Introduction
The cognitive processes involved in human language comprehension are complex and only partially identified. According to the dual-stream model of speech comprehension BIBREF1 , sound waves are first converted to phoneme-like features and further processed by a ventral stream that maps those features onto words and semantic structures, and a dorsal stream that (among other things) supports audio-short term memory. The mapping of words onto meaning is thought to be subserved by widely distributed regions of the brain that specialize in particular modalities — for example visual aspects of the word banana reside in the occipital lobe of the brain and are activated when the word banana is heard BIBREF2 — and the different representation modalities are thought to be integrated into a single coherent latent representation in the anterior temporal lobe BIBREF3 . While this part of meaning representation in human language comprehension is somewhat understood, much less is known about how the meanings of words are integrated together to form the meaning of sentences and discourses. One tool researchers use to study the integration of meaning across words is electroencephelography (EEG), which measures the electrical activity of large numbers of neurons acting in concert. EEG has the temporal resolution necessary to study the processes involved in meaning integration, and certain stereotyped electrical responses to word presentations, known as event-related potentials (ERPs), have been identified with some of the processes thought to contribute to comprehension.
In this work, we consider six ERP components that have been associated in the cognitive neuroscience and psycholinguistics literature with language processing and which we analyze in the data from BIBREF0 (see Figure FIGREF1 for spatial and temporal definitions of these ERP components). Three of these — the N400, EPNP, and PNP responses — are primarily considered markers for semantic processing, while the other three — the P600, ELAN, and LAN responses — are primarily considered markers for syntactic processing. However, the neat division of the ERP responses into either semantic or syntactic categories is controversial. The N400 response has been very well studied (for an overview see BIBREF4 ) and it is well established that it is associated with semantic complexity, but the features of language that trigger the other ERP responses we consider here are poorly understood. We propose to use a neural network pretrained as a language model to probe what features of language drive these ERP responses, and in turn to probe what features of language mediate the cognitive processes that underlie human language comprehension, and especially the integration of meaning across words.
Background
While a full discussion of each ERP component and the features of language thought to trigger each are beyond the scope of this document (for reviews see e.g. BIBREF0 , BIBREF2 , BIBREF4 , BIBREF5 , and BIBREF6 ), we introduce some basic features of ERP components to help in the discussion later. ERP components are electrical potential responses measured with respect to a baseline that are triggered by an event (in our case the presentation of a new word to a participant in an experiment). The name of each ERP component reflects whether the potential is positive or negative relative to the baseline. The N400 is so-named because it is Negative relative to a baseline (the baseline is typically recorded just before a word is presented at an electrode that is not affected by the ERP response) and because it peaks in magnitude at about 400ms after a word is presented to a participant in an experiment. The P600 is Positive relative to a baseline and peaks around 600ms after a word is presented to a participant (though its overall duration is much longer and less specific in time than the N400). The post-N400 positivity is so-named because it is part of a biphasic response; it is a positivity that occurs after the negativity associated with the N400. The early post-N400 positivity (EPNP) is also part of a biphasic response, but the positivity has an eariler onset than the standard PNP. Finally, the LAN and ELAN are the left-anterior negativity and early left-anterior negativity respectively. These are named for their timing, spatial distribution on the scalp, and direction of difference from the baseline. It is important to note that ERP components can potentially cancel and mask each other, and that it is difficult to precisely localize the neural activity that causes the changes in electrical potential at the electrodes where those changes are measured.
Related Work
This work is most closely related to the paper from which we get the ERP data: BIBREF0 . In that work, the authors relate the surprisal of a word, i.e. the (negative log) probability of the word appearing in its context, to each of the ERP signals we consider here. The authors do not directly train a model to predict ERPs. Instead, models of the probability distribution of each word in context are used to compute a surprisal for each word, which is input into a mixed effects regression along with word frequency, word length, word position in the sentence, and sentence position in the experiment. The effect of the surprisal is assessed using a likelihood-ratio test. In BIBREF7 , the authors take an approach similar to BIBREF0 . The authors compare the explanatory power of surprisal (as computed by an LSTM or a Recurrent Neural Network Grammar (RNNG) language model) to a measure of syntactic complexity they call “distance" that counts the number of parser actions in the RNNG language model. The authors find that surprisal (as predicted by the RNNG) and distance are both significant factors in a mixed effects regression which predicts the P600, while the surprisal as computed by an LSTM is not. Unlike BIBREF0 and BIBREF7 , we do not use a linking function (e.g. surprisal) to relate a language model to ERPs. We thus lose the interpretability provided by the linking function, but we are able to predict a significant proportion of the variance for all of the ERP components, where prior work could not. We interpret our results through characterization of the ERPs in terms of how they relate to each other and to eye-tracking data rather than through a linking function. The authors in BIBREF8 also use a recurrent neural network to predict neural activity directly. In that work the authors predict magnetoencephalography (MEG) activity, a close cousin to EEG, recorded while participants read a chapter of Harry Potter and the Sorcerer’s Stone BIBREF9 . Their approach to characterization of processing at each MEG sensor location is to determine whether it is best predicted by the context vector of the recurrent network (prior to the current word being processed), the embedding of the current word, or the probability of the current word given the context. In future work we also intend to add these types of studies to the ERP predictions.
Discussion
In this work we find that all six of the ERP components from BIBREF0 can be predicted above chance by a model which has been pretrained using a language modeling objective and then directly trained to predict the components. This is in contrast to prior work which has successfully linked language models to the N400 BIBREF0 and P600 BIBREF7 but not the other ERP components. We also note that contrary to BIBREF7 , we find that an LSTM does contain information that can be used to predict EEG data, and in particular that it can predict the P600. We speculate that the analysis used in BIBREF7 did not find reliable effects because the language models were related to the EEG data through functions chosen a priori (the surprisal, and the `distance' metric). These functions, though interpretable, might be interpretable at the cost of losing much of the information in the representations learned by the network.
In addition, we show through our multitask learning analysis that information is shared between ERP components, and between ERP components and behavioral data. Although these relationships must be viewed with caution until they can be verified across multiple datasets and with more variation in neural network architectures, here we consider some potential reasons for our findings. The broad point we wish to make is that by better understanding which ERP components share information with each other and with behavioral data through the type of analysis we present here (multitask learning) or other means, we can better understand what drives each ERP component and in turn the processes involved in human language comprehension.
Conclusion
We have shown that ERP components can be predicted from neural networks pretrained as language models and fine-tuned to directly predict those components. To the best of our knowledge, prior work has not successfully used statistical models to predict all of these components. Furthermore, we have shown that multitask learning benefits the prediction of ERP components and can suggest how components relate to each other. At present, these joint-training benefit relationships are only suggestive, but if these relationships ultimately lead to insights about what drives each ERP component, then the components become more useful tools for studying human language comprehension. By using multitask learning as a method of characterization, we have found some expected relationships (LAN+P600 and ELAN+P600) and several more surprising relationships. We believe that this is exactly the kind of finding that makes multitask learning an interesting exploratory technique in this area. Additionally, we have shown that information can be shared between heterogeneous types of data (eye-tracking, self-paced reading, and ERP components) in the domain of human language processing prediction, and in particular between behavioral and neural data. Given the small datasets associated with human language processing, using heterogeneous data is a potentially major advantage of a multitask approach. In future work, we will further explore what information is encoded into the model representations when neural and behavioral data are used to train neural networks, and how these representations differ from the representations in a model trained on language alone.
Acknowledgments
We thank our reviewers for their valuable feedback. This work is supported in part by National Institutes of Health grant number U01NS098969.
Appendix
Here we present a visualization (Figure FIGREF21 ) of the results presented in Table TABREF9 of the main paper, and a visualization (Figure FIGREF22 ) of a more complete set of results from which the information in Table TABREF16 of the main paper is drawn. We also show supplemental results for variants of our primary analysis on multitask learning with eye-tracking, self-paced reading time and ERP data. In the variants we modify the input representation to our decoder network to see whether the relationships between the behavioral data and neural activity appear to be consistent with different choices of encoder architectures. Additional (and more varied) choices or architectures are left to future work. The results in Table TABREF23 reflect using only the forward-encoder (rather than the bi-LSTM) in the encoder network, while the results in Table TABREF24 reflect using only the word embeddings (i.e. bypassing the LSTM entirely). While the results are clearly worse for each of these choices of architecture than for using a bi-LSTM encoder, the relationships between the behavioral data and the ERP signals is qualitatively similar. Finally, TABREF25 shows the Pearson correlation coefficient between different measures. We note that the patterns of correlation are different than the patterns of which measures benefit from joint training with each other.

Question: What datasets are used?
Answer: Answer with content missing: (Whole Method and Results sections) The primary dataset we use is the ERP data collected and computed by Frank et al. (2015), and we also use behavioral data (eye-tracking data and self-paced reading times) from Frank et al. (2013) which were collected on the same set of 205 sentences.
Select:
- ERP data collected and computed by Frank et al. (2015)
- behavioral data (eye-tracking data and self-paced reading times) from Frank et al. (2013)
the ERP data: BIBREF0