Understanding language-elicited EEG data by predicting it from a fine-tuned language model
Electroencephalography (EEG) recordings of brain activity taken while participants read or listen to language are widely used within the cognitive neuroscience and psycholinguistics communities as a tool to study language comprehension. Several time-locked stereotyped EEG responses to word-presentations -- known collectively as event-related potentials (ERPs) -- are thought to be markers for semantic or syntactic processes that take place during comprehension. However, the characterization of each individual ERP in terms of what features of a stream of language trigger the response remains controversial. Improving this characterization would make ERPs a more useful tool for studying language comprehension. We take a step towards better understanding the ERPs by fine-tuning a language model to predict them. This new approach to analysis shows for the first time that all of the ERPs are predictable from embeddings of a stream of language. Prior work has only found two of the ERPs to be predictable. In addition to this analysis, we examine which ERPs benefit from sharing parameters during joint training. We find that two pairs of ERPs previously identified in the literature as being related to each other benefit from joint training, while several other pairs of ERPs that benefit from joint training are suggestive of potential relationships. Extensions of this analysis that further examine what kinds of information in the model embeddings relate to each ERP have the potential to elucidate the processes involved in human language comprehension.
Introduction Background Related Work Discussion Conclusion Acknowledgments Appendix
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.
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.
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.
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.
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.
We thank our reviewers for their valuable feedback. This work is supported in part by National Institutes of Health grant number U01NS098969.
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.
Which two pairs of ERPs from the literature benefit from joint training? What datasets are used?
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Answer with content missing: (Whole Method and Results sections) Self-paced reading times widely benefit ERP prediction, while eye-tracking data seems to have more limited benefit to just the ELAN, LAN, and PNP ERP components.
Select:
- ELAN, LAN
- PNP ERP
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 future work we also intend to add these types of studies to the ERP predictions.

Discussion
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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)
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 future work we also intend to add these types of studies to the ERP predictions.

Discussion
the ERP data: BIBREF0
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.
This work is most closely related to the paper from which we get the ERP data: BIBREF0 . 
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Figure 1: The electrodes from which each event-related potential was recorded in the data from Frank et al. (2015) (after figure 3 in (Frank et al., 2015)). The bottom portion of the figure shows a top-down schematic of the electrode locations with the nose facing towards the top of the page. Each ERP is the mean potential from all of the indicated electrodes during a specific time-window, creating a single scalar value per ERP per word. Overlapping circles indicate multiple ERPs recorded from the same electrode. The ELAN is measured from 125-175ms after stimulus onset, the LAN from 300-400ms, the N400 from 300ms-500ms, the EPNP from 400-600ms, the P600 from 500-700ms, and the PNP from 600-700ms. Figure 2: The model uses an encoder based on the architecture and regularization in Merity et al. (2017) and pretrained by Howard and Ruder (2018). Within this architecture 2 independent 3-layer LSTM models encode a sentence. The context-embeddings output from each encoder are then concatenated together to give a single representation to each word in the sentence. These concatenated context-embeddings are fed into a causal-convolution, which learns a function to combine each pair of context-representations into a pair-embedding. A rectified linear unit (ReLU) non-linearity is applied to the pair-embedding, after which independent linear layers map the pairembedding along with the log-probability of a word and the word-length to a prediction of each ERP or behavioral signal. Table 1: Proportion of variance explained (POVE) for each of the ERP components (mean of 100 training runs). The second column in each cell shows which ERP components in addition to the target ERP component were included in training. All combinations of training signals were explored. Shown is the best combination for each ERP target as well as every combination which is (i) significantly different from training on the target component alone, (ii) not significantly different from the best training combination, and (iii) uses no more than the number of signals used by the best combination. The N400 is predicted best when only the N400 signal is included in training. All values are significantly different from 0. GROUP A refers to (PNP, ELAN, LAN, P600) and GROUP B refers to (EPNP, ELAN, LAN, P600). Figure 3: The mean squared error (MSE) for prediction of each of the ERP signals during each epoch of training (mean of 100 training runs). The first 2 epochs have been omitted for clarity. During the first 20 epochs (lavender background), only the decoder parameters are modified. During the next 20 epochs (light blue background), the parameters in the final layer of the encoder are also modified. During the last 20 epochs (pink background), all of the parameters are modified. Note that in this model architecture, information can be shared between ERP signals even when only the decoder is modified. The figure shows the MSE when separate models are trained for each ERP independently (a), the MSE when a single model is trained on all ERPs jointly (b), and the difference between these two scenarios (c). The top row in each column shows the MSE on the training data while the bottom row shows the MSE on the validation data. In the bottom row right, the dotted vertical lines indicate the epoch at which the minimum MSE is reached in the lower of the independent or joint training. The LAN, EPNP, and PNP all show modest benefits from joint training before overfitting sets in (the minimum value occurs in the joint training scenario), while all ERP signals other than the N400 show reduced overfitting in joint training. Table 2: Proportion of variance explained (POVE) for each of the ERP components (mean of 100 training runs). +ERP indicates the best combination of ERP training signals for the target ERP component, + READ indicates the inclusion of self-paced reading times, +EYE indicates the inclusion of eye-tracking data, and bold font indicates a significant difference from training on the target component alone. Figure 4: The proportion of variance explained for prediction of each of the ERP signals (mean of 100 training runs). The target ERP is indicated by color; each group of bars shows performance for a different target ERP. The top bar in each group shows the proportion of variance explained when the model is trained using only the target ERP. The bottom bar in each group shows the maximum proportion of variance explained over all combinations of training ERPs (or in the case of the N400, the second best). Also shown in each group are any training combinations that (i) used no more than the number of ERP signals used by the combination that achieved the maximum, and (ii) which were not significantly different from the maximum. Bars are statistically different from each other if a black dot on one bar is connected by a contiguous vertical line to a white dot on the other bar. The bars in the N400 group are not significantly different from each other. The N400 signal is best predicted when the model is trained on just that signal. In every other group, there is at least one ERP that, when combined with the target ERP during training, improves the prediction of the target ERP. The results suggest that these pairs are related: (LAN, P600), (LAN, EPNP), (LAN, PNP), (ELAN, N400), (ELAN, EPNP), (ELAN, PNP), (ELAN, P600), (EPNP, P600). Table 3: Proportion of variance explained for each of the ERP components when using only the forward direction of the encoder (mean of 100 training runs). +ERP indicates the best combination of ERP training signals for the target ERP component, + READ indicates the inclusion of self-paced reading times, +EYE indicates the inclusion of eye-tracking data, and bold font indicates a significant difference from training on the target component alone. Figure 5: The proportion of variance explained for prediction of each of the ERP signals (mean of 100 training runs). The target ERP is indicated by color; each group of bars shows performance for a different target ERP. The top bar in each group shows the proportion of variance explained when the model is trained using only the target ERP. Moving down, the next bar in each group, labeled ERP shows the proportion of variance explained by the best combination of ERP signals for the target ERP. The other bars in each group moving from top to bottom show training variations that use behavioral data with either just the target ERP, or with the best combination of ERP signals. READ denotes self-paced reading data, and EYE denotes all four eye-tracking measures (in this analysis we use right-bounded pass time, gaze duration, go-past time, and first-fixation duration). Pairs of bars are significantly different from each other (paired t-test, false discovery rate ¡ 0.01) if a black dot on one bar is connected to a white dot on the other bar by a contiguous vertical line. Self-paced reading time benefits prediction of all target ERP components except the N400. In the case of the ELAN, LAN, and PNP, self-paced reading time also has marginal benefit compared to the best combination of ERP training signals. Eye-tracking data benefits prediction of the ELAN, P600, and PNP components. Table 4: Proportion of variance explained for each of the ERP components when using only the word embeddings as input to the decoder and bypassing the LSTM entirely (mean of 100 training runs). +ERP indicates the best combination of ERP training signals for the target ERP component, + READ indicates the inclusion of self-paced reading times, +EYE indicates the inclusion of eye-tracking data, and bold font indicates a significant difference from training on the target component alone. Table 5: Raw Pearson’s correlation coefficients (computed on content words after the standardization and participant-averaging) between each neural and behavioral measure and each other measure. FIX indicates firstfixation time, PASS indicates first-pass time, GO indicates go-past time, RIGHT indicates right-bounded reading time, and READ indicates self-paced reading. Many of the measures are highly correlated, but the pattern of correlations is different from the pattern of benefits that we find during joint-training. In particular we note that the N400 is correlated with the other ERP signals, and yet we do not see benefit in prediction of the N400 when jointly training a model to predict it and other signals.
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