Semantic constraint, reading control, and the granularity of form-based expectations during semantic processing: Evidence from ERPs

Journal Pre-proof Semantic constraint, reading control, and the granularity of form-based expectations during semantic processing: Evidence from ERPs Nyssa Z. Bulkes, Kiel Christianson, Darren Tanner PII:

S0028-3932(19)30335-5

DOI:

https://doi.org/10.1016/j.neuropsychologia.2019.107294

Reference:

NSY 107294

To appear in:

Neuropsychologia

Received Date: 1 March 2019 Revised Date:

2 December 2019

Accepted Date: 3 December 2019

Please cite this article as: Bulkes, N.Z., Christianson, K., Tanner, D., Semantic constraint, reading control, and the granularity of form-based expectations during semantic processing: Evidence from ERPs, Neuropsychologia (2020), doi: https://doi.org/10.1016/j.neuropsychologia.2019.107294. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

1

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS

Semantic constraint, reading control, and the granularity of form-based expectations during semantic processing: Evidence from ERPs

Nyssa Z. Bulkes 1, Kiel Christianson 2,3, & Darren Tanner 2, 3 1

University of Arizona University of Illinois at Urbana-Champaign 3 Beckman Institute for Advanced Science and Technology 2

Address for correspondence: Kiel Christianson University of Illinois Beckman Institute for Advanced Science and Technology Urbana, IL 61801 Email: [email protected]

2

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS Abstract We investigated the role that semantic constraint and participant control over stimulus presentation have on early stages of visual word recognition. Namely, we tested how the presence of a highly constraining sentential context influences the expectations that readers have during incremental sentence processing. Further, we tested whether allowing participants to selfpace the experiment affected early sensory perceptions of written stimuli. Event-related potentials (ERPs) were recorded in three experiments. Participants read sentences containing a target word from one of four conditions: 1) the target, spelled as expected; 2) the target with two internal characters transposed; 3) a nonword one vowel different from a target; or 4) an illegal consonant string. In Experiment 1, sentences were minimally constraining up to the target word (average cloze at target word: 0.01); in Experiments 2 and 3, sentences were highly constraining (average cloze at target word: 0.93). In both Experiments 1 and 2, sentences were presented using rapid-serial-visual presentation (RSVP). In Experiment 3, participants saw the same sentences used in Experiment 2 but were allowed to self-pace the presentation of each word in every trial. In Experiments 1 and 2, results showed early neural sensitivity to nonsensical consonant strings only, and only when they appeared within high constraint. In Experiment 3, results showed graded N170 effects to all target words containing unexpected visual information. P600 modulations were observed in all three experiments, indexing the difficulty of processing unexpected orthography, particularly in downstream, integrative processing. Results support a nuanced view of early visual processing, namely one arguing that visual processing is more finegrained the more control participants have over how they read.

Keywords: Visual word recognition; ERP; language comprehension; N170; prediction

3

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS Introduction Successful language comprehension requires rapid extraction of information from multiple sources of input. Models of visual word recognition assume recurrent interaction between these sources of information (e.g., Grainger & Holcomb, 2009; Kim & Lai, 2012; McClelland & Rumelhart, 1981), whereby comprehension of text requires the interactivity of both feed-forward (i.e. bottom-up) as well as feedback (i.e. top-down) architectures. While research so far supports the idea of an interactive language processing mechanism, it is an open question to what extent this architecture supports predictive mechanisms in language processing. Proposals within this thread argue that there is constant integration of cues during reading, of both lower-level, perceptual information—such as orthography—as well as higher-level information—such as lexical semantics (e.g., Grainger & Holcomb, 2009; Harm & Seidenberg, 2004; McClelland & Rumelhart, 1981). The current set of experiments investigates the influence of lower-level and higher-level linguistic cues on visual word recognition and, as a consequence, on sentence comprehension. Further, we investigate the effect that methodology has on the kind of neural data elicited during studies of reading using event-related potentials (ERPs). There is a rich body of research accounting for when different sources of linguistic information become available for processing, and this work demonstrates that this time course can depend on the richness of the surrounding context. Focusing strictly on word recognition in isolation, studies using magnetoencephalography (MEG) show that lower-level, sensory features are extracted prior to word-level, lexical features (e.g., Tarkiainen, Helenius, Hansen, Cornelissen, & Salmelin, 1999). For example, Tarkiainen and colleagues show that alphabetic strings presented in isolation are distinguishable from non-linguistic characters within 200ms after stimulus presentation, demonstrating sensitivity to linguistically meaningful visual cues

4

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS early on. Relatedly, Laszlo and Federmeier (2014) report evidence of low-level, sub-lexical effects of orthographic frequency within 130ms to 150ms post-stimulus in work using ERPs for single-item stimuli. On the other hand, their regression analysis showed that characteristics of full lexical items—such as bigram and orthographic neighbor frequency (i.e., frequency of words that differ from the target by only one character)—modulated ERPs within 180ms to 210ms, and semantic effects emerged around 300ms-340ms post-stimulus. Some studies demonstrate that language processing is modulated as early as 130ms after stimulus onset by word category information (e.g., Dikker, Rabagliati, & Pylkkänen, 2009; Friederici, 2002; Neville, Nicol, Barss, Forster, & Garrett, 1991) and lexical predictability (e.g., Dambacher, Rolfs, Göllner, Kliegl, & Jacobs, 2009). Other studies show that the properties of individual lexical items, such as the strength of expectation for a word given a context, affect the amplitude of later effects, such as the N400 (e.g., Federmeier & Kutas, 1999; Kutas & Hillyard, 1980, 1984; Lau, Phillips, & Poeppel, 2008; Wlotko & Federmeier, 2012). DeLong, Urbach, and Kutas (2005) reported diminished N400 effects on nouns that were expected given a prior marked determiner (e.g. “a/an”), which the authors proposed as an example of pre-activation (see also Martin et al., 2013; Ito, Martin, & Nieuwland, 2018, for similar investigations). Nieuwland et al. (2018) reported enhanced N400 effects to expected relative to unexpected nouns in native speakers, but failed to replicate DeLong et al.’s article-elicited N400 results (see also DeLong, Chan, & Kutas, 2019 for related work on wordform and semantic pre-activation). Higher-level processes, such as structural integration and semantic or syntactic reanalysis, have been shown to modulate the P600 amplitude, sometimes referred to as the late-positive component, classically present between 500-800ms (e.g., Hagoort, Brown, & Groothusen, 1993; Osterhout & Holcomb, 1992; 1993; Osterhout & Mobley, 1995; Osterhout & Nicol, 1999, and others; although see

5

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS Coulson, King, & Kutas, 1998; van de Meerendonk, Kolk, Vissers, & Chwilla, 2010; Vissers, Kolk, van de Meerendonk, & Chwilla, 2008, for discussions of semantically motivated P600s). Importantly, all of this work argues that the mechanisms involved in language comprehension are dynamic and interactive, rather than static and modular. Namely, facilitation at one stage of language processing can translate into facilitation at another later stage of processing, and this facilitation is cyclic (McClelland & Rumelhart, 1981; Pylkkänen & Marantz, 2003; Tarkiainen et al., 1999). This dynamicity can be further explained by describing language processing as a feedforward-feedback architecture. Within this framework, the linguistic content from earlier in a sentence, such as the degree of sentential constraint, can affect the expectations a comprehender has for information later as the sentence unfolds. These expectations can be semantic, but they can also be sensory, where expectations for an upcoming lexical item can include how that item should look at the visual feature level. The argument here is that this architecture uses the feedback sytem to impose expectations about specific linguistic properties on early sensory processing, as such making use of the available cues in the most efficient way (e.g., Dambacher et al, 2009; Dikker & Pylkkänen, 2011; Dikker, Rabagliati, Farmer, & Pylkkänen, 2010; Kim & Lai, 2012; Luke & Christianson, 2012). However, there are still several questions about how these predictive mechanisms function, the sorts of information they are sensitive to, and how linguistic and participant factors modulate them. For example, psycholinguistic research shows that the more context or cues to interpretation we have, the more we can anticipate a stimulus. Mounting evidence shows the prominence of predictive mechanisms in language comprehension at multiple levels, including wordform, semantics, discourse, morphology, and syntax (e.g., Altmann & Kamide, 1999; Altmann & Mircovic, 2009; Brothers, Swaab, & Traxler, 2015; Dikker, Rabagliati, & Pylkkänen,

6

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS 2009, Dikker, Rabagliati, Farmer, & Pylkkänen, 2010; Dikker & Pylkkänen, 2011; Farmer, Christiansen, & Monaghan, 2006; Federmeier & Kutas, 1999; Federmeier, Wlotko, De OchoaDewald, & Kutas, 2007; Federmeier, Kutas, & Schul 2010; Fine, Jaeger, Farmer, & Qian, 2013; Kutas & Hillyard, 1984; Levy, 2008; Lew-Williams & Fernald, 2007; Van Berkum, Brown, Zwitserlood, Kooijman, & Hagoort, 2005; Wicha, Moreno, & Kutas, 2004; Wlotko & Federmeier, 2012). A number of studies support the notion that global sentential constraint at the semantic level can encourage lexical prediction (Federmeier et al., 2007; Luke & Christianson, 2012; although see Kuperberg & Jaeger, 2016; Luke & Christianson, 2016; and Staub, 2015, for recent accounts of what is or is not actually predicted rather than loosely anticipated). Other linguistic properties, such as orthography and spelling, have also been shown to enjoy facilitation from a constraining context (e.g., Luke & Christianson, 2012). For example, studies using the transposed-letter (TL) effect show that readers encode letter position and identity information separately. As long as the first and last character in a prime stimulus (letter string) are in place, primes with internal character transpositions (i.e. cholocate) are equally as facilitative in priming correctly spelled targets during masked-prime lexical decision tasks as correctly-spelled primes (chocolate), compared to primes with character substitutions (i.e. choeotate; Duñabeitia, Dimitropoulou, Grainger, Hernández, & Carreiras, 2012; Grainger, 2008; Perea, Duñabeitia, & Carreiras, 2008; Perea & Lupker, 2003, 2004; Rayner, White, Johnson, & Liversedge, 2006). Luke and Christianson (2012) found that in highly constraining contexts, targets containing letter transpositions were more disruptive to processing—and just as disruptive as letter substitutions—than when they appeared in non-biasing prior contexts. Using a novel self-paced reading and masked priming paradigm (SPaM), they found that TL primes and substitution primes yielded equal RTs to target words when the target was highly predictable

7

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS based on the preceding semantic context. This suggests that the more constraining the environment, the more specific predictions become, including predictions for letter identity and position. In cases of more fully specified predictions, this leads to greater disruption downstream when unexpected letter positioning is encountered, more so than if these targets appeared in less constraining contexts. Their work also shows that reading disruptions from TLs can be a robust index of lexical prediction: reading involving TLs in highly predictive contexts is more disrupted than reading involving TLs in weakly predictive contexts. In other words, in less constraining contexts, letter substitutions are more disruptive than transposed letters, and the TLs are responded to equivalently to correctly spelled words. In more constraining contexts, letter substitutions and letter transpositions are equivalently disruptive compared to correctly spelled words. An important caveat about the masked priming method, in general, though, is that masked primes are presented for a short period of time, usually at or below the perceptual threshold (~50ms). While this methodology facilitates insights into the processes implicated in lexical access or retrieval as well as the product of those processes, it is perhaps less ideal for studying the mechanisms underlying skilled reading, namely how and when contextual information from earlier in a sentence affects lexical access and retrieval of a target. There is little work that we know of that is explicitly designed to test how unexpected letter information or anomalous phonology affects processing when a stimulus is presented at durations reflecting normal fixation lengths in normal silent reading (see Bulkes, Tanner, & Christianson, 2016; Laszlo & Federmeier, 2009; Stites, Federmeier, & Christianson, 2016; and White, Johnson, Liversedge, & Rayner, 2008, for four investigations in this thread). White and colleagues (2008) found that, contrary to masked priming findings, targets with internal character transpositions

8

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS took longer to read at speeds that align with normal fixation durations during reading compared to their control counterparts, and that targets containing substitutions took even longer to read than either control counterparts or transposed targets. While predictability was not manipulated in their design, target word frequency was, and it was found that transposed characters in higher frequency words led to less disruption, as measured by reading times, than lower frequency words. This finding is an important piece of behavioral evidence in support of an account of language processing that is interactive, as it shows how characteristics outside the orthographic domain (i.e. spelling) affect visual word recognition during reading. Despite evidence from White et al. (2008) and Luke and Christianson (2012), there are just a few studies that ask how this feedback system manifests neurally. One key study, Kim and Lai (2012), asked how fine-grained the feed-forward feedback processing mechanism is by presenting participants with sentences where a target was highly expected (90% average cloze) given the context. Sentences appeared in one of four conditions: 1) an identity condition, where the predictable target appeared as expected; 2) a phonologically supported pseudoword (i.e. “ceke” for target “cake”); 3) a phonologically unsupported pseudoword (i.e. “tont”); and 4) an illegal string of characters. They hypothesized that if feed-forward processing and anticipation were fine-grained, this should be observable in modulations of early visual components in averaged ERP waveforms—namely, the P130 and the N170 effects, both indices of pre-lexical visual wordform processing. In their results, the authors reported a P130 to phonologically supported pseudowords and an N170 to phonologically unsupported pseudowords. Their theoretical takeaway highlighted the influence of sentential constraint in formulating fine-grained sensory predictions. However, importantly, they did not manipulate constraint and, instead, all of their target words were highly predictable given the context. It is therefore unclear whether these

9

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS effects are modulated by the amount of context preceding a target. Relatedly, work using MEG has also demonstrated that early sensory processing is modulated by the degree to which stimuli match what is expected based on prior context. For example, the M100 response is suggested to index early sensory processing and is sensitive to whether predictions for wordform features are met by what is processed by the visual system (Dikker & Pylkkänen, 2011; Dikker, Rabagliati, Farmer, & Pylkkänen, 2010). Similar to Kim and Lai’s (2012) argument, this assumes interactivity among different levels of the linguistic input and that information from one level of language representation can manifest in both lexical predictions and also clear sensory expectations for how the stimulus should appear. This evidence from the literature suggests that when a context is highly constraining, cortical sensory systems demonstrate early sensitivity to minor form, character mismatches with expected targets. But, there is little work that directly studies how degree of constraint impacts this sensitivity, or exactly how sensitive these sensory systems are to unexpected information. Additionally, it is still unknown how early sensory processing of unexpected wordform information (indexed by early sensory cortical potentials; e.g., the N170 in ERP studies) relates to later neural responses reflecting lexical or structural factors (e.g., the N400 and P600). Behavioral studies suggest that context can affect predictive sensitivity to low-level orthographic information (e.g., Luke & Christiansen, 2012). However, as was noted earlier, methods such as masked priming might obscure or distort the nature of online processing implicit in producing the TL effect, specifically when (or even if) it becomes apparent that the order or identity of characters is not what is expected given the input. Ultimately, any observed effects should replicate across multiple paradigms if those effects are to be incorporated into theories of visual word recognition during reading. Strategic effects, or effects observed only within single-word

10

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS paradigms are still interesting, but only within the confines of the conditions in which they can be reliably elicited. The next step in these cases is to determine how the results feed into a broader theory of reading. Specifically, if we are able to find evidence for contextual effects on low-level processing—as indexed by modulations of early sensory ERPs to unexpected stimuli—we are interested in whether: 1) the degree of sentential constraint in a stimulus modulated this sensitivity; and 2) when this sensitivity becomes apparent in comprehension, as would be indexed by the latency of the observed effect(s). If, as studies such as Kim and Lai (2012) suggest, this architecture is fine-grained, then only in cases of high sentential constraint should we see evidence of specific predictions for letter position, identity, and the phonology of an expected word. Even minor deviations from expected input should elicit early ERPs, in the form of either an enhanced P130 or N170 amplitude. This would suggest that, in cases where the context is highly informative, that expectations for sensory information are, in fact, more fully specified compared to more neutral environments. If, however, early visual processing is more coarse-grained, we do not expect the visual cortex to be sensitive to unexpected orthography. Namely, we should not see modulations of ERPs prior to 200ms in response to stimuli that are near matches to expected targets. In such cases, although we may not see modulations of sensory effect, it is possible that we may see evidence of processing differences in later, integrative measures, namely in the time windows typically associated with lexical retrieval (i.e. 300-500ms for the N400) or structural integration (i.e. 500-800ms for the P600).

11

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS Experiment 1 Experiment 1 was intended to establish a baseline with materials that were minimally constraining. Namely, in the absence of a constraining context, we sought to observe the neural impact of targets visually similar to real English words, with the intent of comparing these results later to the same stimuli embedded in more constraining sentences. Target words in Experiment 1, therefore, were not expected to be predictable, especially with respect to their orthographic forms. Method Participants. Participants were 24 monolingual, native American-English speakers from the University of Illinois at Urbana-Champaign. Participants were right-handed (Oldfield, 1971), had normal or corrected-to-normal vision, and reported no history of neurological impairment, of developmental or reading disorder, or the use of any psychoactive medication. Data from four participants were excluded due to excessive artifact in the raw EEG. Data from 20 participants were included in the final analysis (12 females, ages 18 to 26 years, M=19.75 years). All participants provided informed consent and were compensated with cash for their time.

Materials. One hundred sixty-four sentence frames were created. Sentence contexts were created to be as minimally constraining as possible, with a five-character target word from one of four conditions: 1) the target as expected (the identity condition); 2) the target with two internal characters transposed (the TL condition); 3) a pseudoword that was phonologically close to the expected target (the pseudoword condition); and 4) an illegal consonant string (the letter string condition). Transposed characters in the TL condition were always word-internal, never wordinitial or word-final. Pseudowords were always one vowel different from the target, and the

12

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS substituted vowel was always visually similar to the vowel it replaced (e.g., “a” or “e” for “o”). The consonant string always retained visual features of the target’s characters (i.e. ascenders, descenders; see Table 1 for target word statistics). Table 1. Target word statistics Identity TL Pseudoword Consonant String (i.e. storm) (i.e. sotrm) (i.e. starm) (i.e. zfcnw) Bigram Mean 1415.82 701.64* 1122.65* 71.89* St. Dev. 942.53 625.74 893.28 149.51 Trigram Mean 295.99 57.83* 151.90* 0.00* St. Dev. 339.67 160.66 371.94 0.03 Note: Target word statistics are positional bigram and trigram frequencies (per million) obtained using the MCWord Database (Medler & Binder, 2005). Asterisks (*) indicate significant differences (p < .001) between the identity condition and all other experimental conditions, as determined by paired t-tests.

Sentences began with a preamble intended to not lead in any way to the expectation for any particular lexical item. The final set of items was chosen based on three separate norming studies (n=90) conducted prior to the main study via Amazon Mechanical Turk, in which participants were asked to provide the most likely continuation at the position of the target word in a cloze task. Based on this norming, the final list of sentences was constructed with a goal that participants in the norming provided our target no more than 10% of the time; sentences had an average constraint of 0.01. The target word was never the final word in a sentence (Table 2). Table 2. Experiment 1 example stimuli Condition Example stimulus Identity After the arrival of the storm late that night, the wind howled. TL After the arrival of the sotrm late that night, the wind howled. Pseudoword After the arrival of the starm late that night, the wind howled. Consonant String After the arrival of the zfcnw late that night, the wind howled.

Further, minimally the word prior to the target and the word after the target were kept the same across Experiments 1 and 2, where in some cases more information before or after the target was

13

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS able to be kept the same when it fit felicitously with the new context. The four versions of each stimulus were distributed across four lists using a Latin Square design, where each list contained 41 items per condition, as well as 41 well-formed filler items. Participants never saw two versions of the same sentence. Each experimental list had 205 sentences; 40% of sentences were fully well-formed, and 60% were visually anomalous in some way.

Procedure. Each participant was tested in a single experimental session; while the EEG cap was set up, the participant completed a short language background questionnaire followed by the handedness inventory. After this, individuals completed the sentence processing task, which typically took 45 minutes to an hour to finish. Trials began with a fixation cross, followed by the sentence presented one word at a time. Presentation parameters were identical to Kim and Lai (2012), where the cross and each word appeared onscreen for 250ms followed by a 300ms blank screen. A comprehension question followed each filler sentence (e.g., “Bob went to the store on Friday. Q: Did Bob go to the store on Wednesday?”), with participants asked to make a Yes/No response as quickly and accurately as possible. Average accuracy was 86%, so no participant was excluded from the dataset due to low accuracy. The “Yes” response hand was counterbalanced across participants.

Data acquisition and analysis. Continuous EEG was recorded from 61 tin electrodes mounted in an elastic cap in accordance with the extended 10-20 system (Jasper, 1958) using a BrainAmpDC bioamplifier system (Brain Products, Germany). Participants’ eye movements were monitored by placing electrodes underneath the left eye (referenced offline to FP1) and to the right of the outer canthus of the right eye. Data were recorded with a 1000 Hz sampling rate,

14

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS a 250 Hz low-pass filter, and a 10 second time constant (~ 0.016 high-pass filter); data were recorded on-line using a left mastoid reference. Impedances at all scalp sites were kept below 10 kΩ and below 15 kΩ at ocular sites. Offline processing of the data was conducted using EEGLAB (Delorme & Makeig, 2004) and ERPLAB (Lopez-Calderon & Luck, 2014) toolboxes in Matlab. Individual trials that were characterized by excessive eye movements, including blinks, saccades, drift, skin potentials, or other artifacts, were rejected. For participants with more than 20% of the trials rejected in any one of the experimental conditions (n=10), data were corrected using independent components analysis. Components characterizing eye movement artifacts were chosen by visually inspecting component time course and scalp topography. Following artifact correction, data were rescreened and any remaining aberrant trials were removed from further analysis. Data from participants with 30% or more of trials rejected from any one experimental condition after artifact correction were excluded from further analysis (n=4). Following artifact correct and rejection, an average of 11% of trials were excluded from the experimental conditions. ERPs were averaged time-locked to the onset of the critical word (underlined above) for each participant over each electrode in each condition, relative to a 200ms pre-stimulus baseline. ERPs were quantified using the mean amplitude within three time windows of interest: 160210ms (N170); 300-500ms (N400); 500-800 (P600). Visual inspection of the ERP waveforms showed no differences between the conditions for the P130; we therefore did not conduct further statistical analyses within this time window. For the N170 analysis, the time window was selected as the mean amplitude around the peak negative latency within the timeframe typically attributed to the N170, or within 120ms to 220ms. Within our data, the average peak was 185ms, with a mean amplitude window ±25ms on either side of this latency. For the N170 analysis,

15

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS participants’ data were re-referenced offline to the common average of all scalp electrodes after reconstructing the initial left mastoid reference. Mastoid references for the N170 can give rise to the effect showing up as a positivity over the scalp vertex. Hence, to analyze what is conventionally known as the N170, we chose to use the more appropriate common average reference (Joyce & Rossion, 2005). Further, as the neurological generators of this effect are thought to be in or near the fusiform gyri (Hirshorn, Li, Ward, Richardson, Fiez, & Ghuman, 2016), or in close proximity to the mastoids, using a common average reference avoids attenuating effects during referencing and is in keeping with referencing practices in other work on the visual N170 (e.g., Blau, Maurer, Tottenham, & McCandliss, 2007; Kuefner, De Heering, Jacques, Palmero-Soler, & Rossion, 2010; Maurer, Brandeis, & McCandliss, 2005). For the N400 and P600 analyses, data were referenced offline to the algebraic mean of the left and right mastoids. Repeated-measures analyses of variance (ANOVAs) were computed within each time window separately. N170 analysis focused on occipital and temporal-occipital sites PO7, O1, Oz, O2, and PO8. N400 and P600 analyses focused on midline sites Fz, Cz, Pz, and Oz (Kim & Lai, 2012). For all analyses two factors were included: letter (4 levels) and electrode. For the N170, the electrode factor had five levels and gives an indication of effect laterality; for the N400 and P600, the electrode factor had four levels and gives an indication of the anterior/posterior dimension of the effects. The Greenhouse-Geisser correction for violations of sphericity was applied to any data with more than one degree of freedom in the numerator. In these cases, we provide the corrected p-value. For pairwise comparisons following a significant main effect of letter or interaction between letter and electrode, we corrected for Type I error rate using a

16

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS Holm-Bonferroni correction (Holm, 1979) and indicate where relevant the effects that were significant only after this correction was applied.

Results N170 (160ms-210ms). A repeated-measures ANOVA within the 160ms-210ms time over occipito-temporal sites revealed no effect of letter and no interaction (Table 3; Figure 1). We also conducted an analysis using a mastoid reference as Kim and Lai (2012) did. Neither the main effect of letter nor the interaction between letter and anteriority was significant.

Table 3. Experiment 1 omnibus ANOVA in the 160ms-210ms time window. Contrast Df F MSE p Letter 3, 57 .692 10.54 .56 12, 228 .675 1.23 .59 Letter × Electrode Note: Significant results after Type I error correction are bolded. Ident = Identity, TL = Transposition, Pseudo = Pseudoword, Cons = Consonant String

[Figure 1 about here]

N400 (300ms-500ms). An ANOVA over midline sites within the 300ms-500ms time window revealed a significant main effect of letter (F(3,57) = 15.64, MSE = 165.66, p < .001), and a significant letter × anteriority interaction (F(6,114) = 15.66, MSE = 30.104, p < .001; Table 4 for ANOVA results within this time window). Visual inspection of the waveforms suggests that the ERP to the illegal consonant strings was most positive compared to any of the other conditions, and that the ERP to the TL condition was more positive-going compared to the pseudoword condition (Figure 2). Scalp maps suggest that the positivity to the consonant strings was more posteriorly distributed than that to any of the other conditions. After correcting for

17

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS multiple comparisons, within the factor letter, there were still significant differences between identity and consonant string conditions (F(1,19) = 24.08, MSE = 291.20, p < .001); between TL and pseudoword conditions (F(1,19) = 7.85, MSE = 51.75, p < .001); between TL and consonant string conditions (F(1,19) = 14.18, MSE=104.99, p<.01); and between pseudoword and consonant string conditions (F(1,19) = 68.48, MSE = 304.16, p < .001). Within the interaction, after correction, there were significant differences between identity and consonant string conditions (F(2,38) = 23.38, MSE = 49.01, p < .001); between TL and consonant string conditions (F(2,38) = 20.18, MSE = 41.39, p < .001); and between pseudoword and consonant string conditions (F(2,38) = 30.02, MSE=61.84, p<.001). Ultimately, while this time window is typically utilized for N400 analyses, the direction of the effect and scalp topographies suggests this is an early onset of the P600 (which may be a manifestation of the more general P3b component), particularly for the illegal consonant strings. We return to this claim in the discussion. The significant letter × anteriority interaction replicates the earlier result that the scalp topography of the effect for the illegal consonant strings is more posteriorly distributed than for the other three experimental conditions, suggesting there may be at least partially dissociable underlying neural generators of this effect compared to the effects to either of the word-like conditions.

18

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS Table 4. Experiment 1 omnibus ANOVA and post-hoc pair-wise comparisons in the 300ms500ms time window. Contrast df F MSE p Midline Letter 3, 57 15.64 165.66 <.001 Ident v. TL 1, 19 5.15 46.49 .070 Ident v. Pseudo 1, 19 .01 .14 .913 Ident v. Cons 1, 19 24.08 291.20 <.001 TL v. Pseudo 1, 19 7.85 51.75 <.05 TL v. Cons 1, 19 14.18 104.99 <.01 Pseudo v. Cons 1, 19 68.48 304.16 <.001 6, 114 15.66 30.10 <.001 Letter × Anteriority Ident v. TL 2, 38 .79 1.03 .850 Ident v. Pseudo 2, 38 .21 .23 .850 Ident v. Cons 2, 38 23.380 49.01 <.001 TL v. Pseudo 2, 38 2.65 2.45 .336 TL v. Cons 2, 38 20.18 41.39 <.001 Pseudo v. Cons 2, 38 30.02 61.84 <.001 Note: Significant results after Type I error correction are bolded. P-values provided above are the corrected values following Holm-Bonferroni correction. Ident = Identity, TL = Transposition, Pseudo = Pseudoword, Cons = Consonant String

P600 (500ms-800ms). Within the 500ms-800ms time window, an ANOVA over midline sites did not show a main effect of letter, but did show a significant letter × anteriority interaction (F(6,114) = 27.97, MSE = 52.54, p < .001; see Table 5 for ANOVA results). Visual inspection of the waveforms showed a posterior, sustained positivity in the consonant string condition, with a smaller positivity in the transposition condition, and an even smaller effect in the pseudoword condition (Figure 2). Within the main effect, none of the pairwise comparisons remained significant after applying the Holm-Bonferroni correction. Within the interaction, there remained significant differences between all experimental conditions except between TL and pseudoword conditions (F(2,38) = .60, MSE = .74, p = .488). This shows that the positivity to the consonant string condition was the most negative-going over posterior sites of any of the conditions.

19

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS Table 5. Experiment 1 omnibus ANOVA in the 500ms-800ms time window. Contrast df F MSE P Midline Letter 3, 57 1.56 25.29 .218 6, 114 27.97 52.54 <.001 Letter × Anteriority Ident v. TL 2, 38 17.73 34.36 <.001 Ident v. Pseudo 2, 38 20.38 19.67 <.001 Ident v. Cons 2, 38 55.96 121.44 <.001 TL v. Pseudo .60 .74 .488 2, 38 TL v. Cons 2, 38 22.42 34.33 <.001 Pseudo v. Cons 2, 38 30.06 40.18 <.001 Note: Significant results after Type I error correction are bolded. P-values provided above are the corrected values following Holm-Bonferroni correction. Ident = Identity, TL = Transposition, Pseudo = Pseudoword, Cons = Consonant String

[Figure 2 about here]

Discussion Orthographic manipulations in low-constraint contexts did not modulate early sensory ERP effect. This includes the illegal consonant string condition, where the orthography was most anomalous for a language such as English. Analyses within the 300-500ms time window revealed no significant differences between identity and pseudoword conditions, but significant differences between each of the other experimental conditions. Pseudowords enjoyed complete facilitation in processing, indexed by the absence of any positivity in the 300ms-500ms time window. This suggests that, in the absence of contextual constraint, orthotactic legality becomes a more significant predictor of the effort needed in downstream lexical access and integration. Although both the pseudoword and TL conditions contained targets nearly matching a real word of English, transposed targets were orthotactically less likely in the language compared to pseudowords (review Table 1 for target word statistics per condition). In the absence of strong top-down cues to guide processing, language-specific constraints were more predictive of neural

20

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS responses than the similarity of the targets to a possible word in the language. Further, the finding that TL targets incurred a processing penalty relative to identity controls in measures associated with meaning integration is consistent with results from the larger TL literature. When presented in direction fixation, TL targets incur a processing penalty (White et al, 2008); it is only when TL targets appear as primes that they significantly facilitate activation of a real-word target.

Experiment 2 We conducted Experiment 2 to determine whether increased contextual constraint would result in the sorts of early sensory ERP effects that were not observed in Experiment 1, but which were previously observed in similar high constraint contexts by Kim and Lai (2012). Namely, if contextual constraint establishes a basis for linguistic prediction, both at wordform and semantic levels, we expect to see very early differences in the averaged ERPs to index this.

Method Participants. Participants were 26 monolingual, native American-English speakers from the University of Illinois at Urbana-Champaign. Participants were right-handed (Oldfield, 1971), had normal or corrected-to-normal vision, and reported no history of neurological impairment, of developmental or reading disorders, or the use of any psychoactive medication. Data from two participants were excluded due to equipment malfunction; data from four additional participants were excluded due to excessive artifact in the raw EEG. Data from 20 participants were included in the final analysis (12 females, ages 18 to 30 years, M=20.95 years). All participants provided informed consent and were compensated with cash for their time.

21

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS

Materials. One hundred sixty-four sentence frames were created using the same target words as in Experiment 1, All sentences began with an informative preamble meant to constrain the reader to expect a particular lexical item (see 6).

Table 6. Experiment 2 example stimuli Condition Example stimulus Identity Thunder and lightning signaled the arrival of the storm late that night. TL Thunder and lightning signaled the arrival of the sotrm late that night. Pseudoword Thunder and lightning signaled the arrival of the starm late that night. Consonant String Thunder and lightning signaled the arrival of the zfcnw late that night.

The final set of items was chosen from an initial set of 200 sentence frames based on three separate norming studies (N=30 participants each, or 90 total) conducted prior to the main study via Amazon Mechanical Turk, in which participants were provided with the sentence beginnings, up to, but not including, the target word (e.g., Thunder and lightning signaled the arrival of the…), and were asked to provide the most likely continuation at the position of the target word in a cloze task (Taylor, 1953). Based on this norming, the final list of sentences was constructed with the condition that participants provided our target a minimum of 90% of the time; sentences in Experiment 2 had an average constraint of 0.93 (see supplementary materials for a full list of experimental sentences with cloze probabilities for each item). Minimally three words followed target words. The four versions of each stimulus (identity, TL, pseudoword, and letter string) were distributed across four lists using a Latin Square design, where each list contained 41 items per condition, as well as 41 filler items. Filler sentences were grammatical and varied in structure and in length, and were identical to those used in Experiment 1. Participants never saw two

22

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS versions of the same sentence. Each experimental list had 205 sentences; 40% of sentences were well-formed, and 60% contained a visual anomaly. Procedure. The experimental session, including set-up and the tasks in the experiment, was identical to Experiment 1. As in Experiment 1, a comprehension question followed each filler sentence, and participants were asked to make a Yes/No response as quickly and accurately as possible. Average accuracy in Experiment 2 was 92%. No participant’s data were excluded due to low accuracy. Data acquisition and analysis. Data acquisition methods were identical for both Experiments 1 and 2. Following artifact correction and rejection, an average of 2.5% of trials were excluded from the experimental conditions. ERPs were averaged time-locked to the onset of the critical word (underlined above) for each participant over each electrode in each condition, relative to a 200ms pre-stimulus baseline. ERPs were quantified using the mean amplitude within three time windows of interest: 160210ms (N170); 300ms-500ms (N400); 500ms-800 (P600). As in Experiment 1, visual inspection of the grand-average ERP waveforms did not reveal any differences between the experimental conditions in the P130; no further analyses were conducted on this effect. Referencing protocols and steps taken during statistical analyses were identical to those employed in Experiment 1.

Results N170 (160ms-210ms). Within the 160ms-210ms time window, there was a significant main effect of letter (F(3,57) = 3.70, MSE = 27.40, p < .01; Table 7 for ANOVA results within this time window). Visual inspection of the data shows that ERP modulations were larger over the back of the head during this time window (Figure 3). After correcting for multiple

23

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS comparisons, none of these pairwise differences remained significant. This suggests that while the ERP to targets from the consonant string condition was more negative-going than those to the targets in any of the other three experimental conditions, this result should be interpreted with caution; there were no significant differences between the N170 effects for any of the other conditions. We also conducted an analysis using a mastoid reference, and neither the main effect of letter nor the interaction between letter and anteriority was significant. Table 7. Experiment 2 pairwise contrasts within the factor letter between 160ms-210ms. Contrast df F MSE p Ident v. TL 1, 19 .38 2.23 1.0 Ident v. Pseudo 1, 19 .26 1.03 1.0 Ident v. Cons 1, 19 7.11 59.67 .090 TL v. Pseudo 1, 19 .04 .23 1.0 TL v. Cons 1, 19 4.76 38.85 0.168 Pseudo v. Cons 1, 19 6.28 45.03 0.105 Note: Significant results after Type I error correction are bolded. P-values provided above are the corrected values following Holm-Bonferroni correction. Ident = Identity, TL = Transposition, Pseudo = Pseudoword, Cons = Consonant String [Figure 3 about here]

N400 (300ms-500ms). Within the 300ms-500ms time window, the ANOVA over midline electrodes revealed a significant main effect of letter (F(3,57) = 3.18, MSE = 45.61, p < .05); see Table 8 for ANOVA results within this time window). Visual inspection of the data shows the ERP in the TL condition is more positive-going than the identity condition, indicating the onset of a P600-like positivity. After correcting for Type I error, pairwise comparisons within the factor letter revealed a significant difference between identity and TL conditions (F(1,19) = 11.50, MSE = 91.81, p < .01). So far, we argue that results so far suggest that the difference between the identity and experimental conditions within 300ms-500ms can be characterized as an early positivity (P600) and not an enhanced negativity (N400).

24

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS Table 8. Experiment 2 pairwise contrasts within the factor letter between 300ms-500ms. Contrast df F MSE p Ident v. TL 1, 19 11.50 91.81 <.05 Ident v. Pseudo 1, 19 2.42 22.80 .365 Ident v. Cons 1, 19 3.38 76.97 .453 TL v. Pseudo 1, 19 4.03 27.04 .428 TL v. Cons 1, 19 .13 1.44 .622 Pseudo v. Cons 1, 19 1.44 15.99 .691 Note: Significant results after Type I error correction are bolded. P-values provided above are the corrected values following Holm-Bonferroni correction. Ident = Identity, TL = Transposition, Pseudo = Pseudoword, Cons = Consonant String

P600 (500ms-800ms). Within the 500ms-800ms time window, an omnibus ANOVA over midline sites revealed a significant main effect of letter (F(3,57) = 4.43, MSE = 56.40, p < .01) and a significant letter × anteriority interaction (F(6,114) = 9.89, MSE = 22.19, p < .001; see Table 9 for ANOVA results within this time window). Visual inspection of the waveforms shows long-lasting positivities to each of the three anomaly conditions; scalp maps demonstrate the effect as posteriorly maximal in all three conditions (Figure 4). After correcting for Type I error, pairwise comparisons within the factor letter revealed significant differences between identity and TL conditions (F(1,19)=9.81, MSE=134.50, p<.05). Pairwise comparisons within the interaction showed significant differences between identity and TL (F(1,19)=16.32, MSE=16.87, p<.001); identity and pseudoword (F(1,19)=18.85, MSE=20.15, p<.001); and identity and consonant string conditions (F(1,19)=20.77, MSE=43.39, p<.001). Inspection of the waveforms shows that the ERPs to the word-like conditions were more positive-going than for the identity condition, and that ERPs to the TL and pseudowords were more positive for longer than the ERP to the illegal consonant strings. Finally, a between-subjects ANOVA between Experiments 1 and 2 within the P600 time window revealed a significant interaction between letter and experiment (F(3,102)=2.89,

25

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS MSE=39.52, p<.05). This suggests that modulating the degree of contextual constraint had a significant impact on the amplitude of the P600. Table 9. Experiment 2 omnibus ANOVA in the 500ms-800ms time window. Contrast df F MSE p Midline Letter 3, 57 4.46 56.40 <.01 Ident v. TL 1, 19 9.81 134.50 <.05 Ident v. Pseudo 1, 19 6.90 87.22 .085 Ident v. Cons 1, 19 2.16 32.68 .474 TL v. Pseudo 1, 19 .53 5.10 .538 TL v. Cons 1, 19 4.26 34.59 .212 Pseudo v. Cons 1, 19 1.30 13.12 .538 6, 114 9.89 22.19 <.001 Letter × Anteriority Ident v. TL 2, 38 16.32 16.87 <.001 Ident v. Pseudo 2, 38 18.85 20.15 <.001 Ident v. Cons 2, 38 20.73 43.38 <.001 TL v. Pseudo 2, 38 .25 .32 .627 TL v. Cons 2, 38 3.62 6.23 <.001 Pseudo v. Cons 2, 38 2.12 5.54 <.05 Note: Significant results after Type I error correction are bolded. P-values provided above are the corrected values following Holm-Bonferroni correction. Ident = Identity, TL = Transposition, Pseudo = Pseudoword, Cons = Consonant String

[Figure 4 about here]

Discussion Data from Experiment 2 revealed an increase in the N170 amplitude to targets in the consonant string condition, but no differences in amplitude for either transposed or pseudoword targets, compared to the identity condition. We emphasize, however, that this effect was only significant before correcting for multiple comparisons, and it should therefore be taken with caution. This pattern of results fails to replicate the finding that minor mismatches to expected wordforms elicit early sensory ERPs, namely the P130 and the N170, even under very similar experimental conditions and with a larger number of participants than in prior work. Experiment 1 results

26

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS show that, if anything, the visual system is sensitive only to severely orthographically anomalous stimuli, and after correcting for multiple comparisons, the evidence provided here for early onset of this sensitivity is tenuous (see Nieuwland, 2019, for extensive discussion on the difficulty of replicating early ERP effects such as the P130 and N170). The P600 effect, however, demonstrates that downstream mechanisms are impacted by any sort of deviation from expected input, where minor mismatches as well as obvious anomalies lead to more effortful integrative processing. As such, the results from Experiments 1 and 2—using different levels contextual constraint—show that the primary difference elicited by differing constraint levels arises in downstream processing only. This suggests that unusual visual information can disrupt processing regardless of the degree of constraint, but the effects of mild orthographic anomalies are stronger in high versus low constraint scenarios, as expected. Arguments from the literature suggest that high sentential constraint should facilitate predictions for upcoming visual feature information (Kim & Lai, 2012; Luke & Christianson, 2012). One possibility is that the target words were so predictable in Experiment 2 (0.93 average cloze) that the top-down cues were more heavily relied upon in processing, leaving room for errors in the visual stream. However, if this is the case, similar effects should have been seen in Kim and Lai’s study, where average constraint was 0.90. It could also be that as long as the visual stream resembles what is expected during reading and targets are highly expected, this can help early stages of word recognition. In high constraint, early sensory feature processing may act more as a sanity check to confirm what is expected, and as long as a good-enough match is found, processing is unencumbered (a speculation also raised by Luke and Christianson, 2012). It is also possible that the early sensory results we report here index orthotactic knowledge, namely where the enhanced negativity to the illegal strings of characters may suggest a sensitivity to that

27

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS which is anomalous in the language regardless of the context. In other non-language research, the N170 amplitude is discussed as an index of the resources implicated in expert recognition mechanisms (i.e. processing of faces; e.g., Ghuman et al., 2014; Parvizi et al., 2012). It is possible that, to a skilled reader of the language, both the transposed and pseudoword targets were word-like enough at first glance and the consonant string targets, comparatively, so anomalous as potential words of English, leading to the difference in early sensory processing. If the N170 is modulated by orthotactic, context-independent, language-specific knowledge, then this effect should be modulated in both constraining and non-constraining contexts. The data from Experiments 1 and 2, however, do not conform to this expectation. However, it is also possible that the degree of control a person has over how they are reading may also play a role in developing wordform expectations. Namely, the participants in Experiments 1 and 2 were presented with stimuli using RSVP, a presentation method commonly used in ERP studies that fixes the rate at which participants see each word. The effect of fixed reading time may also have influenced how a reader’s resources were directed during the study, an important distinction that impacts how translatable results are to a broader theory of reading. We were curious whether removing this timed constraint on reading each word would impact processing of targets, misspelled or not. To examine this further, we conducted a third experiment. Experiment 3 In Experiment 3, we allowed participants to self-pace themselves as they read with the goal of understanding how the amount of control a person has during a reading experiment impacts their ability to predict sensory information based on a highly constraining prior context. By allowing participants to self-pace the presentation of stimuli in an ERP experiment, we are

28

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS afforded greater insight into how participant-level factors impact predictive processing. Further, we are also afforded a view into how each word in a sentence contributes to the cognitive load required to process a sentence, where longer reading times are indicative of higher processing demand (e.g., Ditman, Holcomb, & Kuperberg, 2013, Tanner, 2019). We know that presentation rate affects ERPs, namely that slower rates have been shown to allow greater influence from higher-level linguistic influences, such as those at or above the sentence level, and faster rates allow greater influence from lexical information (e.g. Camblin, Ledoux, Boudewyn, Gordon, & Swaab, 2007; Swaab, Camblin, & Gordon, 2004) and discourage predictive processes, at least at the grammatical level (Tanner, Grey, & Van Hell, 2017). Early findings both inside (Metzner, von der Malsburg, Vasishth, & Rösler, 2017) and outside (e.g., Just, Carpenter, & Woolley, 1982) ERP research show that self-paced reading leads to a higher level of engagement with a task compared to rapid-serial-visual presentation, suggesting reading speed or duration might be the important influencer when it comes to downstream predictive strength. By employing a selfpaced methodology in Experiment 3, we test whether a comprehender’s sensitivity to linguistic anomalies can be increased due to the knowledge that participants know they have time to fully process what they read. If self-paced reading elicits not only effects associated with prediction but also in slower reading times at pre-critical and critical words, this would suggest that predictive effects are tied to reading speed; perhaps more time is required to generate predictions of orthographic form. Alternatively, if effects associated with prediction are observed while reading speeds remain equivalent to the presentation speed in RSVP (~250msec), then we can infer that prediction is related more to engagement with the task (reading control) than to reading speed. Note, however, that Tanner (2019) contrasted RSVP versus self-paced formats in the context of grammatical processing and found small quantitative, but not qualitative, processing

29

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS differences between the two tasks for late components like the N400 and P600. The basic pattern of these small differences was that the N400 deflections were slightly more pronounced in selfpaced reading compared to RSVP, and for the P600 deflections, the opposite was observed (RSVP > SPR). It is unknown whether any differences might arise for earlier components like the N170 because, to our knowledge, no one has examined these earlier effects in a self-paced ERP paradigm. We investigate this in Experiment 3.

Method Participants. Participants were 22 monolingual, native American-English speakers from the University of Illinois at Urbana-Champaign. Participants were right-handed (Oldfield, 1971), had normal or corrected-to-normal vision, and reported no history of neurological impairment, of developmental or reading disorder, or the use of any psychoactive medication. Data from two participants were excluded due to excessive artifact in the raw EEG. Data from 20 participants were included in the final analysis (15 females, ages 19 to 25 years, M=21.55 years). All participants provided informed consent and were compensated with cash for their time. Materials. Materials used in Experiment 3 were the same as those used in Experiment 2. Procedure. Set-up and order of tasks was the same as they were in Experiment 1. The sentence processing task typically took participants between 45 minutes to an hour to finish. Trials began with a fixation cross, followed by the sentence presented one word at a time. Participants were responsible for pushing a button on a gamepad to progress to each word in each trial, namely to proceed from the fixation cross to the first word in the sentence and to every word thereafter. The same 300ms ISI was interpolated between successive words as in Experiments 1 and 2. As such, reading times reported below reflect only the time between word

30

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS presentation and the participants’ button push; total reading times per word would include the additional 300ms ISI period. Just as in Experiments 1 and 2, a comprehension question appeared after every filler sentence, where participants had to make a Yes/No response as quickly and accurately as possible. Average accuracy was very high, at 97% (compared to 86% accuracy in Experiment 1 and 92% in Experiment 2), so no participant was excluded from the dataset due to inaccuracy. Response hand was counterbalanced across participants.

Data acquisition and analysis. Data acquisition methods were identical for both Experiments 1 and 2. Following artifact correct and rejection, an average of 6.19% of trials were excluded from the experimental conditions. ERPs were averaged time-locked to the onset of the critical word (underlined above) for each participant over each electrode in each condition, relative to a 200ms pre-stimulus baseline. ERPs were quantified using the mean amplitude within three time windows of interest: 160ms210ms (N170); 300ms-500ms (N400); 500ms-800 (P600). As in Experiments 1 and 2, visual inspection of the grand-average ERP waveforms did not reveal any differences between the experimental conditions in the P130; no further analyses were conducted on this effect. Referencing protocols and steps taken during statistical analyses were identical to those employed in Experiments 1 and 2.

Results Behavioral data. Average reading times on the target word show that although participants were in control of how long they read each word, overall, they were faster than the RSVP target word presentation rate used in Experiments 1 and 2 of 250ms (Table 10). At the

31

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS point of the target word in the sentence, as well as the word following it, TL targets incur slightly longer reading times, followed by pseudowords, followed by the longest times for consonant strings. Table 10. Average reading speed (ms) for target N, N-1, and N+1 Condition Word N-1 Target word N Identity 222.92 (4.01) 230.76 (3.89) Pseudoword 215.94 (3.94) 241.55 (6.34) TL 214.45 (3.58) 233.45 (5.14) Consonant String 210.67 (3.62) 267.10 (9.86) Note: Standard error reported in parentheses.

Word N+1 248.82 (4.08) 260.23 (4.67) 251.05 (4.26) 287.22 (5.61)

ERP data.

N170 (160ms-210ms). An omnibus repeated-measures ANOVA revealed a significant main effect of letter (F(3,57) = 10.94, MSE = 105.85, p < .001) but no significant letter × electrode interaction (F(12,228) = 1.96, MSE = 2.97, p = .122; see Table 11). Visual inspection of the waveforms show the largest N170 effect to the consonant strings, followed by slightly enhanced N170 effects to both the TL and pseudoword targets, all three of which were significantly greater than the effect to the identity condition (Figure 5). After correction, pairwise comparisons within the factor letter showed significant differences between the identity condition and all other experimental conditions.

Further, a between-subjects analysis was conducted to compare the effects elicited in Experiments 2 and 3 within the N170 time window. In this analysis, the main effect of Letter remained significant (F(3,108)=12.83, MSE=113.48, p<.001), and all pair-wise contrasts except between pseudowords and TL words remained significant, as in the analysis of Experiment 3 alone. However, no interactions including both letter and experiment were significant. Although different effects were observed in the two experiments, when each was analyzed independently

32

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS (namely graded N170 effects in Experiment 3, but effects only to strong orthographic violations in Experiment 2), variability is great enough that strong conclusions about differential effects cannot be made at this time.

Table 11. Experiment 3 pairwise contrasts within the factor letter between 160ms-210ms. Contrast df F MSE p Ident v. TL 1, 19 15.87 85.67 <.01 Ident v. Pseudo 1, 19 16.43 54.81 <.01 Ident v. Cons 1, 19 24.59 226.52 <.001 TL v. Pseudo 1, 19 .48 3.43 .498 TL v. Cons 1, 19 5.58 33.58 .087 Pseudo v. Cons 1, 19 5.26 58.48 .087 Note: Significant results after Type I error correction are bolded. P-values provided above are the corrected values following Holm-Bonferroni correction. Ident = Identity, TL = Transposition, Pseudo = Pseudoword, Cons = Consonant String

[Figure 5 about here]

N400 (300ms-500ms). An omnibus repeated-measures ANOVA over midline electrodes between 300ms-500ms showed a significant main effect of letter (F(3,57) = 9.06, MSE = 96.66, p < .001) and a letter × anteriority interaction (F(6, 114) = 5.94, MSE = 11.98, p < .01). Visual inspection shows what appears to be an early negativity, greatest in the consonant string condition, followed by the other two experimental conditions, but this does not have the typical morphology of an N400 effect. Downstream within 300ms-500ms, it appears the grand-averaged ERPs to each of the experimental conditions are more positive relative to the identity condition, with the ERP to TL targets appearing most positive-going (Figure 6). Within the effect of letter, differences still significant after correction are those between identity and TL (F(1,19) = 37.26, MSE = 196.57, p <.001); between TL and pseudoword (F(1,19) = 24.50, MSE = 82.69, p <.001); and between TL and consonant string conditions (F(1,19)=10.80, MSE=83.92, p<.05). Within the interaction, all significant comparisons remained significant after applying the correction (see

33

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS Table 12). As in Experiments 1 and 2, based on the direction and morphology of the effect, this positivity to the experimental conditions within this time frame appear to be a P600 effect with early onset.

Table 12. Experiment 3 omnibus ANOVA in the 300ms-500ms time window. Contrast df F MSE p Midline Letter 3, 57 9.06 96.66 <.001 Ident v. TL 1, 19 37.26 196.57 <.001 Ident v. Pseudo 1, 19 3.29 24.27 .255 Ident v. Cons 1, 19 1.68 23.61 .422 TL v. Pseudo 1, 19 24.50 82.69 <.001 TL v. Cons 1, 19 10.80 83.92 <.05 Pseudo v. Cons 1, 19 .01 .01 .981 6, 114 5.94 11.98 <.01 Letter × Anteriority Ident v. TL 2, 38 .75 .84 .435 Ident v. Pseudo 2, 38 3.19 2.24 .201 Ident v. Cons 2, 38 4.08 9.03 .160 TL v. Pseudo 2, 38 3.36 2.07 .201 2, 38 TL v. Cons 7.39 13.88 <.01 2, 38 Pseudo v. Cons 12.37 23.97 <.001 Note: Significant results after Type I error correction are bolded. P-values provided above are the corrected values following Holm-Bonferroni correction. Ident = Identity, TL = Transposition, Pseudo = Pseudoword, Cons = Consonant String

34

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS P600 (500ms-800ms). An omnibus repeated-measures ANOVA over midline sites between 500ms-800ms revealed a significant main effect of letter (F(3,57) = 21.24, MSE = 260.16, p <.001) and a significant letter × anteriority interaction (F(6,114) = 16.92, MSE = 34.04, p <.001). Within the factor letter, all significant comparisons remained significant after correcting, namely between identity and all other experimental conditions (see Table 13). Within the interaction, all comparisons remained significant after correction. Visual inspection of the waveforms shows a clear, long-lasting positivity to all three of experimental conditions, with the ERP to the consonant strings returning to baseline sooner than either of the two word-like conditions (Figure 6).

Table 13. Experiment 3 omnibus ANOVA in the 500ms-800ms time window. Contrast df F MSE p Midline Letter 3, 57 21.24 260.16 <.001 Ident v. TL 1, 19 43.53 518.00 <.001 Ident v. Pseudo 1, 19 35.77 366.85 <.001 Ident v. Cons 1, 19 19.73 295.26 <.001 TL v. Pseudo 1, 19 3.78 13.00 .067 TL v. Cons 1, 19 3.59 31.10 .073 Pseudo v. Cons 1, 19 .45 3.88 .510 6, 114 16.97 34.04 <.001 Letter × Anteriority Ident v. TL 3, 57 16.40 22.55 <.001 Ident v. Pseudo 3, 57 17.83 22.32 <.001 Ident v. Cons 3, 57 33.19 72.54 <.001 TL v. Pseudo 3, 57 1.43 .70 .253 TL v. Cons 3, 57 8.58 18.83 <.05 Pseudo v. Cons 3, 57 9.43 15.57 <.05 Note: Significant results after Type I error correction are bolded. P-values provided above are the corrected values following Holm-Bonferroni correction. Ident = Identity, TL = Transposition, Pseudo = Pseudoword, Cons = Consonant String

35

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS [Figure 6 about here] Discussion Results of Experiment 3 show enhanced N170 effects to each of the experimental conditions, but most prominently to the orthotactically illegal letter string condition. This early negativity to each of the experimental conditions suggests an increased sensitivity to aberrant visual features when participants are allowed to read at their own pace within high constraint. Compared to the absence of graded N170 effects, particularly to the TL and pseudoword conditions, in either Experiment 1 or 2, this result suggests that the amount of control a reader has during reading modulates the level of detail in the sensory expectations of upcoming visual information when the surrounding context is informative (cf. ERP and eye-tracking coregistration work Kornrumpf, Niefind, Sommer, & Dimigen, 2016). Within the later time windows, although the 300ms-500ms ANOVA revealed a number of significant differences, visual waveform inspection suggests this effect is most likely the early onset of a longer-lasting positivity, as observed by significant differences within 500ms-800ms. P600 findings in Experiment 3 are consistent with those in Experiments 1 and 2. Namely, the enhanced positivities to each of the conditions containing visual anomalies index more effortful combinatorial processing downstream. Note, however, the larger and more robust P600 effects in the TL and pseudoword conditions in this experiment compared to Experiment 1, again indicating a role for contextual constraint in downstream integrative processing.

General Discussion In Experiment 1, when targets were embedded in neutral constraint, we reported no significant differences among any of the experimental conditions in the N170 time window,

36

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS including the condition featuring illegal consonant strings. Using an RSVP paradigm in Experiment 2, we did not find strong evidence of fine-grained sensitivity to visual anomalies, as evidenced by the fact that neither of the conditions visually similar to expected targets elicited early sensory ERPs. Using self-paced reading in Experiment 3, however, when participants read the same sentences from Experiment 2 at their own pace, all three conditions containing unexpected visual material elicited enhanced N170 effects. In all three experiments, P600 effects were elicited by all of the conditions containing anomalous visual information, although these effects were weaker and graded by severity of the anomaly in Experiment 1, where lowconstraint sentences were used. Ultimately, we argue for a depiction of the feed-forward feedback mechanism that is at least partially dependent on methodology. Comparing Experiments 1 and 2, we see clear evidence that the amount of contextual constraint modulates difficulty in downstream processing incurred by reading misspelled targets. In high-constraint sentences, all anomalies elicited large P600 effects with similar amplitudes, whereas the effects were weaker in general and only strongly robust for orthographically illegal consonant strings in weakly-constraining contests. The results for the N170 analyses are less clear. In Experiment 3, the introduction of participant control over the pace of reading in the selfpaced reading paradigm appears to have had the effect of triggering earlier disruption for unexpected letter strings. Comparing Experiment 3 directly with Experiment 2, however—with both studies using high constraint—N170 differences between the experiments that are evident in the descriptive statistics of the between-groups analysis failed to reach significance. In other words, although there appear to be effects of task type on sensitivity to early visual feature anomalies, these effects appear small. Contrasting data from Experiments 1 and 2, we see that when sentential constraint is high, participants can anticipate more broadly upcoming lexical

37

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS items and how they should appear, with orthographic violations causing greater disruption downstream in high constraint. However, we ultimately failed to replicate the P130 that Kim and Lai (2012) describe, and our N170 results suggest that early sensory ERP sensitivity may be modulated by contextual constraint, but perhaps only measurably so under certain task conditions (if at all)1. For word-like targets, as long as the target string’s visual information is similar enough to what the context predicts, this seems to ameliorate penalties to early processing for pseudowords and targets containing internal letter transpositions. Results suggest that these targets may retain enough similarity to an expected target that they can pass a sort of “good-enough” threshold (e.g., Christianson, 2016; Christianson, Hollingworth, Halliwell, & Ferreira, 2001). Once this threshold is reached, readers can proceed without expending extra cognitive effort in early sensory processing. Instead, the incurred penalty manifests downstream in integrative, compositional processing, as evidenced by the P600 we report. Finally, we note that our experimental design did not include a single experiment in which low- and highconstraint setnences were tested directly. Our conclusions are inferred from patterns of results across three incrementally altered experiments, along with a direct comparison of Experiments 2

1

In an effort to exhaust our investigation of the early ERP modulations we observed, we conducted post-hoc analyses of each experiment between 175-325ms, looking for the N250, an effect said to index the mapping of prelexical form information onto whole words (e.g. Hoshino et al., 2010). In a midline analysis (Fz, Cz, Pz), there were significant main effects of Letter in Experiment 2 (F(3,57)=5.237, MSE=24.361, p < .01) and Experiment 3 (F(3,57)=6.053, MSE=31.185, p < .01) only; N250 results for Experiment 1 were not significant. Pairwise comparisons revealed the same significant pairwise effects as in the N170 analysis, namely between the Identity condition and each of the experimental manipulations in both Experiments 2 and 3. Future research is needed to further disentangle the empirical differences between the N170 and N250, specifically as relates to language comprehension and whether these two short, early ERP effects can be truly dissociated. In the present study, it cannot reliably be determined whether the negativities we report are in response to visually unexpected stimuli given prior context (N170s) or indices of enhanced efforts to map pre-lexical information onto full wordforms (N250). Taking a step back, however, we can use our findings to support the claim that the processing system is broadly sensitive to form-based anomalies only when the prior context is predictive, as Experiment 1 did not elicit significant results in the N170 or N250 time windows. We leave the nuances of dissociating the two effects to future work.

38

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS and 3. Ultimately a replication of our Experiment 3 with both low- and high-constraint items would be desirable. Our findings are also reconcilable with the behavioral results from masked-priming studies. We know that a prime with substituted characters is the least facilitative in lexical access compared to a stimulus that either appears as expected or with internal characters transposed; similarly, the illegal strings of characters in the current investigation did not facilitate processing in any way in any of the three experiments. Whereas masked priming studies provide a product of the processing involved (i.e. reaction times comparable across conditions), ERPs provide high temporal resolution for language processing as it unfolds in real time, affording insights into online mechanisms in ways that masked priming cannot. In the present studies, ERP indices of facilitation would be a reduction in the processing ‘penalty’ that a misspelling causes, in comparison to the illegal letter string condition; i.e., a reduction in N170 or P600 amplitude for the TL or pseudoword conditions. It is possible that the facilitation in later processing reported here in response to transposed targets was the same—or a similar—process that masked priming studies report in response to word-like primes with transposed characters. In this case, TL targets showed reduced (Experiment 3) or absent (Experiment 2) early N170 effects, but robust late P600 effects. It is possible that the facilitation enjoyed by transposed primes in masked priming studies is actually akin to the longer-lasting P600 incurred by targets with transposed characters in all three experiments, namely one where the processor acknowledges the visual similarity but cannot integrate it as easily as an expected, correctly-spelled word. A topic for future investigation is the role of word length in early visual form-based linguistic processing. The current study and the study we attempted to replicate (Kim & Lai, 2012) utilized short target words (4-5 characters) to mitigate eye movement artifacts in the EEG

39

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS signal as much as possible. This length restriction in our materials mirrors procedures in a variety of other paradigms in which differences between transposed letters and substituted letters have been observed across word lengths from 4-9 characters, including lexical decision and naming (e.g., Andrews, 1996), masked-priming paradigms (e.g., Perea & Lupker, 2003), and sentencereading paradigms (e.g., Johnson, Perea, & Rayner, 2007). This large body of results can be compared to those of Humphreys, Evett, and Quinlan (1990), who failed to find differences between letter transpositions and letter substitutions in tachistoscopic single-word experiments with short items. Nevertheless, it remains an open question as to whether the relative ease or difficulty of resolving form-based anomalies in processing is helped or hindered by targets that are shorter in length. If a single saccade is needed to process the target containing the anomaly, it is less clear whether this results in greater focus on misspellings, should there be any, or perhaps even less cognitive attention allotted to shorter, presumably easier-to-process words. While our set of experiments are evidence of the visual system’s broad sensitivity to visual anomalies, we cannot claim that this is the case for target words of any length. In addition to word length, future work is needed to further understand the role of reading speed on predictive processing later in a stimulus. When our participants self-paced through Experiment 3, average reading times for the target and the word prior to the target were faster than the 250ms interval employed to display a target word in RSVP (Experiments 1 and 2). This leads us to question the role of reading speed on the pre-critical region, namely as it predicts reading times on regions later in a sentence, those containing a target word or not. In our study, the words in the pre-critical region were identical across conditions, explicitly controlled in order to isolate the target words as the reason behind any differences. In general, reading times across pre-critical regions of the sentences in Experiment 3 were shorter than those in Experiments 1

40

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS and 2. If present, any reading time differences observed pre-critically appeared to be due to individual differences in reading rate and not at the design of the study. It therefore does not appear that orthographic disruptions of more predictable material in Experiment 3 can be attributed to strategically slower reading early on in order to form more specific predictions later on. This interpretation of the present data is consistent with Luke and Christianson’s (2016) general finding that although reading speeds increase over the course of a sentence, and lexical predictions improve (nominally) as sentences unfold, these two observations do not seem to be causally related to each other. We look forward to future work designed to explicitly test these aspects of reading speed/duration as they relate to predictability, however. As to what cognitive process is indexed by the positive component in our data, the proposal by Vissers and colleagues (2006) is most in line with our views. Specifically, the authors suggest that the P600 effect highlights a conflict in what is broadly a monitoring process. Where there are two interpretations—one expected by the processor and one experienced in the input—and there is conflict between the two, this incurs a P600. Our data are consistent with this monitoring account, data which reflect to both context and presentation rate manipulations. In the case of high constraint, where a specific lexical item is anticipated but, instead, an item containing erroneous visual features is encountered, the brain considers this a mismatch, leading to the late positivity. For the illegal consonant strings, we see long, sustained positivities in highly constraining environments such as those in Experiments 2 and 3. In lower constraint, the positivity to the consonant strings appears to have a slightly later onset compared to the other experimental conditions, and this may be an effect of orthotactics. Compared to the other stimuli, the consonant strings bore no similarity to anything remotely resembling a legal word in English; from a monitoring perspective, this would support the positivity to this condition as one of

41

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS categorical mismatch, where without any helpful orthographic information, this type of target registered as completely anomalous and not at all in line with what was expected by the processor. Compare this to the effects elicited by the word-like conditions. In high constraint, we see also see long, sustained positivities to the TL and pseudoword conditions, both of which likely reflect the near-match of these targets to what the contexts predicted. In low constraint, some kind of lexical access appears to be attempted, as indexed by large N400 peaks for all conditions with word-like properties (that is, N400 components, as opposed to N400 effects), in contrast to the consonant string condition, where the N400 peak is nearly eliminated by the onset of the late positive component. While there is little context to support the build-up of an expected sentential outcome, this potentially explains the slightly different time-courses and waveform morphologies observed between high and low constraint. In high constraint, the word-like targets register as conflicts, when the brain checks for the possibility of an error, because the context was so helpful in providing the information needed to facilitate anticipation for a given item. The conflict during this monitoring process, thus, reflects a state of indecision in the brain, leading to the enhanced positivities particularly prevalent in Experiments 2 and 3. The anomalous nature of nonwords is so evident and obviously illegal, they trigger a lesser—albeit still present—conflict, and in all three conditions, the conflict leads to reprocessing. The absence of N400 effects across all three experiments and all experimental conditions seems to suggest little disruption to lexical and semantic access across the board, but for different reasons. Note, however, that for sustained effects like the N400 and P600, there is highly likely a period of co-temporal activity in the cortical regions responsible for these effects, and given the similar scalp topographies of the N400 and P600 effects, the absence of N400 effects in the presence of large P600 effects is difficult to interpret. It could be the case that N400 activity was

42

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS present but was washed out by spatio-temporally intersecting positive-going activity associated with the onset of the P600 (see Tanner, 2019, for further discussion of this sort of issue in ERP interpretation). However, even with this caveat in mind, the lack of N400 effects in our data is still noteworthy, considering the robust N400 effect for unsupported pseudowords reported by Kim and Lai (2012); that condition is akin to the unexpected/unsupported pseudowords in our Experiment 1. Ultimately, our data provide some evidence for the notion of a feed-forward, feedback architecture, but in a weaker version than proposed in other ERP studies (e.g., Kim & Lai, 2012). Namely, individuals in our studies demonstrated keen, late sensitivity to illegal combinations of characters (i.e. the consonant strings) when embedded in sentences, but only weak early effects, and primarily only when participants have control over the reading task.

Acknowledgements This research was conducted with the following support: Graduate Research Fellowship from the Beckman Institute for Advanced Science and Technology, University of Illinois (Bulkes), NSF (BCS-1431324 and BCS-1349110; Tanner). The authors thank the Beckman Institute for facilities support and the audiences at CUNY and Psychonomics conferences, where portions of this work were previously presented. We would also like to thank Mante Nieuwland and two anonymous reviewers for helpful comments on earlier versions of this manuscript. All remaining errors are our own.

43

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS References Altmann, G. T., & Kamide, Y. (1999). Incremental interpretation at verbs: Restricting the domain of subsequent reference. Cognition, 73(3), 247-264. https://doi.org/10.1016/S00100277(99)00059-1 Altmann, G., & Mirković, J. (2009). Incrementality and prediction in human sentence processing. Cognitive Science, 33(4), 583-609. https://doi.org/10.1111/j.15516709.2009.01022.x Andrews, S. (1996). Lexical retrieval and selection processes: Effects of transposed-letter confusability. Journal of Memory and Language, 35, 775–800. https://doi.org/10.1006/jmla.1996.0040 Blau, V. C., Maurer, U., Tottenham, N., & McCandliss, B. D. (2007). The face-specific N170 component is modulated by emotional facial expression. Behavioral and Brain Functions, 3(1), 7. https://doi.org/10.1186/1744-9081-3-7

44

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS

Brothers, T., Swaab, T. Y., & Traxler, M. J. (2015). Effects of prediction and contextual support on lexical processing: Prediction takes precedence. Cognition, 136, 135-149. https://doi.org/10.1016/j.cognition.2014.10.017 Bulkes, N., Christianson, K., & Tanner, D. (October, 2016) Reading between the lines: Effects of frequency and formulaicity in comprehension. Poster presented at the Tenth International Conference on the Mental Lexicon. Ottawa, Canada. Camblin, C. C., Ledoux, K., Boudewyn, M., Gordon, P. C., & Swaab, T. Y. (2007). Processing new and repeated names: Effects of coreference on repetition priming with speech and fast RSVP. Brain Research, 1146, 172-184. https://doi.org/10.1016/j.brainres.2006.07.033 Christianson, K. (2016). When language comprehension goes wrong for the right reasons: Good Enough, underspecified, or shallow language processing. Quarterly Journal of Experimental Psychology, 69, 817-828. https://doi.org/10.1080/17470218.2015.1134603 Christianson, K., Hollingworth, A., Halliwell, J. F., & Ferreira, F. (2001). Thematic roles assigned along the garden path linger. Cognitive Psychology, 42(4), 368-407. https://doi.org/10.1006/cogp.2001.0752 D'Esposito, M., Zarahn, E., Aguirre, G. K., Shin, R. K., Auerbach, P., Thompson, C., ... & Detre, J. A. (1996). Effects of cognitive processing time on fMRI activity. NeuroImage, 3(3), S207. https://doi.org/10.1016/S1053-8119(96)80209-6 Dambacher, M., Rolfs, M., Göllner, K., Kliegl, R., & Jacobs, A. M. (2009). Event-related potentials reveal rapid verification of predicted visual input. PLoS One, 4(3), e5047. https://doi.org/10.1371/journal.pone.0005047 DeLong, K. A., Chan, W. H., & Kutas, M. (2019). Similar time courses for word form and meaning preactivation during sentence comprehension. Psychophysiology, 56(4), e13312. DeLong, K. A., Urbach, T. P., & Kutas, M. (2005). Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8 (8), 1117–1121. doi:10.1038/nn1504 Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9-21. https://doi.org/10.1016/j.jneumeth.2003.10.009 Dikker, S., Rabagliati, H., & Pylkkänen, L. (2009). Sensitivity to syntax in visual cortex. Cognition, 110(3), 293-321. https://doi.org/10.1016/j.cognition.2008.09.008 Dikker, S., Rabagliati, H., Farmer, T. A., & Pylkkänen, L. (2010). Early occipital sensitivity to syntactic category is based on form typicality. Psychological Science, 21(5), 629-634. https://doi.org/10.1177/0956797610367751

45

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS Dikker, S., & Pylkkanen, L. (2011). Before the N400: Effects of lexical–semantic violations in visual cortex. Brain and Language, 118(1), 23-28. https://doi.org/10.1016/j.bandl.2011.02.006 Ditman, T., Holcomb, P. J., & Kuperberg, G. R. (2007). An investigation of concurrent ERP and self-paced reading methodologies. Psychophysiology, 44(6), 927–935. https://doi.org/10.1111/j.1469-8986.2007.00593.x Duñabeitia, J. A., Dimitropoulou, M., Grainger, J., Hernández, J. A., & Carreiras, M. (2012). Differential sensitivity of letters, numbers, and symbols to character transpositions. Journal of Cognitive Neuroscience, 24(7), 1610-1624. https://doi.org/10.1162/jocn_a_00180 Farmer, T. A., Christiansen, M. H., & Monaghan, P. (2006). Phonological typicality influences on-line sentence comprehension. Proceedings of the National Academy of Sciences, 103(32), 12203-12208. https://doi.org/10.1073/pnas.0602173103 Federmeier, K. D., & Kutas, M. (1999). A rose by any other name: Long-term memory structure and sentence processing. Journal of Memory and Language, 41(4), 469-495. https://doi.org/10.1006/jmla.1999.2660 Federmeier, K. D., Wlotko, E. W., De Ochoa-Dewald, E., & Kutas, M. (2007). Multiple effects of sentential constraint on word processing. Brain Research, 1146, 75-84. https://doi.org/10.1016/j.brainres.2006.06.101 Federmeier, K. D., Kutas, M., & Schul, R. (2010). Age-related and individual differences in the use of prediction during language comprehension. Brain and Language, 115(3), 149-161. https://doi.org/10.1016/j.bandl.2010.07.006 Fine, A. B., Jaeger, T.F., Farmer, T.A., & Qian, T. (2013). Rapid expectation adaptation during syntactic comprehension. PLoS ONE 8(10): e77661. https://doi.org/10.1371/journal.pone.0077661 Friederici, A. D. (2002). Towards a neural basis of auditory sentence processing. Trends in Cognitive Sciences, 6(2), 78-84. https://doi.org/10.1016/S1364-6613(00)01839-8 Ghuman, A. S., Brunet, N. M., Li, Y., Konecky, R. O., Pyles, J. A., Walls, S. A., ... & Richardson, R. M. (2014). Dynamic encoding of face information in the human fusiform gyrus. Nature Communications, 5, 5672. https://doi.org/10.1038/ncomms6672 Grainger, J. (2008). Cracking the orthographic code: An introduction. Language and Cognitive Processes, 23(1), 1-35. https://doi.org/10.1080/01690960701578013 Grainger, J., & Holcomb, P. J. (2009). Watching the word go by: On the time‐course of component processes in visual word recognition. Language and Linguistics Compass, 3(1), 128-156. https://doi.org/10.1111/j.1749-818X.2008.00121.x

46

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS 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. https://doi.org/10.1080/01690969308407585 Hagoort, P., Hald, L., Bastiaansen, M., & Petersson, K. M. (2004). Integration of word meaning and world knowledge in language comprehension. Science, 304(5669), 438-441. https://doi.org/10.1126/science.1095455 Harm, M. W., & Seidenberg, M. S. (2004). Computing the meanings of words in reading: cooperative division of labor between visual and phonological processes. Psychological Review, 111(3), 662. https://doi.org/10.1037/0033-295X.111.3.662 Hirshorn, E. A., Li, Y., Ward, M. J., Richardson, R. M., Fiez, J. A., & Ghuman, A. S. (2016). Decoding and disrupting left midfusiform gyrus activity during word reading. Proceedings of the National Academy of Sciences, 113(29), 8162-8167. https://doi.org/10.1073/pnas.1604126113 Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 65-70. Retrieved from http://www.jstor.org/stable/4615733 Hoshino, N., K. J. Midgley, P. J. Holcomb, & J. Grainger. (2010). An ERP investigation of masked cross-script translation priming. Brain Research, 1344, 159-172. Humphreys, G. W., Evett, L. J., & Quinlan, P. T. (1990). Orthographic processing in visual word recognition. Cognitive Psychology, 22, 517-560. https://doi.org/10.1016/00100285(90)90012-S Ito, A., Martin, A. E., & Nieuwland, M. S. (2017). How robust are prediction effects in language comprehension? Failure to replicate article-elicited N400 effects. Language, Cognition and Neuroscience, 32(8), 954-965. Jasper, H. H. (1958). The ten twenty electrode system of the international federation. Electroencephalography and Clinical Neurophysiology, 10, 371-375. Johnson, R. L., Perea, M., & Rayner, K. (2007). Transposed-letter effects in reading: Evidence from eye movements and parafoveal preview. Journal of Experimental Psychology: Human Perception and Performance, 33(1), 209.

47

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS

Joyce, C., & Rossion, B. (2005). The face-sensitive N170 and VPP components manifest the same brain processes: the effect of reference electrode site. Clinical Neurophysiology, 116(11), 2613-2631. Just, M. A., Carpenter, P. A., & Woolley, J. D. (1982). Paradigms and processes in reading comprehension. Journal of Experimental Psychology: General, 111(2), https://doi.org/228. 10.1037/0096-3445.111.2.228 Kim, A., & Lai, V. (2012). Rapid interactions between lexical semantic and word form analysis during word recognition in context: Evidence from ERPs. Journal of Cognitive Neuroscience, 24(5), 1104-1112. https://doi.org/10.1162/jocn_a_00148 Kornrumpf, B., Niefind, F., Sommer, W., & Dimigen, O. (2016). Neural correlates of word recognition: A systematic comparison of natural reading and rapid serial visual presentation. Journal of Cognitive Neuroscience, 28(9), 1374-1391. Kuefner, D., De Heering, A., Jacques, C., Palmero-Soler, E., & Rossion, B. (2010). Early visually evoked electrophysiological responses over the human brain (P1, N170) show stable patterns of face-sensitivity from 4 years to adulthood. Frontiers in Human Neuroscience, 3, 67. https://doi.org/10.3389/neuro.09.067.2009 Kuperberg, G. R., & Jaeger, T. F. (2016). What do we mean by prediction in language comprehension? Language, Cognition and Neuroscience, 31(1), 32-59. https://doi.org/10.1080/23273798.2015.1102299 Kutas, M., & Hillyard, S. A. (1980). Event-related brain potentials to semantically inappropriate and surprisingly large words. Biological Psychology, 11(2), 99-116. https://doi.org/10.1016/0301-0511(80)90046-0 Kutas, M., & Hillyard, S. A. (1984). Brain potentials during reading reflect word expectancy and semantic association. Nature, 307(5947), 161-163. https://doi.org/10.1038/307161a0 Laszlo, S., & Federmeier, K.D. A beautiful day in the neighborhood: An event-related potential study of lexical relationships and prediction in context. Journal of Memory and Language, 61(3), 326-338. https://doi.org/10.1016/j.jml.2009.06.004 Laszlo, S., & Federmeier, K. D. (2014). Never seem to find the time: evaluating the physiological time course of visual word recognition with regression analysis of single-item event-related potentials. Language, Cognition and Neuroscience, 29(5), 642-661. https://doi.org/10.1080/01690965.2013.866259 Lau, E. F., Phillips, C., & Poeppel, D. (2008). A cortical network for semantics:(de) constructing the N400. Nature Reviews. Neuroscience, 9(12), 920. https://doi.org/10.1038/nrn2532

48

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106(3), 1126-1177. https://doi.org/10.1016/j.cognition.2007.05.006 Lew-Williams, C., & Fernald, A. (2007). Young children learning Spanish make rapid use of grammatical gender in spoken word recognition. Psychological Science, 18(3), 193-198. https://doi.org/10.1111/j.1467-9280.2007.01871.x Lopez-Calderon, J., & Luck, S. J. (2014). ERPLAB: an open-source toolbox for the analysis of event-related potentials. Frontiers in Human Neuroscience, 8. https://doi.org/10.3389/fnhum.2014.00213 Luke, S. G., & Christianson, K. (2012). Semantic predictability eliminates the transposed-letter effect. Memory & Cognition, 40(4), 628-641. https://doi.org/10.3758/s13421-011-0170-4 Luke, S. G., & Christianson, K. (2016). Limits on lexical prediction during reading. Cognitive Psychology, 88, 22-60. https://doi.org/10.1016/j.cogpsych.2016.06.002 Martin, C. D., Thierry, G., Kuipers, J.-R., Boutonnet, B., Foucart, A., & Costa, A. (2013). Bilinguals reading in their second language do not predict upcoming words as native readers do. Journal of Memory and Language, 69(4), 574–588. doi:10.1016/j.jml.2013.08.00 Maurer, U., Brandeis, D., & McCandliss, B. D. (2005). Fast, visual specialization for reading in English revealed by the topography of the N170 ERP response. Behavioral and Brain Functions, 1(1), 13. https://doi.org/10.1186/1744-9081-1-13 McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychological Review, 88(5), 375. https://doi.org/10.1037/0033-295X.88.5.375 Metzner, P., von der Malsburg, T., Vasishth, S., & Rösler, F. (2017). The importance of reading naturally: Evidence from combined recordings of eye movements and electric brain potentials. Cognitive Science, 41, 1232-1263. DOI: 10.1111/cogs.12384 Neville, H., Nicol, J. L., Barss, A., Forster, K. I., & Garrett, M. F. (1991). Syntactically based sentence processing classes: Evidence from event-related brain potentials. Journal of Cognitive Neuroscience, 3(2), 151-165. https://doi.org/10.1162/jocn.1991.3.2.151 Nieuwland, M. S., Politzer-Ahles, S. … Huettig, F. (2018). Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. eLife, https://doi.org/10.7554/eLife.33468.001.

Nieuwland, M. S. (2019). Do ‘early’brain responses reveal word form prediction during language comprehension? A critical review. Neuroscience & Biobehavioral Reviews, 96, 367-400.

49

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS Oldfield, R. C. (1971). The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia, 9(1), 97-113. https://doi.org/10.1016/0028-3932(71)90067-4 Osterhout, L., & Holcomb, P. J. (1992). Event-related brain potentials elicited by syntactic anomaly. Journal of Memory and Language, 31(6), 785-806. https://doi.org/10.1016/0749596X(92)90039-Z Osterhout, L., & Holcomb, P. J. (1993). Event-related potentials and syntactic anomaly: Evidence of anomaly detection during the perception of continuous speech. Language and Cognitive Processes, 8(4), 413-437. https://doi.org/10.1080/01690969308407584 Osterhout, L., & Mobley, L. A. (1995). Event-related brain potentials elicited by failure to agree. Journal of Memory and Language, 34(6), 739-773. https://doi.org/10.1006/jmla.1995.1033 Osterhout, L., & Nicol, J. (1999). On the distinctiveness, independence, and time course of the brain responses to syntactic and semantic anomalies. Language and Cognitive Processes, 14(3), 283-317. https://doi.org/10.1080/016909699386310 Parvizi, J., Jacques, C., Foster, B. L., Withoft, N., Rangarajan, V., Weiner, K. S., & GrillSpector, K. (2012). Electrical stimulation of human fusiform face-selective regions distorts face perception. Journal of Neuroscience, 32(43), 14915-14920. https://doi.org/10.1523/JNEUROSCI.2609-12.2012 Perea, M., Duñabeitia, J. A., & Carreiras, M. (2008). Masked associative/semantic priming effects across languages with highly proficient bilinguals. Journal of Memory and Language, 58(4), 916-930. https://doi.org/10.1016/j.jml.2008.01.003 Perea, M., & Lupker, S. J. (2003). Does jugde activate COURT? Transposed-letter similarity effects in masked associative priming. Memory & Cognition, 31(6), 829-841. https://doi.org/10.3758/BF03196438 Perea, M., & Lupker, S. J. (2004). Can CANISO activate CASINO? Transposed-letter similarity effects with nonadjacent letter positions. Journal of Memory and Language, 51(2), 231-246. https://doi.org/10.1016/j.jml.2004.05.005 Pylkkänen, L., & Marantz, A. (2003). Tracking the time course of word recognition with MEG. Trends in Cognitive Sciences, 7(5), 187-189. https://doi.org/10.1016/S1364-6613(03)00092-5 Rayner, K., White, S. J., Johnson, R. L., & Liversedge, S. P. (2006). Raeding wrods with jubmled lettres there is a cost. Psychological Science, 17(3), 192-193. https://doi.org/10.1111/j.1467-9280.2006.01684.x Staub, A. (2015). The effect of lexical predictability on eye movements in reading: Critical review and theoretical interpretation. Language and Linguistics Compass, 9(8), 311-327. https://doi.org/10.1111/lnc3.12151

50

Running head: CONSTRAINT MODULATES FORM EXPECTATIONS Stites, M. C., Federmeier, K. D., & Christianson, K. (2016). Do morphemes matter when reading compound words with transposed letters? Evidence from eye-tracking. Language, Cognition, and Neuroscience, 31, 1299-1319. https://doi.org/10.1080/23273798.2016.1212082 Swaab, T. Y., Camblin, C. C., & Gordon, P. C. (2004). Electrophysiological evidence for reversed lexical repetition effects in language processing. Journal of Cognitive Neuroscience, 16(5), 715-726. https://doi.org/10.1162/089892904970744 Tanner, D. (2019). Robust neurocognitive individual differences in grammatical agreement processing: A latent variable approach. Cortex, 111, 210-237. Tanner, D., Grey, S., & van Hell, J. G. (2017). Dissociating retrieval interference and reanalysis in the P600 during sentence comprehension. Psychophysiology, 54(2), 248-259. Tarkiainen, A., Helenius, P., Hansen, P. C., Cornelissen, P. L., & Salmelin, R. (1999). Dynamics of letter string perception in the human occipitotemporal cortex. Brain, 122 (11), 2119-2132. https://doi.org/10.1093/brain/122.11.2119 Taylor, W. L. (1953). "Cloze procedure": A new tool for measuring readability. Journalism Bulletin, 30, 415-433. https://doi.org/10.1177/107769905303000401 Van Berkum, J. J., Brown, C. M., Zwitserlood, P., Kooijman, V., & Hagoort, P. (2005). Anticipating upcoming words in discourse: evidence from ERPs and reading times. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(3), 443. https://doi.org/10.1037/0278-7393.31.3.443 Vissers, C. T. W., Chwilla, D. J., & Kolk, H. H. (2006). Monitoring in language perception: The effect of misspellings of words in highly constrained sentences. Brain Research, 1106(1), 150-163. https://doi.org/10.1016/j.brainres.2006.05.012 White, S. J., Johnson, R. L., Liversedge, S. P., & Rayner, K. (2008). Eye movements when reading transposed text: the importance of word-beginning letters. Journal of Experimental Psychology: Human Perception and Performance, 34(5), 1261. https://doi.org/10.1037/00961523.34.5.1261 Wicha, N. Y., Moreno, E. M., & Kutas, M. (2004). Anticipating words and their gender: An event-related brain potential study of semantic integration, gender expectancy, and gender agreement in Spanish sentence reading. Journal of Cognitive Neuroscience, 16(7), 12721288. https://doi.org/10.1162/0898929041920487 Wlotko, E. W., & Federmeier, K. D. (2012). So that's what you meant! Event-related potentials reveal multiple aspects of context use during construction of message-level meaning. NeuroImage, 62(1), 356-366. https://doi.org/10.1016/j.neuroimage.2012.04.054

HIGHLIGHTS for Bulkes, Tanner, & Christianson (resubmission) • Contextual constraint modulates P600 effects of unpredicted wordforms. • Reports of P130/N170 effects of unpredicted wordforms were not broadly replicated. • Reader engagement with task may trigger N170 effects due to unpredicted wordforms

FIGURE CAPTIONS

Figure 1. Panel A depicts grand mean waveforms from 4 representative posterior electrode locations from Experiment 1 using a common-average reference. Target word onset is indicated by the vertical axis bar. On the x-axis, each tick mark represents 200 ms of time; negative voltage is plotted up. A 15Hz display filter is used for plotting purposes only in these and all subsequent waveforms. Panel B depicts topographical distribution of experimental effects from deviant minus control difference waves (DWs) between 160 ms-210 ms. Figure 2. Panel A depicts grand mean waveforms from 13 representative frontal, central, and parietal electrodes from Experiment 1 using an averaged mastoid reference. Panel B depicts the topographical distribution of experimental effects from deviant minus control DWs during the 300 ms-500 ms and 500 ms-800 ms time windows. Figure 3. Panel A depicts grand mean waveforms from 4 representative posterior electrode locations from Experiment 2 (common average reference). Panel B depicts topographical distribution of experimental effects between 160 ms-210 ms. Figure 4. Panel A depicts grand mean waveforms from 13 representative electrodes from Experiment 2 (averaged mastoid reference). Panel B depicts the topographical distribution of the experimental effects during the 300 ms500 ms and 500 ms-800 ms time windows. Figure 5. Panel A depicts grand mean waveforms from 4 representative posterior electrode locations from Experiment 3 (common average reference). Panel B depicts topographical distribution of the experimental between 160 ms210 ms. Figure 6. Panel A depicts grand mean waveforms from 13 representative electrodes from Experiment 3 (averaged mastoid reference). Panel B depicts the topographical distribution of experimental effects during the 300 ms-500 ms and 500 ms-800 ms time windows.