Representations from artificial neural network (ANN) language models have been shown to predict human brain activity in the language network. To understand what aspects of linguistic stimuli contribute to ANN-to-brain similarity, we used an fMRI data set of responses to n = 627 naturalistic English sentences (Pereira et al., 2018) and systematically manipulated the stimuli for which ANN representations were extracted. In particular, we (i) perturbed sentences’ word order, (ii) removed different subsets of words, or (iii) replaced sentences with other sentences of varying semantic similarity. We found that the lexical-semantic content of the sentence (largely carried by content words) rather than the sentence’s syntactic form (conveyed via word order or function words) is primarily responsible for the ANN-to-brain similarity. In follow-up analyses, we found that perturbation manipulations that adversely affect brain predictivity also lead to more divergent representations in the ANN’s embedding space and decrease the ANN’s ability to predict upcoming tokens in those stimuli. Further, results are robust as to whether the mapping model is trained on intact or perturbed stimuli and whether the ANN sentence representations are conditioned on the same linguistic context that humans saw. The critical result—that lexical-semantic content is the main contributor to the similarity between ANN representations and neural ones—aligns with the idea that the goal of the human language system is to extract meaning from linguistic strings. Finally, this work highlights the strength of systematic experimental manipulations for evaluating how close we are to accurate and generalizable models of the human language network.

Research in psycholinguistics and cognitive neuroscience of language strives to understand the representations and algorithms that support human comprehension and production abilities. Until recently, mechanistic accounts of human language processing have been out of reach. However, artificial neural network (ANN) language models now hold substantial promise for developing and evaluating computationally precise hypotheses about language processing. In particular, contemporary ANN language models achieve impressive performance on a variety of linguistic tasks (e.g., Brown et al., 2020; Chowdhery et al., 2022; Devlin et al., 2018; Liu et al., 2019; OpenAI, 2023; Rae et al., 2021). Furthermore, representations extracted from ANN language models—especially unidirectional attention transformer architectures like GPT2 (Radford et al., 2019) can explain substantial variance in brain activity recorded from the human language network using regression-based evaluation metrics (e.g., Caucheteux & King, 2022; Gauthier & Levy, 2019; Goldstein et al., 2022; Hosseini et al., 2022; Jain & Huth, 2018; Kumar et al., 2022; Oota et al., 2022; Pasquiou et al., 2022; Schrimpf et al., 2021; Toneva & Wehbe, 2019). This correspondence has been suggested to derive, at least in part, from the convergence of the ANNs’ linguistic representations with those in the human brain (Caucheteux & King, 2022; Goldstein et al., 2022; Hosseini et al., 2022; Schrimpf et al., 2021), despite the vast differences in their learning and architecture (e.g., Huebner & Willits, 2021; Warstadt & Bowman, 2022).

However, many questions remain about the factors that contribute to ANN-to-brain mapping, that is, the ability to predict brain responses from ANN representations. One critical question concerns the aspects of linguistic content and form that play a role. To shed light on this question, we used a published functional magnetic resonance imaging (fMRI) data set (Pereira et al., 2018) where brain responses were collected from 10 native speakers of English as they read syntactically and semantically diverse passages, consisting of several sentences each. We reproduced the result of the top-performing brain-encoding ANN language model in Schrimpf et al. (2021)—GPT2-xl (Radford et al., 2019)—on this data set, and investigated what drives the model’s brain predictivity, or brain score (Schrimpf et al., 2018). In particular, we evaluated the contributions to accurate mapping of sentence meaning (largely carried by content words) and syntactic form (conveyed via word order and function words), along with superficial control features, like sentence length. To do so, we performed 12 sets of experiments: three categories of linguistic manipulations across four variants of what we term here computational experimental design, as elaborated next.

First, we systematically manipulated the linguistic stimuli in three ways: by altering the word order of the sentence (across seven conditions; see the Perturbation Manipulation Conditions section for details), omitting different subsets of words (across five conditions), and replacing a sentence with sentences of different degrees of semantic relatedness (across four conditions). Some of these manipulations disrupt the syntactic form of the sentence (e.g., changing the order of the words or removing the function words); whereas other manipulations affect sentence meaning (e.g., removing the content words or replacing a sentence with a semantically unrelated sentence). We asked how well ANN representations for the resulting altered stimuli (across the 16 conditions) can predict neural responses compared to the ANN representations of (a) the original, unaltered sentence and (b) a control, length-matched condition (a random list of words). Next, we explored possible causes for the differential effects of these manipulations on brain predictivity by examining the changes in the ANN representations and next-word prediction task performance as a function of stimulus alterations. Finally, to evaluate the robustness of the results to the computational experiment design, we performed all three types of linguistic manipulations across four experimental setups, crossing (i) whether the model that maps from stimuli to brain representations was trained on intact or perturbed stimuli; and (ii) whether the ANN representations of the target sentences were contextualized with respect to the preceding sentences in a passage or not.

Manipulations of Linguistic Stimuli

Language allows its users to package meanings into sequences of words. Content words, such as nouns, verbs, and adjectives (which carry the most information in a sentence, as can be quantified using information-theoretic measures; e.g., Shannon, 1948), have to be selected, so as to capture the right word-level meanings, and then they have to be assembled into phrases and sentences according to the rules of the language. The assembly process includes ordering the words in a particular way as well as adding appropriate inflectional morphological markers and function words, like determiners and prepositions. The result of all these operations is a meaningful and well-formed sentence. Here we evaluate the relative importance of different aspects of linguistic stimuli (sentences) for ANN-to-brain mapping. To do so, we systematically manipulate the sentences across three manipulation categories (Figure 1; Table 1) before passing them into the ANN and use the resulting ANN model representations to predict brain responses (Figure 2).

Figure 1.

Overview of perturbation manipulation conditions and the ANN-to-brain mapping approach. An example (original) sentence is illustrated together with an example of the stimulus in a sample condition from each of the three types of perturbation manipulations (word-order manipulations, information-loss manipulations, and semantic-distance manipulations), as detailed in Perturbation Manipulation Conditions.

Figure 1.

Overview of perturbation manipulation conditions and the ANN-to-brain mapping approach. An example (original) sentence is illustrated together with an example of the stimulus in a sample condition from each of the three types of perturbation manipulations (word-order manipulations, information-loss manipulations, and semantic-distance manipulations), as detailed in Perturbation Manipulation Conditions.

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Table 1.

Overview of perturbation manipulation conditions and sample stimuli for each condition.

Manipulation typeCondition nameSample stimulus
original Original it is in every beekeeper’s interest to conserve local plants that produce pollen. 
word-order 1LocalWordSwap it is in every beekeeper’s interest to conserve local plants produce that pollen. 
3LocalWordSwaps in it is every beekeeper’s interest to conserve local plants produce that pollen. 
5LocalWordSwaps in it is every interest beekeeper’s to conserve local plants produce pollen that. 
7LocalWordSwaps in every it is interest beekeeper’s to conserve plants local that pollen produce. 
ReverseOrder pollen produce that plants local conserve to interest beekeeper’s every in is it. 
LowPMI it beekeeper’s conserve plants pollen in to every that is interest local produce. 
LowPMIRandom in that pollen to is plants every beekeeper’s conserve produce interest it local. 
information-loss KeepContentW it is beekeeper’s interest conserve local plants produce pollen. 
KeepNVAdj it is beekeeper’s interest conserve local plants produce pollen. 
KeepNV it is beekeeper’s interest conserve plants produce pollen. 
KeepN it beekeeper’s interest plants pollen. 
KeepFunctionW in every to that. 
semantic-distance Paraphrase conserving regional vegetation that provides pollen is of the utmost importance for beekeepers. 
RandSentFromPassage beekeepers also discourage the use of pesticides on crops because they could kill the honeybees. 
RandSentFromTopic artisanal honey-making emphasizes quality and character over quantity and consistency. 
RandSent mosquitos are thin small flying insects that emit a high-pitched sound. 
control RandWordList of shears metallic is in individual machine for fracture a singer can have. 
Manipulation typeCondition nameSample stimulus
original Original it is in every beekeeper’s interest to conserve local plants that produce pollen. 
word-order 1LocalWordSwap it is in every beekeeper’s interest to conserve local plants produce that pollen. 
3LocalWordSwaps in it is every beekeeper’s interest to conserve local plants produce that pollen. 
5LocalWordSwaps in it is every interest beekeeper’s to conserve local plants produce pollen that. 
7LocalWordSwaps in every it is interest beekeeper’s to conserve plants local that pollen produce. 
ReverseOrder pollen produce that plants local conserve to interest beekeeper’s every in is it. 
LowPMI it beekeeper’s conserve plants pollen in to every that is interest local produce. 
LowPMIRandom in that pollen to is plants every beekeeper’s conserve produce interest it local. 
information-loss KeepContentW it is beekeeper’s interest conserve local plants produce pollen. 
KeepNVAdj it is beekeeper’s interest conserve local plants produce pollen. 
KeepNV it is beekeeper’s interest conserve plants produce pollen. 
KeepN it beekeeper’s interest plants pollen. 
KeepFunctionW in every to that. 
semantic-distance Paraphrase conserving regional vegetation that provides pollen is of the utmost importance for beekeepers. 
RandSentFromPassage beekeepers also discourage the use of pesticides on crops because they could kill the honeybees. 
RandSentFromTopic artisanal honey-making emphasizes quality and character over quantity and consistency. 
RandSent mosquitos are thin small flying insects that emit a high-pitched sound. 
control RandWordList of shears metallic is in individual machine for fracture a singer can have. 

Note. The perturbation manipulation conditions that we use in the current work are motivated by prior theorizing in language research and/or past empirical findings from both neuroscience and natural language processing (NLP). The perturbation manipulations include (i) word-order manipulations of varying severity that preserve or destroy local dependency structure (following Mollica et al., 2020), allowing us to investigate the effect of word order degradation while controlling for local word co-occurrence statistics; (ii) information-loss manipulations with deletion of words of different parts of speech (following O’Connor & Andreas, 2021), allowing us to investigate loss of information from particular classes of words; (iii) semantic-distance manipulations with sentence substitutions that relate to the meaning of the original sentence to varying degrees (inspired by Pereira et al., 2018), allowing us to investigate loss of semantic and more general topical information while retaining sentence well-formedness. As a baseline length-matched control condition, we include a random word list, where each word is substituted with a different random word.

Figure 2.

Overview of the ANN-to-brain mapping approach. Brain data from human participants (n = 10) were recorded while they read intact sentences using functional magnetic resonance imaging (fMRI; Pereira et al., 2018). Brain data consisted of voxel responses within the language-selective network (individually defined using an independent localizer task; Fedorenko et al., 2010) for each of the 10 participants. Following Schrimpf et al. (2021), we divided the stimuli (i.e., sentences) into training/test sets. We then retrieved ANN model representations for the stimuli and fitted a linear ANN-to-brain mapping model (M) from the ANN representations of the training stimuli to each single voxel’s (within the language network) corresponding recordings for those stimuli (the fitting process is not illustrated in this graphic). Next, we tested the ANN-to-brain mapping model (M) on the ANN representations of the held-out test stimuli to generate predicted brain responses for those stimuli, for each voxel (illustrated by the gradient arrows). Lastly, we compared predicted versus actual brain responses for each voxel using the Pearson correlation coefficient. This process was repeated five times, holding out a different set of 20% of stimuli each time. ANN representations were obtained using two approaches (for the motivation and details, see Manipulations of Computational Experimental Design): (1) TrainIntact–TestPerturbed, where ANN representations for the training set were obtained from the original, intact stimuli, whereas the ANN representations for the test set were obtained from the perturbed stimuli; and (2) TrainPerturbed–TestPerturbed, where ANN representations for the training and test set were obtained from the perturbed stimuli. These two approaches were crossed with whether the preceding sentences in the passage were included as contextualizing input for the ANN (not depicted). The GPT2 illustration was adapted from Radford et al. (2018).

Figure 2.

Overview of the ANN-to-brain mapping approach. Brain data from human participants (n = 10) were recorded while they read intact sentences using functional magnetic resonance imaging (fMRI; Pereira et al., 2018). Brain data consisted of voxel responses within the language-selective network (individually defined using an independent localizer task; Fedorenko et al., 2010) for each of the 10 participants. Following Schrimpf et al. (2021), we divided the stimuli (i.e., sentences) into training/test sets. We then retrieved ANN model representations for the stimuli and fitted a linear ANN-to-brain mapping model (M) from the ANN representations of the training stimuli to each single voxel’s (within the language network) corresponding recordings for those stimuli (the fitting process is not illustrated in this graphic). Next, we tested the ANN-to-brain mapping model (M) on the ANN representations of the held-out test stimuli to generate predicted brain responses for those stimuli, for each voxel (illustrated by the gradient arrows). Lastly, we compared predicted versus actual brain responses for each voxel using the Pearson correlation coefficient. This process was repeated five times, holding out a different set of 20% of stimuli each time. ANN representations were obtained using two approaches (for the motivation and details, see Manipulations of Computational Experimental Design): (1) TrainIntact–TestPerturbed, where ANN representations for the training set were obtained from the original, intact stimuli, whereas the ANN representations for the test set were obtained from the perturbed stimuli; and (2) TrainPerturbed–TestPerturbed, where ANN representations for the training and test set were obtained from the perturbed stimuli. These two approaches were crossed with whether the preceding sentences in the passage were included as contextualizing input for the ANN (not depicted). The GPT2 illustration was adapted from Radford et al. (2018).

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Word-order manipulations

The first class of manipulations targets the order of the words in the sentence. Word order is an important cue to how words relate during sentence comprehension (e.g., Bever, 1970; Kimball, 1973). However, word order rigidity varies across languages, with some languages exhibiting flexible orderings, pointing to a more limited role of word order, at least in those languages (e.g., Dryer & Haspelmath, 2013; Hale, 1983; Jackendoff & Wittenberg, 2014). Moreover, work in psycholinguistics has shown that comprehension is highly robust to errors in the linguistic input, including word order errors, as long as a plausible meaning can be recovered. For example, given the sentence The mother gave the candle the daughter, people typically infer the intended meaning to be the more plausible The mother gave the daughter the candle, suggesting that word order information can be overridden in favor of a plausible meaning (e.g., Gibson et al., 2013; Levy et al., 2009). Furthermore, word-order transpositions, as in You that read wrong again!, often go unnoticed during sentence reading (Mirault et al., 2018; Wen et al., 2021).

Similarly, recent evidence from neuroscience has shown that the human language network—a set of brain areas that are selectively and robustly activated when humans process language (e.g., Fedorenko et al., 2011; Fedorenko & Thompson-Schill, 2014; Lipkin et al., 2022; Regev et al., 2013)—exhibits the same amount of activation to word-order-manipulated sentences as to intact ones as long as the pointwise mutual information (PMI) among nearby words remains as high as in the intact sentences (which presumably allows for the formation of local semantic and syntactic dependencies; Mollica et al., 2020). This result aligns with findings by Gauthier & Levy (2019), who showed that fine-tuning the pre-trained bidirectional encoder representations from transformers (BERT) language model (Devlin et al., 2018) on a word prediction task that selects against word-order-based representations of the input (namely, fine-tuning on a corpus where words were randomly shuffled within a sentence) leads to an increase in brain decoding performance for the same fMRI benchmark used here (Pereira et al., 2018). Lastly, recent work in natural language processing (NLP) has shown that word-order information is not necessarily needed to solve many current NLP benchmark tasks (e.g., Papadimitriou et al., 2022; Pham et al., 2021; Sinha et al., 2021; cf. Abdou et al., 2022; Lasri et al., 2022), although these results may be more reflective of how the benchmarks were constructed than about human language or language-processing mechanisms (e.g., McCoy et al., 2019).

Therefore, given this evidence from NLP and language neuroscience, we hypothesized that ANN-to-brain mapping performance on current fMRI data sets may similarly not require ANNs to leverage word-order information. Building on Mollica et al. (2020), we evaluate both local scrambling of words (which may better preserve syntactic and semantic dependencies among nearby words) and more global re-arrangement of words (which does not preserve such dependencies).

Information-loss manipulations

The second class of manipulations targets the information carried by words from specific parts of speech. Research in formal semantics has traditionally posited a categorical difference between the semantics of content (open-class/lexical) words and function (closed-class/logical) words, whereby function words, unlike content words, are assumed to carry little lexical meaning and primarily encode logical relationships between content words (e.g., Chierchia, 2013; Partee, 1992). Further, research in distributional semantics suggests that the semantic properties of function words are not well represented by word co-occurrence distributions, in sharp contrast with the meanings of content words, which are better captured by distributional patterns (Boleda, 2020; though see, e.g., Abrusán et al., 2018; Baroni et al., 2012; Linzen et al., 2016).

Indeed, previous co-occurrence-based models often benefited from the removal of function words and other high-frequency “stop words” for solving various NLP tasks (e.g., Bernardi et al., 2013; Herbelot & Baroni, 2017; Lazaridou et al., 2017). Even for state-of-the-art ANN language models, it has recently been shown that retaining only the content words in linguistic context has little effect on next-word prediction performance, with performance varying as a function of how much of the lexical content is included (i.e., higher performance when keeping all content words vs. keeping only subsets, such as keeping only the nouns and verbs, or keeping only the nouns; O’Connor & Andreas, 2021). Whereas function words have been shown to have a sizable effect on ANN next-word prediction performance within local sentence contexts (because they help ensure grammaticality; Khandelwal et al., 2018), content words strongly influence prediction performance both within local and more extended contexts (Khandelwal et al., 2018; O’Connor & Andreas, 2021).

Research from psycholinguistics similarly shows an asymmetry between content and function words: when reading sentences, people tend to overlook the omission or repetition of function words, but such errors are much more noticeable for content words (Huang & Staub, 2021; Staub et al., 2019). Differences can also be found in language production: Function words tend to have shorter pronunciations than content words of the same length, because they are typically highly predictable from context, which leads to phonological reduction and de-stressing (e.g., Bell et al., 2009).

In line with this previous research, we hypothesized that removing content words, but not function words, should have a strong negative effect on ANN-to-brain mapping and that brain predictivity should increase the more lexical content of the original sentence is available to the ANN for building a representation. Following O’Connor and Andreas (2021), we evaluate the effect of preserving all or some of the content words (e.g., nouns and verbs) or function words.

Semantic-distance manipulations

The third class of manipulations targets sentence-level meanings and serves to test how precisely the meaning of a sentence has to be encoded for a successful ANN-to-brain mapping. Previous research in computational neuroscience has shown that vectors that represent lexical semantics based on word co-occurrence statistics (global vectors for word representation [GloVe]; Pennington et al., 2014) can be decoded from fMRI data recorded while participants read sentences (Pereira et al., 2018). In particular, Pereira et al. (2018) demonstrated that sentence pairwise classification accuracy depended on how semantically similar the sentence pairs were: Sentences from different topics (e.g., a sentence about beekeeping vs. a sentence about skiing) were easier to distinguish than sentences that talk about different ideas within the same general topic (e.g., two sentences about beekeeping that come from distinct passages, such as one passage about the importance of beekeeping for the health of the planet and the other telling a story about a particular beekeeper), with sentences that talk about related ideas within the same topic (e.g., two sentences from the same passage on the importance of beekeeping) being the most challenging to distinguish.

However, in Pereira et al. (2018), even neural responses to sentences from the same passage could still be reliably discriminated, suggesting that semantic representations of linguistic input are relatively fine-grained in both fMRI data and word embeddings. On the other hand, even neural responses to sentences from distinct topics could not be perfectly discriminated (classification accuracy: 81%–84%, chance level: 50%), suggesting that representations of sentences that are unrelated to the target sentence may share some features with the representation of the target sentence that can lead to misclassification (at least when using coarse representations based on averaged decontextualized GloVe embeddings for decoding). This pattern of results raises the question of how much of the fMRI signal that ANN-to-brain models are able to predict represents a sentence’s exact or approximate semantic content.

We manipulate sentence meaning by replacing the original sentence with sentences that vary in how similar they are in meaning to the original. In line with Pereira et al. (2018), we hypothesized that substituting sentences that are semantically more distant from the target sentence would elicit lower ANN-to-brain mapping performance. Further, because ANN representations of compositional sentence meaning (as investigated here) are richer and finer-grained than a simple average of decontextualized word embeddings, we hypothesized that the representations of topically unrelated sentences would be more distant, so that substituting them would result in very low brain predictivity. We leveraged the hierarchical structure of the linguistic materials in the Pereira et al. (2018) fMRI benchmark to vary semantic distance between the original sentences and the manipulated ones, in a similar way to the original study, and additionally created paraphrases for each sentence, which were expected to elicit high ANN-to-brain performance.

Understanding the Effects of Linguistic Manipulations on Brain Predictivity

To complement our findings across the linguistic perturbation manipulations, we present a set of exploratory analyses that aim to uncover potential causes for the differential effects of perturbation manipulations on brain predictivity. In particular, we look for possible correlates of brain predictivity across perturbation manipulation conditions in (a) the ANN’s representational space and (b) the ANN’s performance on the next-word prediction task for the manipulated sentence sets.

The investigation of the ANN representational space is motivated by the use of representational similarity metrics to compare high-dimensional vectors derived from brain recordings, ANNs, or both (Kriegeskorte, 2015; Kriegeskorte et al., 2008). Representational similarity analysis (RSA) relies on the strength of correlation between sets of vectors to make inferences about the information contained in the distributed patterns within the vectors. Although originally developed to compare vectors derived from different brain recording modalities, RSA-style approaches can also be used to compare representations derived from various instantiations of ANNs (e.g., Barrett et al., 2019; Kornblith et al., 2019; Morcos et al., 2018). Here, we investigated how the ANN representational space is transformed by different perturbation manipulations. We hypothesized that larger transformations in the ANN representational space (relative to the representational space for the original, intact stimuli) would be associated with larger changes in brain predictivity.

The investigation of ANN task performance is motivated by research in psycholinguistics and neuroscience, suggesting that predictive processing is a core mechanism for language comprehension (within psycholinguistics: Bicknell et al., 2010; Brothers & Kuperberg, 2021; Demberg & Keller, 2008; Rayner et al., 2006; Smith & Levy, 2013; within neuroscience: Heilbron et al., 2019; Henderson et al., 2016; Lopopolo et al., 2017; Shain et al., 2020; Willems et al., 2016). Converging research from computational neuroscience has reported a positive correlation between the next-word prediction performance of ANN language models and model-to-brain correspondence (Caucheteux & King, 2022; Goldstein et al., 2022; Hosseini et al., 2022; Schrimpf et al., 2021; cf. Antonello & Huth, 2024). In line with this evidence, we hypothesized that ANN brain predictivity would correlate with how well the model can predict upcoming words within a manipulated sentence, such that manipulations that render sentences less predictable would, on average, lead to representations that map less well onto human brain data.

Manipulations of Computational Experimental Design

ANN-based modeling of language provides a novel toolkit for testing theoretically motivated hypotheses about language processing in the mind and brain. However, results derived via such in-silico investigations of human language processing may not be robust to variations in how the ANN-to-brain match comparisons are performed. Here, we evaluate the relative importance of manipulations to what we denote as the computational experimental design for ANN-to-brain match performance to evaluate the robustness of our results. Specifically, we investigate the contribution of two factors, as elaborated below: (i) whether the training data stimuli for the ANN-to-brain mapping model are intact or perturbed, and (ii) whether the target sentence is contextualized with preceding sentences from the passage.

ANN-to-brain mapping model training stimuli

In our main approach (TrainIntact–TestPerturbed, as illustrated in Figure 2, upper two panels), we train a linear ANN-to-brain mapping model using ANN representations for the original (intact) stimuli from Pereira et al. (2018) and human brain responses obtained during the processing of the same, intact versions of the stimuli. This training setup corresponds to the main use case of ANN-to-brain encoding models and follows prior work (e.g., Caucheteux & King, 2022; Goldstein et al., 2022; Schrimpf et al., 2021). Using this standard setup, we investigated our main research question, that is, Which aspects of a linguistic stimulus contribute to successful ANN-to-brain mapping performance?, by evaluating the mapping model’s ability to predict brain responses to intact sentences from ANN representations of sentences that were perturbed in one of the ways described above. (For a detailed description, see Perturbation Manipulation Conditions in Materials and Methods.) If degrading a particular aspect of the sentence decreases the mapping model’s performance (brain predictivity), we would like to conclude that this aspect of the stimulus is a critical contributor to the mapping model’s performance. If, on the other hand, degrading the stimulus does not lead to lower brain predictivity, we would like to conclude that the mapping model does not pay any appreciable attention to the part of the ANN representation of the stimulus that is sensitive to the removed information.

However, there are (at least) two possible explanations for why sentence perturbation manipulations may adversely affect the success of an ANN-to-brain mapping model trained on representations of intact sentences: Either a successful mapping must critically rely on the information removed by a given perturbation (our desired interpretation, as stated above), or perturbing the input to the ANN model only at test time introduces out-of-distribution inputs to the trained ANN-to-brain mapping model; that is, lower brain predictivity can be explained by a distribution shift of the input to the mapping model at test time. To distinguish between these possibilities, we tested how well an ANN-to-brain mapping model can predict brain responses when the input to the ANN is perturbed in the same way during training and testing (Figure 2, TrainPerturbed–TestPerturbed). When the ANN-to-brain mapping models are trained and tested on the same set of perturbations, a decline in brain predictivity relative to the performance using intact sentences (Original benchmark) cannot be explained by a distribution shift in the input to the model at test time. Thus, this approach can reveal the degree to which perturbations remove information from the ANN representation of the stimuli that is useful for a mapping model when it learns a relationship between ANN representations and brain data.

Contextualization of the linguistic stimuli

ANN-to-brain predictivity analyses typically aim to mimic the experimental procedure for which the human brain data were obtained. Linguistic stimuli in human brain imaging studies are often contextualized within a story (e.g., Blank et al., 2014; Huth et al., 2016; Schoffelen et al., 2019) or a passage (e.g., Pereira et al., 2018). Given that large-scale ANN language models (such as transformer language models) are able to condition input representations on large amounts of preceding linguistic context, they enable mimicking the human experimental design by providing the same linguistic stimuli as context to the ANN as were provided to the human participants. However, whether this is the right approach, empirically and conceptually, is not clear.

On the one hand, providing the same context to the ANN language models for representation building as what humans saw/heard during the experiment could improve ANN-to-brain mapping performance by modulating sentence representations in ways similar to how the human brain is affected by context. On the other hand, sentence contextualization could hurt match-to-brain performance. First, the way in which ANNs versus humans represent contextual information in memory is likely very different. In particular, constrained by memory limitations, humans do not retain detailed linguistic representations of the preceding context (e.g., Futrell et al., 2020; Potter, 2012; Potter et al., 1980; Potter & Lombardi, 1990); instead, as they process linguistic input, they appear to extract the representations of the relevant meaning and “discard” the exact word sequences (e.g., Christiansen & Chater, 2016; Potter & Lombardi, 1998). And second, human neuroscience studies have suggested that extended story contexts are represented not in the language network proper (which we focus on here), but in a distinct brain network—the default network (e.g., Blank & Fedorenko, 2020; Lerner et al., 2011; Simony et al., 2016). Thus, neural responses to language stimuli of the language-selective areas may only be capturing the local processing of the current sentence and would therefore align better with decontextualized sentence representations (see Caucheteux et al., 2021, and Jain & Huth, 2018, for evidence that ANN representations with varying amounts of linguistic context lead to differential mapping performance with different brain areas). We therefore evaluated brain predictivity for sentence representations with and without contextualization through inclusion of preceding sentences in the passage; we refer to these as contextualized and decontextualized sentence representations, respectively. We did this for the two ANN-to-brain mapping model training approaches introduced above (i.e., TrainIntact–TestPerturbed and TrainPerturbed–TestPerturbed).

To foreshadow our results, we find that (i) lexical-semantic content of the sentence, rather than syntactic structure (conveyed via word order or function words), is responsible for the ability of ANNs to predict fMRI responses in the human language network. We further show that (ii) linguistic perturbations that decrease brain predictivity have interpretable causes: They lead to (a) more divergent representations in the ANN’s embedding space (relative to the representations of intact sentences) and (b) a decrease in the ANN’s next-word prediction task performance, that is, its ability to predict upcoming tokens in those stimuli. Finally, (iii) the results from the linguistic manipulations are largely robust to variations in the computational experimental design, which impact the overall magnitude of brain scores but not their pattern across conditions.

Here we describe in detail (i) our manipulations of the linguistic stimuli that are designed to isolate the influences of different features of the input, (ii) how we obtain ANN representations for these stimuli, and (iii) how we perform ANN-to-brain mappings. Because our approach is based on the ANN-to-brain mapping framework from Schrimpf et al. (2021), the sections Comparison of ANN Model Representations to Brain Measurements, fMRI Data Set (Pereira et al., 2018), and Estimation of Noise Ceiling (Quantified as Brain-to-Brain Predictivity) are similar to the methods reported in Schrimpf et al. (2021).

fMRI Data Set

We used the data from Pereira et al.’s (2018) Experiments 2 (n = 9) and 3 (n = 6) (10 unique participants, all native speakers of English). (The set of participants is not identical to Pereira et al.: One participant, tested at Princeton, was excluded from both experiments here to keep the fMRI scanner the same across participants; and two participants who were excluded from Experiment 2 in Pereira et al. based on the decoding results in Experiment 1 of that study were included here, to err on the conservative side.) Stimuli for Experiment 2 consisted of 384 sentences (96 text passages, four sentences each), and stimuli for Experiment 3 consisted of 243 sentences (72 text passages, three or four sentences each). The two sets of materials were constructed independently, and each spanned a broad range of content areas. Sentences were 7–18 words long in Experiment 2, and 5–20 words long in Experiment 3. The sentences were presented on the screen one at a time for 4 s each (followed by 4 s of fixation, with additional 4 s of fixation at the end of each passage), and each participant read each sentence three times, across independent scanning sessions (see Pereira et al., for details of experimental procedure and data acquisition).

Preprocessing and response estimation

Data preprocessing was carried out with SPM5 (using default parameters, unless specified otherwise) and supporting, custom MATLAB scripts. Preprocessing included motion correction (realignment to the mean image of the first functional run using second-degree b-spline interpolation), normalization (estimated for the mean image using trilinear interpolation), resampling into 2 mm isotropic voxels, smoothing with a 4 mm full-width at half maximum Gaussian filter and high-pass filtering at 200 s. A standard mass univariate analysis was performed in SPM5 whereby a general linear model estimated the response to each sentence in each run. These effects were modeled with a boxcar function convolved with the canonical hemodynamic response function. The model also included first-order temporal derivatives of these effects (which were not used in the analyses), as well as nuisance regressors representing entire experimental runs and offline-estimated motion parameters.

Functional localization

Data analyses were performed on fMRI blood oxygen level dependent (BOLD) signals extracted from the bilateral fronto-temporal language network. This network was defined functionally in each participant using a well-validated language localizer task (Fedorenko et al., 2010), where participants read sentences versus lists of nonwords. This contrast targets brain areas that support “high-level” linguistic processing, past the perceptual (auditory/visual) analysis. Brain regions that this localizer identifies are robust to modality of presentation (Fedorenko et al., 2010; Malik-Moraleda et al., 2022; Scott et al., 2017), as well as materials and task (e.g., Diachek et al., 2020). Further, these regions have been shown to exhibit strong sensitivity to both lexico-semantic processing (understanding individual word meanings) and combinatorial, syntactic/semantic processing (putting words together into phrases and sentences) (Bautista & Wilson, 2016; Blank et al., 2016; Blank & Fedorenko, 2020; Fedorenko et al., 2010; Fedorenko et al., 2012; Fedorenko et al., 2016; Fedorenko et al., 2020). Following prior work, we used group-constrained, participant-specific functional localization (Fedorenko et al., 2010). Namely, individual activation maps for the target contrast (here, sentences > nonwords) were combined with constraints in the form of spatial “masks”—corresponding to broad areas within which most participants in a large, independent sample show activation for the same contrast. The masks, which are derived in a data-driven way from this independent sample of participants and are available from https://evlab.mit.edu/funcloc/, have been used in many prior studies (e.g., Diachek et al., 2020; Jouravlev et al., 2019; Shain et al., 2020). They include six regions in each hemisphere: three in the frontal cortex (two in the inferior frontal gyrus, including its orbital portion, and one in the middle frontal gyrus), two in the anterior and posterior temporal cortex, and one in the angular gyrus. Within each mask, we selected 10% of most localizer-responsive voxels (voxels with the highest t value for the localizer contrast) following the standard approach in prior work. This approach allows the pooling of data from the same functional regions across participants even when these regions do not align well spatially in the common space.

We constructed a stimulus–response matrix for each of the two experiments by (i) averaging the BOLD responses to each sentence in each experiment across the three repetitions, resulting in one data point per sentence per language-responsive voxel of each participant, selected as described above (13,553 voxels total across the unique 10 participants; 1,355 average, ±6 SD), and (ii) concatenating all sentences (384 in Experiment 2 and 243 in Experiment 3), yielding a 384 × 12,195 matrix for the nine unique participants in Experiment 2, and a 243 × 8,121 matrix for the six unique participants in Experiment 3.

ANN Models

As our computational models, we chose to investigate the GPT2 transformer model family (Radford et al., 2019). These models are trained to predict the next token in a large data set emphasizing document quality (WebText). We focus on this model family for two reasons: (i) As a unidirectional-attention model, GPT2 arguably processes input in a more humanlike manner than bidirectional-attention models such as BERT (Devlin et al., 2018), which have access to the yet unseen input in the future context; and (ii) previous work has shown that GPT2 in particular seems to accurately capture human brain activity in the language system during the processing of the same linguistic stimuli (e.g., Caucheteux & King, 2022; Goldstein et al., 2022; Schrimpf et al., 2021). We report results for GPT2-xl, the top-performing ANN language model in previous work (Schrimpf et al., 2021) and validate that the findings hold across the GPT2 model family (see Figure SI 1A in the Supporting Information, available at https://doi.org/10.11.62/nol_a_00116) to ensure the robustness of our results to idiosyncratic model features. Hence, our primary ANN language model of interest was GPT2-xl (number of layers L = 48, hidden size H = 1,600). Additionally, we tested GPT2 (L = 12, H = 768) and Distil-GPT2, a distilled version (Sanh et al., 2019; L = 6 H = 768). For all three GPT2 models, we used the pretrained models available via the HuggingFace library (Wolf et al., 2020).

Retrieving ANN Model Representations

To retrieve ANN model representations, we treated each ANN model (see ANN Models) as an experimental participant and ran similar experiments on them as would be run on humans. We retrieved ANN representations for each sentence for each ANN layer (i.e., at the end of each transformer block). Given that human participants were exposed to the full sentence at once, we similarly computed a sequence summary representation for each sentence. Our primary approach for obtaining a sequence summary representation was using the last-token representation: We obtained the representation of the last sentence token (which was always the representation of the final period token “.”) as a sequence summary, given that unidirectional models already aggregate representations of the preceding context (i.e., earlier tokens in the sentence; see Figure SI 1B for generalization to average-token representations of sentences).

To retrieve ANN model representations, we fed sentences to the model sequentially (i.e., sentence by sentence). For the contextualized representations (see Manipulations of Computational Experimental Design), we grouped sentences by passage to mimic the experimental procedure for human participants and fed the passage context (if any) before, but not after, each sentence to the ANN model. For the decontextualized representations, we did not feed any passage context to the model.

Comparison of ANN Model Representations to Brain Measurements

Because we were interested in which aspects of the stimulus contribute to high brain predictivity, we compared ANN model representations of systematically manipulated stimuli (see Perturbation Manipulation Conditions) with brain recordings of humans processing the original (intact) version of the sentences (see fMRI Data Set).

We treated the ANN language model representation at each layer separately and tested how well it could predict human brain recordings. (We treated the two experiments in the Pereira et al., 2018 data set separately but averaged the results across experiments for all plots.) Following Schrimpf et al. (2021), we divided the stimuli (i.e., sentences) into an 80%–20% training–held-out split. For each (participant-specific) voxel, we fitted a linear regression model (ordinary least squares) from the ANN’s representations of the training stimuli to that voxel’s corresponding brain recordings for those stimuli. We applied the regression on model representations of the held-out 20% of stimuli to generate predicted brain responses for those stimuli, and then compared predicted versus actual brain responses for that voxel using the Pearson correlation coefficient. This process was repeated five times, holding out a different 20% of stimuli each time. For each voxel, we then took the mean of the resulting five scores to give us that voxel’s mean predictivity score, computed each participant’s median predictivity score across that participant’s voxels, and computed the median and median absolute deviation (MAD) within-participant error within each perturbation condition manipulation category. We report the results for the best-performing layer of the ANN, as well as results across layers, for completeness (Figure SI 2).

Estimation of Noise Ceiling (Quantified as Brain-to-Brain Predictivity)

Due to intrinsic noise in biological measurements, we estimated how well the best possible “average human” model could perform on predicting brain responses in single voxels for held-out “target” participants. In our brain-to-brain predictivity estimation, we included the n = 5 participants that completed both experiments in the Pereira et al. (2018) data set to obtain full overlap in the materials across participants. Following Schrimpf et al. (2021), the ceiling value was estimated using a three-step procedure (see Supplementary Methods for additional details): We (i) iteratively subsampled the data to predict voxel responses in a given target participant from the voxel responses of the remaining “predictor” participants, (ii) extrapolated the procedure to a participant pool of infinitely many participants, and (iii) obtained a final ceiling value by aggregating the estimated voxel-wise predictivity ceilings. Via this procedure, we obtained a ceiling value of 0.32 for the Pereira et al. (2018) data set.

Perturbation Manipulation Conditions

For our baseline Original condition, we stripped the sentence stimuli (from Experiments 2 and 3 in Pereira et al., 2018) of all sentence-internal punctuation, except for hyphens and apostrophes, and lower-cased all words. This was done to ensure that conditions are as comparable as possible across manipulation conditions (e.g., it is unclear where sentence-internal punctuation should go when sentence word order is perturbed). For a baseline Control condition, we created length-controlled random word lists, RandWordList, by gathering all words across the data set into a list and replacing every word in every sentence by a random draw (without replacement). For the critical conditions, we applied a range of controlled manipulations to the stimuli used in the original fMRI experiments reported in Pereira et al. These manipulations can be grouped into three categories: (i) word-order manipulations, designed to understand how degrading word order in various ways affects processing; (ii) information-loss manipulations, designed to understand how loss of words from a particular part of speech category affects processing, and (iii) semantic-distance manipulations, designed to understand how replacing sentences with sentences that are closer versus further semantically affects processing. Manipulations were applied once to the full data set. This perturbed data set was then fed into the ANN language models sentence by sentence and contextualized or decontextualized sentence representations were obtained. (As described in Retrieving ANN Model Representations, contextualized representations of perturbed sentences were obtained using the sentence’s passage context, which was perturbed in the same way as the sentence of interest.)

Word-order manipulations

For the word-order manipulations, we investigated ANN-to-brain mapping performance across different sentence-internal word scrambling conditions. For five of the word-order manipulation conditions, we followed the material creation procedure described in Mollica et al. (2020). Specifically, in four of these conditions, word order was scrambled to different degrees by iteratively and randomly choosing 1, 3, 5, or 7 words from the Original sentence stimuli and swapping them with one of their immediate word neighbors, leading to the creation of the 1LocalWordSwap, 3LocalWordSwaps, 5LocalWordSwaps, 7LocalWordSwaps conditions. To ensure that the desired number of local swaps has in fact been achieved (i.e., within the chosen number of swaps, no swap was undone by another), the pairwise edit distance between the original sentence and the scrambled condition was calculated. As reported in Mollica et al. (2020), these local swap manipulations, even for the 7-swap case (7LocalWordSwaps), typically preserve local semantic dependency structure, as can be measured by PMI among nearby words (as detailed below). The fifth (and last) condition, which also followed the creation procedure described in Mollica et al. (2020) was a condition where the PMI among nearby words is minimized (the LowPMI condition). Here, we assigned the content and function words of every sentence to two lists (creating four lists overall: even- and odd-numbered content words, and even- and odd-numbered function words, according to their position in the sentence). These lists were then re-concatenated into a string such that all function words intervened between the content words in the two lists, creating maximal linear distance between combinable content words (i.e., words that were adjacent/proximal in the original sentence).

We also created two additional word-order manipulation conditions that were not investigated in Mollica et al. (2020). The first used a different strategy for minimizing local combinability than the one used in the LowPMI condition: the LowPMIRandom condition. Here, stimuli were created by generating 10 random permutations of the words within each sentence (which we ensured did not include the versions used in the 1LocalWordSwap-7 condition) and choosing the perturbation with the lowest PMI score (computed as detailed next). Given that the LowPMI condition was the only condition from the original paper that was generated in a deterministic way, the LowPMIRandom condition was included to ensure that the models could not exploit the latent generation procedure. The second was a ReverseOrder condition, in which the order of the words in the sentence was reversed. This condition ensures maximal linear distance between words in the original and the manipulated string, while preserving the PMI profile of the original stimulus.

A string’s PMI score was calculated using the procedure described in Mollica et al. (2020): For each string, we used a sliding four-word window to extract local word pairs (equivalent to collecting the bigrams, 1-skip-grams, and 2-skip-grams from each string). For each word pair, we then calculated its positive PMI score. We used positive pointwise mutual information because negative PMI values are in practice extremely noisy due to data sparsity (Jurafsky & Martin, 2009). Probabilities were estimated using the Google N-gram corpus (Michel et al., 2011) and ZS Python library (Smith, 2014) with Laplace smoothing (α = 0.1). The string’s PMI score was finally calculated by averaging across the positive PMI values for all word pairs occurring within a four-word sliding window (see Equation 1). The PMI scores for all conditions can be found in Figure SI 3.
PMIwiwn=13n2i=1n1j=i+1mini+3nmax0logPwiwjPwiPwj
(1)

Information-loss manipulations

For the information-loss manipulations, we investigated ANN-to-brain mapping performance across five versions of each sentence, for which different subsets of words were retained relative to the original sentence. For the different manipulations, we respectively retained only words whose part of speech tag, as determined by the NLTK part-of-speech tagger (Bird et al., 2009), is in a given set, while preserving the original order of the retained words. Specifically, we examined versions made up of: (i) all the content words, that is, nouns, verbs, adjectives, and adverbs (KeepContentW); (ii) nouns, verbs, and adjectives (KeepNVA); (iii) nouns and verbs (KeepNV); (iv) nouns (KeepN); and (v) only the function words (KeepFunctionW). Following O’Connor and Andreas (2021), we included pronouns and proper names in the set of nouns. Note also that because not all the sentences had adverbs and/or adjectives, some pairs of the conditions (i), (ii), (iii), and (iv) could be identical for some sentences.

Semantic-distance manipulations

For the semantic-distance manipulations, we investigated ANN-to-brain mapping performance across four conditions, for which the original sentence was replaced by a sentence of variable semantic distances. For three of these conditions, we leveraged the hierarchical organization of the materials in Pereira et al. (2018; Figure SI 4). For the fourth condition, we generated sentence paraphrases.

We first describe the three conditions that leverage the hierarchical structure of the Pereira et al. (2018) data set. As described in fMRI Data Set, stimuli for Experiment 2 consisted of 384 sentences grouped into 96 passages with four sentences each, and stimuli for Experiment 3 consisted of 243 sentences grouped into 72 passages with three or four sentences each. Further, the passages in both experiments came from a smaller number of “topics” that spanned a broad and diverse range of content areas, for example, clothes or animals. The 96 passages in Experiment 2 were grouped into 24 topics (with four passages per topic; e.g., for the topic of clothes, there was a passage about a dress and a passage about a glove), and the 72 passages in Experiment 3 were also grouped into 24 topics (with three passages per topic, e.g., beekeeping as a topic, with three different passages), nonoverlapping with the topics in Experiment 2 (e.g., passages from each experiment; see Table SI 1). We created three experimental conditions. In two of them, RandSentFromPassage and RandSentFromTopic, each sentence was replaced by a sentence from the same passage or topic, respectively. And in the third, RandSent, condition, each sentence was replaced by a sentence that was randomly drawn from the entire data set, with the constraint that no sentence ended up in its original position (proportion of sentences in RandSent condition that come from a different topic than the original sentence: 97.1%; proportion of sentences in RandSent condition that come from a different passage than the original sentence: 99.2%).

As described in Comparison of ANN Model Representations to Brain Measurements, the cross-validation scheme used in this paper was a fivefold cross-validation, holding out 20% of stimuli in each fold. In the TrainIntact–TestPerturbed experimental design, the mapping model was trained on ANN representations of stimuli from the Original benchmark. When benchmarks by design shuffled stimuli relative to the fMRI data (all semantic-distance benchmarks except Paraphrase), this procedure could lead to nonindependence in train and test splits. To prevent such overlap between the training and test stimuli in the TrainIntact–TestPerturbed versions of these benchmarks, we proceeded as follows: For each of the five cross-validation splits, we retrieved the representations of the stimuli that belonged to the test set for the same split in the Original benchmark. We then either (a) randomly shuffled the order of these activations relative to the fMRI data and ensured that no sentence representation remained in its original position (RandSent) or we (b) iterated over the passages/topics and, whenever possible (i.e., whenever the test set contained more than one sentence from the given passage/topic), randomly shuffled the sentence representations within the passages/sentences, ensuring that no sentence representation remained in its original position (RandSentFromPassage/RandSentFromTopic). Given this constraint, and because the average number of sentences per passage (4 sentences/passage in Experiment 2, and 3.38 sentences/passage in Experiment 3) was lower than the number of cross-validation splits (n = 5), the average percentage of sentences whose representations were not shuffled relative to the associated fMRI data using the default fivefold cross-validation scheme was 53.72% for RandSentFromPassage and 8.97% for RandSentFromTopic. Although this procedure led to a high proportion of nonshuffled sentence representations relative to the associated fMRI data in the test set, we opted for this method to ensure consistency and comparability across all TrainIntact–TestPerturbed benchmarks, which were thus all trained on the exact same intact sentence representations. To alleviate concerns about the mapping performance being driven mainly by matched fMRI representations, we additionally ran the RandSent, RandSentFromPassage, and RandSentFromTopicTrainIntact–TestPerturbed benchmark versions, as well as the Original and RandWordList benchmarks for comparison, using only two cross-validation splits instead of the default number of five folds. Using this procedure, all but 17.17% of sentence representations could be shuffled relative to its associated fMRI data for RandSentFromPassage and all sentences could be successfully shuffled with the associated fMRI data for RandSentFromTopic, and the key result pattern was not affected (Figure SI 5).

For the fourth and last condition, we generated a paraphrase for each sentence in the set. To do so, we used the online OpenAI ChatGPT interface to generate three paraphrases for each sentence. For each of these paraphrases, we automatically selected the paraphrase that was closest in number of words to the original sentence. These approximately length-matched paraphrases were then manually edited if (i) the absolute difference in number of words between the paraphrased sentence and the original sentence was more than three words; (ii) the paraphrased sentence did not capture the semantic content of the original sentence (as judged by the authors); or (iii) the paraphrased sentence contained different pronouns compared to the original sentence due to ChatGPT history. Out of 627 paraphrase sentences, 111 sentences (17.7%) were manually edited to yield the final set of paraphrased stimuli. Identical to the remaining benchmarks, we stripped the sentence stimuli of all sentence-internal punctuation, except for hyphens and apostrophes, and lowercased all words. On average, the paraphrased sentences were −0.44 words shorter than the original sentences (median: 0). The paraphrased sentences overlapped partly with the original sentences in terms of their lexical content: The average fraction of overlapping words between the paraphrased and original sentences was 0.46 (median: 0.46, min: 0.05, max: 1).

Manipulations of Computational Experimental Design

The computational experimental design conditions aim to investigate factors related to how the comparisons between ANN representations and brain data are performed. Specifically, we investigated two factors: ANN-to-brain mapping model training stimuli and contextualization of the linguistic stimuli.

ANN-to-brain mapping model training stimuli

This condition investigated the effect of the training data for the ANN-to-brain mapping model. In the TrainIntact–TestPerturbed condition we trained the mapping model on intact (i.e., original, same as the humans were exposed to) stimuli, and tested the mapping model on perturbed stimuli. In the TrainPerturbed–TestPerturbed condition we trained the mapping model on perturbed stimuli and tested the mapping model on perturbed stimuli (using the same perturbation manipulation type).

Contextualization of the linguistic stimuli

This condition investigated the effect of preceding linguistic context on the ANN representations derived for each stimulus according to the structure of the materials investigated (see fMRI Data Set). In brief, humans were presented sentences (one at a time) as part of short (3–4 sentence-long) passages. In the Contextualized condition, the ANN representations were obtained using the preceding sentences in the passage of interest as context (if any; i.e., the first sentence in a passage would have no preceding contextual information). Because the perturbations were applied to the full set of materials once, and ANN representations were derived based on the perturbed sentences, the preceding context of a sentence was perturbed in the same manner as the sentence of interest. The contextualization for the test set sentences in the TrainIntact–TestPerturbed_Contextualized semantic-distance manipulation benchmarks where sentence representations were shuffled relative to the fMRI data (RandSentFromPassage, RandSentFromTopic, RandSent) is an exception to this rule. Here, we take the sentence representations of the original sentences and shuffle the order of these (correctly contextualized) sentence representations with the associated fMRI data either randomly or based on the sentence’s membership in a particular passage or topic. In the Decontextualized condition, the ANN representations were obtained without any preceding context, and representations were hence obtained using individual, decontextualized sentence representations.

These two factors were crossed in a 2 × 2 design to yield the four conditions: TrainIntact–TestPerturbed_Contextualized, TrainPerturbed–TestPerturbed_Contextualized, TrainIntact–TestPerturbed_Decontextualized, and TrainPerturbed–TestPerturbed_Decontextualized.

Statistical tests.

For statistical testing of brain predictivity scores within perturbation manipulation conditions (see Figure 3 and Figure 5 in the Results section and Figures SI 4SI 6 in the Supporting Information; see also Lexical-Semantic Content, Not Syntactic Structure, Is the Main Contributor to ANN-Brain Similarity in the Language Network and The Pattern of Brain Predictivity Across Linguistic Perturbation Manipulations Is Robust to Variation in the Computational Experimental Design), we performed pairwise, two-sided, dependent-samples t tests for all comparisons among the participant-wise brain predictivity values (i.e., 10 values given that the Pereira et al., 2018, consisted of 10 unique participants) between pairs of conditions. P values were corrected for multiple comparisons (within each perturbation manipulation condition) using the Bonferroni procedure (i.e., if a perturbation manipulation consisted of seven conditions and, correspondingly, seven pairwise comparisons were performed, with each condition compared to the original condition [or to the baseline, random word list, condition] the correction was performed over these seven tests; for completeness, all pairwise condition comparisons are reported in Table SI 2).

Error bars of brain predictivity scores show MAD within participants using SciPy 1.8.0’s median_abs_deviation function (Virtanen et al., 2020) with a scaling factor of ∼0.67 (scale = “normal”) for approximate consistency with the standard deviation for normally distributed data. Thus, error bars were computed by centering the data across conditions within a manipulation category per participant to remove within-participant differences and finally computing the MAD over participants. The error bars hence demonstrate the ANN-to-brain mapping model’s prediction variance within participants across conditions rather than uncertainty around the median.

For statistical testing between computational experimental design conditions (see Figure 7 in The Pattern of Brain Predictivity Across Linguistic Perturbation Manipulations Is Robust to Variation in the Computational Experimental Design), we concatenated the participant-wise brain predictivity values within a perturbation manipulation condition (i.e., if a perturbation manipulation consisted of seven conditions, we concatenated 10 * 7 = 70 values). Two-sided dependent-samples t tests were performed between these pairs of computational experimental conditions. P values were corrected for multiple comparisons (within each computational experimental design condition) using the Bonferroni procedure. Throughout the figures, significance levels are denoted as follows: *p < 0.05, **p < 0.01, ***p < 0.001.

Lexical-Semantic Content, Not Syntactic Structure, Is the Main Contributor to ANN-to-Brain Similarity in the Language Network

In this section, we investigate which aspects of a linguistic stimulus contribute to successful ANN-to-brain correspondence of canonically trained ANN-to-brain mapping models. In particular, we trained ANN-to-brain mapping models on ANN representations of intact stimuli (with corresponding brain responses to intact stimuli) and tested these models using ANN representations of perturbed stimuli (with corresponding brain responses for intact stimuli; the TrainIntact–TestPerturbed approach, as illustrated in Figure 2). We report results for GPT2-xl, the top-performing ANN language model in previous work (Schrimpf et al., 2021). (See Figure SI 1A for the generalization of the findings across the GPT2 model family, and Figure SI 1B for generalization to a different sequence summarization approach when extracting ANN model representations). In line with Schrimpf et al. (2021), we treat each GPT2-xl layer as an individual model (layer model) and report the brain predictivity score for the best-performing GPT2-xl layer model per perturbation condition. We note that the results derived in this way were comparable to selecting the best-performing GPT2-xl model layer on the Original benchmark and using this layer for evaluating the remaining perturbation manipulation conditions (Figure SI 5). We diverge from Schrimpf et al. (2021) in that the brain predictivity scores throughout the manuscript are raw Pearson r values, rather than r values normalized by the noise ceiling value quantified to be r = 0.32 via extrapolated brain-to-brain predictivity for the Pereira et al. (2018) data set (see Estimation of Noise Ceiling).

First, we investigated the performance of the mapping model on a control condition: a length-matched list of random words. For this condition, the mapping model performed at near-chance level (Figure 3, RandWordList condition in panels A–C). Chance level (zero predictivity) was not fully reached for the best-performing layer model, possibly because this layer is able to exploit some information about the length of the stimulus (see Figure SI 6). We then investigated the effect of our three types of perturbation manipulations—manipulations of word order within the sentence (word-order manipulations), loss of different subsets of words from the sentence (information-loss manipulations), and manipulations of the semantic distance from the original sentence (semantic-distance manipulation)—on the mapping model’s ability to predict brain activity, relative to the original sentence (Figure 3, Original condition in panels A–C).

Figure 3.

Perturbation manipulations that lead to the loss of lexical-semantic or topical content information decrease brain predictivity. Performance of ANN-to-brain mapping models on held-out sentences, trained on ANN representations of intact sentences and evaluated on ANN representations of perturbed sentences (see Figure 2) from the three perturbation manipulation conditions (A–C). For each condition (bar), we plot the raw brain predictivity Pearson r value of the best-performing layer (as in Schrimpf et al., 2021). The ceiling level for the Pereira et al. (2018) data set is r = 0.32, as estimated via brain-to-brain predictivity (see Estimation of Noise Ceiling), which for the Original benchmark leads to ceiling-level predictivity, in line with Schrimpf et al. (2021). Error bars show median absolute deviation within participants. Manipulation condition scores that were significantly different from the Original and RandWordList control benchmarks (dark and light gray dashed lines, respectively; these conditions are identical across the three panels) are marked with asterisks (*p < 0.05, **p < 0.01, ***p < 0.001) above and below the bars, respectively, in each graph. Significance was established via dependent two-sided t tests, with p values corrected for multiple comparisons (within each perturbation manipulation condition and separately for the comparisons to the original vs. the random word list baseline) using the Bonferroni procedure.

Figure 3.

Perturbation manipulations that lead to the loss of lexical-semantic or topical content information decrease brain predictivity. Performance of ANN-to-brain mapping models on held-out sentences, trained on ANN representations of intact sentences and evaluated on ANN representations of perturbed sentences (see Figure 2) from the three perturbation manipulation conditions (A–C). For each condition (bar), we plot the raw brain predictivity Pearson r value of the best-performing layer (as in Schrimpf et al., 2021). The ceiling level for the Pereira et al. (2018) data set is r = 0.32, as estimated via brain-to-brain predictivity (see Estimation of Noise Ceiling), which for the Original benchmark leads to ceiling-level predictivity, in line with Schrimpf et al. (2021). Error bars show median absolute deviation within participants. Manipulation condition scores that were significantly different from the Original and RandWordList control benchmarks (dark and light gray dashed lines, respectively; these conditions are identical across the three panels) are marked with asterisks (*p < 0.05, **p < 0.01, ***p < 0.001) above and below the bars, respectively, in each graph. Significance was established via dependent two-sided t tests, with p values corrected for multiple comparisons (within each perturbation manipulation condition and separately for the comparisons to the original vs. the random word list baseline) using the Bonferroni procedure.

Close modal

Word-order manipulations

Word-order manipulations (Figure 3A) significantly affected brain predictivity, but predictivity scores did not correlate with the severity of word-order manipulations: In particular, predictivity remained relatively high even for the most severe scrambling manipulations, with drops in predictivity values ranging between 8% and 24% for the different manipulations. In particular, one local word swap (1LocalWordSwap) led to an ∼8% drop in brain predictivity (0.35 Original vs. 0.32 1LocalWordSwap: pairwise dependent t test, t = 5.52, p < 0.01; all reported p values were corrected for multiple comparisons within each manipulation category using the Bonferroni procedure). The remaining local word swap conditions ({3,5,7}LocalWordSwaps) all had a comparable numerical effect (∼17% drop) on brain predictivity (from 0.35 to 0.29, ts > 5.81, ps < 0.001). Pairwise comparisons among the {1,3,5,7}LocalWordSwaps conditions showed no significant differences (see Table SI 3 for the pairwise statistical comparisons among all conditions). Even the most extreme local word-order scrambling condition, that is, reversing the order of the words (ReverseOrder), yielded a decrease relative to Original that was statistically comparable to, albeit larger than, the conditions where three or more local pairs were swapped (0.35 Original vs. 0.27 ReverseOrder, ∼24% drop, t = 9.29, p < 0.001).

Critically, all these five conditions ({1,3,5,7}LocalWordSwaps, ReverseOrder) were designed to retain local semantic dependency structure as quantified by PMI (see Perturbation Manipulation Conditions; Figure SI 2). To test whether preserving local combinability of words is critical for brain predictivity (cf. Mollica et al., 2020), we examined two conditions where local dependency structure was destroyed: the LowPMI and LowPMIRandom conditions, both of which decreased local PMI. Strikingly, even for these conditions, the effect on brain predictivity was relatively small, similar to the local-scrambling conditions (0.35 Original vs. 0.30 LowPMI, ∼16% drop, t = 6.35 p < 0.001; and vs. 0.28 LowPMIRandom, ∼20% drop, t = 8.47, p < 0.001). Hence, destroying the local dependency structure does not appear to affect brain predictivity beyond how it is affected by local word swaps that keep local dependency structure more easily inferable.

Information-loss manipulations

Preservation of different subsets of content-carrying words relative to the full sentence was associated with relatively high brain predictivity, though all of these conditions led to a significant drop in performance relative to the Original condition (0.35 Original vs. 0.28–0.21, ts = 9.21–28.58, ps < 0.001; 20%–42% drops; Figure 3B). Preserving fewer content words—preserving all content words (KeepContentW), preserving only the nouns, verbs, and adjectives (KeepNVA), only the nouns and verbs (KeepNV), or only the nouns (KeepN)—led to a gradual decrease in predictivity values, even though scores for KeepContentW and KeepNVA as well as for KeepNVAdj and KeepNV did not differ significantly from each other (see Table SI 3). By contrast, retaining only the function words (i.e., removing all content-carrying words) led to a brain predictivity comparable to that of a random word list (0.03 RandWordList vs. 0.06 KeepFunctionW, t = −2.57, p > 0.05; a ∼83% drop from the Original condition). To ensure that the strong drop in predictivity for the KeepFunctionW condition was not merely an artifact of the length of the condition (a relatively low number of words in each input string, Table SI 4), we included an additional control condition (RandN; Figure SI 7), which was matched for length with the KeepN condition, but in which the nouns were randomly sampled from the nouns in the data set. This RandN control condition was associated with predictivity performance no different than the random word list control condition (0.03 RandWordList vs. 0.04 RandN, t = −0.96, p > 0.05) and similar to the KeepFunctionW condition (0.04 RandN vs. 0.06 FunctionWords, t = −1.88, p > 0.05; Table SI 2). These results highlight a large asymmetry in the contribution of content vs. function words to brain predictivity and suggest that preserving more of the lexical-semantic content leads to higher predictivity.

Semantic-distance manipulations

As expected, replacing the original sentence with a random sentence was associated with chance-level predictivity (0.01, ∼98% predictivity drop; one-sample t test to 0: t = 0.78, p > 0.05), similar to that of a random list of words (RandWordList vs. RandSent, t = 2.1, p > 0.05; ruling out the possibility that any well-formed and meaningful sentence would yield high brain predictivity). Replacing the sentence with a sentence from the same topic was associated with a ∼68% drop relative to Original (0.11; Original vs. RandSentFromTopic, t = 28.35, p < 0.001), much lower than the predictivity associated with word order scrambling manipulations (∼8%–24% predictivity drop range) or manipulations that preserve at least some of the content words (e.g., ∼42% predictivity drop in the KeepN condition). This result (Figure 3C) demonstrates that a rough topical overlap does not suffice for high brain predictivity. However, replacing the original sentence with a sentence from the same passage was associated with a drop in predictivity of ∼13% (0.31; Original vs. RandSentFromPassage, t = 6.84, p < 0.001). (Note that in the RandSentFromPassage and RandSentFromTopic conditions where sentences were shuffled within subparts of the hierarchically structured data set, an unavoidable overlap between train and test sentences was introduced for the experimental setup using five splits. We show that no key pattern of results was affected using a 2-split cross-validation split in Figure SI 8 and report the results for the fivefold experimental paradigm here for consistency across manipulation types.) Finally, replacing the original sentence with a paraphrase led to a drop of ∼11% (0.31; Original vs. Paraphrase, t = 10.78, p < 0.001), which is comparable to the predictivity of the RandSentFromPassage condition, even though the lexical overlap is substantially higher between the Original and the Paraphrase conditions compared to the Original vs. the RandSentFromPassage condition (Figure SI 9). The result from the Paraphrase condition shows that (a) even sentences that are highly similar in overall meaning are still associated with a small but reliable decrease in predictivity relative to the original sentence, which can be taken to suggest that the model-to-brain match is sensitive to subtle differences in wording, which are associated with subtle semantic differences; and (b) when a certain degree of sentence-level semantic similarity with Original is reached (as is the case for both Paraphrase and RandSentFromPassage conditions; see Figure SI 4), stronger lexical overlap does not have much of an effect on predictivity as evidenced from the fact that Paraphrase was not significantly different from RandSentFromPassage (Table SI 3).

Perturbation Manipulations That Are Associated With Larger Representational Distortion in the ANN Embedding Space and Render Linguistic Stimuli More Surprising Lead to Lower Brain Predictivity

In this section, we investigate why certain perturbation manipulation conditions yield lower brain predictivity than others. We explore two potential factors: (1) differences between the original sentences and the perturbed versions in the ANN representational embedding space and (2) the effect of the perturbation manipulations on the ANN’s task performance (i.e., next-word prediction performance.

Perturbation manipulations that are associated with larger representational distortion in the ANN embedding space lead to lower brain predictivity

Do changes in the ANN representational space across perturbed sentence sets (relative to the intact sentences) explain why certain perturbation conditions yield lower brain predictivity than others? To find out, we investigate—for all ANN model layers—what makes some ANN layer representations more suitable than others for predicting brain responses. In particular, we investigated whether layers for which representations of the perturbed stimuli are more similar to the representations of the intact sentences perform better at predicting brain responses. To do so, for each of 18 perturbation manipulations (1 original, 7 word-order manipulations, 5 information-loss manipulations, 4 semantic-distance manipulations, and 1 control [random word list] manipulation) we calculated the degree of representational similarity (as quantified by the Spearman rank correlation coefficient, ρ) between a layer’s representation of the original, intact sentence and the corresponding perturbed sentence. We then averaged these correlation coefficients across all intact-perturbed pairs, to derive a single value per perturbation manipulation per ANN layer. We then correlated these average correlation values with the associated brain predictivity scores (i.e., a total of 864 values: 18 average correlation values × 48 layers).

We observed a strong positive correlation (Pearson r = 0.72 across all perturbation manipulation conditions, p < 0.001; Figure 4A) between (i) the similarity of an ANN layer’s representation of the original and perturbed stimuli for a given manipulation and (ii) how well that layer could predict neural responses for that perturbation manipulation. The positive relationship differed across perturbation manipulation conditions, but was statistically significant in each condition (Figure 4A, panels i–v). This relationship suggests that perturbation manipulation conditions that distort the representation of the original, intact sentences to a larger extent are associated with lower brain predictivity scores. On average, later layers (red colors) yielded higher brain predictivity scores. For some perturbation manipulation conditions, like the KeepFunctionW condition (pentagon symbol), however, all layers (including later ones) exhibited poor brain predictivity performance that was also associated with consistently low representational similarity to the intact sentences.

Figure 4.

Representational similarity to the original sentences is correlated with brain predictivity. (A) Each individual data point shows the correlation between brain predictivity (y axis) and degree of similarity to the intact sentence set (x axis, quantified using the Spearman’s rank correlation coefficient (ρ) for a layer of the GPT2-xl artificial neural network (ANN) model and a certain perturbation manipulation condition. The ANN layer index is denoted by colors. The perturbation manipulation condition is denoted by data point marker symbols. (B) Similarity of the representations from the last layer of GPT2-xl across conditions to its representations of the intact sentences. Note, though, that the brain predictivity scores reported in the previous sections are from the best-performing layer, not the last one.

Figure 4.

Representational similarity to the original sentences is correlated with brain predictivity. (A) Each individual data point shows the correlation between brain predictivity (y axis) and degree of similarity to the intact sentence set (x axis, quantified using the Spearman’s rank correlation coefficient (ρ) for a layer of the GPT2-xl artificial neural network (ANN) model and a certain perturbation manipulation condition. The ANN layer index is denoted by colors. The perturbation manipulation condition is denoted by data point marker symbols. (B) Similarity of the representations from the last layer of GPT2-xl across conditions to its representations of the intact sentences. Note, though, that the brain predictivity scores reported in the previous sections are from the best-performing layer, not the last one.

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Finally, to understand the trends in Figure 4A at a finer grain, we investigated the degree of similarity between the representations of the intact and perturbed stimuli in a selected layer (here: GPT2-xl’s last layer) across perturbation manipulation conditions (Figure 4B). As expected, relatively subtle manipulations (e.g., 1LocalWordSwap) did not strongly affect the representational similarity: The representation of the perturbed sentence versions is very similar to that of the original versions (Spearman ρ = 0.95). Across the word-order manipulations (Figure 4B; panel ii), representational similarity to the intact sentences gradually decreased with the severity of the word-order scrambling. Likewise, across the information-loss manipulations (Figure 4B; panel iii), representational similarity decreased the more lexical content was removed, with representations of only the nouns in the sentence already achieving an average representational similarity of 0.66. Across the semantic-distance manipulations (Figure 4B; panel iv), representational similarity decreased with increasing semantic distance. The most destructive manipulations (e.g., the KeepFunctionW, RandSent, and RandWordList conditions) were the least similar in their representations to the original sentences. Note that the random word list control condition, although showing lower similarity to the original sentences than all the critical perturbation conditions (except the KeepFunctionW condition), still achieved a similarity score of 0.39. This suggests that GPT2-xl’s representations of length-matched random word lists are not orthogonal to those of intact sentences (i.e., some units in the last layer of GPT2-xl respond similarly independent of the specific words).

The overall pattern across perturbation manipulation conditions, shown in Figure 4B, is similar to the pattern of brain predictivity scores shown in Figure 3. This similarity mirrors the main finding from Figure 4A, which includes information on all perturbations across all ANN layers: Perturbation manipulations that render the representations more distinct from those for intact sentences also result in lower brain predictivity scores.

Perturbation manipulations that render linguistic stimuli more surprising lead to lower brain predictivity

In this section, we ask whether the performance of the ANN-to-brain mapping model is linked to the next-word prediction accuracy of a language ANN model. The most widely used training task for large-scale language ANNs is word-in-context prediction, which aims to minimize the surprisal of a word in the input string conditioned on its context. For this analysis, we obtained the average token surprisal of each input string and averaged these surprisal values across items in each linguistic manipulation condition. We then correlated the difference in these average surprisal values for each condition, relative to the surprisal of the original string, with the difference in brain predictivity for each manipulation condition, relative to brain predictivity for the original condition (Figure 5). Sentence surprisal values were always obtained for the last layer of the ANN (given that GPT2 models are trained to predict next tokens using the last layer representation of the context, and not any other layer representation), whereas brain predictivity scores were derived from the best-performing layer, as before.

Figure 5.

Correlation between difference in brain predictivity relative to the original brain predictivity score and difference in average string surprisal relative to the Original sentence condition. Individual data points are perturbation manipulation conditions (Original, Information-loss, Word-order, and Control) colored according to overall perturbation manipulation category. Surprisal values are in nats (logarithm to base e). Note that we excluded the semantic-distance manipulation category for this analysis, because 3 out of 4 of these manipulations by design shuffled sentences across the entire material set and hence (i) each string did not bear relation to the original string and (ii) average surprisal values across the materials would be identical to Original. In contrast, the stimuli in the two other perturbation categories bear relation to the original string: The information-loss conditions retain words of certain parts of speech relative to the intact sentence, word-order manipulations retain all lexical items from the original string, and the control condition RandWordList exchanges every word in the sentence with a different word and is thus length-matched.

Figure 5.

Correlation between difference in brain predictivity relative to the original brain predictivity score and difference in average string surprisal relative to the Original sentence condition. Individual data points are perturbation manipulation conditions (Original, Information-loss, Word-order, and Control) colored according to overall perturbation manipulation category. Surprisal values are in nats (logarithm to base e). Note that we excluded the semantic-distance manipulation category for this analysis, because 3 out of 4 of these manipulations by design shuffled sentences across the entire material set and hence (i) each string did not bear relation to the original string and (ii) average surprisal values across the materials would be identical to Original. In contrast, the stimuli in the two other perturbation categories bear relation to the original string: The information-loss conditions retain words of certain parts of speech relative to the intact sentence, word-order manipulations retain all lexical items from the original string, and the control condition RandWordList exchanges every word in the sentence with a different word and is thus length-matched.

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Across the Original, Word-order, Information-loss, and Control perturbation manipulation conditions, we observed a positive correlation between the difference in average string surprisal (i.e., surprisal averaged across tokens in a string, and then averaged across items in a condition) and the difference in brain predictivity relative to the Original sentence condition (Pearson r = 0.58, p < 0.05), indicating that stimuli with high surprisal yield less predictive ANN representations for encoding brain responses. This finding suggests that sentence perturbations that affect an ANN language model’s mapping onto brain responses also affect the language model’s performance on the next-word prediction task.

The Pattern of Brain Predictivity Across Linguistic Perturbation Manipulations Is Robust to Variation in the Computational Experimental Design

In the above sections, we provided a systematic analysis of the aspects of linguistic stimuli that contribute to the high performance of ANN-to-brain mapping models, as reported in Schrimpf et al. (2021) and investigated why certain perturbation manipulations yield lower brain predictivity than others. In this section, we investigate the robustness of these findings to changes in the computational experimental design.

In particular, we focus on two factors of the experimental design: training the mapping model on intact versus perturbed stimuli and contextualization of sentence representations with respect to the preceding passage context, crossed in 2 × 2 factorial design (as summarized in Figure 6). Figure 7AE shows each of these four factor combinations as individual, colored lines across our perturbation manipulations. The experimental design condition investigated in the above sections is the TrainIntact–TestPerturbed_Contextualized condition (dark purple lines).

Figure 6.

Overview of computational experimental design conditions. We provide a factorial analysis of the contribution of (i) the ANN-to-brain mapping model (either forcing the mapping model to generalize to novel perturbation types during test time (TrainIntact–TestPerturbed) or allowing the mapping model to exploit perturbation types seen at training time (TrainPerturbed–Test-Perturbed), and (ii) linguistic contextualization in the computational experimental design (either mimicking the human experimental design and providing prior passage context (Contextualized) or no sentence-external context (Decontextualized). Note that the condition presented in Word-Order Manipulations and Figure 3 is the TrainIntact–Test-Perturbed_Contextualized.

Figure 6.

Overview of computational experimental design conditions. We provide a factorial analysis of the contribution of (i) the ANN-to-brain mapping model (either forcing the mapping model to generalize to novel perturbation types during test time (TrainIntact–TestPerturbed) or allowing the mapping model to exploit perturbation types seen at training time (TrainPerturbed–Test-Perturbed), and (ii) linguistic contextualization in the computational experimental design (either mimicking the human experimental design and providing prior passage context (Contextualized) or no sentence-external context (Decontextualized). Note that the condition presented in Word-Order Manipulations and Figure 3 is the TrainIntact–Test-Perturbed_Contextualized.

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Figure 7.

Brain predictivity patterns are largely robust to variations in the computational experimental design. (A–E) Comparison of brain predictivity across experimental design conditions (as summarized in Figure 6). Each experimental design condition is shown as an individual line across our perturbational manipulation conditions (individual panels). For each condition, we plot the raw brain predictivity (Pearson r) of the best-performing layer, i.e., the fraction of variance explained that the model can predict relative to the ceiling of the fMRI data set. We note that the condition presented in Lexical-Semantic Content, Not Syntactic Structure, Is the Main Contributor to ANN-to-Brain Similarity in the Language Network and Figure 3 is the TrainIntact–TestPerturbed_Contextualized condition (dark purple line). (F–I) Barplots for each experimental design condition. Each panel (with a series of barplots) corresponds to a single line in panels A–E with box color matching the line color. Manipulation condition scores that were significantly different from the Original and RandWordList control benchmarks (dark and light gray dashed lines, respectively) are marked with asterisks (*p < 0.05, **p < 0.01, ***p < 0.001). Significance was established via dependent two-sided t tests, with p values corrected for multiple comparisons (within each perturbation manipulation condition) using the Bonferroni procedure. Error bars show median absolute deviation within participants.

Figure 7.

Brain predictivity patterns are largely robust to variations in the computational experimental design. (A–E) Comparison of brain predictivity across experimental design conditions (as summarized in Figure 6). Each experimental design condition is shown as an individual line across our perturbational manipulation conditions (individual panels). For each condition, we plot the raw brain predictivity (Pearson r) of the best-performing layer, i.e., the fraction of variance explained that the model can predict relative to the ceiling of the fMRI data set. We note that the condition presented in Lexical-Semantic Content, Not Syntactic Structure, Is the Main Contributor to ANN-to-Brain Similarity in the Language Network and Figure 3 is the TrainIntact–TestPerturbed_Contextualized condition (dark purple line). (F–I) Barplots for each experimental design condition. Each panel (with a series of barplots) corresponds to a single line in panels A–E with box color matching the line color. Manipulation condition scores that were significantly different from the Original and RandWordList control benchmarks (dark and light gray dashed lines, respectively) are marked with asterisks (*p < 0.05, **p < 0.01, ***p < 0.001). Significance was established via dependent two-sided t tests, with p values corrected for multiple comparisons (within each perturbation manipulation condition) using the Bonferroni procedure. Error bars show median absolute deviation within participants.

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The four computational experimental design conditions yielded highly similar brain predictivity patterns across the 18 perturbation manipulation conditions, as was evidenced by an average pairwise Pearson correlation of r = 0.84 (p < 0.001). The lowest pairwise correlation across perturbation manipulations (Pearson r = 0.63) was obtained by comparing TrainPerturbed–TestPerturbed_Contextualized versus TrainIntact–TestPerturbed_Decontextualized, while the highest correlation was obtained for TrainPerturbed–TestPerturbed_Decontextualized versus TrainIntact–TestPerturbed_Decontextualized (Pearson r = 0.96).

We note that even though all computational experimental conditions were highly correlated, there was a substantial difference in the magnitude of brain predictivity scores associated with each profile. For example, brain predictivity for the intact (Original) condition ranged between 0.26 and 0.35 (Figure 7A). Across perturbation manipulation conditions, we observed a boost in brain predictivity performance when including previous in-passage sentences as context (purple lines vs. orange lines): On average brain predictivity improved by 0.06 for TrainIntact–TestPerturbed designs and 0.14 for TrainPerturbed–TestPerturbed designs (cf. How Close Are We to Quantitatively Accurate and Generalizable Models of the Human Language Network?; see Table SI 5 for all pairwise comparison statistics between computational experimental design profiles within a manipulation condition).

For all experimental design conditions, we observed a substantial drop in ANN-to-brain mapping performance for the random word list control condition relative to Original (Figure 7E) (Original vs. RandWordList: p < 0.05 across all four factor combinations). Nevertheless, when the mapping model was trained and tested on contextualized representations of random word lists, the performance of the mapping model was unexpectedly high (TrainPerturbed–TestPerturbed_Contextualized, light purple line). For the same computational experimental design, we also observed surprisingly high ANN-to-brain mapping model performance for the random sentences semantic-distance manipulation (RandSent). These results suggest that this mapping model was still able to extract a substantial amount of useful information for predicting responses to held-out sentences, even for stimuli that we would not expect to carry a lot of useful information for match-to-brain (indicating an undesired interaction between the contextualization and cross-validation schemes; see Discussion). However, not just any input sentence elicited a high brain predictivity score in this design. Replacing all words in the sentence with one and the same word (Figure SI 6B, condition LengthControl), led to a near chance-level performance (see also Figure SI 4). This result shows that, when allowed to exploit meaningful, lexical-semantic content from the context, a mapping model can use ANN representations derived from random word lists and random sentences to obtain relatively high predictivity, even though low-level features of the stimulus, such as its length, are not sufficient to obtain high predictivity (Figure SI 6B).

In sum, our findings suggest that the conclusions from Lexical-Semantic Content, Not Syntactic Structure, Is the Main Contributor to ANN-Brain Similarity in the Language Network regarding the contributions of features of the linguistic input are mostly robust against variation in the computational experimental design. In the Supporting Information, we report results indicating that the conclusions of Perturbation Manipulations That Render Linguistic Stimuli More Surprising Lead to Lower Brain Predictivity are similarly robust: Across computational experimental design conditions, we observed that greater linguistic perturbations lead to (a) more divergent representations in the ANN’s embedding space (relative to the representations of intact sentences; Figure SI 10) and (b) a decrease in the ANN’s next-word prediction task performance, that is, its ability to predict upcoming tokens in those stimuli (Figure SI 11).

A number of independent studies have recently shown that representations from state-of-the-art ANN models—especially unidirectional transformer models—align well with brain responses of humans processing linguistic input (e.g., Caucheteux & King, 2022; Gauthier & Levy, 2019; Goldstein et al., 2022; Jain & Huth, 2018; Kumar et al., 2022; Merlin & Toneva, 2022; Millet et al., 2022; Oota et al., 2022; Pasquiou et al., 2022; Pereira et al., 2018; Schrimpf et al., 2021; Toneva & Wehbe, 2019). However, what makes ANN representations align with human neural responses to language has been little explored (cf. Gauthier & Levy, 2019; Merlin & Toneva, 2022; Oota et al., 2022). Focusing on the top-performing unidirectional-attention GPT model family (Schrimpf et al., 2021), we systematically investigated the effect of diverse linguistic perturbation manipulations, including manipulations that strongly affect sentence meaning (carried largely by content words) and those that primarily affect syntactic structure (carried by word order and function words) on the ability of an ANN-to-brain model to predict brain responses.

The contributions of our work are threefold: First, we found that lexical-semantic content is a stronger contributor to the similarity between ANN language models and brain data than syntactic structure (conveyed by word order or function words), although above a certain level of sentence-level semantic similarity, lexical overlap no longer contributes much. Second, we found that linguistic perturbations that decrease brain predictivity have interpretable causes: They lead to more divergent representations in the ANN’s embedding space (relative to the representations of intact sentences) and decrease the ANN’s next-word prediction task performance, that is, its ability to predict upcoming tokens in those stimuli. Finally, we found that the effects of these linguistic manipulations are largely robust to variations in the computational experimental design, including whether the mapping model is trained on intact versus perturbed stimuli and whether the model is fed contextualized representations that mimic the experimental setup in human experiments. We elaborate on our findings and discuss their implications below.

Lexical-Semantic Content, Not Syntactic Structure, Is the Primary Driver of the ANN-to-Brain Similarity

We showed that ANN language models exploit the lexical-semantic content of the sentence, rather than the sentence’s syntactic form (conveyed via word order or function words) when predicting brain data. Similarly, sentences elicit higher brain predictivity the more topically related they are to the stimulus for which brain representations were obtained. We demonstrated that this pattern is robust across variations in the computational experimental design, indicating that the ANN-to-brain mapping model pays only limited attention to the part of the ANN representation of the stimulus that is sensitive to syntactic information, but rather relies on the representations of the content words’ meanings. These findings align with two growing bodies of evidence: one from (computational) neuroscience that points to the relatively greater importance of meaning for both the magnitude and distributed patterns of activation in the brain’s language system as measured/measurable by fMRI (e.g., Fedorenko et al., 2016; Gauthier & Levy, 2019; Huth et al., 2016; Mollica et al., 2020; Pereira et al., 2018), and another from NLP that shows that ANNs do not necessarily need to use word-order information to solve many current natural language processing benchmark tasks (e.g., O’Connor & Andreas, 2021; Pham et al., 2021; Sinha et al., 2021; cf. Abdou et al., 2022; Lasri et al., 2022).

In our study, we aimed to integrate neuroscientific and NLP perspectives on the role of lexical-semantic content versus syntactic information in the building of linguistic representations, and our perturbation conditions took inspiration from both of these fields. Specifically, the word order manipulations investigated here were inspired by an fMRI study that found the human language network responds as robustly to strings with scrambled word order as to naturalistic input as long as the scrambled order still allows for local composition of words into chunks and phrases (Mollica et al., 2020). Similar to these findings, we found that word-order perturbations that preserve local PMI lead to only a small decrease in a model’s ability to predict brain responses; but unlike the results reported for human participants in (Mollica et al., 2020), we found that even extreme word-order perturbations, which disrupt local semantic and syntactic dependencies, lead to a similarly small decrease in ANN-to-brain mapping performance. Further, we found that the omission of function words does little to decrease brain predictivity.

We hypothesize that these results are due to the fact that mapping models do not strongly rely on syntactic information (as argued above). However, an alternative explanation is that—at least in the word-order scrambling manipulations—ANN language models might implicitly (albeit perhaps noisily) reconstruct the original sentence (Malkin et al., 2021; Sinha et al., 2021), plausibly enabled by their extensive memory capacity. In particular, ANNs have access to the exact words in the sentence context (up to a maximal token length, which is not exceeded in our sentence material), whereas memory limitations in humans lead them to discard the exact word sequences after extracting the relevant meaning from them (e.g., Christiansen & Chater, 2016; Hahn et al., 2022; Potter, 2012; Potter et al., 1998).

Of course, the inability of ANN-to-brain mapping models to detect the fine-grained structure of the original sentence could also be due to other reasons, such as the low temporal resolution of fMRI data, which might impose limitations on the detection of structure effects. Given that the language system is strongly sensitive to syntactic processing difficulty (e.g., Blank et al., 2016; Shain et al., 2020; Shain et al., 2022), it is plausible that modeling linguistic representation construction word by word (cf. for the whole sentence at once as we did here), along with perhaps using more temporally resolved data (e.g., from intracranial human recordings), would reveal stronger effects of syntactic structure than the ones found here. Nevertheless, our results show that syntactic structure is not critical in matching ANN representations with fMRI BOLD responses, at least for the summary representations of sentences.

It is also worth noting that stronger effects of structure might be detected in sentence materials where structure is critical to interpretation, as in cases where word order is the only cue to the propositional meaning, in the absence of animacy/plausibility cues (e.g., The boy introduced the teacher to the girl) or in cases where the identity/location of a particular function word is critical, again in the absence of plausibility biases (e.g., He went out of the building vs. He went into the building, or The book is on the table vs. The book is under the table).

Perturbations That Decrease Brain Predictivity Have Interpretable Causes

Given that different perturbation conditions affected brain predictivity to quite different extents, we investigated potential reasons for these differences and identified two interpretable correlates, one related to the ANN representational space and the other related to ANN task performance. Perturbation manipulations that led to lower brain scores also (i) led to more divergent representations in the ANN’s embedding space (relative to the representations of intact sentences) and (ii) decreased the ANN’s next-word prediction task performance, that is, its ability to predict upcoming tokens in those stimuli.

Related to the ANN representational space, there has been interest in understanding how the units that make up the representations of current large ANN language models change across stimuli and model layers (Biś et al., 2021; Ethayarajh, 2019). In the Results section we quantified the changes in the representational space across our perturbation conditions relative to the intact stimuli and found that perturbations that changed the ANN representation to a greater extent (relative to the representation of the original, intact sentence) also led to larger decreases in brain predictivity scores. Interestingly, even for the most extreme perturbations (e.g., replacing a sentence with a random word list or a random sentence) led to representations that were still moderately correlated with the representations of the intact stimuli (even if these altered representations could not capture human neural responses under most computational experimental design settings). This pattern suggests that in our mapping models, only a subset of the full ANN representational space is being used to represent the stimuli investigated here (naturalistic sentences of length 5–20 words and their perturbed versions). More diverse linguistic materials (e.g., sentences of different length, style, content or context length) may engage a larger subset of the ANN representational space in mapping models.

Related to the ANN task performance, there has been interest in understanding how the next-word prediction performance of ANN language models is related to ANN-to-brain correspondence (e.g., Antonello & Huth, 2024; Caucheteux & King, 2022; Goldstein et al., 2022; Hosseini et al., 2022; Merlin & Toneva, 2022; Schrimpf et al., 2021), motivated by substantial evidence for predictive processing in human language comprehension (e.g., Bicknell et al., 2010; Brothers & Kuperberg, 2021; Demberg & Keller, 2008; Heilbron et al., 2019; Heilbron et al., 2022; Henderson et al., 2016; Lopopolo et al., 2017; Rayner et al., 2006; Shain et al., 2022; Smith & Levy, 2013; Willems et al., 2016). Here, we presented evidence that among our perturbations, those that rendered stimuli less predictable, on average, led to larger decreases in brain predictivity performance (see Merlin & Toneva, 2022, for a similar claim for a naturalistic narratives fMRI data set). This pattern suggests that less predictable strings may yield representations that have features that are less suitable for predicting fMRI brain data.

How Close Are We to Quantitatively Accurate and Generalizable Models of the Human Language Network?

We identified features of linguistic stimuli (namely, lexical-semantic content) that ANN-to-brain mapping models exploit when learning a successful mapping to brain responses. These features are exploited by the mapping model independently of whether the mapping model is trained on intact or perturbed stimuli, and of whether the ANN representations of the target sentences are contextualized with respect to the preceding sentences in a passage. At the same time, we demonstrated that these design choices substantially affect the magnitude of the brain scores, which may lead to different conclusions about the similarity of current ANN language model representations to the ones in the human brain (Figure 7; Original). Furthermore, we showed that certain computational experimental designs lead to high brain scores for perturbations that we would not expect to not carry a lot of informative structure such as a random word list (TrainPerturbed–TestPerturbed, RandWordList; see Figure 3 and Figure 7).

The findings from the computational experimental design manipulations yield three important insights. First, until the effort of relating ANN model representations to neural representations reaches maturity—and the field (hopefully) agrees on a unified framework for performing model-to-brain comparisons—any findings about the similarity between ANN and human representations should be evaluated for robustness to the details of how the comparisons are performed. Contextualization of stimuli with respect to the preceding linguistic context may be especially important as it may introduce nonindependence issues under certain cross-validation setups, as elaborated in the third point below.

Second, these findings highlight the general importance of using careful stimulus-based controls (e.g., replacing the stimuli with random sentences or lists of words) when evaluating ANN-to-brain mappings, in addition to using control (e.g., untrained) models. Examining ANN-to-brain mapping performance only for the original stimuli (those presented to human participants) may lead to flawed inferences about the nature of the similarity. For example, if the ANN representation of a list of random words leads to a similar level of mapping performance with a neural response to some sentence as the representation of that sentence, then we cannot infer that the ANN model is representing the sentence in a similar way to humans.

Third, combining the two previous points, the fact that certain computational experimental designs achieve high predictivity performance on stimuli that are not well-matched with the input to humans showcases an important point that has not received sufficient attention in the recent ANN-to-brain literature: Contextualizing sentences by including the preceding sentences in the story/passage, to match how the stimuli were presented to humans can lead to inflated brain predictivity performance under certain cross-validation setups. In particular, current language models have the ability to keep track of extended contexts, and if contextualization is not properly controlled for, shared context windows for sentences that go into the train set versus the test set can lead to “leakage” of statistical regularities in these contextualized ANN representations, leaving the two sets not truly independent. Furthermore, on the brain side, neural responses to coherent texts can be correlated across time for (at least) two reasons: (1) the participant is still thinking about the content of the previous context when processing the current word/sentence, and/or (2) neural measurements tend to be more similar when they are temporally close (the property known as autocorrelation, which is especially prevalent in methods like fMRI that rely on slow physiological changes; e.g., Bullmore et al., 1996). The two sources of statistical leakage (one in the contextualized sentence representations, one in the neural signal) can be potentially exploited by the ANN-to-brain mapping model.

The passage structure of the benchmark we used in the current study (Pereira et al., 2018) allowed us to perform an exploratory analysis of this issue. Given that contextualization affects sentences and the fMRI BOLD signal within passages, but not across them, we split the stimuli into train and test sets in two ways: by sentence versus by passage. By-sentence splitting is the approach that was adopted in Schrimpf et al. (2021) and that we followed here; this approach disregards the passage structure and is therefore subject to the ANN contextualization leakage problem just described. In contrast, by-passage splitting, whereby all the sentences from the same passage end up in the same set (train or test) rather than being split across those sets, should solve the leakage problem (although note that this splitting approach additionally requires generalization to new semantic domains: e.g., predicting neural responses to sentences about beekeeping when the mapping model has never seen any sentences related to beekeeping).

We found that splitting the train and test sets by passage yielded much lower brain predictivity scores than splitting the data set by sentences: ∼0.10 brain predictivity (Figure SI 12), in comparison with ∼0.35 brain predictivity for by-sentence splitting when preceding within-passage sentences are included as context, and ∼0.26 for by-sentence splitting when preceding sentences are not included as context. In addition, representations of random sentences and random lists of words are no longer predictive of human neural responses under this splitting approach in the TrainPerturbed–TestPerturbed_Contextualized experimental design (in contrast to the same design, i.e., the light purple data points in Figure 7A). As laid out above, this drop in predictivity could be due to the following non-mutually exclusive factors: ANN contextualization leakage, fMRI autocorrelation, and/or the greater difficulty of generalizing to novel semantic domains. Given that noncontextualized sentence representations achieve predictivity of ∼0.26 (substantially higher than ∼0.10; Figure 7 and Figure SI 12), we can tentatively rule out the contextual leakage in ANN representations as the main contributor. Understanding the contributions to higher predictivity in the by-sentence cross-validation approach of (a) temporal autocorrelation in the fMRI signal versus (b) the relative difficulty of generalizing to new semantic domains may require additional data collection (e.g., neural responses to semantically diverse sentences, similar to Pereira et al. [2018], but presented in a random order instead of in passage structure; removing the autocorrelation between semantically related sentences in this new benchmark would enable a direct comparison of generalization of the mapping model to sentences from the same/similar semantic domains versus to sentences from new semantic domains, and comparing the results with those from the current benchmark would allow quantifying the contribution of autocorrelation to neural predictivity). Regardless of what these future investigations reveal, however, it seems clear that current ANN language models still have much room for improvement before they can serve as accurate and generalizable models of the fMRI BOLD responses in the human language network.

In this work, we asked why representations from state-of-the-art ANN language models align with human brain responses (as measured with fMRI) during language processing. To do so, we performed a systematic, large-scale investigation of which linguistic features (across three manipulation categories and four computational experimental designs) reliably contribute to ANN-to-brain mapping performance. We found that the ANN-to-brain mapping model mainly attends to the lexical-semantic content—the key contributor to the sentence’s meaning—rather than to word order or function words, which jointly create the sentence’s syntactic frame. Changes in lexical-semantic content, compared to word order or function words, lead to more divergent representations in the ANN’s embedding space and also decrease the ANN’s ability to predict upcoming tokens in those stimuli. This pattern of results is robust to variations in the computational experimental design, suggesting that the lexical-semantic content of a sentence is reliably encoded in fMRI responses to language. However, our exploratory investigation of different cross-validation settings has also revealed that although current ANN-to-brain mapping models capture a nontrivial amount of variance in human neural data, they do not easily generalize to new semantic contexts, which leaves room for future work to make language models more humanlike.

We thank Martin Schrimpf for his time and help with the Brain-Score framework, and Joe O’Connor for helpful discussions. Carina Kauf and Greta Tuckute contributed equally to this work.

Carina Kauf, K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center, Massachusetts Institute of Technology. Greta Tuckute, Amazon Fellowship from the Science Hub (administered by the MIT Schwarzman College of Computing). Greta Tuckute, International Doctoral Fellowship from American Association of University Women (AAUW). Roger Levy, Paul and Lilah Newton Brain Science award. Roger Levy, National Science Foundation (https://dx.doi.org/10.13039/100000001), Award ID: BCS-2121074. Roger Levy, Quest for Intelligence, Massachusetts Institute of Technology (https://dx.doi.org/10.13039/100019800). Jacob Andreas, MIT-IBM Watson AI Lab. Jacob Andreas, Sony Faculty Innovation Award. Jacob Andreas, Amazon Research Award. Jacob Andreas, Quest for Intelligence, Massachusetts Institute of Technology (https://dx.doi.org/10.13039/100019800). Evelina Fedorenko, National Institutes of Health (https://dx.doi.org/10.13039/100000002), Award ID: R01-DC016607. Evelina Fedorenko, National Institutes of Health (https://dx.doi.org/10.13039/100000002), Award ID: R01-DC016950. Evelina Fedorenko, National Institutes of Health (https://dx.doi.org/10.13039/100000002), Award ID: U01-NS121471. Evelina Fedorenko, McGovern Institute for Brain Research, Massachusetts Institute of Technology (https://dx.doi.org/10.13039/100019335). Evelina Fedorenko, Brain and Cognitive Sciences department, Massachusetts Institute of Technology. Evelina Fedorenko, Simons Center for the Social Brain, Massachusetts Institute of Technology (https://dx.doi.org/10.13039/100018792). Evelina Fedorenko, Middleton Professorship, Massachusetts Institute of Technology. Evelina Fedorenko, Quest for Intelligence, Massachusetts Institute of Technology (https://dx.doi.org/10.13039/100019800).

Carina Kauf: Conceptualization: Lead; Data curation: Lead; Formal analysis: Lead; Investigation: Lead; Methodology: Lead; Validation: Lead; Visualization: Lead; Writing – original draft: Lead; Writing – review & editing: Lead. Greta Tuckute: Conceptualization: Lead; Data curation: Lead; Formal analysis: Lead; Investigation: Lead; Methodology: Lead; Validation: Lead; Visualization: Lead; Writing – original draft: Lead; Writing – review & editing: Lead. Roger Levy: Conceptualization: Supporting; Formal analysis: Supporting; Funding acquisition: Supporting; Methodology: Supporting; Project administration: Supporting; Resources: Equal; Supervision: Equal; Validation: Supporting; Writing – review & editing: Equal. Jacob Andreas: Conceptualization: Supporting; Funding acquisition: Supporting; Methodology: Supporting; Project administration: Supporting; Resources: Equal; Supervision: Equal; Writing – review & editing: Equal. Evelina Fedorenko: Conceptualization: Supporting; Funding acquisition: Supporting; Methodology: Supporting; Project administration: Supporting; Resources: Equal; Supervision: Equal; Writing – review & editing: Equal.

All code and data are available at https://github.com/carina-kauf/perturbed-neural-nlp. Previously published data were used for this work (Pereira et al., 2018).

Unidirectional ANN language models:

A class of ANN language models that only have access to previous linguistic context when performing word-in-context prediction. Unidirectional models can be argued to be more biologically plausible than bidirectional models (which use both left and right context for prediction).

Language network:

A set of brain regions on that lateral surface of the frontal and temporal lobes of the left hemisphere (in most individuals) that selectively support language comprehension and production.

ANN-to-brain similarity:

The similarity between artificial neural networks (ANNs) and human brain activity, quantified as prediction performance of a linear regression model from ANN representations to recordings of brain activity.

Pointwise mutual information (PMI):

A metric from information theory which quantifies the degree to which two words covary systematically.

Lexical semantic sentence content:

The meaning of a sentence as expressed by its lexical items (words), independent of the syntactic structure of the sentence.

Contextualization of ANN representations:

Specification of the amount of linguistic context that is used for representing some target text (e.g., a word or a sentence).

Fivefold cross-validation:

A procedure for splitting a data set into subsets in order to quantify unbiased predictivity performance. The entire data set is split into a training set (80% of the data) and a test set (20% of the data). This procedure is carried out five times such that each data point ends up in the test data set once.

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Competing Interests

Competing Interests: The authors have declared that no competing interests exist.

Author notes

Handling Editor: Milena Rabovsky

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