Incorporating Context into Language Encoding Models for fMRI
Language encoding models help explain language processing in the human brain by learning functions that predict brain responses from the language stimuli that elicited them. Current word embedding-based approaches treat each stimulus word independently and thus ignore the influence of context on language understanding. In this work, we instead build encoding models using rich contextual representations derived from an LSTM language model. Our models show a significant improvement in encoding performance relative to state-of-the-art embeddings in nearly every brain area. By varying the amount of context used in the models and providing the models with distorted context, we show that this improvement is due to a combination of better word embeddings learned by the LSTM language model and contextual information. We are also able to use our models to map context sensitivity across the cortex. These results suggest that LSTM language models learn high-level representations that are related to representations in the human brain.
The authors attempt to study how variable context length relates to the fMRI recordings of subjects who are listening to a story. They investigate variable context lengths through a 3-layer LSTM language model and test how well they can predict brain activity using representations from different layers and different context lengths.  Quality: I completely agree that context is important in representing language in the brain. However, I believe this paper has not done enough to address very likely confounders.   First off, it’s not clear how the encoding model is trained on the actual data set. As far as I understand, Figure 1 shows the pipeline at test time, not train time. How are the beta weights used for the fMRI data prediction in Section 5.1 obtained? If they are trained using the fMRI recordings and the corresponding stimuli from the data described in Section 2, then how did you handle multiple repetitions of a word in different contexts? I believe this is extremely important and can explain some of the results.  The corruption of context results in Section 5.2 are expected because the LSTM is trained to model language, not random “nonsensical” sequences of text, so of course its representations will deteriorate. When the representations are bad, the prediction of fMRI data is also bad, so the encoding model performance decreases. A much more convincing control would be to look at the same word in two different contexts m1 and m2 (of same length), and compare the performance of the encoding model of predicting the brain activity of the word in one context from the LSTM representations of both m1 and m2. Then you can also vary the length of m1 and m2.   The map results are speculative and are only for 1 subject -- why not show a mean over all subjects, or at least include all of the subject maps in the supplementary if these results are consistent?   Clarity: It is not clear how the encoding model was trained, which is extremely important. In general, it would be good to see a lot more detail about the experiments than is offered. Also it is unclear what kind of correlation is used for the encoding model.  Novelty: There does not seem to be methodological novelty. On the neuroscience side, using language models to show that context is important in the brain is also not a novel idea -- the paper even cites (though in an off-hand manner on line 41) one paper that uses an RNN LM to show that one can more accurately predict the brain activity for the next word by using the previous context (hidden layer of the RNN) than by using the embedding of the current word. The idea to study variable length context is interesting and novel, but I don’t believe that the experiments appropriately address this question.  Significance: If my concerns are not correct and the brain map results are consistent across subjects, then this work is significant for the neuroscience community.  Post rebuttal: The authors addressed two of my main concerns in their rebuttal: they showed results across additional subjects and presented results from a context swapping control experiment. However, in reading through the rebuttal and the paper an additional time, I have realized that there may be an additional confounder in the results, that is not controlled for in any of the experiments and could explain the main result.  It is known that the LSTM word representations contain contextual information, and that these representations are sensitive to the nearby context length and word-order within this nearby context (https://arxiv.org/pdf/1805.04623.pdf). So the question is whether the improvement in encoding model performance with longer context is due to the increase in this contextual information in the LSTM representations (as the authors hypothesize), or to something else (a confounder)?  One possible confounder is that increasing the context length of word W makes it more likely that word W will appear in this context, thereby allowing the LSTM representation of W in the longer context to be more influenced by W than it was in the shorter context. In this case, an encoding model trained to predict brain activity elicited by W from the longer context LSTM representations does not necessarily perform better due to the additional context, but possibly due to the fact that the LSTM representation is more similar to W. The more frequent W is, the worse the effect of this confounder could be. Considering that the presented results are over all words in the test set, including very frequent stop words, this alternative explanation could account for much of the improvement of the encoding model when the context length is increased.  Control experiment: The randomized context experiment they perform in the original paper does not control for the number of repetitions of word W, and it also has another downside that I pointed out in my original review. The swapped context experiment, as performed by the authors, also does not control for this confounder, because the swapped context is chosen so that it “has the fewest words in common with the original text” [line 30 in rebuttal]. An experiment that would account for the presented confounder is similar to the swapped context experiment, but would have the same number of occurrences of word W in the original and the swapped context.   Quantification of result significance: it is good that the authors present cortical map results for 3 additional subjects, however they are difficult to compare visually for consistency with the claimed results. In addition, it is difficult to judge how significant an increase in “encoding model performance” is with increased context without any notion of chance or hypothesis testing.   For the above reasons, my score remains the same and I recommend the paper for rejection. I strongly disagree with the other two reviewers.