  Several popular Transformer based language models have been found to be
successful for text-driven brain encoding. However, existing literature
leverages only pretrained text Transformer models and has not explored the
efficacy of task-specific learned Transformer representations. In this work, we
explore transfer learning from representations learned for ten popular natural
language processing tasks (two syntactic and eight semantic) for predicting
brain responses from two diverse datasets: Pereira (subjects reading sentences
from paragraphs) and Narratives (subjects listening to the spoken stories).
Encoding models based on task features are used to predict activity in
different regions across the whole brain. Features from coreference resolution,
NER, and shallow syntax parsing explain greater variance for the reading
activity. On the other hand, for the listening activity, tasks such as
paraphrase generation, summarization, and natural language inference show
better encoding performance. Experiments across all 10 task representations
provide the following cognitive insights: (i) language left hemisphere has
higher predictive brain activity versus language right hemisphere, (ii)
posterior medial cortex, temporo-parieto-occipital junction, dorsal frontal
lobe have higher correlation versus early auditory and auditory association
cortex, (iii) syntactic and semantic tasks display a good predictive
performance across brain regions for reading and listening stimuli resp.
