ARTICLE DE RECHERCHE
A Model of Online Temporal-Spatial Integration
for Immediacy and Overrule in
Discourse Comprehension
un accès ouvert
journal
Takahisa Uchida1, Nicolas Lair2,3
, Hiroshi Ishiguro1, and Peter Ford Dominey2,3
1Ishiguro Lab, Graduate School of Engineering Science, Osaka University, Osaka, Japan
2INSERM UMR1093-CAPS, Université Bourgogne Franche-Comté, UFR des Sciences du Sport, Dijon, France
3Robot Cognition Laboratory, Marey Institute, Dijon, France
Mots clés: discourse context, word embedding, N400, reservoir computing, word2vec
ABSTRAIT
During discourse comprehension, information from prior processing is integrated and appears to
be immediately accessible. This was remarkably demonstrated by an N400 for “salted” and not
“in love” in response to “The peanut was salted/in love.” Discourse overrule was induced by prior
discourse featuring the peanut as an animate agent. Immediate discourse overrule requires a
model that integrates information at two timescales. One is over the lifetime and includes event
knowledge and word semantics. The second is over the discourse in an event context. Nous
propose a model where both are accounted for by temporal-to-spatial integration of experience
into distributed spatial representations, providing immediate access to experience accumulated
over different timescales. For lexical semantics, this is modeled by a word embedding system
trained by sequential exposure to the entire Wikipedia corpus. For discourse, this is modeled by a
recurrent reservoir network trained to generate a discourse vector for input sequences of words.
The N400 is modeled as the difference between the instantaneous discourse vector and the target
word. We predict this model can account for semantic immediacy and discourse overrule.
The model simulates lexical priming and discourse overrule in the “Peanut in love” discourse,
and it demonstrates that an unexpected word elicits reduced N400 if it is generally related to the
event described in prior discourse, and that this effect disappears when the discourse context
is removed. This neurocomputational model is the first to simulate immediacy and overrule in
discourse-modulated N400, and contributes to characterization of online integration processes
in discourse.
INTRODUCTION
An astonishing aspect of human language comprehension is that it brings to bear a vast variety of
information sources to the ongoing interpretation of words in language, and it does so without
temporal penalties for accessing this information. Typiquement, search takes time, particularly when
using artificial arrays of stimuli (Treisman, 1982). Cependant, under naturalistic conditions, atten-
tional mechanisms are recruited and search time is significantly reduced (Peeling & Kastner,
2014). The comprehension system similarly appears to have immediate access to diverse sources
of stored information. This “immediacy assumption” posits that during comprehension, un
attempt is made to relate each content word to its referent as soon as possible (Just &
Carpenter, 1980). The immediacy assumption was initially developed in the context of reading
Citation: Uchida, T., Lair, N., Ishiguro,
H., & Dominey, P.. F. (2021). A model of
online temporal-spatial integration for
immediacy and overrule in discourse
comprehension. Neurobiology of
Language, 1(3), 83–105. https://doi.org
/10.1162/nol_a_00026
EST CE QUE JE:
https://doi.org/10.1162/nol_a_00026
Informations complémentaires:
https://doi.org/10.1162/nol_a_00026
Reçu: 26 May 2020
Accepté: 12 Octobre 2020
Intérêts concurrents: Les auteurs ont
a déclaré qu'aucun intérêt concurrent
exister.
Auteur correspondant:
Peter Ford Dominey
peter.dominey@inserm.fr
Éditeur de manipulation:
Peter Hagoort
droits d'auteur: © 2021 Massachusetts
Institut de technologie. Published
under a Creative Commons Attribution
4.0 International (CC PAR 4.0) Licence.
La presse du MIT
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Immediacy and overrule in discourse comprehension
and the visual fixation of words during reading (Just & Carpenter, 1980; Thibadeau, Just, &
Carpenter, 1982).
N400 responses to words during reading have provided new evidence in favor of the imme-
diacy assumption (Hagoort & van Berkum, 2007; Nieuwland & van Berkum, 2006; van Berkum,
Hagoort, & Brun, 1999). The N400 is a scalp-recorded negativity that appears around 400 ms
after words that are semantically anomalous, such as “socks” in “He spread his warm bread with
socks,” and is one of the most robust EEG effects in language processing (Kutas & Hillyard, 1980).
Since its discovery, the N400 has been extensively investigated and remains a key indicator of
semantic processing (Kutas & Federmeier, 2011).
The immediacy effect has been remarkably demonstrated by a reversal of the expected
response to the sentence “The peanut was salted/in love” with an N400 for “salted” and not
for “in love.” The unexpected effect of an N400 in response to “The peanut was salted” was
induced by providing a prior discourse that featured the peanut as an animate agent who was
dancing, singing, and so forth (Nieuwland & van Berkum, 2006). In this context, Hagoort and
van Berkum (2007, p. 802) elaborated the immediacy assumption as “the idea that every source
of information that constrains the interpretation of an utterance (syntax, prosody, word-level se-
mantics, prior discourse, world knowledge, knowledge about the speaker, gestures, etc.) can in
principle do so immediately.” In such cases where discourse produces a modification to other-
wise expected responses, we can say that there has been a “discourse overrule.”
A related study by Metusalem et al. (2012) further examined discourse overrule where prior
discourse could establish a context that would modulate N400 responses to words by making
them more or less acceptable in the discourse context. Remarquablement, they demonstrated that a
contextually anomalous word elicits a reduced N400 if it is generally related to the described
event, but that this effect disappears when the discourse context is removed. These findings
demonstrate that during the course of incremental comprehension, comprehenders activate
general knowledge about the described event, which can produce a discourse overrule of the
local linguistic stream.
This immediacy seems remarkable in the context of discourse models that require multiple
distinct steps, such as the construction-integration model of Kintsch (1988). This highly produc-
tive and influential model of comprehension is based on the construction of an initial represen-
tation of the discourse based on the input, which requires (un) generating the propositional
representation based on a parse, (b) elaborating these elements based on world knowledge, (c)
inferring additional propositions, and finally (d), the formation of a connectionist network repre-
sentation of the results of this constructive process. Once this construction is achieved, a second
phase of integration takes place over multiple cycles of the network to generate a coherent and
consistent network representation. Despite the fact that Kintsch was well aware of the timing of
different comprehension and inference processes as examined in Till, Mross, and Kintsch (1988),
the multistep specification of the construction-integration model gives the impression that
comprehension over these multiple steps will take time. It is in this context that the immediacy
observations can be considered remarkable.
This poses the puzzle of how these diverse sources of information can physically be made
immediately available and allows us to ask two questions: D'abord, how is the content of prior
discourse made immediately available during language comprehension? Inévitablement, que
discourse context is itself constructed from prior knowledge encoded in the meanings of words,
and so we cannot consider discourse without also considering the world knowledge encoded in
word meaning. This leads to the second question: How is prior knowledge of the world, encoded
in words, made immediately available during comprehension?
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Immediacy and overrule in discourse comprehension
This reveals that there are two timescales of prior information to account for. One timescale is
over the lifetime, and concerns the meaning of words as they have been encountered in contexts
over extended periods of time. The second and shorter timescale is over the discourse and con-
cerns the aggregation of words in the context of an event. We propose that in both cases, a form of
temporal-to-spatial integration takes place, where a temporal sequence of experience is integrated
into a distributed spatial (neural) representation. It is this distributed spatial pattern that will provide
the immediate access to experience accumulated over different timescales.
The Current Study
Our present research addresses these questions via two innovative aspects. The first is to take
into account the accumulation of prior knowledge over the lifetime, through the use of tech-
niques developed in machine learning to create word embeddings, which represent world
knowledge from large natural language corpora that can be encoded in feed-forward net-
travaux. The second is to approximate how this knowledge is integrated during discourse com-
prehension through the use of a temporal-spatial integration function that can be implemented
in recurrent cortical networks.
The use of recurrent networks to integrate word context for explaining the N400 has been
employed in two recent models (Brouwer, Crocker, Venhuizen, & Hoeks, 2017; Rabovsky,
Hansen, & McClelland, 2018). In both cases the models are trained on sentence corpora
generated from relatively small vocabularies (35–74 words) where the semantics of words are
handcrafted based on prespecified semantic features. The training corpora are carefully
controlled by the experimenters, which allows for an explanation of the N400 for words in test
sentences based on their contrast with structure encoded in the training corpora. These models
account for a variety of N400 responses and do not attempt to address how discourse meaning
can influence the N400 beyond the local sentence context. We explore a complimentary
approche, where knowledge imparted to the model originates from the structure inherent in a
large vocabulary, 3.5 billion word corpus of human-generated discourse. Semantic and structural
representations are extracted from this vast corpus to create a vector space that can be used to
generate word embeddings (Mikolov, Sutskever, Chen, Corrado, & Dean, 2013; Yamada et al.,
2020).
Word embeddings or distributed word representations are n-dimensional vector representa-
tions of words that are generated by extracting statistics on neighbor relations between words in
large text training corpora generated from human sources. In one of the classic approaches to this
problem, Landauer and Dumais (1997) assumed that the psychological similarity between words
is determined by how these words co-occur in samples of language and that the language
producer generates language in a way that preserves this orderly mapping between semantic
relatedness and output distance. Their latent semantic analysis (LSA) calculates statistics on
word occurrences in documents based on this distributional hypothesis that assumes that words
that are close in meaning will occur in similar pieces of text. The method can then provide vector
representations for words and documents, and generate estimates of similarity. The model has had
wide success both in information retrieval (for which it was initially developed) and in explaining a
variety of language performance and cultural phenomena, including the psychology of vocabu-
lary learning.
Research in distributional models for word embeddings continues to flourish, particularly as
large corpora and computing resources become increasingly available. A prominent recent
method is word2vec (Mikolov et al., 2013). Whereas LSA is based on counting of co-occurrences,
word2vec is a distributional word embedding neural network model that is based on the
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Immediacy and overrule in discourse comprehension
prediction of words based on the surrounding context. The word2vec algorithm has had great
success in a variety of natural language processing domains, and also captures a remarkable level
of human social psychology and changes over an extended time (Garg, Schiebinger, Jurafsky, & Zou,
2018). In a milestone experiment to establish the link between human brain activity during word
comprehension and high dimensional feature representations extracted from large corpora (similar
to word2vec), Mitchell et al. (2008) tested the assumption that brain activity observed when thinking
about concrete nouns could be derived as a weighted linear sum of contributions from each of its
semantic features. They established a direct, predictive relationship between the statistics of word
co-occurrence in text and the neural activation associated with thinking about word meanings.
Recent research has successfully extended these representation properties from word to text
levels (Le & Mikolov, 2014), and further research indicates that the resulting distributed vector
representations of stories can be related to distributed neural activity in the human default mode
network while people read these stories (Dehghani et al., 2017). This allows us to consider the
possibility that aspects of human neurophysiology in discourse comprehension can be modeled
using distributed vector representations developed in machine learning for natural language pro-
cessation. En effet, these results suggest that distributed representations derived from large corpora
encode not only word semantics, but also knowledge about the structure of human events as
required for understanding stories (Dehghani et al., 2017). En même temps, while such research
examines how word embeddings learned from large corpora may contribute to the understanding
of human discourse processing, in computational linguistics, experimental protocols and results
from human psycholinguistics are being used to improve understanding of what these language
models actually know about language (Ettinger, 2020).
This leaves open the question of how different formats of input to this integrative process,
including knowledge from the ongoing discourse and real-world knowledge, is orchestrated in
the nervous system. Autrement dit, how can word meaning be continuously integrated into a
discourse representation consistent with semantic immediacy and discourse reversal? A response
to this question can be provided by recurrent networks that simulate primate cortex (Dominey,
1995; Enel, Procyk, Quilodran, & Dominey, 2016; Hinaut & Dominey, 2013; Rigotti et al., 2013).
This reservoir family of recurrent networks (Lukosevicius & Jaeger, 2009) eschews learning in the
recurrent connections (Pearlmutter, 1995) and instead uses pre-established connections that
allow much richer high dimensional dynamics. These recurrent networks have the desired
property of maintaining an ongoing history of the past inputs that is continuously and instanta-
neously updated with each new input. Reservoirs have been used to provide online word
processing during sentence comprehension that can generate P600-like responses to unpredicted
grammatical structure (Hinaut & Dominey, 2013). (The P600 is a late positive event-related
potential [ERP] that can be evoked by grammatical structure violations.)
Here we extend this approach by using a reservoir network to continuously maintain and
immediately update a representation of the ongoing discourse, thus providing a response to
the question of how discourse information is continuously and immediately integrated. The input
to this discourse reservoir is the sequence of distributed vector representations of words in a text,
which represents real-world knowledge. Given a sequence of word embeddings as input, le
network is trained to generate the average vector as output. Average vectors have been demon-
strated to serve as a functional approximation of a discourse representation (Cer, Diab, Agirre,
Lopez-Gazpio, & Specia, 2017; Lilleberg, Zhu, & Zhang, 2015). The trained discourse reservoir
thus generates a simple and neurophysiologically valid temporal-spatial integration of the
discourse. This representation is compared with the vector representation of the target word,
and the difference predicts the amplitude of the resulting N400 response. Our objective is to test
the hypothesis that this temporal-spatial integration of word embeddings in a discourse reservoir
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Immediacy and overrule in discourse comprehension
can account for aspects of the immediacy assumption and the discourse overrule of local seman-
tics in human discourse processing, as described in Hagoort and van Berkum (2007) et
Metusalem et al. (2012). This includes prediction of N400 amplitudes in studies where ampli-
tudes varied with experimental conditions (Metusalem et al., 2012). For comparison we will
evaluate performance in predicting N400 for the discourse reservoir and the simple average
vector representations.
The proposed model may be useful in psycholinguistic understanding of discourse compre-
hension because it takes a neurophysiologically motivated stance on the nature of event
representations derived from discourse comprehension. One might intuit that the result of com-
prehension is the elaboration of a situation model with explicit coding of events, relations, agents,
and their roles. An alternative incarnation of the situation model could be a distributed represen-
tation that indeed encodes these elements, but in a distributed embedding. The current study
takes a first step in examining whether such distributed representations can be used to understand
human performance in well-characterized comprehension tasks.
RESULTS AND METHODS
The experiments are implemented in python, and all code and data are openly available for
reproducing these experiments and performing new experiments. The code and data are
available at https://github.com/pfdominey/DiscourseOverrule. See RunningExperiments-log.txt
to get started.
Word Embeddings
Word embeddings for English in all three experiments were generated using Wikipedia2Vec
(Yamada et al., 2020), which learns word embeddings based on the Wikipedia corpus, lequel
includes over 3 billion words in approximately 5.5 million articles. Transformer-based models like
BERT (Devlin, Chang, Lee, & Toutanova, 2019) encode sentence context and yield an impressive
performance on linguistic tasks such as inference classification, question answering, and sentence
continuation. These models can generate different embeddings for the same word, dependent on
the surrounding sentence context. In our case, we wanted to compare the potentially ambiguous
single embedding of the target word to discourse representations that are aggregations of embed-
dings for single words in the text, using an aggregation method for which we have a neurophys-
iologically motivated model (décrit ci-dessous). For this reason, we chose Wikipedia2Vec to
generate these embeddings.
Wikipedia2Vec learns embeddings by jointly optimizing three submodels. The first is the word-
based skip-gram word2vec model of Mikolov et al. (2013). Word2vec takes as its input a large
corpus of text and produces a vector space, typically of several hundred dimensions, with each
unique word in the corpus being assigned a corresponding vector in the space. Word vectors are
positioned in the vector space such that words that share common contexts in the corpus are
located close to one another in the space (Mikolov et al., 2013). The second submodel in
Wikipedia2Vec is a link-graph model that learns entity embeddings by predicting the neighboring
entities of each entity in the Wikipedia’s link graph. The third submodel is an anchor-context
model that aims to place similar words and entities close to one another in the vector space using
hyperlinks and their neighboring words in Wikipedia.
We used a pretrained Wikipedia2Vec model that was trained on the entire Wikipedia pages
for March 20, 2018 (over 3 billion words in approximately 5.5 million articles). It should be noted
that this is an extraordinary volume of human knowledge, in the sense that it has been established
by and is used by an extensive community as an encyclopedic reference. In her analysis of
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Immediacy and overrule in discourse comprehension
Wittgenstein’s view on meaning, Nelson (2009, p. 276) paraphrases: “there is a constellation of
uses of the word that implies conventional rules for use. The meanings of a word then emerge
from its uses by a community of users.” As a form of encyclopedia, Wikipedia is a form of record
of the community’s collective knowledge. While Wikipedia is based on words, the actual articles
that describe each word are well formed text, often explaining historical contexts, summarizing
novel plots, and so forth. This knowledge is represented in the corpus via the juxtaposition of
words in the different Wikipedia articles. The training in Wikipedia2Vec is a form of temporal-
spatial transformation that encodes this knowledge, as the temporal sequence of the training
corpus is used to train a neural network that learns word embeddings.
When given an input word, the trained Wikepdia2Vec model returns that word’s represen-
tation in the high (in our case 100) dimensional space. Similarity between words can be
calculated as the cosine of the angle between their two vectors, avec 1 being identical, et
0 being orthogonal or completely unrelated. Here we provide an example of similarity
measures of word embeddings generated with Wikipedia2Vec for pairs of related and unrelated
words from Chwilla, Brun, and Hagoort (1995, p. 285, Appendix, translated from the Dutch
ader-bloed and zweet-tekst):
Cosine similarity: (Related word pair) vein blood = 0.540219
Cosine similarity: (Unrelated word pair) sweat text = 0.165510
Chwilla et al. (1995) observed that when subjects were presented with such word pairs in a
lexical decision task, the presentation of the second word in the pair produced a larger N400
amplitude for the unrelated word pairs.
Predicting N400 Responses
Given this measure of similarity we can specify a linking hypothesis between these cosine
similarity measures and N400 amplitude (Brouwer et al., 2017). Intuitively, the N400 is linked
to semantic similarity, such that as semantic similarity decreases, N400 increases. Following the
reasoning developed by Brouwer, the difference between the current state of discourse context
and the next target word corresponds to the N400. In the experiments described below, we used
this linking hypothesis as proxy for N400 amplitude.
Þ
N400 ¼ 1 − cos context; target‐word
ð
The context can consist of a single word, c'est, the first word of a word pair, or it can be a more
extended discourse of multiple sentences, as we now describe.
Discourse Representations
Given a tool for generating word embeddings, and a linking hypothesis for predicting N400
amplitude between words, we extend this to generating the same type of representations for word
sequences or discourse, which can correspond to single or multiple sentence texts. Discourse
representations can be generated based on aggregations of single word representations
(Lilleberg et al., 2015). A simple but effective aggregation method is to take the vector average
of the words in the discourse. This includes the semantics of all of the constituent words, without
information provided by word order, referred to as “the bag-of-words approach,” which has been
extensively applied in automatic text classification (Joachims, 1998; Joulin, Grave, Bojanowski,
& Mikolov, 2017). In tests of semantic similarity between sentences, a discourse embedding
baseline was used, which involved averaging word embeddings and then using cosine to
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Immediacy and overrule in discourse comprehension
compute pairwise sentence similarity scores over these average vectors (Cer et al., 2017). Tel
state-of-the-art baselines for obtaining sentence embeddings based on averaging perform well on
the benchmark data, though improved performance was obtained by more exotic algorithms. UN
related averaging method has been employed by Ettinger, Feldman, Resnik, and Phillips (2016) à
simulate N400 responses observed by Federmeier and Kutas (1999).
This justifies our choice to employ the average Wikipedia2Vec as a simple but reliable model
of discourse embedding for our experiments. Our rationale is to adopt a minimalist discourse
aggregation method that is known to be effective and that can realistically be implemented in
the recurrent reservoir model of temporal-spatial integration.
Reservoir Model of Discourse Embedding: The Discourse Reservoir
We employed reservoir computing as a neurophysiologically valid model for the formation of
these discourse representations. Reservoir computing exploits the omnipresent feature of local
recurrent connections in the primate cortex (Goldman-Rakic, 1987) in order to generate high
dimensional representations of the input sequence. It was first developed in the context of
simulating neural activity in the prefrontal cortex of primates that performed sensorimotor
sequence learning tasks (Dominey, Arbib, & Joseph, 1995), then in more general neural compu-
tation (Maass, Natschlager, & Markram, 2002) and signal processing (Jaeger & Haas, 2004).
Plus récemment, reservoir computing has been confirmed as a model of primate cortical function
in higher cognitive processes (Enel et al., 2016; Rigotti et al., 2013). The underlying concept that
differs from other recurrent network models is that the recurrent connections are fixed, et
learning occurs in a readout from the reservoir units. By avoiding simplifications of the recurrent
dynamics required for learning (Pearlmutter, 1995), this allows the recurrent reservoir network to
have extremely rich high dimensional states that can then be used to learn essentially universal
functions of the input (Maass et al., 2002).
We trained the reservoir so that when provided with a sequence of word embeddings as input,
it would generate the average vector as output. The reservoir experiments were performed with
the easyesn toolkit (https://pypi.org/project/easyesn/), a well-documented and easy to use toolkit.
All simulations used a standard echo state network with 100 reservoir units, and input and output
dimensions of 100 (the same as the word and discourse embedding dimensions). The reservoir
was trained on a subset of the Wikipedia corpus made up of 4,000 articles, which generated a
set of 1.5 million words. The input was this sequence of words in their Wikipedia2Vec represen-
tation, and the output was the average vector sequence, c'est, avg(w1), avg(w1, w2), avg(w1,
w2, w3), … avg(w1 … wN). The reservoir was trained using standard parameters specified by
easyesn: leakingRate=0.2, regressionParameters=[1e−2], solver=”lsqr”, feedback=False, avec
no feedback from readout to input. Training required less than two minutes on an Intel®
Core™ i5-8250 CPU @1.6 GHz, avec 16 Go RAM. The training error is on the order of 0.068
over multiple tests.
We evaluated the ability of the trained Discourse Reservoir to generate the average vector
for increasing discourse lengths. The accuracy as measured by the cosine of the predicted and
real average is presented in Figure 1. We found that after five words the similarity is over 0.8,
and after 10 words over 0.9.
In the following experiments we used both methods of aggregation: Average-Embedding and
Discourse Reservoir. In these experiments, we took a systematic approach, first validating the
simple embedding method with an experiment examining semantic priming in word pairs,
Chwilla et al. (1995), and then discourse effects on N400 with experiments from Nieuwland
and van Berkum (2006), Hagoort and van Berkum (2007), and Metusalem et al. (2012).
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Chiffre 1. Cosine similarity (semantic relatedness) of actual average and reservoir generated average.
Experiment 1: N400 Semantic Priming Effect (Chwilla et al., 1995)
To validate that the semantics of Wikipedia2Vec word embeddings can be used to simulate
human N400 responses, we first used our model to predict N400 responses in a classic task of
semantic priming. One of the most robust findings in language psychology is semantic priming,
where words are processed more efficiently when they are preceded by a semantically related
word (Neely, 1991). Semantic priming has been revealed in ERP studies as the N400 semantic
priming effect, where the N400 responses to words are increased when those words are preceded
by a semantically unrelated word (Kutas & Van Petten, 1988). The objective of our first experi-
ment was to test the hypothesis that Wikipedia2Vec encoding of semantic world knowledge can
be used to predict human N400 responses to matching and nonmatching word pairs. In a lexical
decision task, Chwilla et al. (1995) measured N400 responses to words that were distributed in
pairs where the second word was either semantically related or not to the first word. Increased
N400 response was reliably elicited for the unrelated pairs.
In Experiment 1, we thus used the stimuli from Chwilla et al. (1995), translated from Dutch
to English, in order to validate the Wikipedia2Vec method for producing embeddings that can
be used to measure semantic similarity. We generated the embeddings for the 40 related and
40 unrelated word pairs, and then used the cosine similarity, or semantic relatedness, entre
word pairs in the related and unrelated lists to predict N400s. Based on the linking hypothesis,
the N400 amplitude is 1 − cosine similarity.
Chiffre 2 illustrates the predicted N400 measures for related and unrelated pairs. The model
predicts that related pairs have a smaller N400 (mean = 0.39) than unrelated pairs (mean = 0.70).
This is highly significant, with t(39) = −11, p < 0.0001. The predicted N400 amplitude is significantly
smaller for related versus unrelated pairs, just like the N400 amplitude found by Chwilla et al. (1995).
This is consistent with the hypothesis that the Wikipedia2Vec embeddings can be used to predict
human semantic similarity as revealed by the N400 response. It leaves open the question as to how
this works, and whether the vector distance and the N400 are measuring the same process.
Experiment 2: Accommodating the Peanut in Love (Nieuwland & van Berkum, 2006)
Nieuwland and van Berkum (2006) and Hagoort and van Berkum (2007) argued that discourse
comprehension is realized by a single step model for integration of multiple knowledge sources,
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Figure 2. Distribution of predicted N400 for Related and Unrelated pairs. Related and Unrelated word pair list data from Chwilla et al.
(1995). Left panel: Unrelated pair distribution shifted to right, with higher N400s. Right panel: Predicted N400 scores for related and unrelated
pairs. Graphic notation: The box extends from the lower to upper quartile values of the data, with a line at the median. The whiskers extend
from the box to show the range of the data. Circles indicate outliers.
in which locally and globally supplied information can immediately effect semantic processing.
They tested this model in ERP experiments with discourse set-up conditions where an inanimate
object (e.g., a peanut) could take on the semantics of animacy (e.g., being in love). They argued
that if discourse context prevails over animacy and real-world plausibility, then locally plausible
but contextually inappropriate predicates (e.g., “the peanut was salted”) should elicit an N400
effect compared to locally anomalous but contextually appropriate predicates (e.g., “the peanut
was in love”). One of their classic experimental texts that generates such locally plausible but
contextually inappropriate predicates is presented in Table 1.
Using such discourses, Nieuwland and van Berkum (2006) and Hagoort and van Berkum
(2007) were able to demonstrate a discourse overrule of expected N400 amplitudes where,
indeed, a greater N400 was observed in response to the “The peanut was salted” versus “The
peanut was in love.” In order to first determine whether according to Wikipedia2Vec “The peanut
was salted” is more semantically acceptable than “The peanut was in love,” we first simply made
the comparison of Wikipedia2Vec representations for “peanut” versus “love” and “peanut”
versus “salted.” The 100 dimensional vector word embeddings were generated for peanut, love,
and salted, using the trained Wikipedia2Vec model. Semantic relatedness for a pair of words was
calculated as the cosine of their two respective vectors, and the predicted N400 was generated
using the linking hypothesis (N400 = 1 − semantic relatedness) yielding the following results:
Predicted N400 "peanut" "love": 0.69
Predicted N400 "peanut" "salted": 0.34
Given this confirmation, we then set out to determine if the reversal of such effects could be
predicted using our discourse embedding methods. We used the text in Table 1 (after removing
stop-words like “a,” “had,” and “his,” and adding one neutral word at the beginning to prime the
reservoir) as input to the discourse aggregation by average embedding and by the Discourse
Reservoir. We truncated the text at the last occurrence of the word “peanut” (in bold italics in
Table 1) so that the first and last words of the discourse are “peanut.” After each word in the
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Table 1.
A text that creates a locally plausible but contextually inappropriate context for the text in bold “The peanut was salted.”
The Peanut. A woman saw a dancing peanut who had a big smile on his face. The peanut was singing about a girl he had just met. And
judging from the song the peanut was totally crazy about her. The woman thought it was really cute to see the peanut singing and
dancing like that. The peanut was salted/in love and by the sound of it this was definitely mutual. He was seeing a little almond.
Note. Adapted from “When peanuts fall in love: N400 evidence for the power of discourse,“ by M. S. Nieuwland & J. J. van Berkum, 2006. Journal of Cognitive
Neuroscience, 18, p. 1106.
discourse was presented, we calculated the Predicted N400 between the ongoing discourse ag-
gregate and the words “love” and “salted.” Thus, first and last points of comparison are with the
same discourse word “peanut” but without and with the discourse, respectively.
The results are presented in Figure 3, where we observe that the predicted N400 between the
discourse aggregate and “salted” and “love” reverses during the elaboration of the discourse. At
the outset the predicted N400 values are close to those listed above for the direct comparisons of
“peanut” with “love” and “salted,” respectively. As successive words are added, the profile
reverses. Figure 3 also displays results for the same experiment performed using the Discourse
Reservoir. We observe that the differences for “salted” and “love” are smaller at the beginning
of the discourse. This is because the reservoir does not generate a perfect average (which would
be just a copy of the input for a discourse of one word) at the outset. Again we see the reversal of
the predicted N400 over the elaboration of the discourse.
In order to verify the generalization and robustness of this behavior, we performed the exper-
iment with 50 distinct instances of the Discourse Reservoir, formed by using different initializa-
tion conditions for the connections in the reservoir. This corresponds to different subjects in a
human experiment. Distributions of predicted N400 between the online discourse representation
in the Discourse Reservoir and the vectors for “salted” and “love” at the outset of the discourse are
shown in Figure 4, along with distributions after the discourse. We observe a clear and striking
reversal of the predicted N400 between the online discourse representation and the vectors for
“love” and “salted” before and after the discourse. We also observe that overall the predicted
N400 scores are lower after the discourse.
These observations were confirmed with a repeated measures ANOVA on the predicted N400
values, with the factors Discourse (Beginning and End) and Target (Salted vs. Love). There was a
significant main effect for Discourse, F(1, 49) = 1563, p < 0.001, with predicted N400 amplitude
Figure 3. Trajectory of Predicted N400 values comparing the discourse aggregate vector with the vectors for “salted” (blue) and “love” (red),
respectively. Left panel: Simple average aggregate. Right panel: Aggregate as calculated by the reservoir. Note the progressive shift from the
canonical animacy and the real-world plausibility at the beginning of the discourse (where “salted” has a high proximity with the discourse
vector and thus lower N400) to consistency with animacy of the peanut by the end of the discourse (where “love” has a higher proximity with
the discourse vector and thus lower N400).
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Figure 4. Predicted N400 scores for the online discourse embedding versus “love” and “salted” at the beginning and end of the Peanut
discourse. Left and middle panels: Distribution histograms of scores for 50 Discourse Reservoirs at the beginning and end of the discourse,
respectively. Note the N400 reversal with Love > Salted before, and Love < Salted after the discourse. Right panel: Box-whiskers plot with
median, quartile, and range for “love” and “salted” before and after the discourse. The predicted N400 for the online discourse embedding and
“peanut” versus “love” and “salted” are clearly reversed or overruled by the discourse.
significantly smaller for End (0.416) than Beginning (0.510). There was a marginally significant
effect for Target, F(1, 49) = 12, p < 0.005, with Love (0.457) less than Salted (0.468). Importantly,
confirming the principal result, there was a significant Discourse × Ending interaction, F(1, 49) =
683, p < 0.0001, corresponding to the reversal of predicted N400 for Salted and Love between
the Beginning and Ending conditions, as seen in Figure 4.
This demonstrates that online temporal-spatial integration of word embeddings in the
Discourse Reservoir reproduces semantic immediacy and discourse overrule. In the simulation
of the van Berkum Peanut experiments, the N400 response to “Peanut in love” was reversed by
discourse providing context about animacy. The Discourse Reservoir network creates a discourse
representation that models how human responses to words in discourse can be influenced by the
preceding history of the discourse. By using an aggregation model that is updated online, the
influences of the prior discourse are made immediately available. This provides a functional
model of how semantic immediacy and discourse reversal can be obtained.
Experiment 3: Event Knowledge Modulates Discourse Overrule (Metusalem et al., 2012)
Experiment 2 demonstrated how incrementally integrating word vectors into an online discourse
vector provides an explanation for immediate discourse reversal. Experiment 3 examined this
phenomenon in its component parts, strictly controlling the presence or absence of contextual
discourse, to predict relative N400 amplitudes as discourse activates general knowledge about a
described event, so that a contextually anomalous word elicits a reduced N400 if it is generally
related to the described event, as observed by Metusalem et al. (2012). Metusalem et al. tested
subjects on 72 scenarios that allowed manipulation of whether a discourse context would
“rescue” the processing of a target word. An example of their scenarios is illustrated in Table 2.
In Metusalem et al.’s (2012) first experiment illustrating the effects of discourse context, sub-
jects were exposed to a short discourse composed of sentences 1–3, with sentence 3 then ending
in one of the three possible completions 4–6, that is, either Expected (4), Unexpected-Related (5),
or Unexpected-Unrelated (6). N400 responses to the three completions revealed a clear effect of
the context provided by sentences 1–2, as it “rescued” the N400 for the Unexpected-Related
condition (POLICEMAN), which lay statistically between the small N400 for the Expected con-
dition (CRASH) and the large N400 for the Unexpected-Unrelated condition (CONDUCTOR).
N400 amplitude in the Unexpected-Unrelated condition was significantly greater (i.e., more
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Table 2.
Example of one of the 72 discourse context scenarios
1. Elizabeth was standing at the intersection waiting for the light to change.
2. All of a sudden she saw a car barrel through the red light.
3. A moment later, she heard a terrible
4. (Expected) CRASH
5. (Unexpected-Related) POLICEMAN
6. (Unexpected-Unrelated) CONDUCTOR
Note. Adapted from “Generalized event knowledge activation during online sentence comprehension,” by
R. Metusalem et al., 2012. Journal of Memory and Language, 66, pp. 545–567.
negative) than the Unexpected-Related condition, which in turn was greater than the Expected
condition. N400 amplitudes followed the significant progression: Unexpected-Unrelated >
Unexpected-Related > Expected.
Given this demonstration of the effects of discourse context, Metusalem et al. (2012) then per-
formed a second experiment using “No Context” to determine what would happen under the same
conditions but in the absence of the context provided by the first two sentences of the scenario. Dans
terms of the example in Table 2, subjects were exposed only to sentence 3—thus the target word 4
“CRASH” remains expected; the target words 5 “POLICEMAN” and 6 “CONDUCTOR” are both
inattendu; and “POLICEMAN” loses its status of being related to the event. Metusalem et al.
observed that the rescuing of the N400 for the Unexpected-Related disappeared, avec le
N400 profile of a large and equivalent N400 for both Unexpected cases with respect to the small
N400 for the Expected case: Unexpected-Unrelated = Unexpected-Related > Expected.
En résumé, when a discourse context was present, the N400 effect for an unexpected but
semantically related word was rescued, reducing its N400 amplitude.
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We set out to determine if such discourse rescue effects could be predicted based on our hypoth-
esis that a temporal-spatial integration of word embeddings can account for aspects of immedi-
acy and discourse overrule. We first tested discourse aggregation using the average vector in two
conditions for each of the 72 scenarios. Dans le premier, only sentence 3 was used as input to the vector
average. We then calculated the predicted N400 as the 1 − cosine of the vectors representing the
aggregated discourse and the target word for each of the three target words. This resulted in three
predicted N400 measures, Sentence versus Expected, Sentence versus Unexpected-Related, et
Sentence versus Unexpected-Unrelated. We then repeated this procedure, using sentences 1–3
as the input to generate the average vector. Encore, we measured the predicted N400 given the
discourse representation, in response to the three target words. This yielded six measures for each
of the 72 scenarios. Expected, Unexpected-Related, and Unexpected-Unrelated with Sentence
versus Discourse context. The results for these measures are illustrated in Figure 5.
The distributions of predicted N400 scores for the 72 scenarios are shown in Figure 5. In the
Sentence context, predicted N400s for the Expected responses are distributed to the left, with lower
valeurs (voir la figure 5, left panel). The impact of using the extended Discourse to generate the context
is evident, as the scores for the Unexpected-Related words shift to the left, with smaller N400s (voir
Chiffre 5, middle panel). In the Sentence condition, a smaller predicted N400 is shown (Chiffre 5,
right panel) for the Expected words and larger predicted N400 for the Unexpected-Related and
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Chiffre 5. Predicted N400 for Expected, Related, and Unrelated words as a function of discourse context using Wikipedia2Vec average
discourse aggregation. Left and middle panels: Distribution histogram of Predicted N400 scores for the three target word types as a function
of the context—Sentence versus Discourse—using the Wikipedia2Vec average discourse aggregation. Left panel: Sentence context. Predicted
N400 scores for Expected words are distributed to the left, with respect to Related and Unrelated, which are superimposed with similar in-
creased values. Middle panel: Discourse context. The distributions for Related and Unrelated become separated with values for Related words
shifting to the left, reflecting the decrease in their Predicted N400 induced by the discourse. Vertical colored arrows indicate means: Note the
shift of Related to the left when passing from sentence to discourse. Right panel: Comparison of Predicted N400s between discourse vector
and target words after the Sentence and after the complete discourse with context. With sentence only, the Expected word has a smaller
Predicted N400 than the Unexpected-Related and Unexpected-Unrelated. With complete discourse, the N400 for the Unexpected-Related
ending is rescued.
Unexpected-Unrelated. In the Discourse condition, again a lower predicted N400 is shown
(Chiffre 5, right panel) for Expected words, but now the predicted N400 for the Unexpected-
Related words has diminished, and appears smaller than that for the Unexpected-Unrelated
words. This rescue of the N400 by discourse is the effect of discourse overrule.
These observations were confirmed using 2 × 3 ANOVA on the Predicted N400 score, with factors
Context (Sentence, Discourse) and Relatedness (Expected, Unrelated-Related, Unexpected-
Unrelated). There was a significant main effect for Context, F(1, 71) = 138.3, p < 0.001, with the
predicted N400s smaller for Discourse (0.491) than Sentence (0.547) conditions. There was a sig-
nificant main effect for Relatedness, F(2, 71) = 36.7, p < 0.0001, with Expected (0.487) smaller
than Unexpected-Related (0.517), smaller than Unexpected-Unrelated (0.563). Importantly there
was a significant Context × Relatedness interaction, F(2, 142) = 14.8, p < 0.0001. This corresponds
to the observation that for the Sentence condition, the predicted N400 for Expected (0.495) is sig-
nificantly less than Unexpected-Related (0.559), which is significantly less than Unexpected-
Unrelated (0.598), while in the Discourse condition, Expected (0.459) and Unexpected-Related
(0.465) are not significantly different ( p = 0.1), and both are less than Unexpected-Unrelated
(0.598). Thus, the presence of the extended discourse reduces the predicted N400 for the
Unexpected-Related, but not Unexpected-Unrelated words. This reflects the discourse overrule
behavior observed in human subjects by Metusalem et al. (2012), where the Unexpected-
Related is rescued by the extended discourse. This is consistent with their observation that gener-
alized event knowledge provided by discourse contributes to mental representations of described
events, is immediately available to influence language processing, and likely drives linguistic ex-
pectancy generation.
Simulation of 72 Metusalem et al. (2012) scenarios with the discourse reservoir
In order to evaluate its discourse aggregation capability, we then tested a single instance of the
Discourse Reservoir in two conditions for each of the 72 scenarios in Metusalem et al. (2012),
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following exactly the same procedure as for the vector average, but now using the reservoir. As in
the previous experiment, in the first condition, only sentence 3 was used as input, this time to
the Discourse Reservoir network that had been trained to generate the vector average of its inputs
as described above. We then calculated the predicted N400 as 1 − cosine of the vectors represent-
ing the aggregated discourse and the target word for each of the three target words. This resulted in
three predicted N400 measures: Sentence versus Expected, Sentence versus Unexpected-Related,
and Sentence versus Unexpected-Unrelated. We then repeated this procedure, using sentences
1–3 as the input to the reservoir, which had been trained to generate the average vector. Again,
we calculated the predicted N400 between the discourse representation and the three target
words. This yielded six measures for each of the 72 scenarios: Expected, Unexpected-Related,
and Unexpected-Unrelated with the Sentence and Discourse contexts. The results for these
measures are illustrated in Figure 6.
The distributions of predicted N400 scores for the 72 scenarios are shown in Figure 6. In the
Sentence context, these are comparable to the results of the vector average discourse aggre-
gation results in Figure 5. N400 predictions for the Expected responses are distributed to the
left, with lower values. The impact of using the extended Discourse to generate the context is
evident (see Figure 6, middle panel), as the scores for the Unexpected-Related words shift to
the left, with smaller N400s. For the Sentence condition, a smaller predicted N400 for the
Expected words is shown (Figure 6, right panel), along with increased predicted N400 for
the Unexpected-Related and Unexpected-Unrelated. In the Discourse condition, again is
shown (Figure 6, right panel) a reduced predicted N400 for Expected words, but now the pre-
dicted N400 for the Unexpected-Related words has decreased, and appears smaller than that
for the Unexpected-Unrelated words.
These observations were confirmed using 2 × 3 ANOVA on the predicted N400 score, with
factors Context (Sentence, Discourse) and Relatedness (Expected, Unexpected-Related,
Unexpected-Unrelated). There was a significant main effect for Context, F(1, 71) = 13.48, p <
0.001, with the predicted N400 smaller for Discourse (0.492) than Sentence (0.501) conditions.
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Figure 6. Predicted N400 as a function of discourse context using Wikipedia2Vec reservoir discourse aggregation. Left and middle:
Distribution histogram of Predicted N400 scores for the three target word types as a function of the Context: Sentence versus Discourse.
Vertical arrows indicate mean and allow visualization of shifts between sentence and discourse contexts. Left panel: Sentence context.
Predicted N400 scores for Expected words are distributed to the left, with respect to Related and Unrelated, which are superimposed with
similar reduced values. Middle panel: Discourse context. The distributions for Related and Unrelated become separated, with values for
Related words shifting to the left, reflecting the decrease in their Predicted N400 induced by the discourse. Right panel: Predicted N400s
for Discourse Reservoir vector and target words after the sentence and after the complete discourse with context. With sentence only, the
Expected word has a smaller Predicted N400 than the Unexpected-Related and Unexpected-Unrelated. With complete discourse, the
Predicted N400 for the Unexpected-Related ending is rescued.
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There was a significant main effect for Relatedness, F(2, 71) = 28.17, p < 0.0001, with N400s for
Expected (0.468) smaller than Unexpected-Related (0.503), smaller than Unexpected-Unrelated
(0.518). Importantly there was a significant Context × Relatedness interaction, F(2, 142) = 18.95,
p < 0.0001. This corresponds to the observation that for the Sentence condition, N400s for
Unexpected-Related (0.511) and Unexpected-Unrelated (0.515) are not significantly different
( p = 0.72), while in the Discourse condition, they are significantly different with Unexpected-
Related (0.496) significantly smaller than Unexpected-Unrelated (0.522) p < 0.0001. This
decrease in the predicted N400 of the Unexpected-Related words reflects the discourse overrule
behavior observed in human subjects by Metusalem et al. (2012).
Simulation of 72 Metusalem et al. (2012) scenarios with 50 discourse reservoirs
In order to evaluate the generalization and reliability of these results, we then performed the same
experiment on 50 instances of the reservoir, corresponding to 50 experimental subjects. As in the
previous case, each reservoir was tested with just the sentence and with the complete discourse for
the Expected, Unexpected-Related, and Unexpected-Unrelated target words, for the 72 scenarios.
The results are presented in Figure 7.
The distributions of mean predicted N400 values for the 50 subjects (i.e., instances of the res-
ervoir) are shown in Figure 7. First, when compared with the results for a single reservoir in
Figure 6, much less variability in the scores is found. This is similar to the variability across trials
for a single subject (single reservoir) that is reduced when the means of those values are then
averaged across multiple subjects (multiple reservoirs). This corresponds to the observation that
for a single reservoir there is variability between scores in the different scenarios, and across
multiple reservoirs there is less variability in the means averaged over the 72 scenarios for each
reservoir. Importantly, a shift from right to left for the Unexpected-Related comparisons between
Sentence to Discourse condition is shown (Figure 7, left and middle panels). In the Sentence
condition we see a smaller predicted N400 for Expected words and increased predicted N400
for Unexpected-Related and Unexpected-Unrelated (see Figure 7, right panel). What is most
Figure 7. Distribution histogram of Predicted N400 scores for the three target word types as a function of the context—Sentence versus
Discourse—for the 50 reservoirs, comparable to Figure 6 but with a more pronounced effect. Left panel: Sentence context. Predicted N400
scores for Expected words are distributed to the left, with respect to Related and Unrelated, which are superimposed with similar increased
values. Middle panel: Discourse context. The distributions for Related and Unrelated become separated with values for Related words shifting
to the left, reflecting the decrease in their Predicted N400s, rescued by the discourse. Right panel: Predicted N400s for Discourse Reservoir
vector and target words after the sentence and after the complete discourse with context. With sentence only, the Expected word has a smaller
Predicted N400 than the Unexpected-Related and Unexpected-Unrelated. With complete discourse, the Predicted N400 for the Unexpected-
Related ending is rescued.
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remarkable is the effect of the extended Discourse in reducing the predicted N400 for the
Unexpected-Related words, and increasing it for the Unexpected-Unrelated words.
These observations were confirmed using 2 × 3 ANOVA on the Predicted N400 score, with factors
Context (Sentence, Discourse) and Relatedness (Expected, Unexpected-Related, and Unexpected-
Unrelated). There was a significant main effect for Context, F(1, 49) = 3307, p < 0.001, with
the predicted N400 smaller for Discourse (0.495) than Sentence (0.502) conditions. There
was a significant main effect for Relatedness, F(2, 49) = 12490, p < 0.0001, with Predicted
N400s for Expected (0.471) less than Unexpected-Related (0.505), less than Unexpected-
Unrelated (0.520). Importantly there was a significant Context × Relatedness interaction, F(2,
98) = 3600, p < 0.0001. All post hoc comparisons were highly significant. The two Unexpected
endings vary significantly from the Expected baseline in both Sentence and Discourse contexts.
For the Sentence condition, Unexpected-Related (0.512) and Unexpected-Unrelated (0.518)
significantly differ only by 0.006, while in the Discourse condition the significant difference
between Unexpected-Related (0.497) and Unexpected-Unrelated (0.523) is greater at 0.026. A
paired t test between these differences in Sentence versus Discourse is highly significant (t = 71,
−10). In the presence of Discourse, the predicted N400 for Unexpected-Related decreases
p < 10
and Unexpected-Unrelated increases. Again, this decrease in the predicted N400 of the
Unexpected-Related words in the Discourse condition reflects discourse rescue or overrule
behavior observed in human subjects by Metusalem et al. (2012).
In summary, the principal result is that the model predicts that in the Discourse condition,
the N400 for Unexpected-Related ending words is rescued from its high value in the Sentence
condition, while this is not the case for the Unexpected-Unrelated endings. This corresponds
to the principal result of the two experiments in Metusalem et al. (2012).
DISCUSSION
This research was motivated to answer questions about how multiple sources of knowledge can
be accessed during discourse processing in a way that respects the immediacy assumption and
discourse overrule: How can world knowledge from lexical semantics and the ongoing knowl-
edge from the accumulated discourse be immediately applied to the processing of the next word
in the discourse to overrule expectations based on local semantics? A striking experimental ob-
servation that motivates such a question is the N400 response to the word “salted” in “The peanut
was salted” when that sentence was preceded by a discourse context where the peanut was
framed as an animate agent who was singing and dancing with happiness (Hagoort & van
Berkum, 2007; Nieuwland & van Berkum, 2006). These experiments illustrated the effects of se-
mantic immediacy and discourse overrule, where the local semantics are overruled by those of
the preceding discourse in the immediate time frame of the N400.
We determined that satisfying the immediacy assumption and discourse overrule requires
two coordinated capabilities corresponding to lexical retrieval and online discourse process-
ing. In both cases if the data were processed in a purely serial manner, then the size of the
dataset would tend to increase processing time. The larger the lexicon, the longer the process-
ing. Similarly, as words accumulate in a discourse, serial retrieval would require time propor-
tional to the length of the discourse to access information (such as the animacy of the peanut)
that appeared earlier in the discourse. Yet in both cases, there seems to be no time penalty in
human language processing. We believe that this is because the brain has adopted solutions
that transform serial representations into distributed spatial representations. Sequential infor-
mation is recoded in a distributed spatial form which can be immediately accessed by distrib-
uted neural connections.
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For lexical retrieval and word semantics, this serial-to-spatial transformation is exploited in
distributed vector representations that have been developed in the domain of natural language
processing, which capture rich linguistic and conceptual information (Bengio, Schwenk,
Senécal, Morin, & Gauvain, 2006; Landauer & Dumais, 1997; Mikolov et al., 2013). These
systems can be implemented as statistical models or neural networks. The point is that they are
corpus-based learning algorithms that generate distributed representations of word meanings
and, thus, transform the temporal and sequential information in the training corpora into distrib-
uted spatial vector spaces that allow immediate access.
In Experiment 1, we illustrated how a current leading incarnation of these systems,
Wikipedia2Vec, which is based on word2vec, displays human-like predictions of N400 responses
for word pairs from a classic study of semantic priming and N400 (Chwilla et al., 1995). Clearly we
do not want to claim that Wikipedia2Vec or other word embedding algorithms understand word
meanings in all the ways that humans do. These methods do, however, capture a certain degree of
the knowledge contained in the human-produced corpora on which they are trained, in the vector
embeddings they generate. Characterizing the limits of these models remains an open topic of
research (Ettinger, 2020). The geometric relationships between these vectors capture meaningful
semantic relationships between the corresponding words in a variety of domains from analogical
reasoning and developmental psychology to neuroscience (Dehghani et al., 2017; Landauer &
Dumais, 1997; Mikolov et al., 2013; Mitchell et al., 2008). With respect to the neural correlates,
studies of human brain imaging indicate that human brain activity during language comprehen-
sion can be represented and decoded using models based on distributed word representations
from natural language processing in machine learning (Huth, de Heer, Griffiths, Theunissen, &
Gallant, 2016). This suggests that understanding the highly distributed nature of the human seman-
tic system may be aided by understanding the representational structure of high dimensional word
and discourse embeddings. Indeed, these representations can be used to predict the meaning of
brain activation at the level of words (Mitchell et al., 2008) and stories (Dehghani et al., 2017). It
has even been shown that these methods can capture societal shifts reflected in language over
decades, for example, the women’s movement in the 1960s and Asian immigration to the
United States (Garg et al., 2018).
For integrating these word representations during discourse processing, we considered an
approach where the temporal-sequential information in the discourse is transformed into a spatial
representation via recurrent connections in a reservoir network. Previous research demonstrated
how a reservoir model for learning grammatical constructions could take word sequences as
input and generate a distributed spatial representation that was used to generate immediate
responses based on the grammatical structure in the input, including simulation of the P600
ERP for structure violations (Hinaut & Dominey, 2013). It is now accepted that these recurrent
reservoir networks model the functional neurophysiology of local recurrent connections in pri-
mate cortex in a variety of higher cognitive functions (Enel et al., 2016; Fusi, Miller, & Rigotti,
2016; Rigotti et al., 2013). In Experiments 2 and 3 we used the Discourse Reservoir to integrate
word embeddings in a discourse, and we observed that this provided immediate access to
accumulated information from the discourse, consistent with observations of human N400
responses (Hagoort & van Berkum, 2007; Metusalem et al., 2012; Nieuwland & van Berkum,
2006). Our results from Experiments 2 and 3 demonstrated how prior discourse could influence
the processing of the current lexical item, shifting and even reversing predicted N400 responses
based on prior discourse.
Again, we do not claim that the Discourse Reservoir (nor an average vector of the words in the
discourse) models full human discourse comprehension. However, such simple discourse
embeddings, which involve averaging word embeddings and then using cosine to compute
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pairwise sentence similarity scores, perform well and are used as baselines for evaluation of state-
of-the-art algorithms for Semantic Textual Similarity evaluation tasks (Cer et al., 2017) and also
have been used for modeling N400 in sentence contexts (Ettinger et al., 2016). Similarly, the use
of averaging Wikipedia2Vec embeddings to characterize sentences was employed in the win-
ning system at the Human–Computer Question Answering Competition at the Thirty-first
Annual Conference on Neural Information Processing Systems (Yamada, Tamaki, Shindo, &
Takefuji, 2018). Likewise, reservoir models have been demonstrated to be capable of integrat-
ing multiple words in short discourse for subsequent thematic role assignment (Hinaut &
Dominey, 2013). Tasks that require access to discourse history, such as semantic role labeling,
where the role of a current noun requires access to the verb and grammatical words that may
be far behind in the past can benefit from this history preserving capability of recurrent net-
works (Zhou & Xu, 2015).
Neural Implementation of the Model
The integration of discourse and world knowledge, and their interaction in terms of overall coher-
ence in comprehension, has been shown to recruit distinct neural substrates (Menenti, Petersson,
Scheeringa, & Hagoort, 2009). The left inferior frontal gyrus (IFG) was associated with processing
world knowledge, and the right IFG was more sensitive to the effects of a “rescuing” in local
discourse similar to the rescuing in discourse in the Metusalem et al. (2012) task. Interestingly,
the left angular gyrus showed a significant interaction between world knowledge and discourse
context, suggesting a role in tracking the coherence between the discourse context and the cur-
rent sentence (Menenti et al., 2009).
The immediacy of these world knowledge and local context effects was characterized by
Hald, Steenbeek-Planting, and Hagoort (2007) using EEG. They observed that the effects of
manipulating world knowledge and discourse context modulated N400 responses in the same
325–525 ms time frame, illustrating the immediacy of these effects. The interplay between these
two levels of lexical and discourse processing has been further revealed in recent studies of
human electrophysiology during reading. Investigating the underlying mechanisms with MEG,
Hultén, Schoffelen, Uddén, Lam, and Hagoort (2019) showed that within 500 ms of seeing a
word, the word’s lexical information has been retrieved and unified with the sentence context.
This happens via co-modulation between the left posterior temporal cortex and the left inferior
frontal cortex for individual words around 400 ms after their onset across the sentence progres-
sion. The results provide a detailed description of the temporal orchestration related to single
word processing in the context of ongoing language.
In terms of the spatial organization of these representations, Dehghani et al. (2017) calculated
the distributed representations of stories and demonstrated that by using these representations,
they could identify the specific story that a participant was reading from the neural data, based
on a collection of brain regions most prominently located in the default mode network. That is,
they established a predictive relation between high dimensional distributed story representations
generated by doc2vec and high dimensional distributed story representations in the human
default mode network. Once trained, their classifier could predict the doc2vec representation
of a story, given the human subject’s brain activation from reading that story. This argues that,
in addition to providing powerful tools for natural language processing, these distributed repre-
sentations can help us to better understand biological mechanisms for discourse comprehension.
This allows the elaboration of a model of narrative processing where the left IFG is associated
with processing lexical semantics and world knowledge, corresponding to our temporal-spatial
transformation realized by the Wikipedia2Vec word embedding model. In this model, the right
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IFG is associated with the temporal-spatial integration processing of local discourse that contrib-
utes to the rescuing effects observed by Metusalem et al. (2012) and implemented as a recurrent
reservoir network in our model. Such a model should also include observation of default mode
network activity during narrative processing (Metusalem et al., 2012), which may correspond to
embodied simulation of the represented events (Jouen et al., 2015).
Related Neurocomputational Models
Our present study can be situated in the context of existing models that simulate the N400.
Rabovsky et al. (2018) developed a semantic gestalt (SG) model that explains N400 results from
a variety of human experimental studies. In their model, the incoming word is processed through
a hidden layer where it combines with the previous SG activation, thus implementing a form of
recurrent network. The model has an input vocabulary of 74 words that are associated with hand-
crafted semantic representations. Using this vocabulary, the model is trained on [sentence, event]
pairs, where events are characterized as sets of role-filler pairs (e.g., agent, man; action, eat; pa-
tient, eggs; location, kitchen; situation, breakfast). The N400 response is modeled as the semantic
update (SU) or sum of changes in activation in the SG network in response to the current word.
The authors note that the SU at any given point is determined by the statistical regularities in the
training corpus, and they explain how the observed effects depend on the training corpus. This
research provides a detailed analysis of how the N400 arises as a function of how the current
sentence is related to the probabilistic structure of sentences that the system has been previously
exposed to. The SG representation itself clearly embodies the notion of temporal-spatial transfor-
mation that we advocate, and it has the further advantage that it can be used to generate an event
representation of the meaning of the sentence. It will be interesting in the future to extend this
model to address how discourse meaning can influence the N400 over and above the local
sentence context (Rabovsky et al., 2018) as in the experiments of Nieuwland and van Berkum
(2006) and Metusalem et al. (2012).
Brouwer et al. (2017) developed a model that addresses the N400 in the larger context of the
N400 and P600. They develop a theory and model where the N400 reflects the retrieval of word
meaning from semantic memory, and the P600 component indexes the integration of this mean-
ing into the unfolding utterance interpretation. The word retrieval is contingent on the context of
the utterance, which is accumulated by recurrent connections in the integration network. This
accumulation allows for the immediacy effect. The results of the retrieval then feed forward into
the integration network where the utterance level representation is developed, and where the
integration effects corresponding to the P600 are simulated. Both the retrieval (N400) and the
integration (P600) effects displayed by the model result from a confrontation at test time with
the statistics that were accumulated by the model from the training corpus during training time.
It will be interesting to determine how this model will address discourse effects as in the exper-
iments of Nieuwland and van Berkum (2006) and Metusalem et al. (2012).
The above two models and the model we have developed here all share the use of recurrent
networks to perform the temporal-spatial integration that provides for semantic immediacy. The
models of Rabovsky et al. (2018) and Brouwer et al. (2017) rely on regularities that are introduced
by the construction of the training corpus. Their models learn meaning representations and thus
require labeled corpora of [sentence, meaning] pairs that must be created by the experimenters.
Our research exploits regularities that are encoded in the distributional properties of a large, natural,
human-generated corpus. We do not, however, include an explicit representation of the meaning.
Future research should consider how these modeling approaches can be combined, for example,
by bootstrapping a system using labeled training data to account for event coded meaning repre-
sentations, and then extending this using large unlabeled data encoded in word embeddings.
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The observation that we can simulate N400 effects, including multiple sentence discourse
effects related to human event semantics, indicates that a trace of these regularities is represented
in the information extracted by the Wikipedia2Vec algorithm. This is supported by studies from
Ettinger (Ettinger, 2020; Ettinger et al., 2016) that confront state of the art language models,
including word2vec (Mikolov et al., 2013) and BERT (Devlin et al., 2019), with psycholinguistics
to determine what information the models are able to use when assigning probabilities to
words in context. This research demonstrated how word2vec averages (Ettinger et al., 2016)
and BERT (Ettinger, 2020) were able to account for data from several psycholinguistic experiments,
including Federmeier and Kutas (1999). Federmeier and Kutas (1999) used stimuli similar to those
of Metusalem et al. (2012). Context was established in a first sentence, followed by a second
neutral sentence with final words that were either expected exemplars, within-category violations,
or between-category violations, as in the example, “Checkmate,” Rosaline announced with glee.
She was getting to be really good at chess/monopoly/football. Like Metusalem et al. (2012), they
showed that N400s were present for the violations, but reduced for within category violations,
particularly when the context was highly constrained. Ettinger et al. (2016) demonstrated this effect
using word2vec averages to model the discourse context, and Ettinger (2020) demonstrated a pref-
erence for the expected versus violation endings using BERT. As Federmeier and Kutas (1999) did
not perform a second experiment (as in Metusalem), they did not address the discourse induced
effects in the presence and absence of the context sentence, but it is likely that they would have
found similar effects, as we did for the Metusalem et al. (2012) experiments.
Future research should continue to exploit this fruitful interaction between computational
linguistics and psycholinguistics. Fine characterization of language processing via psycholinguis-
tics can continue to be used to better understand what computational language models are
capable of (Ettinger, 2020), and computational models can continue to be used to characterize
what the brain is doing (Dehghani et al., 2017; Huth et al., 2016; Huth, Nishimoto, Vu, & Gallant,
2012; Mitchell et al., 2008). A new domain of scientific discovery is emerging where these two
approaches can actively cooperate.
In conclusion, we have presented results confirming our hypothesis that a temporal-spatial
integration of word embeddings in a Discourse Reservoir can account for aspects of the imme-
diacy assumption and discourse reversal in human discourse processing. This research offers
several future perspectives. One is related to the limits of immediacy. Inference takes time
(Calvo, 2000; Estevez & Calvo, 2000; Till et al., 1988), and it will be interesting to explore in more
depth to what extent inferred knowledge is present in these systems, and how more extended
inference mechanisms might be incorporated. Likewise, while we have focused on more imme-
diate N400 effects, manipulations of discourse predictability can generate much later positivities
(Brothers, Wlotko, Warnke, & Kuperberg, 2020; Brouwer, Fitz, & Hoeks, 2012), which reveal
processes that should be accounted for in models of discourse processing (Brouwer et al.,
2017; Brouwer et al., 2012).
One might consider that the type of modeling presented here does not help us understand
human language, but rather that it helps us understand whether or not and how a computational
model behaves like a human. Interestingly we can also proceed in the other direction. Our model
defines a class of computations based on its two computational components, the language model
that generates word embeddings, and the reservoir that accumulates discourse representations.
The ability of the model to predict N400s during discourse comprehension allows us to hypoth-
esize that some aspect of human neurophysiology is isomorphic to the model. This allows spe-
cific predictions to be made. One of the properties of the computational and cortical reservoir is
the presence of mixed selectivity, that is, a nonlinear mixture of different task parameters in res-
ervoir units that has been observed in the primate cortex and in reservoir models (Enel et al., 2016;
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Rigotti et al., 2013). We should thus expect to see evidence for mixed selectivity in human cor-
tical electrophysiology during discourse comprehension, as potentially observable in MEG
(Schoffelen et al., 2019).
Our research contributes to a more general principal of temporal-spatial transformation for
cognitive immediacy. Standard conceptions of computation have been constrained by the
Turing machine model and the Von Neumann architecture, which are inherently sequential in
nature (Graves, Wayne, & Danihelka, 2014; Hopcroft, Motwani, & Ullman, 2001). If temporally
structured sequences can be projected into a two dimensional space, and if computations can be
realized in one step on representations in that space, then computational complexity is signifi-
cantly reduced. Interestingly, this is exactly the organization of spatial maps in the brain, with
extensive parallelism in the duplicated organization of the cortico-striato-thalamo-cortical
circuits (Alexander, DeLong, & Strick, 1986). The further exploration of inherently spatial distrib-
uted computation will likely play a role in understanding human cognition and developing
increasingly powerful learning systems.
ACKNOWLEDGMENTS
Research supported by Region Bourgogne Franche-Comté (AAP 2019 RobotSelf ), and the
French Convention CIFRE N 2018/0434 Cloud-Temple/INSERM.
FUNDING INFORMATION
Peter Ford Dominey, Conseil Régional de Franche-Comté (FR), Award ID: (AAP 2019 RobotSelf ).
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AUTHOR CONTRIBUTIONS
Takahisa Uchida: Conceptualization, Investigation, Methodology, Software, Writing – Review
& editing. Nicolas Lair: Conceptualization, Investigation, Methodology, Software, Writing –
Review & editing. Hiroshi Ishiguro: Project administration, Supervision, Validation, Writing –
Review & editing. Peter Ford Dominey: Conceptualization, Investigation, Methodology,
Software, Supervision, Writing – Original draft.
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