Word meaning sentence structure

Abstract

To understand what you are reading now, your mind retrieves the meanings of words from a linguistic knowledge store (lexico-semantic processing) and identifies the relationships among them to construct a complex meaning (syntactic or combinatorial processing). Do these two sets of processes rely on distinct, specialized mechanisms or, rather, share a common pool of resources? Linguistic theorizing and empirical evidence from language acquisition and processing have yielded a picture whereby lexico-semantic and syntactic processing are deeply inter-connected. In contrast, most current proposals of the neural architecture of language continue to endorse a view whereby certain brain regions selectively support lexico-semantic storage/processing whereas others selectively support syntactic/combinatorial storage/processing, despite inconsistent evidence for this division of linguistic labor across brain regions. Here, we searched for a dissociation between lexico-semantic and syntactic processing using a powerful individual-subjects fMRI approach across three sentence comprehension paradigms (n=49 participants total): responses to lexico-semantic vs. syntactic violations (Experiment 1); recovery from neural suppression across pairs of sentences differing in lexical items vs. syntactic structure (Experiment 2); and same/different meaning judgments on such sentence pairs (Experiment 3). Across experiments, both lexico-semantic and syntactic conditions elicited robust responses throughout the language network. Critically, no regions were more strongly engaged by syntactic than lexico-semantic processing, although some regions showed the opposite pattern. Thus, contra many current proposals of the neural architecture of language, lexico-semantic and syntactic/combinatorial processing are not separable at the level of brain regions – or even voxel subsets – within the language network, in line with strong integration between these two processes that has been consistently observed in behavioral language research. The results further suggest that the language network may be generally more strongly concerned with meaning than structure, in line with the primary function of language – to share meanings across minds.

Introduction

What is the functional architecture of human language? A core component is a set of knowledge representations, which include knowledge of words and their meanings, and the probabilistic constraints on how words can combine to create compound words, phrases, and sentences. During comprehension (decoding of linguistic utterances), we look for matches between the incoming linguistic signal and these stored knowledge representations in an attempt to re-construct the intended meaning, and during production (encoding of linguistic utterances), we search our knowledge store for the right words/constructions and combine and arrange them in a particular way to express a target idea.

How is this rich set of representations and computations structured? Which aspects of language are functionally dissociable from one another? Traditionally, two principal distinctions have been drawn: one is between words (the lexicon) and rules (the grammar) (e.g., Chomsky, 1965, 1995; Fodor, 1983; Pinker & Prince, 1988; Pinker, 1991, 1999); and another is between linguistic representations themselves (i.e., our knowledge of the language) and their online processing (i.e., accessing them from memory and combining them to create new complex meanings and structures) (e.g., Chomsky, 1965; Fodor et al., 1974; Newmeyer, 2003). Because these two dimensions are, in principle, orthogonal, we could have distinct mental capacities associated with i) knowledge of word meanings, ii) knowledge of grammar (syntactic rules), iii) access of lexical representations (in comprehension or production), and iv) parsing (in comprehension) or construction (in production) of syntactic structures (Fig. 1a).

However, both of these distinctions have been long debated. For example, as linguistic theorizing evolved and experimental evidence accumulated through the 1970s-90s, the distinction between the lexicon and grammar began to blur, for both linguistic knowledge representations and processing (e.g., Fig. 1b; see Snider & Arnon, 2012, for a summary and discussion). Many have observed that much of our grammatical knowledge does not operate over highly general categories like nouns and verbs, but instead requires reference to particular words or word classes (e.g., Lakoff, 1970; Bybee, 1985, 1998, 2010; Levin, 1993; Goldberg, 1995, 2002; Jackendoff, 2002, 2007; Sag et al., 2003; Culicover & Jackendoff, 2005; Levin & Rappaport Hovav, 2005). As a result, current linguistic frameworks incorporate lexical knowledge as part of the knowledge of the grammar, although they differ as to the degree of abstraction that exists above and beyond knowledge of how particular words combine with other words (see e.g., Hudson, 2007, for discussion), and in whether abstract syntactic representations (like the double object, passive, or question constructions) are always associated with meanings or functions (e.g., Pinker, 1989; Goldberg, 1995; cf. Chomsky, 1957; Branigan & Pickering, 2017a; see Jackendoff, 2002, for discussion).

In line with these changes in linguistic theorizing, experimental and corpus work in psycholinguistics have established that humans i) are exquisitely sensitive to contingencies between particular words and the constructions they occur in (e.g., Clifton et al., 1984; MacDonald et al., 1994; Trueswell et al., 1994; Garnsey et al., 1997; Traxler et al., 2002; Reali & Christensen, 2007; Roland et al., 2007; Jaeger, 2010), and ii) store not just atomic elements (like morphemes and non-compositional lexical items), but also compositional phrases (e.g., “I don’t know” or “give me a break”; e.g., Wray, 2005; Evert, 2008; Arnon & Snyder, 2010; Christiansen & Arnon, 2017) and constructions (e.g., “the X-er the Y-er”; Goldberg, 1995; Culicover & Jackendoff, 1999). The latter suggested that the linguistic units people store are determined not by their nature (i.e., atomic vs. not) but instead, by their patterns of usage (e.g., Bybee 1998, 2006; Goldberg 2006; Barlow and Kemmer 2000; Langacker 1986, 1987; Tomasello 2003). Further, people’s lexical abilities have been shown to strongly correlate with their grammatical abilities – above and beyond shared variance due to general fluid intelligence – both developmentally (e.g., Bates et al., 1995; Bates & Goodman, 1997; Dixon & Marchman, 2007; Hoff et al., 2018) and in adulthood (e.g., Dabrowska, 2018). Thus, linguistic mechanisms that have been previously proposed to be distinct are instead tightly integrated with one another or, perhaps, are so cognitively inseparable as to be considered a single apparatus.

The distinction between stored knowledge representations and online computations has also been questioned (see Hasson et al., 2015, for a recent discussion of this issue in language and other domains). For example, by using the same artificial network to represent all linguistic experience, connectionist models dispense not only with the lexicon-grammar distinction but also the storage-computation one, and assume that the very same units that represent our linguistic knowledge support its online access and processing (e.g., Rumelhart and McClelland, 1986; Seidenberg, 1994; see also Goldinger, 1996; Bod, 1998, 2006, for exemplar models, which also abandon the storage-computation divide).

Alongside psycholinguistic studies, which inform debates about linguistic architecture by examining the behaviors generated by language mechanisms, and computational work, which aims to approximate human linguistic behavior using formal models, a different, complementary approach is offered by cognitive neuroscience studies. These studies examine how the relevant cognitive mechanisms are neurally implemented. Here, the assumption that links neuroimaging (and neuropsychological patient) data to cognitive hypotheses is as follows: to the extent that two mental capacities are functionally distinct, they may be implemented in distinct brain regions or sets of regions. Such brain regions would be expected to show distinct patterns of response, and their damage should lead to distinct patterns of deficits.

A (non-exhaustive) set of theoretically possible neural architectures is schematically illustrated in Figure 1, with distinct boxes corresponding to distinct brain regions (or sets of regions). These architectures differ in whether they draw a (region-level) distinction between the lexicon and grammar (1a vs. 1b-f), between storage and access of linguistic representations (1a-b vs. 1c-f), and in whether syntactic/combinatorial processing is a separable component (1a-d vs. 1e-f). (Here, and in subsequent discussions of possible linguistic architectures, we talk about not just “syntactic”, but “syntactic/combinatorial” processing given that combining words into complex – phrase- and sentence-level – representations requires both syntactic structure building but also semantic composition, and different researchers construe this combinatorial process with different foci / at different grain levels. However, in the discussion of the paradigms used in the current study, we talk about “syntactic” processing, following the terminology of prior studies that have relied on these paradigms.) Over the years, a number of paradigms have been developed in an effort to constrain these architectures. Some paradigms have varied the presence or absence of lexico-semantic and syntactic information in the signal; others have tried to more strongly tax the processing of word meanings vs. syntactic/combinatorial processing; yet others have made the meaning of a particular word vs. the structure of the sentence more salient / task-relevant. In many cases, researchers have not been clear as to whether their manipulation specifically targeted linguistic knowledge (i.e., knowledge of word meanings vs. syntactic structures), online processing (i.e., access of word meanings vs. syntactic rules vs. combining linguistic elements to create new complex representations), or both, perhaps because the storage/computation distinction is difficult to evaluate empirically (cf. Mirman and Britt, 2013). Nevertheless, if a brain region (or set of regions) engages selectively, or at least preferentially (more strongly), in response to lexico-semantic information or processing demands, and another brain region (or set of regions) selectively/preferentially responds to syntactic/combinatorial information or processing demands, this would give weight to architectures that draw a distinction between the two (i.e., 1a-d) over those that do not (i.e., 1e-f).

Indeed, in the 1990s and 2000s – when brain imaging methods became available – many have searched for and claimed to have observed a dissociation between brain regions that support lexico-semantic storage/processing and those that support syntactic, or more general combinatorial (e.g., compositional semantic), processing (e.g., Dapretto & Bookheimer, 1999; Embick et al., 2000; Friederici et al., 2000; Noppeney & Price, 2004; Cooke et al., 2006, among others). The alleged syntax-selective regions have sparked particular excitement due to claims that syntax is what makes human language unique (e.g., Hauser et al., 2002; Berwick et al., 2013; Friederici, 2018). However, taking the available evidence from cognitive neuroscience en masse, the picture that has emerged is rather complex.

First, the specific regions that have been argued to support lexico-semantic vs. syntactic/combinatorial processing, and the construal of these regions’ contributions, differ widely across studies and proposals (e.g., Friederici, 2011, 2012; Baggio and Hagoort, 2011; Tyler et al., 2011; Bemis & Pylkkanen, 2011; Duffau et al., 2014; Ullman, 2004, 2016). Second, although a number of diverse paradigms have been used across studies to probe lexico-semantic vs. syntactic/combinatorial processing, any given study has typically used a single paradigm, raising the possibility that the results reflect paradigm-specific differences between conditions rather than a general difference between lexico-semantic and syntactic/combinatorial computations. Further, many studies that claimed to have observed a dissociation have not reported the required region by condition interactions, as needed to argue for a functional dissociation (Nieuwenhuis et al., 2011). Third, many studies that have argued for syntax selectivity did not actually examine lexico-semantic processing, focusing instead on syntactic complexity manipulations (e.g., Stromswold et al., 1996; Ben-Shachar et al., 2003; Bornkessel et al., 2005; Fiebach et al., 2005; see Friederici, 2011, for a meta-analysis). Although such studies (may) establish sensitivity of a brain region to syntactic complexity, they say little about its selectivity for syntactic over lexico-semantic processing. Finally, a number of neuroimaging studies have failed to observe a dissociation between lexico-semantic and syntactic processing (e.g., Keller et al., 2001; Roder et al., 2002; Fedorenko et al., 2010; Fedorenko et al., 2012; Bautista & Wilson, 2016; Blank et al., 2016; Fedorenko et al., 2016). Relatedly, studies of patients with brain damage have failed to consistently link syntactic deficits with a particular locus within the language network, leading some to argue that syntactic processing is supported by the language network as a whole, including regions that are implicated in lexico-semantic storage/processing (e.g., Caplan et al., 1996; Dick et al., 2001; Wilson and Saygin, 2004; Mesulam et al., 2015).

Here we build on prior neuroimaging studies to search for a potential dissociation between lexico-semantic and syntactic storage/processing within the left-lateralized high-level language network (Fedorenko et al., 2010) using fMRI. Critically, the current study goes beyond prior fMRI studies in two important aspects. First, we adopt a powerful individual-subjects analytic approach, where all the key comparisons are performed within individual participants. This approach contrasts with traditional fMRI analyses, which average individual activation maps in a common anatomical space and assume voxel-wise functional correspondence across individuals (e.g., Holmes & Friston, 1998). These traditional analyses stand the risk of missing dissociations between brain regions/voxels selective for lexico-semantic vs. syntactic processing even if these are present in each individual brain, because precise activation loci exhibit high inter-individual variability, especially in the lateral frontal and temporal cortex (e.g., Fischl et al., 2008; Frost & Goebel, 2011); this risk also characterizes meta-analyses of activation peaks (e.g., Rodd et al., 2015; Hagoort & Indefrey, 2016). The individual-level functional localization approach we adopt here has been shown to yield higher sensitivity and functional resolution (e.g., Saxe et al., 2006; Nieto-Castañon & Fedorenko, 2012; Glezer & Riesenhuber, 2013) and thus gives us the best chance to discover selectivity for lexico-semantic or syntactic processing if such exists within the language network.

And second, we systematically examine three paradigms from the literature: responses to lexico-semantic vs. syntactic violations (Experiment 1); recovery from neural suppression across pairs of sentences differing in lexical items vs. syntactic structure (Experiment 2); and same/different meaning judgments on such sentence pairs (Experiment 3). Each paradigm thus has a condition that targets lexico-semantic processing, and another condition that targets syntactic processing. Experiments 1 and 3 are designed to tax lexico-semantic vs. syntactic processing more strongly by having a critical word be incompatible with the context in terms of its meaning or structural properties (in Experiment 1), or by forcing participants to focus on the meanings of the critical words vs. the structure of sentences (in Experiment 3). Experiment 2 relies on the well-established neural adaptation to the repetition of the same information across stimuli: here, repeating the words vs. the structure. Given that all three manipulations target the same theoretical distinction, they should, in principle, yield similar patterns of response. And if the observed patterns are indeed stable across paradigms, we can more confidently take them to reflect lexico-semantic vs. syntactic processing demands, as opposed to potentially paradigm-specific differences between conditions. Note that each of these paradigms can be potentially criticized for some flaw(s). However, as discussed in more detail below, these are exactly the paradigms that have been used in prior studies to argue for a dissociation between lexico-semantic and syntactic processing. We thus follow the literature in adopting these paradigms.

To foreshadow the results, we find that every brain region in the language network supports both lexico-semantic and syntactic processing. No region (or even set of non-contiguous voxels within these regions) shows a consistent preference, in the form of a stronger response, for syntactic processing, although some regions show the opposite preference. These results are in line with current linguistic theorizing, psycholinguistic evidence, and computational modeling work that all suggest tight integration between the lexicon and grammar at the level of both knowledge representations and processing, and contrary to the view that syntactic storage/processing is a separable component within the language architecture.

Materials and Methods

The potential dissociation between lexico-semantic and syntactic processing was evaluated across three language comprehension paradigms. The first paradigm is commonly used in ERP investigations of language processing and relies on violations of expectations about an incoming word set up by the preceding context. In particular, the critical word does not conform to either the lexico-semantic or the syntactic expectations (e.g., Kutas & Hilliyard, 1980; Osterhout & Holcomb, 1992; Hagoort et al., 1993). This paradigm has been used in a number of prior fMRI studies (e.g., Embick et al., 2000; Cooke et al., 2006; Friederici et al., 2010; Herrmann et al., 2012). The second paradigm relies on neural adaptation, wherein repeated exposure to a stimulus leads to a reduction in response, and a change in some feature(s) of the stimulus leads to a recovery of response (see e.g., Krekelberg et al., 2006, for a general overview of the approach). This paradigm has also been used in prior fMRI studies that examined adaptation, or recovery from adaptation, to the lexico-semantic vs. syntactic features of linguistic stimuli (e.g., Noppeney & Price, 2004; Santi & Grodzinsky, 2010; Menenti et al., 2012; Segaert et al., 2012). Finally, the third paradigm was introduced in a classic study by Dapretto & Bookheimer (1999): pairs of sentences vary in a single word vs. in word order / syntactic structure. Either of these manipulations can result in the same meaning being expressed across sentences (if a word is replaced by a synonym, or if a syntactic alternation, like active→passive, is used) or in a different meaning (if a word is replaced by a non-synonym, or if the thematic roles are reversed). Participants make same/different meaning judgments on the resulting sentence pairs. Note that all three paradigms use sentence materials, which necessarily require both lexico-semantic and syntactic processing. As a result, all conditions are expected to elicit reliable (above-baseline) responses throughout the language network. The critical question is whether we will observe a consistent preference (stronger responses) for lexico-semantic over syntactic conditions in some regions, and the opposite preference in other regions.

In an effort to maximize sensitivity and functional resolution (e.g., Nieto-Castanon & Fedorenko, 2012), we adopt an approach where all the key contrasts are performed within individual participants. We perform two kinds of analyses. In one set of analyses, we identify language-responsive cortex in each individual participant with an independent language localizer task based on a contrast between the reading of sentences vs. sequences of nonwords (Fedorenko et al., 2010), and examine the engagement of these language-responsive areas in lexico-semantic vs. syntactic processing in each critical paradigm. (The use of the same language localizer task across experiments allows for a straightforward comparison of their results, obviating the need to rely on coarse anatomy and reverse-inference reasoning for interpreting activations in functional terms (Poldrack, 2006, 2011).) To further ensure that we are not missing the critical dissociation by averaging across (relatively) large sets of language-responsive voxels, we supplement these analyses with analyses where we only use data from the critical paradigms. In particular, we use some of the data from a given critical task to search for the most lexico-semantic-vs. syntactic-selective voxels (e.g., in Experiment 1, voxels that respond more strongly to lexico-semantic than syntactic violations), and then test the replicability of this selectivity in left-out data (as detailed below).

Results

Discussion

We conducted three fMRI experiments to search for a dissociation between lexico-semantic and syntactic storage/processing, a distinction that continues to permeate proposals of the neural architecture of language (e.g., Friederici, 2012; Baggio and Hagoort, 2011; Tyler et al., 2011; Duffau et al., 2014; Ullman, 2016). Each used a paradigm from the prior literature that included conditions targeting lexico-semantic vs. syntactic processing. Our results can be summarized as follows. First, as expected given the use of sentence-level materials, both lexico-semantic and syntactic processing elicited robust responses throughout the language network across experiments. Further, in Experiment 1, we found sensitivity to both lexico-semantic and syntactic violations, although the latter did not elicit a response stronger than that elicited by low-level font violations. In Experiment 2, we found that changes in either the lexical items or syntactic structure led to recovery from adaptation. Second, in Experiments 1 and 3, we found a bias (i.e., stronger responses) for lexico-semantic processing across language fROIs (defined using a language localizer; Fedorenko et al., 2010), with no fROIs showing the opposite pattern (i.e., stronger responses for syntactic processing). And third, when we searched for the most lexico-semantic-selective and syntactic-selective voxels, we again found replicably stronger responses to lexico-semantic than syntactic processing (in Experiments 1 and 3), but not the opposite pattern (except a small effect in one region in Experiment 3).

Conclusions

To conclude, across three fMRI experiments, we found robust responses to both lexico-semantic and syntactic processing throughout the language network, with generally stronger responses to lexico-semantic processing, and no regions, or even sets of non-contiguous voxels within those regions, that respond reliably more strongly to syntactic processing. These results constrain the space of possible neural architectures of language. In particular, they rule out architectures that postulate a distinct region (or set of regions) that selectively supports syntactic/combinatorial processing (i.e., architectures shown in Fig. 1a-d). These constraints on neural architectures, in turn, inform cognitive theories. Of course, the lack of regional, or even voxel-level, dissociations between mental processes need not imply cognitive inseparability – there may exist neuronal assemblies selective for syntactic or combinatorial storage/processing at the sub-voxel level (e.g., the architecture in Fig. 1e). However, to the extent that prior fMRI studies have been taken as establishing syntax-selective mechanisms, those findings do not seem to be robust given our data.

We have here focused on cortical mechanisms. May syntax selectivity be present at the level of white-matter tracts? Indeed, some have argued that the arcuate / superior longitudinal fasciulus (the dorsal tracts connecting posterior temporal and inferior frontal language areas) may be selective for syntactic processing (e.g., Friederici, 2009; Brauer et al., 2011; Papoutsi et al., 2011; Wilson et al., 2011). However, others have implicated this tract in non-syntactic linguistic computations, including, most commonly, articulation (e.g., Duffau et al., 2003; Hickok & Poeppel, 2007; Rauscheker & Scott, 2009), but also aspects of semantic processing (e.g., Glasser & Rillings, 2008) and even reading (Yeatman et al., 2011). Thus, at present, no clear evidence of syntax selectivity exists for white matter tracts either.

In summary, taking all the available data into consideration, it seems that a cognitive architecture whereby the processing of individual word meanings is not separable from syntactic processing is most likely. The fact that connectionist networks, especially deep neural nets, have been shown to achieve remarkable performance on a wide variety of tasks (e.g., Mikolov et al., 2010; Sutskever et al., 2014; Bahdanau et al., 2016), including those that involve complex syntactic phenomena (e.g., Linzen et al., 2016; Gulordava et al., 2018; Futrell et al., 2018), may be taken to further support the latter kind of an architecture.

Author contributions

EF conceived and designed the study. All authors collected the data and performed data analyses. MS and IB created the figures. EF drafted the manuscript and IB provided critical revisions, with MS and ZM providing additional comments. All authors approved the final version of the manuscript.

Conflict of interest

The authors declare no competing financial interests.

Acknowledgements

We would like to acknowledge the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research at MIT, and its support team (Steve Shannon, Atsushi Takahashi, and Sheeba Arnold). We thank former and current EvLab members (especially Zuzanna Balewski and Brianna Pritchett) for their help with data collection, Gina Kuperberg for providing the materials used in adapted form in Experiment 1, Michael Behr for creating the script for Experiment 1, Zuzanna Balewski for help with creating the materials and script for Experiment 2, Nancy Kanwisher for discussions of the experimental design for all three experiments, and Ted Gibson, Adele Goldberg, Inbal Arnon, Jayden Ziegler, and Yonatan Belinkov for comments on the manuscript. We also thank the audience at the 2017 CUNY Sentence Processing conference (Cambridge, MA) for feedback. EF was supported by award R01-DC-016607 from NIH and by a grant from the Simons Foundation to the Simons Center for the Social Brain at MIT.

References

1. Ambridge, B., Pine, J. M., Rowland, C. F., Freudenthal, D., & Chang, F. (2014). Avoiding dative overgeneralisation errors: semantics, statistics or both? Language Cognition and Neuroscience, 29(2), 218—243. doi:10.1080/01690965.2012.738300

2. ↵

   Arnon, I., & Snider, N. (2010). More than words: Frequency effects for multi-word phrases. Journal of Memory and Language, 62(1), 67—82.

3. ↵

   Baggio, G., & Hagoort, P. (2011). The balance between memory and unification in semantics: A dynamic account of the N400. Language and Cognitive Processes, 26(9), 1338—1367.

4. ↵

   Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., & Bengio, Y. (2016). End-to-end attention-based large vocabulary speech recognition. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference (pp. 4945—4949). .

5. ↵

   Barlow, M., & Kemmer, S. (Eds.). (2000). Usage Based Models of Language.

6. ↵

   Bates, E. & Goodman, J.C. (1997). On the inseparability of grammar and the lexicon: Evidence from acquisition, aphasia and real time processing. Language and Cognitive Processes, 12, 507–586.
7. ↵
8. ↵

   Bautista, A., & Wilson, S. M. (2016). Neural responses to grammatically and lexically degraded speech. Language Cognition and Neuroscience, 31(4), 567—574. doi:10.1080/23273798.2015.1123281

9. ↵

   Bedny, M., Pascual-Leone, A., Dodell-Feder, D., Fedorenko, E., & Saxe, R. (2011). Language processing in the occipital cortex of congenitally blind adults. Proceedings of the National Academy of Sciences, 108(11), 4429—34. doi:10.1073/pnas.1014818108

10. ↵

    Ben-Shachar, M., Hendler, T., Kahn, I., Ben-Bashat, D., & Grodzinsky, Y. (2003). The neural reality of syntactic transformations: Evidence from functional magnetic resonance imaging. Psychological Science, 14(5), 433—440.

11. ↵

    Benjamini, Y., & Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29(4), 1165—1188.

12. ↵

    Berwick, R. C., Friederici, A. D., Chomsky, N., & Bolhuis, J. J. (2013). Evolution, brain, and the nature of language. Trends in Cognitive Sciences, 17(2), 89—98. doi:10.1016/j.tics.2012.12.002

13. ↵

    Blank, I., Balewski, Z., Mahowald, K., & Fedorenko, E. (2016). Syntactic processing is distributed across the language system. NeuroImage, 127, 307—323. doi:10.1016/j.neuroimage.2015.11.069

14. ↵

    Bock, J. K. (1986). Meaning, sound, and syntax: Lexical priming in sentence production. Journal of Experimental Psychology: Learning, Memory, and Cognition, 12(4), 575—586. doi:10.1037//0278-7393.12.4.575

15. ↵

    Bock, K., & Loebell, H. (1990). Framing sentences. Cognition, 35(1), 1—39. doi:10.1016/0010-0277(90)90035-i

16. ↵

    Bod, R. (1998). Beyond grammar: An experience-based theory of language. Stanford, CA: CSLI Publications.
17. ↵

    Bod, R. (2006). Exemplar-based syntax: How to get productivity from examples. The linguistic review, 23(3), 291—32

18. ↵

    Bornkessel, I., Zysset, S., Friederici, A. D., Von Cramon, D. Y., & Schlesewsky, M. (2005). Who did what to whom? The neural basis of argument hierarchies during language comprehension. Neuroimage, 26(1), 221—233.

19. Bornkessel-Schlesewsky, I., Kretzschmar, F., Tune, S., Wang, L. M., Genc, S., Philipp, M., Roehm, D., Schlesewsky, M. (2011). Think globally: Cross-linguistic variation in electrophysiological activity during sentence comprehension. Brain and Language, 117(3), 133—152. doi:10.1016/j.bandl.2010.09.010

20. ↵

    Branigan, H. P., & Pickering, M. J. (2017). An experimental approach to linguistic representation. Behavioral and Brain Sciences, 40. doi:10.1017/S0140525X16002028, e282

21. ↵

    Bybee, J. L. (1985). Morphology: A Study of the Relation between Meaning and Form (Vol. 9). Amsterdam: John Benjamins Publishing Company. https://doi.org/10.1075/tsl.9

22. ↵

    Bybee, J. L. (1998). A Functionalist Approach to Grammar and Its Evolution. Evolution of Communication, 2(2), 249—278.

23. ↵

    Bybee, J. (2006). From usage to grammar: The mind’s response to repetition. Language, 82, 711—733.

24. ↵

    Bybee, J. (2010). Language, usage and cognition (Vol. 98). Cambridge University Press.

25. ↵

    Cai, Z. G. G., Pickering, M. J., & Branigan, H. P. (2012). Mapping concepts to syntax: Evidence from structural priming in Mandarin Chinese. Journal of Memory and Language, 66(4), 833—849. doi:10.1016/j.jml.2012.03.009

26. Chang, F., Bock, K., & Goldberg, A. E. (2003). Can thematic roles leave traces of their places? Cognition, 90(1), 29—49. doi:10.1016/s0010-0277(03)00123-9

27. Charniak, E. (1997). Statistical parsing with a context-free grammar and word statistics. AAAI/IAAI, 2005(598-603), 18.

28. ↵

    Chomsky, N. (1965). Aspects of the theory of syntax. Cambridge, MA: MIT Press.

29. ↵

    Chomsky, N., & Dinozzi, R. (1972). Language and mind: Harcourt Brace Jovanovich.

30. ↵

    Christiansen, M. H., & Arnon, I. (2017). More than words: The role of multiword sequences in language learning and use. Topics in cognitive science, 9(3), 542—551.

31. ↵

    Clifton, C., Frazier, L., & Connine, C. (1984). Lexical expectations in sentence comprehension. Journal of Memory and Language, 23(6), 696.

32. ↵

    Cooke, A., Grossman, M., DeVita, C., Gonzalez-Atavales, J., Moore, P., Chen, W., … Detre, J. (2006). Large-scale neural network for sentence processing. Brain and Language, 96(1), 14—36. doi:10.1016/j.bandl.2005.07.072

33. ↵

    Coulson, S., King, J. W., & Kutas, M. (1998). Expect the unexpected: Event-related brain response to morphosyntactic violations. Language and Cognitive Processes, 13(1), 21—58. doi:10.1080/016909698386582

34. ↵

    Culicover, P. W., & Jackendoff, R. (1999). The view from the periphery: The English comparative correlative. Linguistic inquiry, 30(4), 543—571.

35. ↵

    Culicover, P. W., Jackendoff, R. S., & Jackendoff, R. (2005). Simpler syntax. Oxford University Press on Demand.

36. D□browska, E. (2018). Experience, aptitude and individual differences in native language ultimate attainment. Cognition, 178, 222—235.

37. ↵

    Dale, A. M. (1999). Optimal experimental design for event-related fMRI. Human Brain Mapping, 8(2-3), 109—114. doi:10.1002/(sici)1097-0193(1999)8:2/3<109::aid-hbm7>3.0.co;2-w

38. ↵

    Dapretto, M., & Bookheimer, S. Y. (1999). Form and content: Dissociating syntax and semantics in sentence comprehension. Neuron, 24(2), 427—432. doi:10.1016/s0896-6273(00)80855-7
39. ↵

    Dick, F., Bates, E., Wulfeck, B., Utman, J. A., Dronkers, N., & Gernsbacher, M. A. (2001). Language deficits, localization, and grammar: Evidence for a distributive model of language breakdown in aphasic patients and neurologically intact individuals. Psychological Review, 108(4), 759—788. doi:10.1037//0033-295x.108.4.759

40. ↵

    Dixon, J. A., & Marchman, V. A. (2007). Grammar and the lexicon: Developmental ordering in language acquisition. Child Development, 78(1), 190—212.

41. ↵

    Duffau, H., Moritz-Gasser, S., & Mandonnet, E. (2014). A re-examination of neural basis of language processing: Proposal of a dynamic hodotopical model from data provided by brain stimulation mapping during picture naming. Brain and Language, 131, 1—10. doi:10.1016/j.bandl.2013.05.011

42. ↵

    Embick, D., Marantz, A., Miyashita, Y., O’Neil, W., & Sakai, K. L. (2000). A syntactic specialization for Broca’s area. Proceedings of the National Academy of Sciences, 97(11), 6150—6154. doi:10.1073/pnas.100098897

43. ↵

    Evert, S. (2008). Corpora and collocations. Corpus linguistics. An international handbook, 2, 1212—1248.

44. ↵

    Fedor, A., Varga, M., & Szathmáry, E. (2012). Semantics boosts syntax in artificial grammar learning tasks with recursion. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(3), 776.

45. ↵

    Fedorenko, E. (2014). The role of domain-general cognitive control in language comprehension. Frontiers in Psychology, 5, 335. doi:10.3389/fpsyg.2014.00335

46. ↵

    Fedorenko, E., Behr, M. K., & Kanwisher, N. (2011). Functional specificity for high-level linguistic processing in the human brain. Proceedings of the National Academy of Sciences, 108(39), 16428–16433. https://doi.org/10.1073/pnas.1112937108

47. ↵

    Fedorenko, E., Hsieh, P. J., Nieto-Castanon, A., Whitfield-Gabrieli, S., & Kanwisher, N. (2010). New method for fMRI investigations of language: Defining ROIs functionally in individual subjects. Journal of Neurophysiology, 104(2), 1177—1194. doi:10.1152/jn.00032.2010

48. ↵

    Fedorenko, E., Nieto-Castanon, A., & Kanwisher, N. (2012). Lexical and syntactic representations in the brain: An fMRI investigation with multi-voxel pattern analyses. Neuropsychologia, 50(4), 499—513. doi:10.1016/j.neuropsychologia.2011.09.014

49. Fedorenko, E., & Varley, R. (2016). Language and thought are not the same thing: Evidence from neuroimaging and neurological patients. Annals of the New York Academy of Sciences, 1369(1), 132—153. doi:10.1111/nyas.13046

50. ↵

    Fedorenko, E., Williams, Z. M., & Ferreira, V. S. (2018). Remaining Puzzles about Morpheme Production in the Posterior Temporal Lobe. Neuroscience, 392, 160—163.

51. ↵

    Ferreira, F., Bailey, K. G. D., & Ferraro, V. (2002). Good-enough representations in language comprehension. Current Directions in Psychological Science, 11(1), 11—15. doi:10.1111/1467-8721.00158

52. Ferreira, V. S., & Bock, K. (2006). The functions of structural priming. Language and Cognitive Processes, 21(7-8), 1011—1029. doi:10.1080/01690960600824609

53. ↵

    Fiebach, C. J., Schlesewsky, M., Lohmann, G., Von Cramon, D. Y., & Friederici, A. D. (2005). Revisiting the role of Broca’s area in sentence processing: syntactic integration versus syntactic working memory. Human brain mapping, 24(2), 79—91.

54. ↵

    Fischl, B., Rajendran, N., Busa, E., Augustinack, J., Hinds, O., Yeo, B. T., … Zilles, K. (2008). Cortical folding patterns and predicting cytoarchitecture. Cereb Cortex, 18(8), 1973—1980. doi:10.1093/cercor/bhm225
55. ↵

    Frankland, S. M., & Greene, J. D. (2015). An architecture for encoding sentence meaning in left mid-superior temporal cortex. Proceedings of the National Academy of Sciences of the United States of America, 112(37), 11732—11737. doi:10.1073/pnas.1421236112

56. ↵

    Friederici, A. D., Bahlmann, J., Heim, S., Schubotz, R. I., & Anwander, A. (2006). The brain differentiates human and non-human grammars: functional localization and structural connectivity. Proceedings of the National Academy of Sciences, 103(7), 2458—2463.

57. ↵

    Friederici, A. D. (2011). The brain basis of language processing: from structure to function. Physiological reviews, 91(4), 1357—1392. doi:10.1152/physrev.00006.2011

58. ↵

    Friederici, A. D. (2012). The cortical language circuit: from auditory perception to sentence comprehension. Trends in Cognitive Sciences, 16(5), 262—268. doi:10.1016/j.tics.2012.04.001

59. ↵

    Friederici, A. D. (2018). The neural basis for human syntax: Broca’s area and beyond. Current opinion in behavioral sciences, 21, 88—92.

60. ↵

    Friederici, A. D., Chomsky, N., Berwick, R. C., Moro, A., & Bolhuis, J. J. (2017). Language, mind and brain. Nature Human Behaviour, 1(10), 713.

61. ↵

    Friederici, A. D., Kotz, S. A., Scott, S. K., & Obleser, J. (2010). Disentangling syntax and intelligibility in auditory language comprehension. Human Brain Mapping, 31(3), 448—457. doi:10.1002/hbm.20878

62. ↵

    Friederici, A. D., Meyer, M., & von Cramon, D. Y. (2000). Auditory language comprehension: An event-related fMRI study on the processing of syntactic and lexical information. Brain and Language, 74(2), 289—300. doi:10.1006/brln.2000.2313

63. ↵

    Friston, K. J., Rotshtein, P., Geng, J. J., Sterzer, P., & Henson, R. N. (2006). A critique of functional localisers. Neuroimage, 30(4), 1077—1087. doi:10.1016/j.neuroimage.2005.08.012

64. Frost, M. A., & Goebel, R. (2012). Measuring structural-functional correspondence: Spatial variability of specialised brain regions after macro-anatomical alignment. Neuroimage, 59(2), 1369—1381. doi:10.1016/j.neuroimage.2011.08.035

65. ↵

    Futrell, R., Wilcox, E., Morita, T., & Levy, R. (2018). RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency. arXiv preprint arXiv:1809.01329.

66. ↵

    Garnsey, S. M., Pearlmutter, N. J., Myers, E., & Lotocky, M. A. (1997). The contributions of verb bias and plausibility to the comprehension of temporarily ambiguous sentences. Journal of Memory and Language, 37(1), 58—93. doi:10.1006/jmla.1997.2512

67. ↵

    Gibson, E., Bergen, L., & Piantadosi, S. T. (2013). Rational integration of noisy evidence and prior semantic expectations in sentence interpretation. Proceedings of the National Academy of Sciences of the United States of America, 110(20), 8051—8056. doi:10.1073/pnas.1216438110

68. ↵

    Glezer, L. S., & Riesenhuber, M. (2013). Individual Variability in Location Impacts Orthographic Selectivity in the “Visual Word Form Area”. Journal of Neuroscience, 33(27), 11221—11226. doi:10.1523/jneurosci.5002-12.2013

69. Goldberg, A. (2002). Construction Grammar. In Encyclopedia of Cognitive Science: Macmillan Reference Limited Nature Publishing Group.

70. ↵

    Goldberg, A. E. (1995). Constructions: A Construction Grammar Approach to Argument Structure: University of Chicago Press.

71. ↵

    Goldberg, A. E. (2006). Constructions at work: The nature of generalization in language. Oxford University Press on Demand.

72. ↵

    Goldinger, S. D. (1996). Words and voices: episodic traces in spoken word identification and recognition memory. Journal of experimental psychology: Learning, memory, and cognition, 22(5), 1166.

73. ↵

    Gulordava, K., Bojanowski, P., Grave, E., Linzen, T., & Baroni, M. (2018). Colorless green recurrent networks dream hierarchically. arXiv preprint arXiv:1803.11138.

74. ↵

    Hagoort, P., Brown, C., & Groothusen, J. (1993). The syntactic positive shift (SPS) as an ERP measure of syntactic processing. Language and Cognitive Processes, 8(4), 439—483. doi:10.1080/01690969308407585

75. Hagoort, P., & Indefrey, P. (2014). The Neurobiology of Language Beyond Single Words. Annual Review of Neuroscience, Vol 37, 37, 347-+. doi:10.1146/annurev-neuro-071013-013847

76. ↵

    Hare, M. L., & Goldberg, A. E. (1999). Structural priming: Purely syntactic? Proceedings of the Twenty First Annual Conference of the Cognitive Science Society, 208—211.

77. ↵

    Hasson, U., Chen, J., & Honey, C. J. (2015). Hierarchical process memory: memory as an integral component of information processing. Trends in Cognitive Sciences, 19(6), 304—313. doi:10.1016/j.tics.2015.04.006

78. ↵

    Hauser, M. D., Chomsky, N., & Fitch, W. T. (2002). The faculty of language: What is it, who has it, and how did it evolve? Science, 298(5598), 1569—1579. doi:10.1126/science.298.5598.1569

79. ↵

    Herrmann, B., Obleser, J., Kalberlah, C., Haynes, J. D., & Friederici, A. D. (2012). Dissociable neural imprints of perception and grammar in auditory functional imaging. Human Brain Mapping, 33(3), 584—595. doi:10.1002/hbm.21235

80. ↵

    Hoff, E., Quinn, J. M., & Giguere, D. (2018). What explains the correlation between growth in vocabulary and grammar? New evidence from latent change score analyses of simultaneous bilingual development. Developmental science, 21(2), e12536.

81. ↵

    Holmes, A., & Friston, K. (1998). Generalisability, random effects and population inference. NeuroImage, 7(4), S754.

82. ↵

    Jackendoff, R. (2002). English particle constructions, the lexicon, and the autonomy of syntax. Verb-particle explorations, 67—94.

83. ↵

    Jackendoff, R. (2002). Foundations of Language: Brain, Meaning, Grammar, Evolution: Oxford University Press.

84. ↵

    Jackendoff, R. (2007). A Parallel Architecture perspective on language processing. Brain Research, 1146, 2—22. doi:10.1016/j.brainres.2006.08.111

85. ↵

    Jaeger, T. F. (2010). Redundancy and reduction: Speakers manage syntactic information density. Cognitive psychology, 61(1), 23—62.

86. ↵

    Keller, T. A., Carpenter, P. A., & Just, M. A. (2001). The neural bases of sentence comprehension: a fMRI examination of syntactic and lexical processing. Cerebral Cortex, 11(3), 223—237. doi:10.1093/cercor/11.3.223

87. Kolk, H., & Chwilla, D. (2007). Late positivities in unusual situations. Brain and Language, 100(3), 257—261. doi:10.1016/j.bandl.2006.07.006

88. Kolk, H. H. J., Chwilla, D. J., van Herten, M., & Oor, P. J. W. (2003). Structure and limited capacity in verbal working memory: A study with event-related potentials. Brain and Language, 85(1), 1—36. doi:10.1016/s0093-934x(02)00548-5

89. Kouider, S., de Gardelle, V., Dehaene, S., Dupoux, E., & Pallier, C. (2010). Cerebral bases of subliminal speech priming. Neuroimage, 49(1), 922—929. doi:10.1016/j.neuroimage.2009.08.043

90. ↵

    Krekelberg, B., Boynton, G. M., & van Wezel, R. J. A. (2006). Adaptation: from single cells to BOLD signals. Trends in Neurosciences, 29(5), 250—256. doi:10.1016/j.tins.2006.02.008

91. ↵

    Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S., & Baker, C. I. (2009). Circular analysis in systems neuroscience: the dangers of double dipping. Nature Neuroscience, 12(5), 535—540. doi:10.1038/nn.2303

92. ↵

    Kuperberg, G. R., Holcomb, P. J., Sitnikova, T., Greve, D., Dale, A. M., & Caplan, D. (2003). Distinct patterns of neural modulation during the processing of conceptual and syntactic anomalies. Journal of Cognitive Neuroscience, 15(2), 272—293. doi:10.1162/089892903321208204

93. ↵

    Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: Finding meaning in the N400 component of the event-related brain potential (ERP). Annual Review of Psychology, Vol 62, 62, 621—647. doi:10.1146/annurev.psych.093008.131123

94. Kutas, M., & Hillyard, S. A. (1980). Event-related brain potentials to semantically inappropriate and surprisingly large words. Biological Psychology, 11(2), 99—116. doi:10.1016/0301-0511(80)90046-0

95. ↵

    Lakoff, G. (1970). Irregularity in syntax. New York: Holt, Rinehart, and Winston.

96. ↵

    Langacker, R. W. (1986). An introduction to cognitive grammar. Cognitive science, 10(1), 1—40.

97. ↵

    Langacker, R. W. (1987). Foundations of cognitive grammar: Theoretical prerequisites (Vol. 1). Stanford University Press.

98. ↵

    Lau, E. F., Phillips, C., & Poeppel, D. (2008). A cortical network for semantics: (de)constructing the N400. Nature Reviews Neuroscience, 9(12), 920—933. doi:10.1038/nrn2532

99. Lee, D., Simon, M. V., Fedorenko, E., Cahill, D. P., Curry, W. T., Nahed, B., & Williams, M. Z. (under review). The neural architecture of functional morpheme production in the left temporal-parietal junction.

100. ↵

     Levin, B. (1993). English verb classes and alternations: A preliminary investigation. University of Chicago press.

101. ↵

     Levin, B., & Rappaport-Hovav, M. (2005). Argument Realization. Cambridge, UK: Cambridge University Press.

102. Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106(3), 1126—1177. doi:10.1016/j.cognition.2007.05.006

103. Linzen, T. (2016). Issues in evaluating semantic spaces using word analogies. arXiv preprint arXiv:1606.07736.

104. ↵

     Macdonald, M. C., Pearlmutter, N. J., & Seidenberg, M. S. (1994). The lexical nature of syntactic ambiguity resolution. Psychological Review, 101, 676—703.

105. ↵

     Mahowald, K., James, A., Futrell, R., & Gibson, E. (2016). A meta-analysis of syntactic priming in language production. Journal of Memory and Language, 91, 5—27.

106. McElree, B., & Griffith, T. (1998). Structural and lexical constraints on filling gaps during sentence comprehension: A time-course analysis. Journal of Experimental Psychology-Learning Memory and Cognition, 24(2), 432—460. doi:10.1037//0278-7393.24.2.432

107. ↵

     Menenti, L., Petersson, K. M., & Hagoort, P. (2012). From reference to sense: how the brain encodes meaning for speaking. Frontiers in Psychology, 3. doi:10.3389/fpsyg.2011.00384

108. ↵

     Mikolov, T., Karafiát, M., Burget, L., □ernocký, J., & Khudanpur, S. (2010). Recurrent neural network based language model. In Eleventh Annual Conference of the International Speech Communication Association.

109. Mirman, D., Britt, A. E., & Chen, Q. (2013). Effects of phonological and semantic deficits on facilitative and inhibitory consequences of item repetition in spoken word comprehension. Neuropsychologia, 51(10), 1848—1856.

110. Morgan, E., & Levy, R. (2016). Abstract knowledge versus direct experience in processing of binomial expressions. Cognition, 157, 384—402. doi:10.1016/j.cognition.2016.09.011

111. ↵

     Nelson, M. J., El Karoui, I., Giber, K., Yang, X. F., Cohen, L., Koopman, H., … Dehaene, S. (2017). Neurophysiological dynamics of phrase-structure building during sentence processing. Proceedings of the National Academy of Sciences of the United States of America, 114(18), E3669—E3678. doi:10.1073/pnas.1701590114

112. ↵

     Newmeyer, F. J. (2003). Grammar is grammar and usage is usage. Language, 79(4), 682—707.

113. ↵

     Nieto-Castañón, A., & Fedorenko, E. (2012). Subject-specific functional localizers increase sensitivity and functional resolution of multi-subject analyses. NeuroImage, 63(3), 1646—1669. doi:10.1016/j.neuroimage.2012.06.065

114. ↵

     Nieuwenhuis, S., Forstmann, B. U., & Wagenmakers, E. J. (2011). Erroneous analyses of interactions in neuroscience: a problem of significance. Nature Neuroscience, 14(9), 1105—1107. doi:10.1038/nn.2886

115. ↵

     Noppeney, U., & Price, C. J. (2004). An fMRI study of syntactic adaptation. Journal of Cognitive Neuroscience, 16(4), 702—713. doi:10.1162/089892904323057399

116. O’Donnell, T. J. (2015). Productivity and Reuse in Language: A Theory of Linguistic Computation and Storage: MIT Press.

117. ↵

     Osterhout, L., & Holcomb, P. J. (1992). Event-related brain potentials elicited by syntactic anomaly. Journal of Memory and Language, 31(6), 785—806. doi:10.1016/0749-596x(92)90039-z

118. ↵

     Pallier, C., Devauchelle, A. D., & Dehaene, S. (2010). Cortical representation of the constituent structure of sentences. Proceedings of the National Academy of Sciences of the United States of America, 108(6), 2522—2527. doi:10.1073/pnas.1018711108

119. Petersson, K. M., Folia, V., & Hagoort, P. (2012). What artificial grammar learning reveals about the neurobiology of syntax. Brain and language, 120(2), 83—95.

120. ↵

     Pickering, M. J., & Ferreira, V. S. (2008). Structural priming: A critical review. Psychological Bulletin, 134(3), 427—459. doi:10.1037/0033-2909.134.3.427

121. ↵

     Pinker, S. (1989). Learnability and Cognition: The Acquisition of Argument Structure. Cambridge, MA: MIT Press.

122. ↵

     Pinker, S. (1995). The language instinct: William Morrow and Company.

123. Pinker, S. (1999). Words and rules: The ingredients of language. London: Weidenfeld & Nicolson.

124. ↵

     Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, 10(2), 59—63. doi:10.1016/j.tics.2005.12.004

125. ↵

     Poldrack, R. A. (2011). Inferring Mental States from Neuroimaging Data: From Reverse Inference to Large-Scale Decoding. Neuron, 72(5), 692—697. doi:10.1016/j.neuron.2011.11.001

126. Reali, F., & Christiansen, M. H. (2007). Processing of relative clauses is made easier by frequency of occurrence. Journal of memory and language, 57(1), 1—23.

127. ↵

     Rodd, J. M., Vitello, S., Woollams, A. M., & Adank, P. (2015). Localising semantic and syntactic processing in spoken and written language comprehension: An Activation Likelihood Estimation meta-analysis. Brain and Language, 141, 89—102. doi:10.1016/j.bandl.2014.11.012

128. ↵

     Roder, B., Stock, O., Neville, H., Bien, S., & Rosler, F. (2002). Brain activation modulated by the comprehension of normal and pseudo-word sentences of different processing demands: A functional magnetic resonance Imaging study. Neuroimage, 15(4), 1003—1014. doi:10.1006/nimg.2001.1026

129. ↵

     Santi, A., & Grodzinsky, Y. (2010). fMRI adaptation dissociates syntactic complexity dimensions. Neuroimage, 51(4), 1285—1293. doi:10.1016/j.neuroimage.2010.03.034

130. ↵

     Saxe, R., Brett, M., & Kanwisher, N. (2006). Divide and conquer: A defense of functional localizers. NeuroImage, 30(4), 1088—1096. doi:10.1016/j.neuroimage.2005.12.062

131. ↵

     Scheepers, C., Raffray, C., & Myachykov, A. (2017). The lexical boost effect is not diagnostic of lexically-specific syntactic representations. Memory and Language, 95, 102—115.

132. ↵

     Scott, T. L., Gallee, J., & Fedorenko, E. (2016). A new fun and robust version of an fMRI localizer for the frontotemporal language system. Cognitive Neuroscience, 1—10. doi:10.1080/17588928.2016.1201466

133. ↵

     Segaert, K., Menenti, L., Weber, K., Petersson, K. M., & Hagoort, P. (2012). Shared syntax in language production and language comprehension — an fMRI Study. Cerebral Cortex, 22(7), 1662—1670. doi:10.1093/cercor/bhr249

134. ↵

     Siegelman, M., Mineroff, Z., Blank, I., & Fedorenko, E. (2017). An attempt to replicate a dissociation between syntax and semantics during sentence comprehension reported by Dapretto & Bookheimer (1999, Neuron). bioRxiv.

135. ↵

     Stromswold, K., Caplan, D., Alpert, N., & Rauch, S. (1996). Localization of syntactic comprehension by positron emission tomography. Brain and language, 52(3), 452—473.

136. ↵

     Tomasello, M. (2003). Constructing a Language: A Usage-Based Theory of Language Acquisition. Cambridge, MA: Harvard University Press

137. ↵

     Traxler, M. J., Morris, R. K., & Seely, R. E. (2002). Processing subject and object relative clauses: Evidence from eye movements. Journal of Memory and Language, 47(1), 69—90. doi:10.1006/jmla.2001.2836

138. ↵

     Trueswell, J. C., Tanenhaus, M. K., & Garnsey, S. M. (1994). Semantic influences on parsing: Use of thematic role information in syntactic ambiguity resolution. Journal of memory and language, 33, 285—285.

139. ↵

     Tyler, L. K., Marslen-Wilson, W. D., Randall, B., Wright, P., Devereux, B. J., Zhuang, J., … Stamatakis, E. A. (2011). Left inferior frontal cortex and syntax: function, structure and behaviour in patients with left hemisphere damage. Brain, 134, 415—431. doi:10.1093/brain/awq369

140. ↵

     Ullman, M. T. (2004). Contributions of memory circuits to language: The declarative/procedural model. Cognition, 92(1-2), 231—270.

141. ↵

     Ullman, M. T. (2016). The Declarative/Procedural Model : A Neurobiological Model of Language Learning, Knowledge, and Use. In Neurobiology of Language: Academic Press.

142. ↵

     van de Meerendonk, N., Kolk, H. H. J., Vissers, C., & Chwilla, D. J. (2010). Monitoring in Language Perception: Mild and Strong Conflicts Elicit Different ERP Patterns. Journal of Cognitive Neuroscience, 22(1), 67—82. doi:10.1162/jocn.2008.21170

143. van de Meerendonk, N., Kolk, H. J., & Chwilla, D. J. (2009). Monitoring in language perception Language and Linguistics Compass, 3(5), 1211—1224.

144. ↵

     Vissers, C., Chwilla, D. J., & Kolk, H. H. J. (2006). Monitoring in language perception: The effect of misspellings of words in highly constrained sentences. Brain Research, 1106, 150—163. doi:10.1016/j.brainres.2006.05.012

145. ↵

     Vissers, C., Chwilla, D. J., & Kolk, H. H. J. (2007). The interplay of heuristics and parsing routines in sentence comprehension: Evidence from ERPs and reaction times. Biological Psychology, 75(1), 8—18. doi:10.1016/j.biopsycho.2006.10.004

146. ↵

     Wang, L., Uhrig, L., Jarraya, B., & Dehaene, S. (2015). Representation of numerical and sequential patterns in macaque and human brains. Current Biology, 25(15), 1966—1974.

147. ↵

     Wang, J., Cherkassky, V. L., Yang, Y., Chang, K. M. K., Vargas, R., Diana, N., & Just, M. A. (2016). Identifying thematic roles from neural representations measured by functional magnetic resonance imaging. Cognitive neuropsychology, 33(3-4), 257—264. doi: 10.1080/02643294.2016.1182480

148. ↵

     Wray, A. (2005). Formulaic language and the lexicon. Cambridge University Press.

149. Ye, Z., & Zhou, X. L. (2008). Involvement of cognitive control in sentence comprehension: Evidence from ERPs. Brain Research, 1203, 103—115. doi:10.1016/j.brainres.2008.01.090

150. ↵

     Ziegler, J. & Snedeker, J. (2018). How broad are thematic roles? Evidence from structural priming. Cognition, 179, 221—240.

151. ↵

     Ziegler, J., Snedeker, J., & Wittenberg, E. (2018). Event structures drive semantic structural priming, not thematic roles: Evidence from idioms and light verbs. Cognitive Science.

• Сейчас обучается 268 человек из 64 регионов

• Сейчас обучается 396 человек из 63 регионов