Implicit multisensory associations influence voice recognition
Katharina von Kriegstein (1,2*) and Anne-Lise Giraud (1,3)
PLoS Biol 4(10): e326 (2006)
1 Department of Neurology, Johann Wolfgang Goethe University,
Frankfurt am Main, Germany
2 Wellcome Department of Imaging Neuroscience, Functional Imaging
Laboratory, London, United Kingdom
3 Département d'Études Cognitives, Ecole Normale Supérieure, Paris,
France
* To whom correspondence should be addressed.
E-mail:
Natural objects provide partially redundant information to the brain
through different sensory modalities. For example, voices and faces
both give information about the speech content, age, and gender of a
person. Thanks to this redundancy, multimodal recognition is fast,
robust, and automatic. In unimodal perception, however, only part of
the information about an object is available. Here, we addressed
whether, even under conditions of unimodal sensory input, crossmodal
neural circuits that have been shaped by previous associative learning
become activated and underpin a performance benefit. We measured brain
activity with functional magnetic resonance imaging before, while, and
after participants learned to associate either sensory redundant
stimuli, i.e. voices and faces, or arbitrary multimodal combinations,
i.e. voices and written names, ring tones, and cell phones or brand
names of these cell phones. After learning, participants were better
at recognizing unimodal auditory voices that had been paired with
faces than those paired with written names, and association of voices
with faces resulted in an increased functional coupling between voice
and face areas. No such effects were observed for ring tones that had
been paired with cell phones or names. These findings demonstrate that
brief exposure to ecologically valid and sensory redundant stimulus
pairs, such as voices and faces, induces specific multisensory
associations. Consistent with predictive coding theories, associative
representations become thereafter available for unimodal perception
and facilitate object recognition. These data suggest that for natural
objects effective predictive signals can be generated across sensory
systems and proceed by optimization of functional connectivity between
specialized cortical sensory modules.
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