Published in

Cerebral Cortex Communications, 2021

DOI: 10.1093/texcom/tgab002



Export citation

Search in Google Scholar

Meta-analyses support a taxonomic model for representations of different categories of audio-visual interaction events in the human brain

Distributing this paper is prohibited by the publisher
Distributing this paper is prohibited by the publisher

Full text: Unavailable

Red circle
Preprint: archiving forbidden
Red circle
Postprint: archiving forbidden
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO


Abstract Our ability to perceive meaningful action events involving objects, people and other animate agents is characterized in part by an interplay of visual and auditory sensory processing and their cross-modal interactions. However, this multisensory ability can be altered or dysfunctional in some hearing and sighted individuals, and in some clinical populations. The present meta-analysis sought to test current hypotheses regarding neurobiological architectures that may mediate audio-visual multisensory processing. Reported coordinates from 82 neuroimaging studies (137 experiments) that revealed some form of audio-visual interaction in discrete brain regions were compiled, converted to a common coordinate space, and then organized along specific categorical dimensions to generate activation likelihood estimate (ALE) brain maps and various contrasts of those derived maps. The results revealed brain regions (cortical “hubs”) preferentially involved in multisensory processing along different stimulus category dimensions, including (1) living versus non-living audio-visual events, (2) audio-visual events involving vocalizations versus actions by living sources, (3) emotionally valent events, and (4) dynamic-visual versus static-visual audio-visual stimuli. These meta-analysis results are discussed in the context of neurocomputational theories of semantic knowledge representations and perception, and the brain volumes of interest are available for download to facilitate data interpretation for future neuroimaging studies.