Journal of Experimental Psychology: Learning, Memory, and Cognition 2016, Vol. 42, No. 10, 1632–1642

© 2016 American Psychological Association 0278-7393/16/$12.00 http://dx.doi.org/10.1037/xlm0000259

Are Nonadjacent Collocations Processed Faster? Laura Vilkaite˙

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University of Nottingham Numerous studies have shown processing advantages for collocations, but they only investigated processing of adjacent collocations (e.g., provide information). However, in naturally occurring language, nonadjacent collocations (provide some of the information) are equally, if not more frequent. This raises the question whether the same kind of processing advantage holds for nonadjacent collocations as for adjacent ones. This paper reports on an eye-tracking experiment in which participants read sentences containing either adjacent or nonadjacent collocations or matched control phrases. The results replicated the finding that collocations are processed faster than control phrases, and extended this finding to nonadjacent collocations. However, the results also suggest that the facilitative effect might be larger for adjacent collocations than for nonadjacent ones. Keywords: multiword sequences, collocations, nonadjacent collocations, eye-tracking, mental lexicon

phrases that carry a meaning which is not a sum of the meanings of their parts (e.g., Rommers, Dijkstra, & Bastiaansen, 2013; Siyanova-Chanturia, Conklin, & Schmitt, 2011; Tabossi, Fanari, & Wolf, 2009; Vespignani, Canal, Molinaro, Fonda, & Cacciari, 2010). These studies suggest that idioms are processed faster than matched novel phrases. The same effect of faster processing holds for binomials: phrases, consisting of two content words of the same class with a conjunction in between them, for example, bride and groom (Siyanova-Chanturia, Conklin, & van Heuven, 2011). The Siyanova-Chanturia et al. (2011) study showed that the original form of a binomial is processed faster, but when binomials are reversed (even if individual words remain exactly the same), the processing advantage disappears. Phrasal verbs also showed a facilitative phrase frequency effect in a self-paced reading study (Kim & Kim, 2012). Lexical bundles (fixed and very frequent sequences of three or more words, even if not necessarily semantically and structurally complete) showed processing advantages in a self-paced reading study, which was taken to suggest that regular and frequent multiword sequences leave a trace in the brain (Tremblay, Derwing, Libben, & Westbury, 2011). Thus, all these findings seem to point in the same direction: multiword sequences are processed faster than matched control phrases due to their frequency, familiarity and predictability (Siyanova-Chanturia, 2015). There are different theoretical suggestions to account for this faster processing, for example, faster mapping of the elements (Wray, 2012) or lexical priming theory (Hoey, 2005, 2012). One class of multiword sequences that has received particular attention in both processing studies and language pedagogy is collocations. Collocations are defined as “associations between lexical words, so that the words co-occur more frequently than expected by chance” (Biber, Johansson, Leech, Conrad, & Finegan, 1999, p. 998). As this definition implies, collocations are usually extracted from language corpora based on statistical measures of probability of co-occurrence and also a selected frequency cutoff, which makes them both frequent and predictable. Collocations are also very heterogeneous, including both grammatical collocations, such as ask for or look at, and lexical collocations such as dark night or spend time.

Multiword sequences have received considerable attention in both psycholinguistics and applied linguistics because they are extremely frequent and appear to have a special status in the mental lexicon (Siyanova-Chanturia & Martinez, 2015). Some researchers went as far as claiming that these multiword sequences are stored as wholes in the mental lexicon and accessed as such when needed (see Siyanova-Chanturia, 2015, for a detailed discussion). Siyanova-Chanturia (2015) pointed out, though, that the studies these claims are based upon were not designed to evaluate the idea of holistic storage, and therefore do not give any evidence either in favor or against this idea. The studies only suggest that there is an effect of phrasal frequency on processing, and this is generally in line with usage based models of language acquisition (Barlow & Kemmer, 2000; Tomasello, 2000, 2005) Various studies have estimated very large numbers of multiword sequences in language. For example, Pawley and Syder (1983) hypothesized that adult native speakers of English know hundreds of thousands of memorized word sequences. Reviewing a number of studies that tried to quantify this phenomenon in English, Conklin and Schmitt (2012) concluded that multiword sequences make up from one third to one half of the English language. Multiword sequences vary considerably in terms of structure, completeness, length, opacity of the meaning, and so forth. Nonetheless, studies that looked at the processing of various types of multiword sequences show relatively consistent results. For example, there is a vast body of literature on processing idioms, that is,

This article was published Online First February 25, 2016. I would like to express my gratitude to my supervisor Norbert Schmitt for his guidance when developing this study. I am particularly grateful to Gareth Carrol and Kathy Conklin for their useful comments on the earlier drafts of this paper. I would also like to thank the three anonymous reviewers for their constructive recommendations on improving this paper. Correspondence concerning this article should be addressed to Laura Vilkaite˙, School of English, The University of Nottingham, Trent Building, University Park, Nottingham NG7 2RD, United Kingdom. E-mail: [email protected] 1632

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ARE NONADJACENT COLLOCATIONS PROCESSED FASTER?

Studies of collocation processing point in the same direction as the studies of other multiword sequences: collocations seem to be processed faster than novel language. For example, Siyanova and Schmitt (2008) looked at collocation processing by native and nonnative speakers of English using a variation of a lexicaldecision task. Their participants were asked to make frequency judgments about common and noncommon collocations. Both of their participant groups judged frequent collocations faster than noncollocations. Durrant and Doherty (2010) also carried out a lexical decision experiment to investigate if the faster processing of collocations is driven by their phrasal frequency or simply by the semantic association between the collocates. They found that very frequent collocations were indeed processed faster, even if the collocates were not semantically associated. Wolter and Gyllstad (2011, 2013) looked at congruency effects on collocation processing in a second language (L2). They found that collocations are processed faster in an L2 if they have an equivalent in a first language (L1; i.e., are congruent in both languages). Wolter and Yamashita (2015) followed up on these studies, investigating whether collocations that do not exist in the L2 (English), but exist in one’s L1 (Japanese), are still facilitated when processed in the L2. Their study found no facilitation effect for the L1-only collocations translated into the L2. However, the native speakers of English, unexpectedly processed the Japaneseonly collocations (which were translated into English) significantly faster than the noncollocations. Curiously, this was the case only for the verb–noun, but not the adjective–noun items. This led the authors to tentatively suggest that there might be inherent differences between verb–noun and adjective–noun collocations. The studies discussed above mostly presented collocations to the participants in a word-by-word manner (as in a lexical-decision task) and without any context. Presenting collocations word-byword could arguably disrupt their processing, encouraging the processing of single words rather than phrases. Therefore, it is interesting to look at studies that investigated collocation processing using more naturalistic reading tasks. Some studies of eye-movement looked at how the probability of words co-occurring together in a text (transitional probabilities) affects processing. McDonald and Shillcock (2003a, 2003b) suggested that transitional probabilities are good predictors of reading ease, showing an effect on early eye-movement measures. However, Frisson, Rayner, and Pickering (2005) replicated and expanded this research and claimed that transitional probabilities no longer have an effect on processing if contextual predictability is controlled for. On the other hand, the authors acknowledged that contextual predictability (as measured by cloze tests) inevitably captures some information about transitional probabilities as well, as learners probably draw on their distributional knowledge of language when completing cloze tests. This study thus casts some doubts on the faster processing of collocations, showing that it is very hard to disentangle the effect of the statistical association between the words (collocational status) and contextual predictability. More recently, Sonbul (2015) carried out a study which included both online (eye-tracking) and off-line (rating) measures of collocation processing. She carried out her study with both native and nonnative speakers of English. She presented her participants with high-frequency, lower frequency, and nonattested synonymous adjective–noun pairs, and analyzed how phrase frequency affected processing of a word sequence. She found that both L1 and L2

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speakers were sensitive to collocational frequency in early measures of eye-movements, but not in late measures. This led her to conclude that collocations are not entirely fixed phrases. When reading an unexpected word combination, readers initially spend more time dealing with it, but once they incorporate it into a more general adjective–noun schema, they tend to read nonattested phrases equally fast. Overall, studies on collocation processing mostly confirm that there is a processing facilitation effect for collocations. However, these studies looked at processing of adjacent collocations only (e.g., provide information). In natural language use, collocations are very often nonadjacent, that is, there are words intervening between collocates (provide some of the information). When analyzing collocations in corpus linguistics, this fact is always taken into account (e.g., Krishnamurthy, Sinclair, Daley, & Jones, 2004; Stubbs, 1996), and a certain span for the co-occurrence of words (e.g., ⫾4) is allowed. The frequency of nonadjacent collocations raises the question as to whether the same processing facilitation holds for nonadjacent collocations as well. There has been one study that looked at the effect of variation on the processing of multiword sequences. Molinaro, Canal, Vespignani, Pesciarelli, and Cacciari (2013) looked at modified complex prepositions (e.g., in the capable hands of) in Italian. They compared reading times of the core form of the complex preposition (without an adjective) with the modified form (with an adjective inserted before the noun). In a self-paced reading task, they found that in the modified condition, participants took longer to read the noun and the second preposition, probably because of the additional information to be integrated when processing them. However, their ERP results showed that the insertion did not disrupt the processing, with indeed a smaller N400 for the noun following the inserted adjective. However, Molinaro et al. also found that the modified condition yielded left anterior negativity when reading the noun which followed the modified preposition. They interpreted this finding as a reflection of higher requirements of working memory in the insertion condition, as there was more information to be integrated. Thus, their findings would suggest that when processing complex prepositions, we do not access them as wholes, but rather analyze them into their component parts, and that modification of one of the components does not hinder the processing and is not perceived as a violation. Based on this study, it would seem that nonadjacent collocations should also show some processing advantage despite the words intervening between the collocates. However, in the literature, there have been suggestions that statistical probabilities of word co-occurrences would probably not have an effect on processing when words are separated. For example, based on the relatively weak effects for even adjacent words (e.g., Frisson et al., 2005), Rayner, Warren, Juhasz, and Liversedge (2004) concluded that “it is somewhat unlikely that transitional probability effects would survive intervening words” (p. 1298). Overall, the issue of the processing advantage of nonadjacent collocations remains an empirical question. The present study sets out to test if nonadjacent collocations show the same processing advantage as adjacent ones. First of all, it explores whether the processing advantage for adjacent collocations will be replicated using an eye-tracking methodology, when the contextual predictability is controlled for. Second, it investigates whether the adjacent collocation advantage extends to non-

VILKAITE˙

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adjacent collocations. If nonadjacent collocations were shown to be processed faster than control phrases, this would be in line with the findings that modifications of multiword sequences do not disrupt their processing (Molinaro et al., 2013). Also, it would suggest that nonadjacent collocations that are very frequent in corpus data are cognitively real multiword units. Conversely, if nonadjacent collocations did not show any processing advantage, this would give some initial evidence that transitional probability effects hold only for the words that directly follow each other.

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Method Participants Twenty-eight native speakers of English (23 females, five males, mean age: 19.89 years, range: 18 –21 years) participated in the study. They all had normal or corrected to normal vision. All participants were undergraduate students at a British university and received course credit for participation.

Materials Collocations for the study were extracted from the British National Corpus (BNC; Davies, 2004). There are numerous different metrics for extracting collocations that have been suggested in the literature (see Gries, 2013 for an overview and discussion). In this study, mutual information (MI; Church & Hanks, 1990) was used to identify collocations. MI is calculated as the logarithmic ratio between the observed and expected occurrences of a word pair in a corpus. This measure is very commonly used in applied linguistics and corpus linguistics, with a cutoff point of 3 usually adopted to identify collocations (Schmitt, 2010). In order to compare the processing of collocations with the processing of novel phrases, four groups of stimuli were created (see Table 1). As Table 1 shows, the same nouns were retained in all four conditions, while the verbs were replaced for the control conditions. Therefore, when comparing the reading times of the final word (noun) of the sequence, the length, frequency, concreteness and all the other features of the words in all four conditions compared were perfectly matched, as exactly the same list of words was used. For the nonadjacent conditions, the same three words were inserted in both sentences. The beginning of the sentence before the target phrase was always kept the same, and whenever possible, the ending of the sentence was kept identical as well. In sentences where changes were necessary, the changes were always minimal and at least two words after the target noun remained the same in all four conditions. Care was taken to ensure that the collocations would not appear in the first two positions or in the last two positions of the sentences.

Table 1 Experimental Conditions Condition

Example

Adjacent collocation Nonadjacent collocation Adjacent control Nonadjacent control

Provide information Provide some of the information Compare information Compare some of the information

All words for collocations and controls were selected from the list of the first 2,000 most frequent lemmas (Kilgarriff, n.d.) from the BNC. Only collocations with MI ⱖ3 were used for the study. For the control conditions, the verb chosen was matched with the word in the collocation in terms of length and frequency and it was a noncollocate of the noun (MI ⬍ 1). The intervening words used for the study were checked in the BNC to ensure that they were not strong collocates of the target nouns (MI ⬍ 2). This restriction was chosen to make sure that the relationship between the insertion and the noun would not override the collocational effect between the verb and the noun. To make sure that any processing advantage was due to the collocational status of the words rather than their semantic association, association norms of both verbs and nouns were checked in two databases (Kiss, Milroy, & Piper, 1973; Nelson, McEvoy, & Schreiber, 1998). Word pairs that were semantically associated (either the verb was listed as an associate of the noun or vice versa) were not included in the study. As the contextual predictability has been shown to play a major role in word processing (e.g., Rayner, 1998; Rayner & Well, 1996; Starr & Rayner, 2001), leading to skipping predictable words and reading them faster, it was important to control for predictability of the nouns in the collocations. To start with, the sentences were written to include only a very neutral context to try and keep the nouns in all four conditions equally unpredictable. To check for this, a norming study was carried out. Initially, 51 blocks of sentences (each containing four sentences: one per condition) were written to use in the norming stage of the study to control for the contextual predictability and for the naturalness of the sentences. To establish the predictability scores, participants were presented with a pseudo cloze test with sentence fragments up to the noun (e.g., John was asked to provide _____) and were asked to guess the word that comes next. To obtain the naturalness ratings, each participant was presented with a Likert scale (from 1 to 5) and was asked to judge how natural the sentences sounded. Thirty grammatically correct, but unnaturally sounding, filler items were included to provide the nonnatural contrast. Participants saw the predictability test first, followed by the naturalness rating task. The sentences were presented in four counterbalanced lists, combined in such a way that the same participant would never see the sentence in the same condition in both parts of the norming study. Twenty participants from the same population as the participants of the main study took part in the Norming study. Individual items with a predictability proportion of above 0.4 were excluded from the study. Also, items that had an average naturalness rating of below 3 in at least one of the conditions were excluded. The naturalness and predictability scores for the remaining items were used in the statistical analysis of the main study to account for potential differences in the predictability or naturalness between the conditions. After the norming phase, 40 sentence blocks were retained for the main study. The final list of collocations and verbs used for the control phrases are presented in the Appendix. The main characteristics of these items are summarized in Table 2.

Procedure Eye movements were recorded using an Eye-Link 1000 Plus eye-tracker. The experiment began with a 9-point grid calibration, followed by five practice sentences to allow the participants to

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Table 2 Summary of Stimuli Characteristics

Noun length Verb length (collocations) Verb length (controls) Noun frequency Verb frequency (collocations) Verb frequency (controls) Mutual information (collocations) Mutual information (controls) Phrase frequency (collocations) Phrase frequency (controls) Naturalnessa (adjacent collocations) Naturalness (adjacent controls) Naturalness (nonadjacent collocations) Naturalness (nonadjacent controls) Predictability (adjacent collocations) Predictability (adjacent controls) Predictability (nonadjacent collocations) Predictability (nonadjacent controls) a

M

SD

Range

7.3 6.0 6.0 15,864.65 18,219.10 15,864.65 4.11 ⫺.94 366.8 16.2 4.46 4.10 4.38 4.17 .04 0 .07 .01

1.23 1.33 1.23 10,574.50 11,861.77 10,574.50 .97 1.11 486.14 17.14 .34 .48 .46 .47 .08 0 .13 .04

4–12 4–9 4–9 4,398–50,109 5,222–49,571 4,398–50,109 3.02–6.28 ⫺3.21–0.58 59–2,653 0–66 3.4–5 3.2–5 3.4–5 3.2–5 0–0.2 — 0–0.4 0–0.2

Naturalness was measured on the Likert scale, from 1 (very unnatural) to 5 (very natural).

familiarize themselves with the task. Participants were instructed to read the sentences for comprehension and press a button to advance. Each trial started with a fixation point that appeared on the top left corner of the screen to check the calibration, followed by a sentence presented across one line in the middle of the screen. Experimental sentences were presented across four counterbalanced lists so that the participant always saw a collocation in only one of the four conditions. The experimental items were mixed with 40 filler items. Thirty of these filler items were followed by yes/no comprehension questions, to make sure the participants paid attention while reading. The rest were followed by a prompt: “Ready?” Sentences were presented in two blocks with a short break and a recalibration in between.

Analysis and Results The data were cleaned using the four stage procedure of Eyelink Data Viewer software (fixations shorter than 100 ms and longer than 800 ms were removed). Overall, this accounted for 2.97% of raw data being removed from the analysis. Participants showed no difficulty in answering the comprehension questions (the mean percentage of the correct answers was 95%) Following the suggestions of Carrol and Conklin (2014), both the reading times of the whole phrase and of the single word at the end of the sequence were analyzed. Carrol and Conklin (2014) have noted that most eye-movement research on reading so far has focused on individual words, which makes it challenging to directly apply the same measures for analyzing multiword sequences. Their suggestion is to use a hybrid approach: analyze both the final word of the phrase and the whole phrase reading. Final word reading measures in this case allow investigating the predicted locus of the facilitation (final word is where the effect of collocational effect could theoretically occur because the whole collocation is encountered). However, the final word analysis excludes items that are skipped during the first pass reading. As nouns in collocations were skipped more frequently than in controls (7.5% of words for the adjacent collocations, 5.5% for adja-

cent controls, 14.6% for nonadjacent collocations, and 10.7% for nonadjacent controls), by excluding these items from the analysis, we might be ignoring the cases where the facilitation of collocations is the largest. Also, it might be that the effect of collocational status occurs not at the final word only, but by rereading the verb that is used before it. So the analysis of the entire phrase reading allows the capture of the collocational effect at the phrase level. Therefore, two different areas of the interest were selected in the current study: the final noun (always the same noun across the four conditions) and the whole phrase. It has been suggested that comparing early and late measures of processing is one of the key advantages of the eye-tracking methodology. It is argued that early measures show lexical access and very early integration, while late measures show reanalysis of the information and discourse level integration (Roberts & SiyanovaChanturia, 2013). Therefore four different eye-movement measures were chosen for the final word reading analysis. Two of them are defined as early measures: first fixation duration (the amount of time spent fixating the word for the first time) and gaze duration1 (sum of all fixations in the area of interest, before the eye leaves it and moves to the left or to the right). Total reading time2 (sum of all fixation durations in the region, including regressions to the region) was also analyzed as a late measure of processing, as it includes regressions and rereading. Also, the results include a fixation count analysis, even if fixation counts do not measure the time spent reading, but rather the number of times the region is fixated. For the whole phrase interest area, the first pass reading time (the same measure as the gaze duration for an individual word), the 1 Gaze duration time is different from the first fixation time only if the word gets fixated on more than once during the first-pass reading (before the eye leaves the area of interest). In this experiment, on average 27% of the final words in the phrases were fixated on more than one time during the first-pass reading. 2 In this dataset, for the final word, participants reread to the area of interest on average in 33% of the trials. On average, 46% of the trials included rereading of parts of the whole phrase interest area.

VILKAITE˙

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total reading time and the fixation count were analyzed. The mean results for all the eye-movement measures considered in the study are presented in Table 3. Linear mixed effects models using the lme4 package (Bates, Maechler, Bolker, & Walker, 2014, Version 1.1–7) were fitted to analyze the data. All the continuous reading measures were log-transformed. All the frequency measures used in the analysis were log-transformed as well, and all the continuous predictors were centered in order to avoid multicollinearity (Siyanova-Chanturia, Conklin, & van Heuven, 2011). To check if there were any potential problems with the multicollinearity of the predictors, a Kappa value was calculated for all the continuous predictors to be used in the model (following Baayen, 2008), and the result confirmed that after centering the predictors, there was no problematic collinearity (K ⫽ 5.86). The four experimental conditions were inserted in the models as two two-level categorical variables: collocational status (collocation or not) and adjacency (adjacent phrase or not). These categorical variables were coded specifying custom contrasts to ⫺0.5 and 0.5, to make sure that in the models that contain interactions, the main effects of collocational status and adjacency were evaluated across all levels of factors. Seven separate models (one for each eye-tracking measure) were fitted. For the fixation count models, as the outcome variable was a count, the generalized linear models using the Poisson distribution were fitted. Model fitting always started with the model of the main interest (a core model) where the outcome variable was predicted by the collocational status and adjacency of the phrase and the interaction between those two variables. Collocational status, adjacency, and the interaction between them were always left in the models, regardless of their significance values, to address the main research question. This core model included random intercepts for subjects and items (see Tables 4 and 5 for the random structures included in the models). Following the suggestions of Barr, Levy, Scheepers, and Tily (2013), the structure of random effects was kept maximal by including random slopes for main predictors— collocational status and adjacency (as well as the interaction between the two in the models where it was a significant fixed effect). Then all the potential covariates were added to the model. For the final word reading models, the potential covariates included: list number (one of the four experimental lists that the participant was assigned to), trial number of the sentence, noun frequency, noun length, phrase frequency, MI score, insertion length, noun predictability, sentence naturalness, and the participant’s age as

potential covariates. For the whole phrase reading models, all the same covariates as for the final word were included, but verb frequency, verb length, and phrase length were also added. Starting from this full model, a backward step-by-step model selection process was adopted: the covariates which did not reach t values of 2 in the model were dropped one by one, starting from the ones with the lowest t scores. Each new model was then explicitly compared to the previous one, using likelihood ratio tests to make sure excluding the predictor did not change the model significantly. However, the predictability score was always left in the model, regardless of its significance, to show that whenever the collocational status was a significant predictor in the model, it was not the predictability of the item that drove the facilitative effect. The models that contained significant interactions were further investigated statistically using phia package (Rosario-Martinez, 2015) and graphically plotted using effects package (Fox, 2009).

Final Word Reading Times The coefficients of the fixed effects and random effect structures of the selected models for the interest area of the final word are reported in Table 4. Adjacency status, noun length and the insertion length (the length of the inserted words in letters) came out as significant predictors in all of the models. In early eye-movement measures (first fixation duration and gaze duration), predictability of the noun was also a significant predictor, but it was no longer significant in the late measures, suggesting that predictability plays a role in early automatic processing. Collocational status was clearly a significant predictor in the total reading time and fixation count, but was not a significant predictor for the early measures. After fitting the models, the interactions between the collocational status and adjacency in the total reading time and in the gaze duration (as it approached significance: t ⫽ ⫺1.78, p ⫽ .08) models were further analyzed. They both showed the same trends, suggesting that the facilitative effect for the final word reading time is larger for adjacent than for nonadjacent collocations (see Figure 1 for the comparison of both interactions). The follow up analysis of the interaction was carried out using phia package (Rosario-Martinez, 2015). Pairwise comparisons showed that for the gaze duration the effect of collocational status approached the significance for adjacent collocations with adjacent collocations being read faster than adjacent controls, ␹2(1, 1,013) ⫽ 4.29, p ⫽ .077; while it was not significant for nonadjacent phrases (nonadjacent collocations vs. nonadjacent controls):

Table 3 Summary of the Measures Measures analyzed Final word First fixation duration (ms) Gaze duration (ms) Fixation count Total reading time (ms) Whole phrase First pass reading time (ms) Fixation count Total reading time (ms)

Adjacent collocations

Controls

Nonadjacent collocations

Nonadjacent controls

232.83 270.83 1.38 315.79

245.54 292.02 1.78 408.34

226.27 253.65 1.26 275.15

229.34 259.55 1.50 333.99

444.01 2.76 611.91

521.24 3.45 777.44

852.41 5.40 1167.70

928.46 6.20 1389.06



ⴱⴱ

ⴱⴱⴱ

ⴱⴱⴱ

ⴱⴱⴱ

.06

.041 .051 .050 .055 .086 .031 .044 .021 .203 .002 .003 .003 .003 .007 .001 .002 .000 .041

SD Variance SD

.000 .000 .000

.199 .073 .019

Variance

.000 .000 .000

.040 .005 .000

.08



ⴱⴱ

ⴱⴱⴱ

ⴱⴱⴱ

.20

.040 .031 .019 .027 .054 .026 .033 .027 .161 .002 .001 .000 .001 .003 .001 .001 .001 .026 .029 .012 .009

SD Variance SD

.132 .018

.001 .000 .000

Item Collocation | Item Adjacency | Item Collocation ⫻ Adjacency | Item Subject Collocation | Subject Adjacency | Subject Collocation ⫻ Adjacency | Subject Residual

.003 .000 .000

Variance Random effects

.051 .014 .021

.17

ⴱⴱⴱ

ⴱⴱ



ⴱⴱⴱ

.61

Note. Significance values were estimated using the R package lmerTest (Version 2.0 –20; Kuznetsova, Brockhoff, & Bojesen Christensen, 2015). ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .001.

.93 .41



ⴱⴱⴱ

ⴱⴱ

ⴱⴱⴱ

p t

132.99 3.96 ⫺3.59 6.01 3.02 ⫺1.88 ⫺2.02 .019 .017 .052 .004 .003 .097 .027

SE ␤

2.489 .067 ⫺.186 .025 .011 ⫺.182 ⫺.055 ⴱⴱⴱ

p z

7.71 3.60 ⫺3.27 6.13 2.56 .09 ⫺.83

SE

.045 .054 .149 .012 .010 .345 .099



.351 .196 ⫺.486 .071 .025 .032 ⫺.082

p

ⴱⴱⴱ

182.52 1.30 ⫺3.67 4.27 3.04 ⫺2.44 ⫺1.78

t SE

.013 .013 .036 .004 .002 .075 .021 2.385 .017 ⫺.131 .017 .007 ⫺.182 ⫺.038

␤ p

ⴱⴱⴱ

t

203.00 .52 ⫺3.46 2.42 3.17 ⫺3.73 ⫺1.36

SE

.012 .010 .028 .003 .002 .059 .017 2.340 .005 ⫺.096 .007 .006 ⫺.222 ⫺.023

␤ Fixed effects

Intercept Collocation Adjacency Noun length Insertion length Predictability Collocation ⫻ Adjacency

Total reading time Fixation count Gaze duration First fixation duration

Table 4 Selected Models for the Final Word Reading

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ⴱⴱⴱ

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␹2(1, 1,013) ⫽ .02, p ⫽ .88. Hence, for the gaze duration, there was no facilitation for nonadjacent collocations, but an almost significant facilitation for the adjacent ones. As for the total reading time, pairwise comparisons revealed that there was a significant facilitation for adjacent collocations when compared to adjacent controls: ␹2(1, 1,013) ⫽ 17.72, p ⫽ .000; while for the nonadjacent phrases, the difference between collocations and controls only approached significance, ␹2(1, 1,013) ⫽ 3.45, p ⫽ .063. Thus, Figure 1 and the follow-up interaction analysis would suggest that there is a stronger facilitative effect for adjacent than for nonadjacent collocations, when looking at the final word reading. While for the early measures there was no facilitation at all for the nonadjacent collocations, for the total reading time, the difference between the reading of the nonadjacent collocations and the controls approached significance. Also, for the fixation count, there was the main effect of collocational status and no significant interaction, showing that the collocations were overall fixated fewer times than the control items. Hence, even if the effect for nonadjacent collocations at the final word is not as clear as for the adjacent ones, it still seems to occur.

Whole Phrase Reading Another area of interest that was analyzed was the whole phrase (collocation with or without intervening words). The models selected for the whole phrase reading times are presented in the Table 5. As it can be seen from the models, collocational status, phrase length and noun length were significant predictors in all the models. The interaction between collocation status and adjacency was not significant in any of these models. Also, the adjacency status did not come out as a significant predictor when the whole phrase reading was analyzed. This is to be expected, as the phrase length was included in the model and it was a clearly significant predictor, overriding the adjacency effect. Nonadjacent phrases by definition were longer, so they were also read for longer or fixated more times. What is important to note, though, when looking at the whole phrase reading, is that there was a consistent advantage for collocations over the control phrases, as collocational status came out as a significant predictor in all three models. What is more, there was no interaction between collocational status and adjacency for the whole phrase reading models. This would suggest that the phrases containing collocations were consistently read faster than the control phrases no matter what their adjacency was.

Discussion First of all, the results of the study replicated the finding that adjacent collocations are read faster than matched control phrases. This result is in line with the previous studies, which showed that collocations (as well as other multiword sequences) are processed faster than noncollocations (e.g., Durrant & Doherty, 2010; Sonbul, 2015; Wolter & Gyllstad, 2011, 2013). As contextual predictability was included in the analysis, and collocational status still came out as a significant predictor, it supports the suggestions of McDonald and Shillcock (2003a, 2003b) that transitional probabilities do indeed play a role in processing. In this case, it is not the contextual predictability and not the semantic association that drove the facilitative effect. This processing advantage probably

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1638 Table 5 Selected Models for Whole Phrase Reading

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First pass reading time

Fixation count

Total reading time

Fixed effects



SE

t

p



SE

z

p



SE

t

p

Intercept Collocation Adjacency Noun length Predictability Phrase length Collocation ⫻ Adjacency

2.756 .040 ⫺.077 ⫺.013 ⫺.002 .022 ⫺.016

.018 .016 .056 .006 .097 .003 .026

155.89 2.48 ⫺1.36 ⫺2.24 ⫺.02 6.18 ⫺.64

ⴱⴱⴱ

1.410 .177 ⫺.050 ⫺.028 .167 .044 ⫺.086

.041 .033 .111 .012 .208 .007 .060

34.33 5.38 ⫺.45 ⫺2.38 .81 6.35 ⫺1.43

ⴱⴱⴱ

2.915 .080 ⫺.052 ⫺.014 ⫺.012 .022 ⫺.026

.022 .011 .047 .006 .082 .003 .021

134.21 6.97 ⫺1.09 ⫺2.47 ⫺.14 7.19 ⫺1.22

ⴱⴱⴱ

Random effects

Variance

SD

Variance

SD

Variance

SD

Item Collocation | Item Adjacency | Item Subject Collocation | Subject Adjacency | Subject Residual

.002 .003 .004 .006 .000 .000 .046



.18 ⴱ

.98 ⴱⴱⴱ

.52

.050 .051 .061 .077 .014 .018 .214

.006 .002 .011 .036 .000 .001

.079 .041 .104 .190 .009 .030

ⴱⴱⴱ

.65 ⴱ

.42 ⴱⴱⴱ

.15

.003 .000 .003 .011 .000 .000 .031

ⴱⴱⴱ

.28 ⴱ

.89 ⴱⴱⴱ

.22

.051 .010 .058 .103 .013 .021 .175

Note. Significance values were estimated using the R package lmerTest (Version 2.0 –20; Kuznetsova, Brockhoff, & Christensen, 2014). p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .001.



occurs due to both the mutual expectancy (or transitional probability) of the collocates and their phrasal frequency. This finding is in line with the usage based approach to language learning, according to which frequency plays a major role in language acquisition and processing (see Kemmer & Barlow, 2000, for an overview). The results of this study, as well as of previous studies (e.g., Siyanova-Chanturia, Conklin, & van Heuven, 2011), would suggest that it is not only the frequency of individual words, but also a frequency of co-occurrence that matters. Control items in this study, even though semantically plausible, had very low phrasal frequencies in the BNC, as well as very low or even negative MI scores. Conversely, the collocations were frequently used phrases with high mutual expectancy between their constituent words. It is important to note, though, that the fact that the MI score and the phrasal frequency did not come out as significant predictors in the models, by no means refutes the effect

of these two variables. Rather, these variables may have been accounted for in the collocational status, which was a better predictor in the model, making the other two redundant and therefore nonsignificant. When analyzing the whole phrase reading, collocational status consistently came out as a significant predictor in all three models (both early and late measures), showing a consistent effect of collocational status for the phrase level processing. However, the reading of the final word of the collocation showed a somewhat different result. Presumably, the final word is the locus where the collocational effect could first occur, as after reading the first element of the collocation a person should enjoy a processing advantage for the second one. However, in the early measures, collocational status was not a significant predictor, suggesting that for the very early stages of processing and the lexical access of the word, collocational status seems not to facilitate the processing. It

Figure 1. Interaction between the collocational status and adjacency. See the online article for the color version of this figure.

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ARE NONADJACENT COLLOCATIONS PROCESSED FASTER?

appears to become important, though, during later integrative processes, as it was a significant predictor for the fixation count and the total reading time models for the adjacent collocations. It is interesting to compare the facilitation effect for adjacent collocations found in this study with Sonbul’s (2015) study, as it used a very similar methodology, but looked at a different type of collocations (adjective–noun). As it has been suggested that there might be inherent processing differences between adjective–noun and verb–noun collocations (Wolter & Yamashita, 2015), comparing the results of the current study and Sonbul’s (2015) study might prove informative. Sonbul found a processing advantage for adjective–noun collocations; however, this effect was present in only early, but not late, measures of eye movement. She took the results to mean that once a reader incorporates word sequences into a more generalized adjective–noun scheme, they process noncollocations in the same way as collocations. In the current study on verb–noun collocations, the results showed that the collocational status was a significant predictor in all the measures of eye movements for the entire phrase.3 While the comparison of these two studies cannot provide a definitive answer to whether adjective–noun and verb–noun collocations are processed similarly or differently, it could be argued that it does not necessarily provide any supportive evidence for differences in processing. The collocations selected in the current study were stronger both in terms of chosen frequency and MI cutoff points (especially when compared to the ones in Sonbul’s midfrequency condition), and they also showed the additional collocational effect for the late measures. These studies together would suggest that both adjective–noun and verb–noun collocations enjoy a processing advantage. While Sonbul’s study did not show the effect of collocations for late measures of eye-movement, the results of the current study are in line with the results of Siyanova-Chanturia et al.’s (2011) binomial study, which also found the phrase frequency and the phrase type (binomial or not) to be significant predictors in models of both early and late measures of eye-movements. However, the most important finding of the study is that nonadjacent collocations also showed a processing advantage, even if there were three words inserted in between the collocates. For all the models of the reading times for the whole phrase, collocational status was a significant predictor while the interaction between collocational status and adjacency was not. This would suggest that when looking at the processing of the whole phrase, both adjacent and nonadjacent collocations get an equal or at least similar facilitative effect. However, the models of the final word reading showed a somewhat different trend. When looking at the late measures, the nouns in the collocations were read faster than in the control phrases, but the interaction between collocational status and adjacency for the total reading time in particular showed a significant facilitation only for the adjacent phrases, while for the nonadjacent ones, it only approached significance. This result could be interpreted as initial evidence of there being a somewhat stronger facilitation for adjacent than for nonadjacent collocations. It still remains to be investigated if this interaction holds only for reading (where a reader could potentially obtain some preview benefit for a word that directly follows) or if it holds for language processing overall. This difference between the results for the final word reading and the whole phrase reading would suggest that while there is

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some facilitation for the final word of collocations, especially for the adjacent phrases, the final word is not the only locus of facilitation. Hence, as Carrol and Conklin (2014) suggested, looking both at the entire phrase and at the word where the effect can in theory be expected seems to be a fruitful approach. The finding that nonadjacent collocations showed processing facilitation (at least at the phrasal level) argues against the notion that the transitional probabilities do not have any effect if there are words intervening in between (Rayner et al., 2004). On the contrary, it seems to suggest that the effect of statistical probability of two words occurring together in a language holds even if three words intervene in between them. However, it has to be noted that these specific multiword sequences (verb–noun collocations) are very frequently used in naturally occurring language in their nonadjacent form, and this might be the reason for why they are learned/entrenched in the mental lexicon as nonadjacent dependencies. For collocations that tend not to allow any intervening words, the effect of transitional probability may not hold if extended to the nonadjacent condition. The results of this study are in line with the complex prepositions study (Molinaro et al., 2013), which showed that even modifying multiword sequences does not interfere with their processing. Wray (2012) noted that it is not clear if the facilitation in processing of multiword sequences occurs due to holistic processing or faster mapping of the elements of the phrase. The results of Molinaro et al. (2013) and the current study seem to argue against the holistic processing hypothesis and support the idea that collocates have some sort of links between words that allow activating them together faster. This is in line with the usage-based theory of language acquisition, which claims that language as a system is frequency driven (Barlow & Kemmer, 2000). The idea that more frequent items or phrases become more entrenched in memory (Barlow & Kemmer, 2000; Bybee, 2006; Tomasello, 2005) would explain why frequent phrases are processed faster than novel control phrases. The results of the current study replicated this finding showing that frequent collocations are indeed processed faster than nonfrequent verb–noun phrases. The current study also extended this finding by suggesting that the elements do not necessarily have to co-occur adjacently; the faster processing effect seems to hold for nonadjacently co-occurring collocates as well. This study is also in line with Hoey’s theory of lexical priming (Hoey, 2005, 2012). This theory is largely based on corpus data, and it tries to account for the pervasive use of collocations in language. It claims that each time a language user encounters a collocation, it leaves a memory trace and after repeated encounters these cumulative patterns of word uses become a part of our lexical knowledge—they enter our mental lexicon. While this theory is generally in line with the usage based approach, as it is largely frequency based, it is specifically concerned with collocations. The results of the current study seem to support the idea that once the collocation is encountered, its elements prime each other and are activated together to facilitate its processing. 3 Sonbul (2015) only analyzed and reported the results of the interest area of the whole phrase, so the final word reading times cannot be compared.

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The results of this study are also informative in light of the studies on the importance of statistical learning and probabilities in language processing. Learning dependencies of nonadjacent elements has been of interest in many research studies in first language acquisition, incidental learning and statistical learning. This line of research has shown that adjacent dependencies are easier to learn than nonadjacent dependencies (Gómez, 2002; Newport & Aslin, 2004), nonadjacent dependencies seem to be learned better if there is a variation of the intervening elements (Gómez & Maye, 2005), and the distance between the nonadjacent elements seems to play a role in learning (Santelmann & Jusczyk, 1998). However, these studies looked at the learning of artificial languages. The findings of the current study (based on lexical items from a natural language) support the idea that people learn nonadjacent dependencies. The results suggest that adult native speakers possess information about words co-occurring together and this information about the probabilities of co-occurrence is tracked for nonadjacent elements as well as for adjacent ones. Looking at the effect of probabilities for language processing, recent findings of both behavioral and neural studies show the importance of prediction in language processing. After reviewing a body of literature, Kuperberg and Jaeger (2016) emphasized the importance of making probabilistic predictions at all the levels of language representation. The current study suggests that the predictions based on co-occurrence information can possibly be made not only about the items that are adjacent to each other, but also about the nonadjacent lexical items. It remains to be investigated, though, how far these predictions apply. Overall, this study extends the previous results of the faster processing of collocations, showing that collocational status adds something to the word processing beyond the contextual predictability and that nonadjacent collocations have a processing advantage as well as adjacent ones.

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(Appendix follows)

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Appendix

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Collocations and Control Verbs Used in the Study Collocation

Insertion

Control verb

Achieve status Acquire land Adopt policies Allow access Attract investment Cause damage Commit murder Confirm findings Create reality Develop skills Develop software Earn respect Ensure safety Express concern Extend knowledge Gain control Gain experience Hold views Improve performance Improve quality Increase capacity Influence behavior Lose confidence Meet demand Meet expectations Offer advice Perform duties Promote development Protect interests Provide information Provide services Raise cash Receive treatment Reduce costs Reduce pressure Require attention Require effort Save energy Seek help Spread news

A more secure A bit of A number of All its members All the needed Any kind of More than one All the typical Some aspects of Some of the Some of the At least some The best possible The entire society’s The degree of At least some A bit of Their own individual The level of The level of The average available The type of Some of the The widely discussed The almost impossible Some much better Basic and simple New kinds of Only his own Some of the Free or cheap A lot of Any form of At least some The existing academic All of their Any sort of All forms of Your older brother’s All the positive

Ignore Receive Choose Sell Obtain Admit Ignore Address Assess Present Present Want Expect Explain Change Leave Want Keep Support Discuss Examine Recommend Keep Avoid Stand Expect Complete Control Support Compare Consider Leave Arrange Divide Accept Involve Imply Sell Avoid Accept

Received August 31, 2015 Revision received January 15, 2016 Accepted January 15, 2016 䡲

Are nonadjacent collocations processed faster?

Numerous studies have shown processing advantages for collocations, but they only investigated processing of adjacent collocations (e.g., provide info...
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