC)
The current review summarizes experimental findings, theories, and model approaches of audiovisual unimodal and crossmodal selective attention from psychology, neuroscience, and computer science perspective
Summary
“The art of being wise is knowing what to overlook.” –William James, 1842-1910. The real world is complex, uncertain and rich in dynamic ambiguous stimuli. It is considered to be instinctive and spontaneous and often results in a reflexive saccade (Smith et al, 2004; Styles, 2006) Another point of view distinguishes between “covert” and “overt” orienting attention: covert attention can attend events or objects with the absence of eyes movement, while overt attention guides the fovea to the stimulus directly with eyes or head movements (Posner, 1980). The development and application of technical measurements and methods like functional magnetic resonance imaging (fMRI), Magnetoencephalography (MEG), and state-ofthe-art artificial neural networks (ANN) and deep learning (DL) open up a new window for studies on humans, primates, and robots Such new findings should be valuated and integrated into the current framework. We aim to integrate selective attention concepts, theories, behavioral, and neural mechanisms studied by the unimodal and crossmodal experiment designs. We discuss the current limitations and the future trends of utilization and implications of human selective attention models in artificial intelligence
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