Disrupting morphosyntactic and lexical semantic processing has opposite effects on the sample entropy of neural signals

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Disrupting morphosyntactic and lexical semantic processing has opposite effects on the sample entropy of neural signals Andre´ Fonsecaa, Vezha Boboevab, Sanne Brederooc, Giosue` Baggiod,n a

Center of Mathematics, Computation and Cognition, ABC Federal University, Rua Santa Adélia 166, 09210-170 Santo André, Brazil b SISSA International School for Advanced Studies, via Bonomea 265, 34136 Trieste, Italy c Center for Language and Cognition and NeuroImaging Center, University of Groningen, Postbus 716, 9700 AS Groningen, Netherlands d Language Acquisition and Language Processing Lab, Norwegian University of Science and Technology, 7491 Trondheim, Norway

art i cle i nfo

ab st rac t

Article history:

Converging evidence in neuroscience suggests that syntax and semantics are dissociable in

Accepted 18 January 2015

brain space and time. However, it is possible that partly disjoint cortical networks,

Available online 26 January 2015

operating in successive time frames, still perform similar types of neural computations. To test the alternative hypothesis, we collected EEG data while participants read sentences containing lexical semantic or morphosyntactic anomalies, resulting in N400 and P600 effects, respectively. Next, we reconstructed phase space trajectories from EEG time series, and we measured the complexity of the resulting dynamical orbits using sample entropy – an index of the rate at which the system generates or loses information over time. Disrupting morphosyntactic or lexical semantic processing had opposite effects on sample entropy: it increased in the N400 window for semantic anomalies, and it decreased in the P600 window for morphosyntactic anomalies. These findings point to a fundamental divergence in the neural computations supporting meaning and grammar in language. & 2015 Elsevier B.V. All rights reserved.

1.

Introduction

The distinction between meaning and grammar is at the heart of human language. ‘Sylvester chased Tweety’ and ‘Tweety was chased by Sylvester’ express the same meaning by different syntactic forms, whereas ‘Sylvester chased Tweety’ and ‘Wile Coyote chased the Road Runner’ convey n

Corresponding author. E-mail address: [email protected] (G. Baggio).

http://dx.doi.org/10.1016/j.brainres.2015.01.030 0006-8993/& 2015 Elsevier B.V. All rights reserved.

different meanings by means of the same syntactic form. Meaning and grammar are subtly entwined in ordinary language, but they may nonetheless vary independently. As is suggested by these examples, moreover, the kind of mental operations that are required to transform a sentence into another differ greatly depending on whether the result will preserve the form or the content of the original sentence.

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Theoretical linguistics has indeed analyzed the syntax and semantics of natural languages using different mathematical tools, e.g., formal grammars (Chomsky, 1957) and predicate logics (Montague, 1974), and psycholinguistics has followed suit in recognizing their relative independence in the cognitive architecture of language (Levelt, 1978; Jackendoff, 2002). Four converging strands of evidence suggest that brain systems for syntax and semantics are dissociable in space (i.e., they involve partly non-overlapping neural networks) and time (successive neural computations), though the precise contours of these networks, as well as of the computations they carry out, remain to a large extent elusive. One line of evidence is provided by research on patients with brain lesions. Caramazza and Zurif (1976) showed that Broca's and conduction aphasics were able to comprehend sentences whose meaning could be recovered based on semantic relations between words alone, without having to decode the syntax, but they failed on sentences in which syntax was crucial to establish meaning. Further dissociations were found in single-case studies. The aphasic patient JG (Ostrin and Tyler, 1995) had suffered from a left temporo-parietal stroke that had largely spared lexical semantics and some capacity for phrasal integration of word meanings, but impaired certain syntactic and morphological processes. A different case is patient Oscar (Garrard et al., 2004), who was affected by a widespread neurodegeneration of frontotemporal gray matter. His ability to make grammatical distinctions (e.g., of count and mass nouns) was normal but his performance in semantic judgments (e.g., on the natural/ manmade distinction) was impaired. A second line of evidence is constituted by fMRI experiments (see Bookheimer, 2002 for a review). Earlier research has established that the posterior segment of the left inferior frontal gyrus (BA44) is implicated in syntactic processing, while the lower portion (BA47) is recruited by semantic processes (Dapretto and Bookheimer, 1999). Further work showed that pars triangularis (BA45) of the left inferior frontal gyrus, intermediate between BA44 and BA47, activated more strongly to violations of argument structure, and BA44 responded more to agreement violations (Newman et al., 2003). This has led some authors to propose that the left inferior frontal gyrus is organized following a rostro-caudal gradient, with more posterior regions being involved in phonological (BA6 and BA44) and syntactic (BA44 and BA45) processing, and more anterior portions supporting the integration of lexical meanings into a sentence or discourse context (BA45 and BA47) (Hagoort, 2005; Uddén and Bahlmann, 2012). Third, studies using diffusion tensor imaging (DTI) showed syntactic processes hinge on dorsal white matter pathways of the left hemisphere connecting superior temporal with posterior inferior frontal cortices (superior longitudinal and arcuate fasciculi) (Wilson et al., 2011). In contrast, electrical stimulation during brain tumor resection has implicated ventral pathways (i.e., inferior occipito-frontal fasciculus) in lexical semantic processing (Matsumoto et al., 2004; Duffau et al., 2005, 2009). Further evidence for the involvement of a ventral pathway in semantic processing is provided by DTI data (Saur et al., 2008). In an attempt to characterize this dual-pathway system in the brain, Bornkessel-Schlesewsky and Schlesewsky (2013) conjecture that the ventral stream

underlies conceptual unification while the dorsal stream subserves syntactic structuring (but see Hickok and Poeppel, 2004 for a different functional account). Finally, a large number of studies have shown that the amplitude of distinct ERP waves, the N400 and P600, can be independently modulated, among other factors (see below), by violations and other manipulations of lexical semantic constraints (e.g., ‘He took a sip from the transmitter’ evokes a larger N400 than the alternative sentence closure ‘waterfall’, whose N400 is in turn larger than the N400 produced by ‘bottle’) and grammatical constraints (e.g., ‘The spoilt child throw the toy on the floor’; ‘throw’ yields a larger P600 wave than ‘throws’) (Kutas and Hillyard, 1980; Osterhout and Holcomb, 1992; Hagoort et al., 1993; Osterhout and Nicol, 1999). These lines of evidence, however, compelling in suggesting the brain represents and handles grammatical and semantic information in different ways, do not draw clear boundaries between morphosyntactic and semantic processing – but neither does so linguistic theory. More specifically, syntax and semantics at the phrase and sentence levels are tightly enmeshed, and build on representations and operations which link semantic to syntactic structures in complex ways (the ‘syntax–semantics interface’), partly depending on the specific language being examined (Pylkkänen and McElree, 2006; Bornkessel and Schlesewsky, 2006; Bornkessel-Schlesewsky and Schlesewsky, 2009). The same observation can be extended to word-level processes and representations, linking lexical semantics to morphology. Therefore, the same structural or semantic properties of phrases or sentences across languages may be realized in functionally and anatomically different ways in the brain (Choudhary et al., 2009; Tune et al., 2014). This crosslinguistic variation of neural substrates has momentous consequences for general theories of language comprehension in the brain (Bornkessel and Schlesewsky, 2006). Crucially, however, this conclusion does not undermine the approach whereby neural correlates of meaning and grammar are established in particular languages independently, based on a limited range of linguistic phenomena that may be unambiguously assigned to either the domain of semantics or morphosyntax. Here, we harness the accepted distinction between word meaning and morphological agreement in Dutch to probe one stretch of the boundary between meaning and grammar in the brain, using N400 and P600 data. This suggests some necessary qualifications concerning the N400 and the P600. Although the correlations between N400 and lexico-semantic processing, and P600 and morphosyntactic processing, are well established, their specificity (one-to-one mapping) relative to these processes is not upheld by the data. It should be noted that the early N400 and P600 studies did not claim that these ERP effects were specific to lexical semantic or grammatical processes (Kutas and Hillyard, 1980; Osterhout and Holcomb, 1992; Hagoort et al., 1993; Osterhout and Nicol, 1999). Nevertheless, specificity is a legitimate issue arising from subsequent N400 and P600 works. For example, Munte et al. (1998) showed that a P600 is elicited by orthographic violations. Furthermore, a P600 can follow semantic argument structure violations (Kim and Osterhout, 2005; van Herten et al., 2005), despite the sentences being grammatically well formed. Here, three remarks are in order. First, recent research suggests that

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different instances of what appears to be a P600 might reflect different underlying brain processes (Regel et al., 2014; DeLong et al., 2014). Therefore, some prudence is advised when comparing P600 effects across experiments and paradigms. Second, these findings highlight a many-to-many mapping between ERPs (and active brain areas) and linguistic processes, but they do not compromise the non-arbitrary link between the P600 and grammar. One-to-one relationships may be obtained by considering ‘complex neural signatures’, instead of single dependent measures – i.e., sets of brain responses (e.g., ERPs, oscillations, fMRI activations, and variations in nonlinear measures like entropy) that jointly uniquely correlate with each linguistic process (Baggio et al., 2015). Viewed in this light, the N400 and P600 might prove essential to establish complex signatures for meaning and grammar. Third, newer theories, e.g., monitoring (van Herten et al., 2005) and the extended Argument Dependency Model (eADM; Bornkessel and Schlesewsky, 2006) must also account for the early P600 results, and thereby follow earlier reanalysis (Friederici, 2002) and unification models (Hagoort, 2003) at least in connecting the P600 to forms of structural input processing. Indeed, the object of monitoring must be a structural representation of the input, else the P600 would have a much broader set of antecedent conditions. In the eADM, the P600 is seen as reflecting the computation of ‘linking relations’, which map arguments onto the hierarchical argument structure of the verb, taking into account agreement and other grammatical properties (see Brouwer and Hoeks, 2013 for a related, yet broader functional account of the P600). A few ERP studies reported N400-like negativities in response to manipulations of word order (Bornkessel et al., 2002) as well as word class in participants that did not show P600 effects in the same experimental conditions (Osterhout, 1997). These findings have led authors to propose models of the N400 that depart radically from accounts based on lexical retrieval or semantic integration (Kutas and Federmeier, 2000, 2011; Lau et al., 2008; Baggio and Hagoort, 2011). For example, Haupt et al. (2008) found N400 effects to sentences in which incremental interpretation requires a revision to an object-initial word order as compared with subject-initial sentences. They claim the ‘N400 is a robust correlate of grammatical function reanalysis that occurs independently of any lexical factors’. It is unclear how this fits with the widely replicated N400 effects of lexical semantic expectancy. Bornkessel-Schlesewsky and Schlesewsky (2008) also argue that ‘the plausibility-based N400 which occurs in sentences with semantically unassociated arguments and verbs should be distinct from N400 effects observed as correlates of argument linking or prominence computation’, but this does not account for the N400 when arguments are semantically associated, as in classic expectancy-based N400 effects. Non-syntactic accounts of the P600, as well as non-semantic accounts of the N400, are welcome contributions to the ongoing debate. However, they are best viewed as explorations of manyto-many mappings holding between cognitive processes and ERP effects, and not as new theories superseding earlier morphosyntactic and lexical semantic accounts. Above we presented four converging lines of evidence suggesting some degree of separation between brain systems underlying syntax and semantics. Crucially, these spatial and

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temporal differences in the brain networks supporting meaning and grammar do not entail computational differences. Indeed, partly disjoint circuits that operate over successive time frames might perform the same type of computation (Marr and Poggio, 1976; Marr, 1982; Churchland and Sejnowski, 1990) or instantiate similar representations (Knudsen et al., 1987). We tested the alternative hypothesis, that meaning and grammar differ also in the underlying neural computations. We collected EEG data while participants read sentences that contained incongruities of grammar (1) and meaning (3), and their congruent versions (2) and (4). Critical words are marked in bold in the examples below:

(1) De trein werd gisteren repareren nadat er een raam ingegooid was. [The train was yesterday repair after there a window smashed was.] The train was repair yesterday after a window was smashed. (2) De trein werd gisteren gerepareerd nadat er een raam ingegooid was. [The train was yesterday repaired after there a window smashed was.] The train was repaired yesterday after a window was smashed. (3) De thee werd in glazen theekannen meegemaakt voordat de receptie begon. [The tea was in glass teapots experienced before the reception began.] The tea was experienced in glass teapots before the reception began. (4) De thee werd in glazen theekannen geschonken voordat de receptie begon. [The tea was in glass teapots served before the reception began.] The tea was served in glass teapots before the reception began.

We obtained ERPs by averaging amplitude values across EEG trials time locked to the visual onset of critical words, and by computing the mean difference between incongruent vs. congruent trials (Section 5.5). Once we had derived the N400 and P600 effects, we estimated their onset latencies (Section 5.5). Next, we applied a measure of the complexity of brain signals – sample entropy – to single EEG trials, as reconstructed in a multi-dimensional phase space (Sections 5.7 and 5.9) before and after the onset of the N400 and P600 effects, and in the baseline interval preceding visual word onset. Our aim here is to use sample entropy to assess whether neural information processing differs between lexical semantics (i.e., in the time window of the N400) and morphosyntax (P600). We describe sample entropy in full formal detail in Section 5.9. Here, we introduce the idea briefly and informally. Classical entropy is a measure of dynamical systems uncertainty. Entropy levels depend on the number of states a dynamical system can access in a given time interval, so the higher the number of states, the less orderly the dynamics, the higher the entropy. In information theory, entropy measures the unpredictability of systems behavior through the expected value of the associated probability

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Fig. 1 – Results of event-related potential (ERP) analyses of EEG data. Top and bottom rows show results for incongruities of meaning and grammar, respectively. Panels (a) and (e) display the number of electrodes at which the effects are significant as a function of time. The N400 (a) and P600 (e) effects, each defined as an uninterrupted sequence of non-empty clusters of electrodes in which the conditions are statistically different, are highlighted in red. The onset time of the N400 and P600 is shown in light blue. ERP waveforms from channel Cz are shown in (b) and (f). Panels (c) and (g) display ERP effects (average signal in incongruent minus congruent trials) in all 64 channels overlaid, with Cz highlighted in red. Panels (d) and (h) show topographic maps of ERP effects (incongruent minus congruent) before and after the onset of N400 or P600 effects. function in a logarithmic scale (Shannon, 1948). Several estimators have been developed to quantify entropy based on samples of systems dynamics of limited duration, i.e., short time series. These estimators, such as approximate entropy (Pincus, 1991) and sample entropy (Richman and Moorman, 2000), typically compute a statistic for the recurrence rate of states as reconstructed in a multi-dimensional phase space. The more frequently the system visits a set of states (e.g., amplitude values in the EEG), the higher the recurrence rate, and the lower the entropy. Due to the lack of biologically inspired computational models of language, it is impossible to formulate clear predictions as to whether entropy should increase or decrease in response to lexical semantic and morphosyntactic anomalies. However, a definite prediction can be derived on the assumption that entropy reflects a system's information processing dynamics. If sample entropy were to change in opposite ways (increasing vs. decreasing) for the N400 and P600, this would be evidence that semantic and morphosyntactic information are processed in computationally different ways in the brain.

2.

Results

Semantically incongruent words resulted in a larger N400 compared to congruent controls, and grammatically incongruent words produced a larger P600 relative to controls. These effects are fully consistent with previous work (Kutas and Hillyard, 1980; Osterhout and Holcomb, 1992; Hagoort et al., 1993). Besides analyzing ERPs, we obtained timefrequency representations (TFRs) of EEG data (see Section 5.6). The N400 was not accompanied by modulations of oscillatory responses in TFRs, while the P600 was associated

with power changes in the δ (2–4 Hz) band. Sample entropy increased in all comparisons from the baseline to the preonset intervals, and from pre-onset to post-onset in congruent trials. Crucially, however, whereas sample entropy increased from pre-N400 onset to post-N400 onset following lexical semantic anomalies, it decreased from pre-P600 onset to post-P600 onset in response to grammatically incongruities. These changes in entropy were not correlated with either ERP amplitudes or power changes in specific frequency bands. We present these results in greater detail below.

2.1.

Event-related potentials

Incongruities of meaning and grammar modulated different ERP waves relative to controls (Fig. 1). Semantically incongruent words produced a larger N400 compared to controls (Fig. 1a–d; Tsum ¼  4480:2; p ¼ 0:007, cluster size S¼ 1301 samples), and grammatical incongruities resulted in a larger P600 (Fig. 1e–h; Tsum ¼ 12 227; po0:001; S ¼ 3409). The N400 started at 324 ms from visual word onset and lasted until 734 ms (Fig. 1a; see Experimental procedures for the calculation of ERP onset and offset times). The P600 started at 452 ms and lasted until the end of the epoch (Fig. 1e). Both effects had centro-parietal maxima (Fig. 1d and h). These ERP results provide us with two pieces of information to be used in phase space analyses. The first is the estimated onset times of the N400 (324 ms) and P600 (452 ms) in the present data set. We will use these estimates to define pre-onset and post-onset intervals of equal size for which sample entropy will be calculated. The size of pre-onset and post-onset intervals is 270 ms (135 points). This choice meets three key constraints: (1) the statistical maxima (Fig. 1a and e) and the amplitude peaks (Fig. 1b and f) of the N400 and P600

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Fig. 2 – Results of time–frequency representations (TFR) of EEG data. Top and bottom rows show data form congruent and incongruent conditions, respectively. Panels (a), (b) and (e), (f) display TFRs from the channel Cz. Power is expressed as percent change relative to the baseline. Topographic maps show average power relative to the baseline in the delta (2–4 Hz) and gamma (80–100 Hz) frequency bands for grammatically congruent (c) and (d) and incongruent (g) and (h) stimuli. are in each case included in the post-onset interval; (2) the intervals do not exceed in time the visual onset of words (0 ms) or the offset of the N400 (734 ms); (3) the intervals are comparable in extent with a pre-stimulus baseline, where sample entropy was also computed. The second piece of information provided by our ERP results is a representative channel that is included in the topographic maxima of the N400 and P600: here the vertex electrode Cz (Fig. 1c and g). This is consistent with previous research reporting modulations of the N400 and P600. The typical distribution of these effects indeed covers the vertex region. Moreover, the vertex channel occupies a neutral position exactly at the intersection of the sagittal (i.e., midline) and frontal axes. Time series from Cz will be reconstructed in phase space, and sample entropy for the resulting dynamical trajectories will be calculated in the baseline, pre-onset and postonset intervals separately. Finally, sample entropy will be compared between pre-onset and post-onset intervals using a larger sample of electrodes, i.e., the vertex Cz, left (F5) and right (F6) frontal, and left (P5) and right (P6) parietal sites. This additional analysis was conducted to test whether our results are consistent across channels, and do not depend on the initial choice of the representative electrode Cz.

signals to establish whether sample entropy is statistically (in) dependent of oscillations in frequency bands at which differences occur relative to baseline in each condition. TFRs showed small modulations of phase-locked responses relative to baseline between 0 and 200 ms from stimulus onset, between 10 and 30 Hz, corresponding to the exogenous evoked responses N100 and P200 (Fig. 2a, b and e, f and Fig. 1b and f). The N400 was not accompanied by any significant power modulations, hence TFRs for semantically congruent and incongruent words are comparable (Fig. 2a and e). The δ power increase for semantically congruent words, visible in the TFR (Fig. 2a), was statistically not significant (Wilcoxon signed rank test on average δ power from Cz in the 600–1000 ms window relative to baseline: V ¼ 136; p ¼ 0:2109). Incongruities of grammar resulted in clearer changes in TFRs (Fig. 2e and f). Power increased in the δ frequency band over frontal electrodes (2–4 Hz, 400–800 ms, Cz: V ¼ 81:5; p ¼ 0:01; Fig. 2c and g). The γ burst over right parietal sites, discernible in the TFR at 400 ms from word onset (Fig. 2f and h), was not significant (80–100 Hz, 350–450 ms, Cz: V ¼ 206; p ¼ 0:696; Fig. 2c and g). No other TFR modulations were observed.

2.2.

Single EEG trials were reconstructed in a multi-dimensional phase space using the formal embedding procedure described in Section 5.7. The sample entropy (Section 5.9) of the resulting dynamic trajectories was computed in each trial first, and was then averaged across trials. Figs. 3–4 show the results for the meaning (N400) and grammar (P600) conditions, respectively. Both figures show, in the left-hand panels a representative EEG trial (details in Section 5.11) in each condition (congruent and incongruent), the reconstructed trajectory in phase space in the middle panels (with dimensions m ¼2 for ease of visualization), and boxplots for sample entropy in the right-hand panels.

Oscillations

When studying the complexity of EEG signals it is necessary to understand how the power spectra of such signals change over time. An oscillator that becomes phase-locked with a particular frequency wave, if signal dominates over noise, enters a more regular dynamic regime. Thus, sample entropy decreases accordingly. However, the reverse does not hold. Changes in sample entropy may be produced by changes in signal regularity that are not consistently produced by phase-locked responses in any one particular frequency band across trials or subjects. We computed time–frequency representations of EEG

2.3.

Sample entropy

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Fig. 3 – Results of phase space analyses of EEG data from channel Cz for semantically congruent and incongruent stimuli, shown in the top and bottom rows, respectively. Panels (a) and (d) display representative EEG trials (S is subject and T is trial), (b) and (e) show their phase space trajectories (for m ¼2 and τ ¼ 1), and (c) and (f) show average sample entropy in three time intervals: baseline (  200 to 0 ms; green), pre-onset (54–324 ms; blue) and post-onset (326–596 ms; green). Significance codes of Wilcoxon signed rank tests: n 0:01opo0:05; nn 0:001opo0:01; nnn po0:001 (details in Table 1).

Fig. 3 shows the results for semantically congruent and incongruent words. Sample entropy increases from baseline ( 200 to 0 ms) to the pre-N400 onset (54–324 ms) interval, and from pre-onset to post-onset (324–594 ms) for congruent (Fig. 3c) as well as incongruent words (Fig. 3f; Table 1). Sample entropy follows a different pattern in grammatically congruent and incongruent words, as shown in Fig. 4. Again sample entropy increases from baseline (  200 to 0 ms) to the pre-onset interval (182–452 ms), for congruent (Fig. 4c) and incongruent words (Fig. 4f; Table 1), but it shows only a weak pre-/post-onset increase for congruent words (Fig. 4c; Table 1), and it decreases from pre-onset to post-onset in incongruent trials (Fig. 4f; Table 1). Therefore, grammatically incongruent words seem the exception to the overall pattern where sample entropy increases in each of the time windows considered. We constructed surrogate data preserving the power spectrum and probability distribution of the original EEG signals, and in which the non-linear properties of the EEG are lost (Section 5.10). Entropy changes, reflecting non-linear dynamics, should therefore disappear in surrogate data. Indeed, that is what we found (Table 1). In surrogate data, the baseline/preonset effects were even larger compared to the observed data. However, all the pre-/post-onset effects disappeared altogether, showing that the observed post-onset sample entropy changes are true non-linear phenomena, rather than a consequence of linear properties of EEG signals, i.e., the probability distribution of states (reflected in ERPs) and power spectrum (TFRs).

These surrogate data tests establish that sample entropy changes in post-onset intervals are independent of the probability distribution of states and of the power spectrum of signals. To show that is the case also on a subject-bysubject basis, we computed all pairwise correlations between mean sample entropy in the pre-onset and post-onset intervals of N400 and P600, for congruent and incongruent words, with delta power (2–4 Hz, 400–800 ms), gamma power (80– 100 Hz, 350–450 ms; Fig. 2), N400 and P600 amplitudes. We found no correlations (p40:1). The exception was that mean ERP amplitudes were correlated between pre-onset and postonset intervals in both congruent and incongruent N400 and P600 trials (po0:001). To further corroborate our experimental findings, we provide evidence that the observed pattern of sample entropy across conditions is stable for different values of the reconstruction parameters m and τ (Section 5.7; Table 1). Further, we re-run the entire data analysis protocol on a larger set of 5 electrodes, now including the vertex Cz, left and right frontal (F5 and F6), and left and right parietal (P5 and P6) sites. Sample entropy increases for semantically congruent (V ¼ 43; po0:001) and incongruent (V ¼ 37; po0:001; Fig. 5b) words from preonset to post-N400 onset (Fig. 5a), it shows but a weak increase in the post-P600 onset interval for congruent words (V ¼ 191; p ¼ 0:972), whereas it decreases post-P600 onset for grammatically incongruent words (V ¼ 330; po0:001; Fig. 5b). Therefore, the results reported for channel Cz (Figs. 3 and 4; Table 1) hold as well for a larger set of channels.

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Fig. 4 – Results of phase space analyses of EEG data from channel Cz for grammatically congruent and incongruent stimuli, shown in the top and bottom rows, respectively. Panels (a) and (d) display representative EEG trials (S is subject and T is trial), (b) and (e) show their phase space trajectories (for m¼2 and τ ¼ 1), and (c) and (f) show average sample entropy in three time intervals: baseline (  200 to 0 ms; green), pre-onset (182–452 ms; blue) and post-onset (454–724 ms; green). Significance codes of Wilcoxon signed rank tests: n 0:01opo0:05; nn 0:001opo0:01; nnn po0:001 (details in Table 1).

3.

Discussion

The selection of studies reviewed in the Introduction suggest that brain networks for morphosyntax and lexical semantics can be dissociated in space and time. Still, differences in the spatio-temporal organization of brain circuits do not necessarily entail computational differences. Indeed, it is conceivable that partly disjoint brain networks that operate in successive time frames perform the same (or very similar) types of neural computations. The N400 and P600 obtained here for incongruities of meaning and grammar entail nonoverlapping sources (differences in polarity) and serial processing (differences in latency). However, they are equally consistent with the idea that the underlying computations are the same or indistinguishable, although they are carried out in different brain regions at different times. Contrary to this theoretical possibility, we report that disrupting either morphosyntactic or lexical semantic processes has opposite effects on the complexity of neural signals. Sample entropy increased in the N400 window for incongruities of meaning, and it decreased in the P600 window for grammatical anomalies. These results point to a fundamental divergence in the neural computations supporting lexical meaning and grammatical agreement. In what follows we address three issues in an effort to clarify our results from the perspective of cognitive processing and its biological and physical and implementation.

3.1.

What do N400 and P600 really reflect?

The choice of the time intervals used in our phase space analyses, and the interpretation of sample entropy results, rely on the N400 and P600 being robust markers of semantic and syntactic processing. Two views of the N400 have been contrasted in the literature, suggesting that this ERP component reflects either semantic integration or contextual preactivation of lexical meanings (Lau et al., 2008). These two models are not incompatible (Baggio and Hagoort, 2011), and neither one would challenge the established inverse linear relation between the amplitude of the N400 and the degree of semantic relatedness between the eliciting word and its left context. Moreover, this relation is not called into question by evidence that other content-bearing stimuli, besides words, elicit N400 effects (Nigam et al., 1992). The issue is rather more intricate, but not substantially different, regarding the P600. At least four competing functional accounts of the P600 exist. One views the P600 as an index of the costs of integrating a word into the syntactic structure of a phrase or a sentence (Hagoort, 2003). The second account treats the P600 as a late marker of syntactic reanalysis or repair (Friederici, 2002). The third proposal builds upon the finding that certain semantic anomalies (e.g., ‘The cat that fled from the mice’, where thematic roles are reversed) also elicit a P600, which is interpreted as reflecting a monitoring process ‘that checks upon the veridicality of one's sentence perception’ (van Herten et al., 2005). Finally, the extended Argument Dependency Model

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Table 1 – Wilcoxon signed rank tests on average sample entropy for observed and surrogate data from the vertex channel, for different values of the parameters m (embedding dimension) and τ (time lag). Abbreviations following the N400 and P600 labels: ‘C’ congruent; ‘I’ incongruent. Key effects are highlighted in yellow.

Fig. 5 – Average sample entropy in pre-onset and post-onset intervals in five channels: the vertex Cz, left and right frontal (F5 and F6), and left and right parietal (P5 and P6) electrodes for semantically (a) and grammatically (b) congruent and incongruent words. Significance codes of Wilcoxon signed rank tests: n 0:01opo0:05; nn 0:001opo0:01; nnn po0:001 (details in the main text). (eADM; Bornkessel and Schlesewsky, 2006) regards the P600 as reflecting the construction of the hierarchical argument structure of verbs, given the relevant grammatical constraints.

The unification and eADM accounts can explain the result that modulations of the P600 were also reported for sentences in which morphosyntactic complexity or ambiguity are

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increased (Hagoort, 2003), but no violation occurs. In all these cases, reanalysis and repair are not required, and there is not necessarily a conflict to be monitored. Moreover, the monitoring account assumes that violations of thematic role relations are semantic in nature. Thematic roles are typically seen by linguists as lying at the syntax–semantics interface, linking syntactic argument structure to lexical semantics. Accordingly, there is some leeway to argue that the P600 in these cases still involves syntactic processing. Finally, as was argued above for the N400, evidence that the P600 is also produced by, e.g., processing structural relations in music (Patel et al., 1998), merely detracts from its putative languagespecificity but does not affect its established connection to linguistic syntax. In short, although a many-to-many relation between ERP waves and functional processes may apply – i.e., even if N400 and P600 also reflect other processes than meaning and grammar in natural languages, and even if these operations are also reflected by other ERPs – our sample entropy data show that instances of semantic and morphosyntactic processing exist that can be dissociated computationally, and not just spatially or temporally, in the human brain.

3.2. How could meaning and grammar be computationally similar? Syntax and semantics have traditionally been described using different tools by linguists, i.e., formal grammars (Chomsky, 1957) and predicate logics (Montague, 1974), each of the result of abstraction on properties of phrase structure and compositional meaning. Our sample entropy data highlight a fundamental dichotomy in the way semantic and syntactic information is processed in the brain, and broadly support accounts of language comprehension mirroring the above theoretical distinction. However, within that class of models, our results do not necessarily favor classical modular theories (Fodor, 1983) and their modern incarnations (Hauser et al., 2002) over non-modular accounts. Positing two brain systems abiding by different computational principles (see below for a hypothesis on what these may be) does not imply that these systems are either domain-specific or informationally encapsulated. Alternative models of sentence comprehension propose a uniform treatment of morphosyntactic and lexical semantic processing. For instance, parallel interactive theories (MacDonald et al., 1994) describe how multiple information streams, e.g., meaning and grammar, jointly determine interpretation at each processing stage. Specifically, syntactic and semantic information is modeled as sets of probabilistic constraints that are evaluated and satisfied in parallel during sentence processing. A generative perspective on parallel processing is provided by Jackendoff (1999), in which a single operation termed ‘unification’ is required to combine simple into complex structures in phonology, morphosyntax and semantics. Our data cannot directly rule out the possibility that in normal processing circumstances, i.e., with well formed and interpretable linguistic inputs, the cortical dynamics underlying unification in syntax and semantics is the same or very similar. However, we can conclude that disrupting lexical semantic and

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morphosyntactic unification results in divergent brain responses computationally, and not just spatio-temporally. This is evidence that the large-scale neural information processing principles by which those two systems operate are at least in some circumstances different.

3.3.

How can sample entropy changes be accounted for?

The ‘null hypothesis’ above, whereby lexical semantic and morphosyntactic processing are amenable to a form of probabilistic context-sensitive constraint satisfaction, can be understood at the neural level through the lens of ‘predictive coding’ (Friston, 2010). One may apply predictive coding to language processing, and assume that the brain uses the left context to compute parallel predictive models for each incoming word at different levels of linguistic representation. Semantically plausible, well-formed words will satisfy the internally generated expectations, prediction errors will be minimized, and entropy (here a measure of surprise or uncertainty) will remain low. However, if the incoming word is either semantically or grammatically incongruent, or is for some other reason contextually unexpected, prediction errors will be large. On the assumption that both semantic and grammatical processing follow the principles of predictive coding, entropy should increase for both anomalies of meaning and grammar. This prediction does not agree with our findings. To our understanding, the predictive coding scheme cannot account for decreasing entropy values,1 such as those observed here in the post-P600 onset window for grammatical anomalies. Indeed, entropy can either increase if prediction errors are positive, or remain low (or 0) if prediction errors are low (or 0). This informal line of reasoning should not lead to conclusions regarding the status of predictive coding as a theory of cortical computation. It merely suggests a different kind of mechanism should be invoked to account for the sample entropy changes observed in the present study. A preliminary answer, to serve more as an ‘intuition pump’ (Dennett, 2013) than a theory, can be found in a metaphor attributed to Polish mathematician Mark Kac (Cohen and Stewart, 1994, pp. 258–259). Imagine two cats, one black and one white, each with fleas. Suppose we paint the fleas the color of their respective cat to keep track of them. As long as the cats do not meet, the fleas will hop around randomly on their own cat, and each system will exhibit a specific degree of entropy, i.e., total entropy remains constant until the cats interact. When that happens, fleas will hop from one cat to the other and, after some time, each cat will have a mixture of black and white fleas. Such a process of flea exchange increases total entropy because the new coupled system exhibits a greater number of possible interactions between its components, i.e., a larger number of physical states that the system can occupy. If we now separate the cats again, and we forbid interactions, entropy will 1 Here we assume that entropy as surprise or uncertainty will be reflected in the complexity of brain signals. Without this parsimonious working hypothesis, linking classical informationtheoretic entropy to approximate or sample entropy, certain consequences of predictive coding would seem untestable.

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decrease and return to the original level, or to a lower level, if energy is being dissipated. During language processing, interactions between spatiotemporally separated neural systems may increase as the sentence unfolds. A grammatical anomaly may result in an uninterpretable sentence, and may accordingly disrupt these dynamic interactions between brain systems – total entropy would be expected to decrease. Pincus and Goldberger (1994) propose a ‘general hypothesis’ associating normal physiology with greater irregularity, and systems perturbations with more regular or patterned processing. The EEG is the result of multiple interacting mechanisms at the cortical level, typically including coupling and feedback, and processing of the input from internal or external sources. Pincus and Goldberger (1994) further argue that perturbations (e.g., incongruent stimuli) decouple some of the systems involved in processing the input, and isolate one system (e.g., morphosyntax) from its ambient universe. Entropy is expected to increase with greater system coupling or external inputs, as our baseline vs. pre-onset contrasts indicate. Our data would suggest that the brain responds to lexical semantic anomalies with increased coupling (i.e., increased entropy) across semantic networks, while it copes with grammatical incongruities by decoupling some of the relevant cortical circuits. Note, however, that in certain circumstances a decrease in sample entropy might be produced by an increase in the strength of coupling between cortical oscillators (Vakorin et al., 2011). Our EEG data exhibited no correlations between sample entropy and power changes in specific frequency bands across participants, but we cannot exclude a more intricate scenario in which coupling occurs across different frequency bands across trials or participants, such that no systematic pattern can be detected at the group level.

4.

Conclusion

Through a combination of ERPs, TFRs and phase space analyses of EEG signals in a language processing task, we showed that meaning and grammar in the human brain are not only subserved by spatially and temporally disjoint networks, as the N400 and P600 demonstrate. It is the nature of the underlying computation which differs. We found that sample entropy increases following semantic incongruities in a time window in which meaning is integrated, and it decreases for grammatical incongruities in a temporal frame in which morphosyntactic features of words are processed. Complexity measures, as sample entropy, are being used to ‘effectively discriminate dynamical systems’ (Pincus and Goldberger, 1994) In this paper, we used sample entropy to suggest that, besides being dissociable in time and space, brain networks for syntax and semantics function as different dynamical systems. A deeper understanding of the connections between sample entropy and cognitive brain processing awaits for further experimental work. Conversely, however, as an explicit computational account of language processing in the brain might lie years ahead in the future, new formal approaches to brain signals may contribute to our understanding of the neural bases of language, and to selecting among competing psycholinguistic theories. Here, we

report dynamically different neural responses to incongruities of meaning and grammar. This suggests that theories that describe semantic and syntactic processes in formally similar ways are unlikely to succeed in connecting with key aspects of the biology of language.

5.

Experimental procedures

5.1.

Participants

Twenty-seven right-handed native speakers of Dutch (18 female; mean age 21.6 years) took part in the study. All had normal or corrected-to-normal vision, and received monetary compensation for participating in the experiment.

5.2.

Materials

The stimuli comprised 115 fillers with varying syntax, content and length, and 78 critical sentences. The latter set comprised 13 sentences containing a morphosyntactic incongruency, and 26 correct sentences, adapted from Sabourin (2003), 13 sentences containing a semantic incongruency and 26 correct sentences, adapted from Hoeks et al. (2004). The average position within each sentence of the critical word was 7.7 for grammatical anomalies and 7.9 for semantic anomalies.

5.3.

Procedure

Stimulus sentences were presented visually word-by-word in the center of a computer screen, in a white font against a black background. Each word was shown for 240 ms and was followed by a blank screen for 240 ms. Participants were instructed to read each sentence, and to determine whether it was acceptable or not by pressing either the z-key or the /-key on a standard QWERTY-keyboard. Participants had to respond immediately after the end of each sentence, which was signaled by a full stop at the final word. Participants were recommended to blink during the 500 ms appearance of a fixation mark after the response time.

5.4.

Recording

EEG signals were sampled from 64 tin electrodes mounted in an elastic cap (Electro-Cap International Inc.) and arranged according to the extended international 10–20 system. Two more electrodes were placed on the left and right mastoids and served as a reference. The EOG was recorded using four bipolar tin electrodes on the outer canthus of each eye, and above and below the left eye. Data were digitized with a sampling rate of 500 Hz, a low pass filter at 140 Hz, and a 10 s time constant. The ground electrode was mounted on the sternum.

5.5.

Event-related potentials

Segments were extracted from the EEG from each channel starting 200 ms before and ending 1000 ms after the onset of each critical word, and were baseline-corrected using the 200 ms pre-stimulus interval. Artifact identification was

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carried out in 3 steps: (1) epochs containing activity exceeding a 7200 μV threshold were detected; (2) segments containing muscle artifacts were identified by thresholding the z-transformed values of the 100–125 Hz band-pass filtered EEG; (3) epochs containing eye movements and blinks were identified by means of thresholding the z-transformed values of the 1–15 Hz band-pass filtered EOG. Steps (2)–(3) detect highamplitude changes in the specified frequency bands in each channel: first, the amplitude of the signal over time (the Hilbert envelope) is computed; second, its mean and standard deviation are computed; third, every time point is z-normalized (i.e., its mean is subtracted and divided by the standard deviation); last, z-values are averaged per time point. Trials selected in steps (1)–(3) were discarded. On average, 84% of the trials (SD¼ 15%) from the critical sentences survived artifact rejection. A 30 Hz low-pass filter was used for ERP analyses only. ERPs were obtained by averaging over artifact-free trials in each condition, and were statistically analyzed by means of a cluster-based randomization procedure (Maris and Oostenveld, 2007), involving the following steps. A sample was defined as a (time, electrode) pair. The experimental conditions (incongruent/congruent) were compared by means of a dependent-samples t-test on each sample. Samples for which the t-tests yielded a p-value smaller than 0.05, and that were moreover connected in space (neighboring electrodes) and time (adjacent time points), were clustered together, and the sum of t-values from sample-level t-tests in the cluster was used as a cluster-level t-value (tsum). The cluster-level p-value was calculated using a Monte Carlo simulation: ERP averages in each spatio-temporal cluster for each subject from both conditions were merged into a single set; the resulting set was randomly partitioned into two subsets with equal size, and a t-test was used to compare the means of the two resulting subsets. These steps were repeated 1000 times, and the cluster-level p-values were estimated as the proportion of random partitions resulting in a larger t-statistics than in the observed ERP averages. The first point in the time series showing a significant difference (poα ¼ 0:05) between conditions in a cluster of two neighboring channels was chosen as the onset of the ERP effect, and the last point with that property was selected as its offset.

5.6.

Oscillations

Time–frequency representations (TFRs) were constructed for the two experimental conditions (incongruent/congruent) relative to the onset of each critical word. Power changes over time across frequency bands were quantified in each condition relative to a 500 ms baseline. EEG segments relative to the onset of each critical word were analyzed using the same procedure used for ERP analyses, with the following differences: the pre-stimulus interval was 1000 ms; the 500 ms interval preceding stimulus onset was used for baseline correction; epochs ended 2000 ms after word onset. As with phase space analyses (5.7–5.11), no high-pass or lowpass filters were applied to the EEG data. Artifact identification and rejection were as described in Section 5.5. TFRs were computed using Morlet wavelets obtained by composing a carrier wave oscillation in each particular frequency with a

11

Gaussian envelope (Grossmann et al., 1989). Wavelet width was 7 cycles and wavelet length was 3 standard deviations of the implicit Gaussian kernel. TFRs for each trial were derived as the squared norm of the convolution of Morlet wavelets with the EEG time series (Tallon-Baudry et al., 1996). TFRs were computed from 1 to 120 Hz in steps of 2 Hz. TFRs were then averaged over trials in each condition. To normalize the data for individual differences in EEG power, and for differences in absolute power between frequency bands, average power was expressed as percent change relative to power in the 500 ms baseline. The statistics were Wilcoxon signed rank tests on average power values in the frequency bands and time intervals of interest (see Results; Fig. 2).

5.7.

Phase-space reconstruction

Differences between experimental conditions in ERPs or TFRs typically entail differences in the spatial or temporal organization of the underlying brain circuits. Amplitude or power modulations of successive ERP components or phase-locked responses in different frequency bands reflect neural processes occurring in different time windows or time scales, whereas differences in the topographic distribution of ERP or TFR effects entail partly non-overlapping brain generators (Luck, 2005; Nunez and Srinivasan, 2006; Buzsáki, 2006). However, differences in the spatial or temporal structure of two neural processes X and Y do not necessarily entail that X and Y instantiate qualitatively different computations. The same computation might be carried out in different brain regions (resulting in topographic differences), in distinct time windows (latency differences), or within time frames of different extent (oscillation frequency differences). Complexity and information-theoretic measures may be applied to human EEG recordings to gain insights into the nature of the computation (Stam, 2005), if they can for a particular data set be shown to be independent of variations in ERPs and in TFRs (see Section 5.12). Such measures are not directly applied to EEG signals, but to trajectories in a phase space derived from the original EEG time series (Kantz and Schreiber, 2004). For each artifact-free EEG time series X ¼ fx1 ; …; xn g we reconstructed its phase space: o ! n!
ð1Þ X ¼ xi ¼ xi ; xiþτ ; …; xiþðm  1Þτ with i ¼ 1; …; n ðm 1Þτ. Two parameters occur in this expression: a time delay τ and the embedding dimension m. These are related, respectively, to the time scale of the processes of interest and to attractor geometry (Whitney, 1936; Takens, 1981). Because the parameter spaces for τ and m are infinitely large, it may in practice be useful to be able to estimate their optimal values. For instance, one may estimate the optimal τ^ by means of the minimum mutual information (Fraser   and Swinney, 1986) between the sets fxi g and xiþτ , for i ¼ 1; …; n τ. Once the delay is set, one then chooses a range of values for the embedding dimension and, for each m, one ! looks for close vectors in X ðm; τÞ that are no longer close in ! X ðm þ 1; τÞ. Those vectors are termed ‘false neighbors’, and ^ is the one that provides the minimum amount the optimal m of them (Kennel et al., 1992). This is a method for estimating the optimal values of τ and m independently, hence it does

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not take into account their correlation. Here, we will use instead the Gautama–Mandic–Hulle (GMH) method based on the non-parametric Kozachenko–Leonenko (KL) estimator that sets τ and m jointly (Gautama et al., 2003): N !  1X
 ln Ndj þ lnð2Þ þ E H X ðm; τÞ ¼ Nj¼1

ð2Þ

! where X ðm; τÞ is the phase space of the reconstructed vectors, N ¼ nðm 1Þτ is the number of reconstructed vectors, dj is ! the distance between the vector xj and its nearest neighbor, and E is Euler's constant. GMH defines the ratio: !  H X ðm; τÞ lnðNÞ E þ m ð3Þ Rðm; τÞ ¼ D ! N H S ðm; τÞ k

k

where Sk are surrogates of X (details in Section 5.10) preserving both its power spectrum and probability distribution (Schreiber and Schmitz, 1996) and the second additive term introduces a penalty for higher embedding dimensions. Therefore, Eq. (3) is a standardization of the KL estimator that uses average estimate values over the reconstructed ^ and τ^ are obtained phase space for surrogates. The optimal m as the minimum of Rðm; τÞ, computed for each EEG time series, in each interval (baseline, pre-N400/P600 onset, postonset), lying in the range m; τ A f1; …; 10g. As in our previous work (Baggio and Fonseca, 2012) we restricted ourselves to this initial portion of an infinite parameter space. We chose the highest value among optimal embedding dimensions and the lowest value of optimal time lags obtained across intervals, trials and participants. Those values were m¼ 3, τ ¼ 1. The robustness of results for the complexity measure used here (sample entropy, see Section 5.9) was tested on other values of m and τ as well.

5.8.

Time series normalization

Prior to reconstruction in phase space, each time series was normalized by subtracting its running mean, and by dividing the result by the running standard deviation (Schreiber, 1997; Richman and Moorman, 2000). This procedure is used to ensure that sample entropy values are comparable across intervals in which the original time series exhibits different linear properties, such as range of amplitude values, mean and variance.

5.9.

Sample entropy

Based on vector subsets as reconstructed by Eq. (1), for each state ! xi we define its ε-recurrence set (Richman and Moorman, 2000): n o ! ! ! ! ð4Þ Vε ð xi Þ ¼ xj : ‖ xi  xj ‖oε ; iaj where the recurrence tolerance was set to ε ¼ 0:25 (Baggio and Fonseca, 2012) for the normalized time series. The probability ! distribution of xi is given by ! #V ε ð xi Þ ð5Þ Pi ¼ n ðm 1Þτ where # is the cardinality operator. The mean recurrence pro bability of a state in embedding dimension m is therefore P m ¼ Pi . Performing the same computation for ðm þ 1Þ-embedding, we

define sample entropy as

 P mþ1 SampEnðm; τ; εÞ ¼ ln Pm

ð6Þ

Average sample entropy over trials in each time interval (baseline, pre-N400/P600 onset and post-onset) was compared between conditions (congruent/incongruent) using Wilcoxon signed-rank tests.

5.10.

Surrogate time series

Surrogates of the EEG time series X were constructed that preserved the power spectrum and the probability distribution of X (Schreiber and Schmitz, 1996). Given X ¼ fx1 ; …; xn g we calculated its sorted values Xs and its discrete Fourier transform amplitudes: Ak ¼

n X

xi e2πki=n

ð7Þ

i¼1

We obtained Xð0Þ by performing a permutation of X without replacement and then we iterated the following steps: (1) we calculated the Fourier transform of Xð0Þ and replaced its amplitudes with the original amplitudes Ak; the resulting time series is denoted as Xð1Þ ; (2) we computed a probabilitydistribution correction by applying to Xð1Þ the rank ordering of Xs; the outcome is called Xð2Þ . The first step, resulting in Xð1Þ , changes the probability distribution of the states; the second step, producing Xð2Þ , changes the signal's power spectrum. Both steps are repeated until the error Pn ð2Þ 2 i ¼ 1 ðXi Xi Þ ð8Þ E¼ P n ð2Þ 2 i ¼ 1 ðX Þ is less than α ¼ 0:05. For surrogate data, sample entropy was computed for m¼ 3 and τ ¼ 1. The procedure was applied to Xð1Þ and Xð2Þ surrogates separately.

5.11.

Representative trials

The relationship between EEG time series, their phase space trajectories, and sample entropy is shown in Figs. 3 and 4. Representative trials were selected by first calculating absolute differences in sample entropy between the pre-onset and post-onset intervals. We computed mean absolute differences in sample entropy across participants and trials and, for each ERP effect (N400/P600) and condition (congruent/ incongruent) we chose, from all participants’ data, the trial that was closest to the mean value. Using these criteria, we selected: for N400 congruent, subject 24 trial 25 (deviation from the mean, D¼0.000036998); for N400 incongruent, subject 19 trial 9 (D¼ 0.00019318); for P600 congruent, subject 3 trial 26 (D¼0.0010568) and for P600 incongruent, subject 11 trial 2 (D¼ 0.00096624).

5.12.

Cross-measure correlations

We constructed 4 cross-correlograms for N400 and P600 data in the congruent and incongruent conditions to study dependencies between 6 variables: pre-onset and post-onset ERP amplitudes (μV), power (μV2 ) in two frequency bands (2–4 Hz, 80–100 Hz; see Section 2.3 for motivation) post-onset, and

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sample entropy pre-onset and post-onset. Six vectors (variables) with 27 elements each (participants) were fed into a multiple-test algorithm computing a Pearson's product– moment correlation coefficient for all 62 pairs of measures. The resulting p-values were Bonferroni-corrected. The average power values that were entered in these correlations corresponded to noticeable bursts in TFRs but not necessarily to sample-level statistically significant effects (see Section 2.2). More specifically, to ensure sample entropy results are not spurious, one should prove that they are not driven by changes in the power spectrum, however weak. Indeed, in by-subject correlations a marginal effect at the sample level (e. g., holding for just a few participants) could well drive a correlation (e.g., if the participants that show the effect also show the strongest sample entropy changes).

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