Recurrent Neural Networks (RNNs) are widely used in both psycholinguistics and Natural Language Processing (NLP). Psycholinguists have looked to RNNs as an architecture for modelling human sentence processing (for a recent review, see Frank et al., Frank2017). RNNs have been used to account for human reading times [29, 15] and N400 amplitudes in the EEG signal during reading [13, 30, 5, 12]. Since the introduction of the Simple Recurrent Network (SRN) 
, different RNN architectures have been proposed that address the issue that SRNs had with capturing long term patterns by adding gating mechanisms that control the flow of information over time; allowing the networks to weigh old and new inputs and memorise or forget information when appropriate. The most well known of these are the Long Short-Term Memory (LSTM)
and the Gated Recurrent Unit (GRU).
These gated RNNs have successfully been applied to NLP tasks such as translation, caption generation and speech recognition [3, 37, 38, 27]. However, a recent study which compared SRN, GRU and LSTM models’ ability to predict reading times and N400 amplitudes found no significant differences between the three recurrent architectures .
In essence, all these RNN types process sequential information by recurrence. Previous input is represented as the hidden state of the recurrent computation and each new input is processed and combined with the hidden state. Recently, a new neural network architecture called the Transformer has been introduced . Importantly, the Transformer is not simply an improved type of RNN as the LSTM and GRU were. The Transformer is a fundamentally different type of architecture that does not use recurrence at all. A Transformer cell as originally proposed by Vaswani et al. Vaswani2016, consists of self-attention layers  followed by a linear feed forward layer. In contrast to recurrent processing, self-attention layers are allowed to ‘attend’ to parts of previous input directly.
. Pre-trained Transformer based models such as BERT and GPT-2 make it possible to employ the power of networks trained on huge amounts of data. Many studies have fine-tuned such models and broken benchmark NLP scores. Not much is known however, about how the Transformer fares as a model of human sentence processing. The success of RNNs in explaining human behavioural and neurophysiological data suggests that something akin to recursive processing might be involved in human sentence processing and as such RNNs seem more cognitively plausible than the Transformer. Especially the direct access that attention operations have to past input regardless of temporal distance is not biologically plausible. Even though the Transformer seems less biologically plausible it has not yet been confirmed that the Transformer is a worse model of sentence comprehension.
We will compare the Transformer with the GRU and investigate how well they perform as models of human sentence processing. We model human sentence processing using Transformer and GRU based language models (LMs). We compare how well their word-by-word surprisal predicts human processing effort as measured by self-paced reading, eye tracking and electroencephalography (N400 response) using the same human reading data as Aurnhammer and Frank Aurnhammer2017 used to compare RNN architectures. We think the introduction of the Transformer merits a similar comparison as the differences between Transformers and RNNs are more fundamental than the differences between RNN types. Looking ahead to our results, we surprisingly find that the Transformer outperforms our RNN models.
2.1 Models of human sentence processing
An important question in human sentence processing research is why some words are more difficult to process than others. It has long been known that more predictable words are generally read faster than less predictable words and are more likely to be skipped 
. Predictability has been formalised as surprisal, a measure which can be derived from LMs. To generate surprisal values, LMs are trained to estimate the probability of the next word in a sentence given the preceding context. In this sense, a LM has an expectation of a wordat time given the preceding words . We formally measure this as the surprisal of a word given by: . In surprisal theory, surprisal acts as a ‘causal bottleneck’ between computational models and behavioural observations , meaning that for instance the model architecture (Transformer or RNN) only affects predictions about human processing difficulty through the generated word probabilities.
Surprisal has long been related to human word processing effort in sentence comprehension. The central idea of such expectation-based theories of sentence comprehension is that less expected words lead to more processing effort. For instance, in psycholinguistics it is common to take reading times as a measure of word processing difficulty and the positive correlation between reading time and surprisal has firmly been established [16, 24, 21, 29, 34, 22] with Goodkind and Bicknell Goodkind2018 recently showing that the predictive power of surprisal values increases linearly with the quality of the language model.
High surprisal has been shown to correlate with greater neural activity in fMRI and MEG studies [10, 18]. The N400, a brain potential peaking around 400 ms after stimulus onset and associated with semantic incongruity , has been shown to correlate with word surprisal in both EEG and MEG studies [36, 13].
Most recent models of processing difficulty used RNN-based LMs. In this paper, we will compare RNN and Transformer based LMs on their ability to predict human reading data. The work most closely related to the current study is that by Aurnhammer and Frank Aurnhammer2017. They compared SRNs, LSTMs and GRUs; three RNN types differing in how they integrate their ‘memory’ of past input with the next input in the sequence, on human reading data from three psycholinguistic experiments. Despite the GRU and LSTM generally outperforming the SRN on NLP tasks, Aurnhammer and Frank found no difference in how well the models’ surprisal predicted human processing effort in the self-paced reading, eye-tracking and EEG experiments. The Transformer has to the best of our knowledge not yet been evaluated as a model of human sentence processing.
2.2 Comparing RNN and Transformer architectures
For a complete overview of the Transformer and our implementation we refer to Vaswani2016 and our code https://github.com/DannyMerkx/next_word_prediction. Here we briefly highlight the difference between RNNs and the Transformer in how the models process sequential information. The way activation flows through the network is represented in Figure 1, which shows an example with a five-word sentence. We only consider uni-directional versions of both the Transformer and RNNs here, since language modelling is trivial for a bi-directional network. Note that Figure 1 only shows how activation is propagated through time and across layers, not how specific architectures compute the hidden states (see [9, 19, 6, 35] for specifics on the SRN, LSTM, GRU, and Transformer respectively).
In an RNN, incoming information is immediately processed and represented as a hidden state. The next token in the sequence is again immediately processed and combined with the previous hidden state to form a new hidden state. Across layers, each time-step also only gets to see its corresponding hidden state from the previous layer and the hidden state of the previous time-step. So, processing is immediate and incremental. Information from previous time-steps is encoded in the hidden state, but this state is limited in how much it can encode so decay of previous time-steps is implicit.
The Transformer’s attention layer allows each input to look at all previous time-steps. Hidden states are a weighted combination of all time-steps seen so far. This basically unlimited memory is a big conceptual difference with RNNs, where long-distance dependencies can only be propagated through the hidden states. Processing is not incremental over time as in a single layer: the processing of word is not dependent on the results of processing words through . While the RNN is inherently sequential, the Transformer can only use order information if it is explicitly added to the input or if the network is multi-layered. Consider in the first layer which is based on and . Hidden state does not depend on the order of the previous inputs (any order will result in the same hidden state). However, depends on and . If the order of the inputs is different, will be the same hidden state but and will not, resulting in a different hidden state at .111Note that this is only true for uni-directional Transformers. Bi-directional Transformers are not sensitive to order unless explicit order information is given.
RNNs handling of sequential inputs make them seemingly more plausible as a cognitive model. Christiansen and Chater Christiansen2015 argue for a ‘now-or-never’ bottleneck in language processing; incoming inputs need to be rapidly recoded and passed on for further processing to prevent being interfered with by the rapidly incoming stream of new material. In line with this theory, Futrell et al. Futrell2020 proposed a model of lossy-context surprisal based on a lossy representation of memory. Recurrent processing, where input is forgotten as soon as it is processed and only available for subsequent processing through a bounded size hidden state, is more compatible with these theories than the Transformer’s attention operation.
We train LMs with Transformer and GRU architectures and compare how well their surprisal explains human behavioural and neural data. It has been shown that the predictive power of surprisal is a linear function of language model quality . So, to separate the effects of LM quality from the effects of the architectural differences, we compare the architectures at equal language modelling capability. A state-of-the-art pre-trained model such as GPT-2 
can likely achieve a lower perplexity, but we opt for training our own LMs to have control over the training material and hyperparameters such that a fair comparison between the two architectures can be made.
3.1 Language model architectures
In this work we test only the Gated Recurrent Unit (GRU). Aurnhammer and Frank Aurnhammer2017 extensively compared GRUs, LSTMs and SRNs on the same behavioural and electrophysiological data that we use here and found no significant differences. We use the GRU because of the three it was most recently introduced and it has fewer weights than the LSTM . We train single-layer and two-layer LMs for both the GRU and the Transformer, to also investigate the effect of network depth.
First we trained a GRU model where we used the same architecture as used in Aurnhammer2017: an embedding layer with 400 dimensions per word, a 500-unit GRU layer followed by a 400-unit linear layer with a tanh activation function and finally an output layer with log-softmax activation function. All LMs used in this experiment use randomly initialised embedding layers, that is, no pre-trained word embeddings were used.
To minimise the differences between the LMs we picked parameters for the Transformer such that the total number of weights is as close as possible to the GRU model. We also make sure the embedding layers for the models share the same initial weights. The Transformer model has an embedding layer with 400 dimensions per word followed by a single Transformer layer with 8 attention heads and a fully connected layer with 1024 units and finally an output layer with log-softmax activation function. The Transformer was described as an encoder-decoder model Vaswani2016. We use the encoder side of the model, that is, the Transformer with one instead of two attention operations.222 The decoder has a self-attention operation and a second attention operation attending to a context vector given by the encoder. In the current setting the encoder with only self-attention is more appropriate, since there is no context vector.
The decoder has a self-attention operation and a second attention operation attending to a context vector given by the encoder. In the current setting the encoder with only self-attention is more appropriate, since there is no context vector.
We implement the Transformer in PyTorch following and make our implementation including all training and analysis scripts available on https://github.com/DannyMerkx/next_word_prediction. The total number of parameters for our single-layer GRU and Transformer models are 9,673,137 and 9,581,961 respectively.
We also train two-layer GRU and Transformer models. It is known that neural networks tend to increase in expressiveness with depth, learning more complex representations (e.g. [14, 1]). Furthermore, an additional layer allows the Transformer to make use of implicit order information. The analysis (see Section 3.5) showed that the two-layer Transformer outperformed the single-layer Transformer in explaining the human reading data so we decided to train a four-layer Transformer as well too see if this trend continues. We did not see a performance increase in the two-layer GRU over the the single-layer GRU however and did not increase the layer depth further. As the analysis showed that the Transformer outperformed the GRU we trained a GRU model with a Transformer self-attention operation in between the GRU layer and the linear layer to see whether the Transformer’s advantage was solely due to the unlimited access to past states.
3.2 Training materials
We train our LMs on Section 1 of the English Corpora from the Web (ENCOW 2014) , consisting of random sentences taken from the web. We first exclude tokens containing numerical values or punctuation other than hyphens and apostrophes. We treat common contractions such as ‘don’t’ as a single token instead of two. Following Aurnhammer2017 we then select the 10,000 most frequent word types from ENCOW. 134 word types from the test data (see Section 3.4) that were not covered by these most frequent words were added for a final vocabulary of 10,134 words. We selected the sentences from ENCOW that consisted only of words in the vocabulary and limit the sentence length to 39 tokens, reflecting the longest sentence in the test data. Our training data contains 5,855,671 sentences with a total of 84,938,722 tokens.
3.3 Language model training
We use a standard next word prediction task with cross-entropy loss to train the LMs. Because the attention operation in the Transformer inherently allows each position in the sentence to attend to all other words in the sentence (including future words) we apply a mask to the upper diagonal of the attention matrix such that each position can only attend to itself and previous positions.
To account for random effects of weight initialisation and data presentation order we train eight LMs of each type described in section 3.1 and share the random seeds between the LM types so each random presentation order and embedding layer initialisation is present in each of the LM types. The LMs were trained for two epochs using stochastic gradient descent with a momentum of 0.9. The initial learning rates were chosen such that the models still improved near the end of the second epoch while keeping the language modelling performance of the GRU and Transformer models as similar as possible. The initial learning rate for the GRU models was 0.02 and for the Transformer models 0.005. The learning rate was halved afterand all of the sentences during the first epoch and then kept constant over the second epoch. The LMs were trained on minibatches of ten sentences.
3.4 Human reading data
We use the LMs to calculate surprisal values for sentences used in human sentence processing experiments and evaluate our models on how well the surprisal values predict human processing effort. We use the self paced reading (SPR) and eye-tracking (ET) data from Frank2013 and the electroencephalography (EEG) data from Frank2015. In these experiments, participants read English sentences from unpublished novels. In the SPR and EEG experiments, the participants were presented sentences one word at a time. In the SPR experiment the reading was self paced (i.e., participants proceed to the next word with a key-press) while in the EEG experiment words had a fixed presentation time. In the ET experiments, participants were shown full sentences while an eye tracking device monitored which word was fixated. The SPR stimuli consists of 361 sentences, with the EEG and ET stimuli being a subset of the SPR stimuli. The experimental measures representing processing effort of a word are reading time for the SPR data (time between key presses), gaze duration for the ET data (time a word is fixated before the first fixation on a different word) and N400 amplitude for the EEG data (average amplitude at the centroparietal electrodes between 300 and 500 ms after word onset ).
For our analysis we exclude the word at the start and end of each sentence, and words attached to a comma. For the SPR and ET data we also exclude the word following a comma. For the EEG data we exclude datapoints that were marked by Frank et al. Frank2015 as containing artifacts. For the SPR and ET data we excluded words with a reading time under 50 ms or over 3500 ms and for the ET data we exclude words that were not fixated. Table 1 gives an overview of the test data.
|Data||Participants||Sentences||Sentence length||Mean sentence length||Tokens||Datapoints|
3.5 Analysis procedure
We save each LM’s parameters at 10 different points during training (1K, 3K, 10K, 30K, 100K, 300K, 1M, 3M sentences and after the first and second epoch). We use each of these parameter states to generate surprisal values for the 361 test sentences for a total of 480 sets of surprisal values (10 (parameter states) 8 (repetitions) 6 (LMs)).
3.5.1 Linear mixed effects regression
Following Aurnhammer2017, we analyse how well each set of surprisal values predicts the human sentence processing data using linear mixed effects regression (LMER) models with the MixedModels package in Julia . For each of the human behavioural datasets (SPR, ET and EEG) we fit a baseline LMER model which takes into account several factors known to influence processing effort. The dependent variables of the SPR and ET models are reading time (ms) and gaze duration (ms) respectively (both log transformed). The dependent variable of the EEG model is the size of the N400 response. All LMER models include log transformed training corpus word frequency, word length (characters) and the word’s position in the sentence as fixed effects. Spill-over is known to affect reading time and can occur when processing of a word is not yet fully done by the time the next word is read (e.g., [32, 28, 11]
). In order to account for spill-over in the SPR and ET data we add in the previous word’s frequency and length. For the SPR data we add the previous word’s reading time to account for the high correlation between consecutive word’s reading times in SPR paradigms. For the EEG data, we include the baseline activity which is the average amplitude in the 100 ms before word onset. All LMER models have by-subject and by-item (word token) random intercepts and by-subject random slopes for all fixed effects. We included interaction effects between all fixed effects and all fixed effects were normalised for mean and standard deviation.
After fitting the baseline models, we fit LMER models where we add in the surprisal values and for the SPR and ET data also the previous word’s surprisal as fixed effects. We do not include interaction effects between the surprisal and the other fixed effects. Table 2 gives an overview of the variables used in each model.
For each LMER model with surprisal, we calculate the log-likelihood ratio with its corresponding baseline model indicating the decrease in model deviance due to adding the surprisal measures. The more the surprisal values decrease the model deviance, the better these values predict the human reading data. We call this log-likelihood ratio the goodness-of-fit between the surprisal and the data. If the surprisal values actually predict effects contrary to what we expect, we show this by reversing the sign of the goodness-of-fit so that negative values indicate the LMER models where high surprisal predicts lower gaze duration, reading time, or N400 size. The LMER analysis results in a set of 480 goodness-of-fit measures which are used in the second stage of the analysis.
|SPR||log(RT)||surp + prev surp + log(freq) * char * word pos * prev log(freq) * prev char|
|* prev log(RT)|
|ET||log(gaze dur)||surp + prev surp + log(freq) * char * word pos * prev log(freq) * prev char|
|EEG||N400||surp + log(freq) * char * word pos * baseline|
3.5.2 Generalised additive modelling
As said before, it is well known that surprisal values derived from better LMs are a better fit to human reading data [29, 13, 15]. We use generalised additive modelling (GAM) to assess whether the LMs differ in their ability to explain the human reading data at equal language modelling capability, that is, because of their architectural differences and not due being a better LM. We used the R package mgcv by Wood Wood2004. We take the log-likelihood ratios obtained in the previous analysis step as a measure of how well each LM explains the human reading data. We use each LM’s average log probability (i.e., negative average surprisal) over the datapoints used in the LMER analysis as a measure of the LM’s language modelling capability. Separate GAMs were fitted for each of the three datasets. We used the LM type (single-layer GRU, two-layer GRU etc.) as an unordered factor so that separate smooths are fit for each LM type. Furthermore, we add training repetition (i.e. the random training order and embedding initialisation) as a random smooth effect.
4.1 LM quality and the effects on goodness-of-fit
Figure 2 shows the goodness-of-fit values resulting from the linear mixed effects models and the smooths fitted by the GAMs. As in Aurnhammer2017, we see that for lower LM quality the surprisal values actually predict effects contrary to what we expect, especially in the gaze duration and the N400 size (as indicated by negative goodness-of-fit). This effect occurs in both our GRU LMs and our Transformer LMs, showing this is not particular to RNNs. Overall we see the expected relationship where higher LM quality generally results in higher goodness of fit. The Transformer models notably have a higher minimum LM quality than the GRUs. The models do seem to reach similar levels of LM quality at the end of training. The average log probability of the best LM (two-layer Transformer) is only 0.17 higher than worst LM (two-layer GRU). The LM quality increases monotonically during training meaning the clusters seen in the scatter-plots correspond to the points during training where the network parameters were stored.
4.2 GAM comparisons
. We indicate where the 95% confidence interval of the estimated differences does not include zero. The two-layer GRU does not seem to improve over the single-layer GRU. It outperforms the single-layer GRU only in the early stages of training on the EEG/ET data with the single-layer GRU outperforming it on the SPR data. The two-layer GRU also reaches a lower maximum LM quality on all datasets. For the Transformers we see the opposite, with the two-layer Transformer outperforming the single-layer Transformer on the N400 data at the end of training and never being outperformed by its shallower counterpart. The two-layer Transformer reaches a higher maximum LM quality on all datasets.
Here we only compare the best model of each type, i.e., the single-layer GRU and the two-layer Transformer333The comparisons between the two-layer GRU and the Transformers and the single-layer GRU and single-layer Transformer can be found in Appendix A.. We see that while the GRU outperforms the Transformer in the early stages of training (10K/30K sentences) on the N400 data, the Transformer clearly outperforms the GRU at the end of training on both the SPR and N400 data. Note that even these stretches of language model quality at the end of training where the Transformer outperforms the GRU seem small, they represent roughly 75% of the training cycle (1st and 2nd epoch) for the EEG data and roughly 91% (3M sentences, 1st and 2nd epoch) for the SPR data. On the gaze duration data, there are some performance differences with the Transformers and GRUs outperforming each other at different points during training but there are no differences in the later stages of training.
4.3 Alternative LM architectures
The results prompted us to investigate further and train two more LM types. The Transformer benefited from the additional complexity of increased depth as shown by the difference on the N400 data and by reaching overall higher LM quality. We trained a four-layer Transformer model to see if this trend continues. The GRU did not seem to benefit from additional depth and instead we trained a single-layer GRU that was followed by a Transformer attention operation in between the GRU output and the linear classification layer. The resulting smooths and plots of the estimated differences can be found in Appendix B. The addition of attention to the GRU did not improve its performance. The attention GRU outperforms the GRU on the SPR data corresponding to the end of the first epoch but the difference is gone after the second epoch. The four-layer Transformer does not seem to improve the performance of the Transformer more than the addition of a second layer did. The four-layer model outperforms the two-layer model early on in the N400 data and near the end of training in the SPR data but this difference disappears at after the second epoch.
4.4 Shorter and longer sentences in SPR
While the EEG and ET datasets used the same test stimuli, the SPR experiment included sentences that were not in the EEG/ET data. The SPR data contains a subset of sentences longer (in terms of characters) than those seen in the EEG/ET data. We repeated the analysis of the single- and two-layer GRUs and Transformers but only on those sentences from the SPR data that also occurred in the EEG/ET data. On this data we surprisingly find the exact opposite of our previous results, where the two-layer GRU outperforms both Transformers at the end of training. When we test on only those sentences that were not included in the EEG/ET experiments (i.e. the longer sentences), we see that the Transformers outperform the GRUs as they did on the complete SPR dataset. The plots for these results can be found in Appendix C.
We trained several language models based on Transformer and GRU architectures in order to investigate if there is any difference in how well these neural networks are able to model human reading data. We compared the architectures at equal LM quality and found that in general the Transformers seem to outperform the GRUs. Previous work had shown there are no significant differences between different RNN types despite differences in their gating mechanisms . It seems that the Transformer’s attention-based computation allow it to better capture the human self paced reading time data and to a lesser extent the EEG data.
Somewhat surprisingly, adding more depth to the GRU models did not seem to improve performance and even hurt it in the case of the reading time data, despite previous research showing that increasing layer depth in RNNs allows them to capture more complex patterns in linguistic data [14, 1]. The Transformers did show improvement when adding a second layer and improving slightly further on the SPR data for the even deeper 4-layer model. This could be explained by the fact that our single-layer Transformer cannot make use of implicit order information in the sequence, and hence we add explicit order information to the input embeddings following Vaswani2016. When adding more layers to our Transformer, the subsequent layers operate no longer on raw input embeddings but on contextualised hidden states allowing the model to utilise implicit information in the order of the input. Further research could investigate how powerful this implicit order information is, and whether multi-layer Transformer LMs no longer require the additional explicit order information.
It is notable that the Transformer outperformed the GRU on the two datasets which consist of a reading task where sentences were presented to the subjects word by word (SPR and EEG). There is neurophysiological evidence that natural reading (whole sentences) places different demands on the reader than reading in a word-by-word setting, leading to different encoding and reading strategies . Metzner et al. speculate that a word-by-word setting places greater demand on the reader’s working memory, leading to faster retrieval of previously processed material. This seems to be supported by our results; the Transformer has direct access to previous inputs and hidden states is better at explaining the RT and N400 data from the word-by-word reading experiments. However, when we split the SPR data by sentences that were also present in the ET/EEG data, the results seem to suggest that the Transformers’ advantage is mainly due to performing better on longer sentences. This question could be resolved with new data gathered in experiments where the same set of stimuli is used for the SPR and EEG experiment.
In conclusion, we investigated how the recently introduced Transformer architecture holds up as a model of human sentence processing compared to the GRU. Our Transformer LMs are better at explaining the EEG and SPR data even though the Transformer’s attention operation contradicts the widely held idea that human sentence processing involves recurrent and immediate processing with lossy retrieval of previous input.
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