Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning
Soto D, Sheikh UA, Mei N, Santana R. 2020 Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning. R. Soc. Open Sci. 7: 192043. http://dx.doi.org/10.1098/rsos.192043
Resumen
How the brain representation of conceptual knowledge varies
as a function of processing goals, strategies and task-factors
remains a key unresolved question in cognitive neuroscience.
In the present functional magnetic resonance imaging study,
participants were presented with visual words during
functional magnetic resonance imaging (fMRI). During
shallow processing, participants had to read the items.
During deep processing, they had to mentally simulate the
features associated with the words. Multivariate classification,
informational connectivity and encoding models were used to
reveal how the depth of processing determines the brain
representation of word meaning. Decoding accuracy in
putative substrates of the semantic network was enhanced
when the depth processing was high, and the brain
representations were more generalizable in semantic space
relative to shallow processing contexts. This pattern was
observed even in association areas in inferior frontal and
parietal cortex. Deep information processing during mental
simulation also increased the informational connectivity
within key substrates of the semantic network. To further
examine the properties of the words encoded in brain activity,
we compared computer vision models—associated with the
image referents of the words—and word embedding.
Computer vision models explained more variance of the brain
responses across multiple areas of the semantic network.
These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to
visual representations and is highly distributed, including prefrontal areas previously implicated in
semantic control.