Introduction
Language users have been shown to be sensitive to the frequency of linguistic units along the continuum from the smallest units in language (i.e., phonemes) to the largest units (i.e., lexical constructions) (see Ellis, Reference Ellis2002, for an overview of evidence). This evidence is compatible with the emergentist theories of language acquisition. The most prominent of these are usage-based models which posit that linguistic knowledge is tightly related to language experience. Within these theories, language development is viewed as the associative learning (or entrenchment) of constructions (chunks) and their frequency-related aspects (Bybee, Reference Bybee2001, Reference Bybee2006; Langacker, Reference Langacker, Barlow and Kemmer2000). Every exposure to a linguistic unit (from the smallest to the largest) acts as a memory trace which either reinforces or modifies existing knowledge. In contrast with this emergentist view, the words-and-rules approach (Pinker, Reference Pinker1999; Ullman, Reference Ullman2001) assumes that lexical knowledge is declarative (in the form of memorized forms) while grammatical knowledge is procedural (set of rules applied to combine these forms). According to this approach, frequency can affect the processing of memorized lexical forms but cannot influence the processing of longer constructions (which should be computed based on rules only).
Most of the available research on frequency effects on language processing has been concerned with the processing of individual words and has indisputably shown a clear effect of distributional properties (see, for example, Balota & Chumbley, Reference Balota and Chumbley1984; Morton, Reference Morton1969; Rayner & Duffy, Reference Rayner and Duffy1986). More recently, a number of research studies have shown that language users are also sensitive to frequency effects on constructions larger than words. These constructions are collectively referred to as formulaic sequences (i.e., holistically processed sequences of words) and are categorized into various types: chunks (by the way), frozen metaphors (fishing for compliments), idioms (kick the bucket), and collocations (fatal mistake). However, there are reasons to believe that collocations (word combinations which typically co-occur) should be viewed differently. One characteristic of collocations which distinguishes them from other types of formulaic sequences is what Wray (Reference Wray2002) calls “fluidness” as opposed to the “fixedness” of idioms. Collocations have more to do with tendencies than exclusiveness. While the idiom kick the bucket is fixed in form and meaning and cannot be modified, the collocation fatal mistake is not as fixed. Fatal tends to occur frequently with mistake, but this does not exclude other (less frequent) combinations such as awful mistake which might sound acceptable as well.
Existing research exploring frequency effects on the processing of formulaic sequences, and more specifically collocations, (e.g., Arnon & Snider, Reference Arnon and Snider2010; Bell, Jurafsky, Fosler-Lussier, Girand, Gregory & Gildea, Reference Bell, Jurafsky, Fosler-Lussier, Girand, Gregory and Gildea2003; Sosa & MacFarlane, Reference Sosa and MacFarlane2002; Tremblay, Derwing, Libben & Westbury, Reference Tremblay, Derwing, Libben and Westbury2011) is limited in a number of ways. First, most studies employed either off-line or on-line measures not allowing for the direct comparison of on-line/off-line processes.Footnote 1 Second, with the exception of two recent studies (i.e., Siyanova-Chanturia, Conklin & van Heuven, Reference Siyanova-Chanturia, Conklin and van Heuven2011b; Wolter & Gyllstad, Reference Wolter and Gyllstad2013), most of the available research explored the issue with regard to native speakers only. Including both natives and non-natives in the same study would allow for testing the assumptions of usage-based models in both L1 (first language) and L2 (second language) learning contexts. Finally, semantic relatedness was only controlled for in one study (Siyanova-Chanturia et al., Reference Siyanova-Chanturia, Conklin and van Heuven2011b) which did not specifically look into the processing of collocations. Controlling for semantic relatedeness is essential in on-line measures to minimize the potential influence of implausibility/unrelatedness on any facilitative frequency effects revealed.
The present study combines both off-line and on-line measures in order to assess native and non-native English language users’ sensitivity to the corpus frequency of semantically-related (i.e., synonymous) adjective-noun collocations. Moreover, a rough estimate of proficiency (scores in the Vocabulary Levels Test) will be included to investigate whether sensitivity to collocational frequency is influenced by level of proficiency.
Frequency Effects on Formulaic Language Processing
Evidence for frequency effects on the processing of formulaic sequences has surfaced in the past few years. Arnon and Snider (Reference Arnon and Snider2010) employed a timed off-line judgment task instructing their native-speaker participants to decide whether four-word sequences were possible in English or not (a YES/ NO phrasal decision task). In Experiment 1, the high cut-off bin (the condition with a big frequency difference) involved a 10 per million gap while the low cut-off bin (the condition with a small frequency difference) involved a 1 per million gap only.Footnote 2 Results showed sensitivity to frequency; a significant reaction time (RT) advantage was found for both cut-off bins. In Experiment 2 the effect was also established for a mid cut-off bin (5 per million gap). Finally, a meta-analysis (regression analysis run on data from both experiments) confirmed the effect; raw sequence frequency predicted RT behaviour.
Another similar recent study (Wolter & Gyllstad, Reference Wolter and Gyllstad2013) looked specifically at the effect of frequency on the off-line processing of collocations. The study employed a timed acceptability judgment task (similar to the one used by Arnon & Snider, Reference Arnon and Snider2010) and included adjective-noun collocations from various frequency levels. Both native speakers of English and advanced non-natives (L1 Swedish, L2 English) completed the task. Target collocations were extracted from the Corpus of Contemporary American English, COCA (Davies, Reference Davies2008), to reflect both congruent (L1 = L2, e.g., human rights) and incongruent (L1≠L2, e.g., bottom line) collocations. In addition to the target collocations, non-collocate pairs were constructed (through randomly matching adjectives with nouns, e.g., angry use). Natives showed sensitivity to collocational status (with a clear advantage for true collocations over non-collocate pairs) and to collocational frequency (with log collocational frequency predicting RT performance) regardless of congruence. As for the Swedish non-native learners, they showed a similar sensitivity both to collocational status and collocational frequency. However, unlike natives, their behaviour was clearly affected by congruence with shorter RTs for congruent items than incongruent items (leading to the conclusion that L1 exercises an effect on L2 processing even for advanced non-natives). Although these two studies provided clear evidence for frequency effects on native/non-native collocational processing, the task required an explicit judgment and, thus, did not assess on-line processing. On-line measures should tap into performance as it unfolds in real time (without explicit judgment) using RT methods such as priming/monitoring or the eye-tracking methodology.
Two studies have employed on-line RT measures in exploring frequency effects on natives’ on-line collocational processing. Sosa and MacFarlane's (2002) native-speaker participants completed a monitoring task in which they had to detect the node of in two-word combinations with various parts of speech (e.g., kind of, because of). They divided their items (derived from the Switchboard Corpus, available online at http://catalog.ldc.upenn.edu/LDC98S75) into four frequency levels. Results showed that natives’ reaction times were longer (i.e., slower processing) under the highest frequency level in comparison with the other three levels combined. It was concluded that more frequent collocations are stored holistically hindering the access to individual items. In a similar study employing the priming paradigm, Durrant and Doherty (Reference Durrant and Doherty2010) investigated both strategic and automatic collocational priming in two experiments.Footnote 3 Four frequency levels were included: low frequency combinations, moderate collocations, pure non-associated frequent collocations, and associated frequent collocations. Each level was paired with randomly constructed non-collocate pairs (e.g., armed concept as a control pair for main concept). Results of the strategic priming experiment showed facilitative effects for both types of highly frequent collocations over non-collocate pairs. On the other hand, automatic priming effects were only found for associated frequent collocations. The two studies presented above seem to suggest that native speakers are sensitive to collocational frequency on-line. It should be noted, however, that these studies suffer from an important limitation: they looked at arbitrarily divided levels of frequency but did not attempt to look at raw frequency effects across the continuum.
Reali and Christiansen (Reference Reali and Christiansen2007) explored raw frequency effects on collocational processing. They employed both off-line (complexity-rating task, Experiment 1) and on-line (self-paced reading task, Experiment 2) to test natives’ processing of pronominal object-relative clauses comprising chunks of various frequency levels (high, who I met disturbed, versus low, who I disturbed met). Off-line, results showed that natives judged the sentences comprising the high-frequency chuncks to be less complex than those containing the low-frequency ones. Similarly, on-line, sentences with highly frequent sequences were read faster (shorter RTs) than those with low-frequency ones: log raw clause frequency predicted RT performance.
The three studies reviewed above employed three RT measures (i.e., priming, monitoring and self-paced reading) to tap into the on-line processing of formulaic sequences. In comparison with these RT measures, an eye-tracking methodology allows for the separation of early (first pass reading time) and late (total reading time/number of fixations) comprehension processes during real-time reading (see Liversedge, Paterson & Pickering, Reference Liversedge, Paterson, Pickering and Underwood1998). Two of the earlier researchers who aimed at investigating whether collocational frequency exercises an influence on eye-movement behaviour were McDonald and Shillcock (Reference McDonald and Shillcock2003). Two eye-tracking experiments explored natives’ processing of highly predictable as opposed to low predictable verb-noun collocations (avoid confusion versus avoid discovery, respectively). Predictability was defined here as transitional probability based on BNC (British National Corpus) frequency counts. Items were embedded in a short context in Experiment 1 but were inserted into a more natural reading passage in Experiment 2. Results of both experiments showed that natives’ fixation durations were predicted by transitional probability (i.e., highly predictable collocations were fixated less often).
Up until now, all the studies reviewed did not control for an important item-related aspect: that is, semantic relatedness. Accordingly, it is not clear whether the facilitative frequency effects are real or false (i.e., emerging from the implausibility of control sequences or from variance in semantic relations among target sequences). One recent study by Siyanova-Chanturia et al. (Reference Siyanova-Chanturia, Conklin and van Heuven2011b) overcame this limitation. The study employed the eye-tracking methodology in exploring natives’ and advanced non-natives’ (assumed to reflect variation in exposure to language) sensitivity to the frequency of one specific type of a formulaic sequence controlled for semantic relatedness (i.e., the binominal phrase). Participants’ eye movements were monitored as they read sentences containing frequent binominal phrases (safe and sound) or their less frequent reversed forms (sound and safe). The analysis included three main variables and the interaction between them: raw BNC phrasal frequency, phrase type (binominal versus reversed), and proficiency (based on self-reported ability scores). Results demonstrated that: (1) raw BNC frequency had an overall facilitative effect on reading times (with fewer fixations and shorter reading times: first pass and total) but did not interract with proficiency and (2) binominals were read faster (early and late in processing) than their reversed forms with a clear effect of proficiency (i.e., sensitivity to binominals’ configuration improved with proficiency). These results were taken as evidence for usage-based models of language acquisition (more exposure to language, reflected in higher self-reported proficiency, leads to clearer sensitivity). It should be noted, however, that the lack of interaction between raw BNC frequency and proficiency might leave us cautious about the above conclusion. It might be assumed that the effect of proficiency (which in itself is a subjective measure) observed in this study is more related to the special nature of binominals (with a specific, fixed configuration) per se than to the frequency of exposure to formulaic sequences in general.
In sum, research has generally shown that language users (both L1 and L2) are sensitive to the frequency of formulaic sequences both off-line and on-line providing clear evidence for usage-based theories over the words-and-rules approach. However, most of the available research has only looked either at the distinction between natives and non-natives or that between off-line/on-line processes but not both. Moreover, almost all studies used control items which were not semantically plausible (e.g., angry use, armed concept) and might, thus, have biased the results. Two of these issues (i.e., including both L1 and L2 users and controlling for semantic relatedness) were tackled in a recent study (Siyanova-Chanturia et al., Reference Siyanova-Chanturia, Conklin and van Heuven2011b). However, the study looked at a very special type of formulaic sequences, that is, binominals. It is not clear, thus, whether the facilitative frequency effects reported would apply to other, less fixed, categories such as collocations.
Nesselhauf (Reference Nesselhauf2005) distinguishes between three types of word pairs: free combinations (no sense restrictions on either word, e.g., want a car, where both words can be used in other combinations freely), collocations (sense of one word is restricted, e.g., take a picture, where the verb take in this sense cannot be used with nouns like film), and idioms (sense of both words is restricted, e.g., sweeten the pill). Hence, the question is whether Nesselhauf's second category follows the same pattern of fixed formulaic sequences (with corpus frequency predicting processing both for natives and non-natives).
Current Study
The current study deals with limitations of the previous research through employing various tasks in the assessment of natives’ and non-natives’ sensitivity to the frequency of synonymous adjective-noun collocations. The term collocation is defined here according to corpus-derived frequency as:
A non-idiomatic pair comprising two open class lemmas (adjective + noun) which occurs in a corpus (within a window of 4 characters to the right and left of the node, ±4) above chance (Mutual Information, MI > 1).Footnote 4
The first aim of the present experiment is to establish whether native and non-native speakers of English are sensitive to corpus-derived collocational frequency both off-line in a rating task and on-line in an eye-tracking experiment. Another aim of the study is to investigate whether sensitivity to frequency (both off-line and on-line) is influenced by level of proficiency (as indicated by objective scores in the Vocabulary Levels Test, VLT).Footnote 5 The following research questions will be addressed:
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(1) Are native speakers of English and non-natives sensitive to corpus-derived collocational frequency off-line (typicality rating scores)? What effect does proficiency have on sensitivity to collocational frequency off-line?
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(2) Are native speakers of English and non-natives sensitive to corpus-derived collocational frequency on-line (real-time eye-movement behaviour)? What effect does proficiency have on sensitivity to collocational frequency on-line?
A list of synonymous collocations belonging to a variety of frequency levels were extracted from the BNC along with non-collocations (two-word combinations with zero occurrences or negative MI scores). The research questions are addressed through exploring effects of raw corpus-derived frequency (irrespective of the arbitrary division of frequency levels) on natives’ and non-natives’ off-line/on-line collocational performance (through a number of Linear Mixed Effects, LME, models). If frequency exerts an influence on off-line/on-line processing, raw BNC frequency should significantly predict collocational performance. Also, if level of proficiency plays a role on how sensitive language users are to collocational frequency, then VLT score should significantly interact with collocational frequency, supporting the usage-based view that linguistic units (of various sizes) are modified based on the cumulative experience with language.
Methods
Participants
Thirty natives and 30 non-native participants (12 males and 48 females) took part in the present study. They all had normal or corrected to normal vision and ranged in age between 19 and 43 (M = 24.27, SD = 6.27). The native participants’ average age was 19.40 (SD = 0.50). Non-natives, on the other hand, had an average age of 29.13 (SD = 5.54). An independent samples t-test revealed a significant difference between the two groups’ average age: (t (58) = −9.59, p < .001, eta squared = 0.61). Thus, age was added as a variable in the LME models to control for its effect (see Data analysis section below).
The natives were undergraduate students at Nottingham University who participated for course credit. The non-natives, on the other hand, were postgraduates (master's = 12, Ph.D = 18) who had all met the university entry requirement (minimum IELTS score of 6.0 or TOEFL score of 550) and were offered a payment of £6 for their participation. These non-native participants came from a variety of L1 backgrounds (Arabic = 5, Chinese = 4, Farsi = 1, Finnish = 1, French = 1, German = 1, Hungarian = 1, Icelandic = 1, Indonesian = 2, Malay = 1, Russian = 2, Spanish = 3, Tamil = 3, Thai = 3, Vietnamese = 1) and had spent a mean of 26.07 months in English-speaking countries (SD = 27.63, Min = 6, Max = 132). They were first exposed to English at an average age of 8.92 years (SD = 3.86, Min = 3, Max = 17). Their self-rated proficiency scores (on a scale from 1 = very poor to 5 = excellent) were: reading M = 4.23, SD = 0.82; writing M = 3.80, SD = 0.96; speaking M = 4.13, SD = 0.73, and listening M = 4.23, SD = 0.73. Their overall self-rated proficiency score (averaged across skills) was 4.55, SD = 0.67.
All participants were administered the 3K and 5K levels of the VLT (Schmitt, Schmitt & Clapham, Reference Schmitt, Schmitt and Clapham2001) as a rough objective estimate of their proficiency. It was decided that these two levels were the most relevant to the non-native participants in the present study (the 2K level might be redundant while the 10K level might be too difficult). The native participants scored almost a perfect score (Mean = 59.77 out of a maximum 60, SD = 0.43). Non-natives, on the other hand, scored an average of 52.73 (SD = 6.05) out of 60 with a mean score of 28.17 out of 30 on the 3K level (SD = 2.00) and a mean score of 24.57 out of 30 on the 5K level (SD = 4.45). An independent samples t-test revealed a significant difference between the two groups’ overall VLT scores: (t (58) = 6.35, p < .001, eta squared = 0.41).
Stimuli
Initially, the aim was to find three different synonymous adjective collocates for a number of noun nodes in English representing three levels of collocational frequency (lower: 5–15 occurrences in the whole BNC; mid: 25–45 occurrences; and higher: over 55 occurrences) along with a fourth non-collocate adjective. However, since an important aim was to control for semantic relatedness, I ended up with three different sets reflecting the different combination of collocational levels where each noun node is matched with 2 collocates and one non-collocate (Set 1: non, lower and mid; Set 2: non, mid and higher; Set 3: non, lower and higher). The division of levels was arbitrary (based on frequency range), thus these levels were not employed in the analysis. Instead, raw BNC frequency (dealt with in the form of quantiles) was included as a more reliable predictor in the LME models (see Data analysis section for details).
In order to arrive at a set of candidate collocational arrays, a number of steps were followed:
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(1) Davies’ BNC interface (Davies, Reference Davies2004) was consulted to find adjective collocates for the most frequent 2,000 lemmas in the BNC (Leech, Rayson & Wilson, Reference Leech, Rayson and Wilson2001) in a window of ±4.
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(2) The extracted adjective collocates for each noun were examined to find two candidate synonymous adjectives which fit the following criteria:
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a. The collocations belong to two of the three specified levels (lower, mid and higher).
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b Each collocation has an MI-score of 1 or above.
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(3) Concordance lines for each adjective-noun collocation were checked to make sure that the adjective modifies the noun. Any occurrences where the adjective is not describing the noun (e.g., where the adjective is occurring outside sentence boundary) were excluded from the frequency count of the collocation.
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(4) For each candidate item, a non-collocate synonymous adjective was searched for. This adjective returned 0–2 occurrences in the BNC within the ±4 span and a negative MI score.Footnote 6
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(5) Finally, two association databases were checked for the forward and backward association strength of the target collocates and non-collocates (Kiss, Armstrong, Milroy & Piper, Reference Kiss, Armstrong, Milroy, Piper, Aitken, Bailey and Hamilton-Smith1973; Nelson, McEvoy & Schreiber, Reference Nelson, McEvoy and Schreiber1998). This step was intended to control for association strength since previous research (e.g., Durrant & Doherty, Reference Durrant and Doherty2010) revealed its significant role in collocational processing. Only adjectives that were not produced in response to (and did not elicit) the target nouns were included in the item pool.Footnote 7
It should be noted here that I did not experimentally control for individual words’ length or frequency. This is due to the fact that LME modelling is employed in analysing the results. This type of analysis allows for the statistical control of item-related variables and thus eliminates the need for their experimental control.
In the end, 30 adjective-noun arrays were selected (10 under each set, see the appendix). Sentence contexts were adapted from the BNC for the off-line/on-line tasks. For each noun node, three versions of the same sentence were created with the difference being in the adjective collocating with each noun. Here is an example:
The engineer made one fatal mistake which weakened the bridge. Frequency=26
The engineer made one awful mistake which weakened the bridge. Frequency=4
The engineer made one extreme mistake which weakened the bridge. Frequency=0
The final sentences were piloted with 10 native speakers of English who did not participate in the main study to ensure naturalness. Results showed that the sentences were rated very high on a scale from 1 (not natural) to 6 (very natural): Mean = 5.79, SD = 0.20, Min = 5.37, Max = 6.00.
Measures
On-line eye-tracking experiment
To assess on-line processing of collocations, an eye-tracking experiment was designed on Experiment Builder and run using the SR head-mounted eye-tracker (see procedures below for more details). In this experiment, participants were presented with the target sentence contexts and were instructed to read as naturally as possible for comprehension.
Critical stimuli for this experiment included the 60 adjective-noun collocations and the 30 non-collocate pairs. The collocations and non-collocations were divided into three counterbalanced lists such that one adjective from each one of the 30 arrays is combined with the noun node. A noun which was matched with an adjective from a specific level (non-collocate level, or one of the two collocational levels) in one list was matched with an adjective from a different level in the other two lists. No noun or adjective was used more than once in any of the lists. In addition, four practice trials and 62 filler trials were included in which non-target combinations were inserted. Some filler sentences included non-collocations (K = 20) while others included collocations from the three frequency levels (low, K = 14; mid, K = 14; high, K = 14). Fillers were intended to include collocations (and non-collocations) so that targets are not particularly marked (and are not noticeable by participants). Thus, in each stimuli list, participants were presented with 96 trials (30 targets, 62 fillers, and 4 practice trials).
Off-line rating task
The off-line sensitivity measure was a rating task with three counterbalanced lists of sentences (the same as the eye-tracking list for each participant). In this task, participants were presented only with the 30 experimental sentences under each list (with no fillers) with the target combinations underlined. They were instructed to rate each underlined adjective-noun combination on how typical it is in English on a scale from 1 (not typical) to 6 (very typical).
Procedures
The study was performed in three stages. First, upon arrival at the lab, the participant signed the consent form in which he/she was only briefed on the purpose of the study (without a detailed account of its various stages). Then, the on-line eye-tracking experiment started. Eye movements were recorded using SMI EyeLink I (SR Research Ltd., Mississauga, Ontario, Canada). A 9-point grid calibration procedure was done prior to the experiment. The first four trials were always practice trials. The eye-tracker was calibrated at least four times during the experiment. Each trial started with a fixation point that appeared in the middle of the screen. After participants fixated it and a calibration check was conducted, a sentence appeared across one line in the middle of the screen (in Courier New, 14 point, font). The task was to read the sentences as quickly as possible for comprehension. One quarter of the sentences were followed by a comprehension question. The rest were followed by “Ready?” Both groups of participants had no difficulty answering the comprehension questions (Natives: 94.80%; Non-natives: 91.33%). The order of trials was randomized across subjects to avoid potential order effects. This task took around 20 minutes.
Immediately after that, participants performed a distractor task which was mainly intended to minimize any effect of the on-line measure on the subsequent off-line task. This task consisted of only two levels of the VLT: the 3K and 5K levels plus the language background questionnaire. It was checked very carefully that none of the words constituting the target collocate and non-collocate pairs were used in the VLT. Another purpose of this test was to arrive at a rough estimate of non-natives’ proficiency.
Finally, participants moved on to the final stage of the study: the off-line typicality rating task (conducted on E-Prime for randomization of trials) which took no more than 10 minutes. There were four practice trials in the beginning. This was followed by the 30 target items under each list.
Data analysis
I conducted the analysis with R version 2.15.0 (R Development Core Team, 2010) using LME models. Four LME models were fit: one model was fit on the off-line rating scores and three models were fit on three eye-tracking measures (first pass reading time, total reading time, and fixation count of the target combinations). The analysis was conducted both in the forward and backward step-wise model selection procedures.Footnote 8 The resulting best-fit models in both directions for all dependent measures were identical. Positive t values in the best-fit LME model indicate a direct relation (i.e., the higher the value of the predictor, the higher the value of the dependent measure) while negative t values indicate an inverse relation (i.e., the higher the value of the predictor, the lower the value of the dependent measure).
The above-mentioned eye-movement measures were chosen as they are the most commonly used in multi-word sequences research (e.g., Siyanova-Chanturia, Conklin & Schmitt, Reference Siyanova-Chanturia, Conklin and Schmitt2011a; Siyanova-Chanturia et al., Reference Siyanova-Chanturia, Conklin and van Heuven2011b). First pass reading time is the sum of fixation durations within the area of interest before it was left (to the left or to the right). It is the primary measure of interest when target items are longer than single words reflecting early/immediate comprehension processes. Total reading time is the sum of all fixation durations made within the area of interest (including re-reading) and is assumed to reflect later comprehension processes (e.g., textual integration). Fixation count is the number of all fixations made within the area of interest and is also assumed to reflect later comprehension processes (see Liversedge et al., Reference Liversedge, Paterson, Pickering and Underwood1998; Rayner, Reference Rayner1998, for an overview).
The following predictors (fixed independent factors) were tested for each dependent measure: age, trial number, VLT score, pair length (number of characters), log Word1 frequency, log Word2 frequency, and log collocational frequency (all frequency counts are based on occurrences per 100 million words).Footnote 9 As I did not experimentally control for pair length, individual word frequency, or participants’ age, I statistically controlled for these variables prior to testing for the effect of collocational frequency. Finally, I tested the interaction between the two major predictors (log collocational frequency and VLT score) and each one of the other predictors (for each dependent measure). All dependent measures (rating scores, first pass reading times, total reading times, and fixation counts) were log-transformed to reduce skewness in the distribution.
As for the on-line eye-movement measures, data cleaning was essential prior to fitting the models. Single fixation durations shorter than 100 ms and longer than 800 ms were excluded. The missing data accounted for 4.31% of the total data for native speakers and 5.27% for non-natives. I also excluded cumulative fixation durations shorter than 200 ms and longer than 2,000 ms (per combination) to further reduce skewness in the data distribution. This resulted in the loss of 10.71% of the data for total reading time and 10.15% of the data for first-pass reading time. For the fixation count measure, I also excluded fixation counts of 12 or more (only 2.51% of the data).Footnote 10
When fitting mixed-effects models, variables which correlate strongly can cause serious problems, so residuals should be calculated for strongly correlated variables (see Baayen, Reference Baayen2008). Collinearity among item-related variables was checked prior to fitting the LME models, and residuals were calculated. I orthogonalized pair length by fitting a linear model in which pair length was predicted by log W2 frequency. Residuals of this model (ResidPairLength) correlated with the original (pair length) predictor (r = 0.97, p < .001). I also checked for collinearity between the two participant-related factors (VLT and age). They were found to correlate, so residuals were calculated by fitting a linear model in which VLT score was predicted by age. The residuals (ResidVLT) correlated with the original VLT predictor (r = 0.91, p < .001). Finally, all continous predictors were centred. A summary of the continuous variables is presented in Table 1.
Table 1. Summary of the Continuous Variables.
Note: The second column shows the range of the variables. The adjusted range after transformation, partialing out correlated predictors and/or centering, is presented in parentheses. Standard deviations and medians refer to the predictor values in the models. All variables are centred, and their means are zero.
Results
On-line eye-tracking
Table 2 presents the mean reading times and fixation counts for both natives and non-natives under various collocational frequency quantiles. It can be clearly seen that there is not a clear trend for any of the eye movement measures across frequency quantiles. This might be related to the fact that length and frequency of individual words were not experimentally controlled for, and might, thus, have concealed frequency effects. As stated above, the analysis started with controlling for these lower-level variables and then tested for collocational frequency effects.
Table 2. Mean (SE) on-line reading times and fixations counts for both natives and non-natives under various collocational frequency quantiles.
Note: a The first and second quantiles are identical due to the presence of zero frequency values (comprising non-collocates).
First pass reading time
The best-fit model for variables predicting first pass reading time is presented in Table 3. There are a number of significant main effects. First, the VLT score had a significant effect on reading times: the higher the VLT score, the shorter the reading times (i.e., the more proficient the participant, the faster he/she read the combinations). Second, pair length influenced reading times in that the longer the pair, the longer the first pass reading time was. Third, the more frequent the second word of the pair (noun node), the shorter the reading time was. Fourth, collocational frequency had a significant effect on the first pass reading time measure: the higher the frequency of the pair, the shorter the reading time. Fifth, participants’ age had a significant main effect on reading times (the older the participant, the longer the reading time). Finally, it is interesting to note that trial number had no significant direct effect on reading times, though it interacted with the VLT score. The interaction is depicted in Figure 1. It can be clearly seen that the usual direction of trial number effects (shorter reading times with increase in trial number) is actually reversed for participant with low VLT scores. This result might be related to the fact that the non-native participants with lower proficiency levels paid extra attention to answering the comprehension questions properly. This might have led them to spend more time in the initial reading of sentences as the experiment proceeded in order to make sure that they do not lose any detail.
Table 3. Summary of the Best Fit LME Model for Variables Predicting Log First Pass Reading Time (N = 1611, R2 = 0.47).
Note: The model has random intercepts for participants and items; MCMC = Monte Carlo Markov chain; pMCMC = p values estimated by the MCMC chain method using 10,000 simulations; Pr(>|t|) = p values obtained with the t test using the difference between the number of observations and the number of fixed effects as the upper bound for the degrees of freedom.
Figure 1. Interaction between VLT score and Tiral Number for the on-line first pass reading time measure.
Above all, results of the first pass reading time measure show that both natives and non-natives are clearly sensitive to collocational frequency early in processing. Once pair length and frequency of individual words were controlled for, collocational frequency had a clear effect on collocational processing. Interestingly, this sensitivity was not affected by level of proficiency (no interaction between VLT scores and collocational frequency). No difference was observed between the lower-level non-natives and the higher-level participants (i.e., natives and non-natives achieving native-like scores in the VLT).
Total reading time
Table 4 presents the best-fit LME model for the total reading time measure. Only four main predictors were found to be significant: trial number (the bigger the number, the shorter the total reading time), VLT score (the larger the score, the shorter the total reading time), pair length (the longer the pair, the longer the total reading time), and age (the older the participant, the longer the total reading time). Collocational frequency did not surface as a significant (main or interacting) predictor.
Table 4. Summary of the Best Fit LME Model for Variables Predicting Log Total Reading Time (N = 1601, R2 = 0.58).
Note: The model has random intercepts for participants and items; MCMC = Monte Carlo Markov chain; pMCMC = p values estimated by the MCMC chain method using 10,000 simulations; Pr(> |t|) = p values obtained with the t test using the difference between the number of observations and the number of fixed effects as the upper bound for the degrees of freedom.
Thus, results of the total reading time measure suggest that collocational frequency has no effect on later integrative processes during reading. This lack of sensitivity was not modulated by proficiency. Natives and non-natives (across proficiency levels) did not experience any late processing difficulty when reading less frequent collocations/non-collocate pairs than more frequent ones.
Fixation count
The best-fit model for the on-line fixation-count measure is presented in Table 5. As both measures (total reading time and fixation count) are late measures indicative of textual integration (see Data analysis section above), their best-fit models were identical. Only trial number, VLT score, pair length, and age significantly predicted fixation counts (the first two with a negative and the last two with a positive effect). Thus, similar to the total reading time measure, results of the fixation count show that collocational frequency had no effect on later integration collocational processes.
Table 5. Summary of the Best Fit LME Model for Variables Predicting Log Fixation Count (N = 1748, R2 = 0.58).
Note: The model has random intercepts for participants and items; MCMC = Monte Carlo Markov chain; pMCMC = p values estimated by the MCMC chain method using 10,000 simulations; Pr(> |t|) = p values obtained with the t test using the difference between the number of observations and the number of fixed effects as the upper bound for the degrees of freedom.
To recap on-line results, natives and advanced non-natives showed similar effects. Collocational frequency had a significant main effect on initial reading times (not modulated by proficiency) but did not influence later reading processes for either group of participants. It is also worthy of notice that participants’ age had a significant positive main effect on all eye-tracking measures but did not interact with the main factors of collocational frequency or proficiency.
Off-line rating
Mean typicality rating scores both for natives and non-natives under various collocational frequency quantiles are presented in Table 6. It can be clearly seen that the mean rating score increased with frequency for both groups. The difference between the 3rd and 4th quantiles was not very clear for the natives, though. It is also notable that the mean scores for the 1st/2nd quantiles (representing non-collocates) were surprisingly moderate (around 3.50 out of 6 for both groups). This might be due to the fact that non-collocations in the present study were semantically controlled and, thus, did not look totally implausible to participants.
Table 6. Mean (SD) off-line rating scores for both natives and non-natives under various collocational frequency quantiles.
Note: a The first and second quantiles are identical due to the presence of zero frequency values (comprising non-collocates).
The final best-fit model is presented in Table 7. Significant main effects can be summarized as follows: (1) the higher the frequency of the second content word in the pair, the lower the rating score, (2) the higher the frequency of the collocation, the higher the typicality rating score. The VLT score had no significant main effects on rating scores, but it interacted with first word frequency and with collocational frequency modulating some of the main effects. First, the interaction between the VLT score and the log frequency of the first content word is depicted in Figure 2. The higher the frequency of the first word (i.e., the adjective), the higher the rating score was for the lower-level non-natives. This interaction went in the opposite direction for the higher-level non-natives and natives. Also, this VLT effect was clearer for the first frequency quantile. In order to explain this interaction, it should be noted that while the effect of the second word frequency on rating scores was significant regardless of proficiency (with lower rating scores as the frequency increased, see above), the effect of the first word frequency on rating scores was modulated by proficiency (i.e., highly proficient participants showed a similar effect to that of the second word frequency while the lower-level participants showed the effect in the opposite direction). It seems that for the lower-level non-natives, collocations comprising very non-frequent adjectives are more likely to form part of a less typical collocation.
Table 7. Summary of the Best Fit LME Model for Variables Predicting Log Rating Score (N = 1793, R2 = 0.45).
Note: The model has random intercepts for participants and items; MCMC = Monte Carlo Markov chain; pMCMC = p values estimated by the MCMC chain method using 10,000 simulations; Pr(> |t|) = p values obtained with the t test using the difference between the number of observations and the number of fixed effects as the upper bound for the degrees of freedom.
Figure 2. Interaction between VLT score and log W1 frequency for the off-line measure.
Second, and more importantly, the VLT score interacted with log collocational frequency (see Figure 3). As can be clearly seen, the difference between the collocational frequency quantiles was present for participants across all proficiency levels. However, sensitivity to frequency increased with proficiency: participants with higher VLT scores showed bigger differences across frequency quantiles.
Figure 3. Interaction between VLT score and log collocational frequency for the off-line measure (Note: the first and second log collocational frequency quantiles are identical).
In summary, results of the off-line task show a clear effect of collocational frequency on the typicality rating behaviour of both natives and non-natives. This effect was modulated by proficiency in that the natives and highly proficient non-natives (those achieving native-like scores in the VLT) showed stronger sensitivity to collocational frequency. Age did not have any main or interacting effect on off-line results.
Discussion
Previous research addressing frequency effects on the processing of formulaic sequences focused on fixed sequences (e.g., binominals). The present study looked at a different type of formulaic sequences (i.e., collocations) which are far less fixed. More importantly, the present study (1) employed both off-line (rating) and on-line (the highly sensitive eye-movement) measures, (2) tested both natives and non-natives, and (3) controlled for the semantic relatedness of word pairs across frequency levels.
Results of the off-line typicality rating task showed very clear effects of frequency with higher rating scores as the frequency of the collocation increased. More interestingly, level of proficiency influenced rating scores with bigger differences across frequency levels as the proficiency increased. This effect was present over and above all lower-level item-related variables. Thus, to answer the first research question, both natives and non-natives show off-line sensitivity to corpus-derived collocational frequency with higher levels of sensitivity as proficiency increases. This result replicates Arnon and Snider's (Reference Arnon and Snider2010) finding that natives are sensitive to the frequency of formulaic sequences off-line. Moreover, similar to Wolter and Gyllstad (Reference Wolter and Gyllstad2013), current findings show that both natives and non-native language users are similarly sensitive (during off-line performance) to the frequency of a specific type of formulaic sequences, i.e., collocations. These findings surfaced in the present study even when semantic plausibility was controlled for (an aspect which was not considered in Wolter and Gyllstad's (Reference Wolter and Gyllstad2013) study). Additionally, the present study shows that non-natives’ off-line sensitivity is affected by their level of proficiency.
This proficiency effect was not present during on-line processing. Both groups of participants (natives and non-natives) manifested only early sensitivity to collocational frequency during reading, which surfaced over and above lower-level factors and which was not modulated by proficiency. This early sensitivity to collocational frequency disappeared later in processing; collocational frequency was not a significant main or interacting predictor of total reading time or fixation count. These findings provide a clear answer to the second research question: both natives and non-natives show early on-line sensitivity to the corpus-derived frequency of collocations with no clear effect of proficiency.
Collocations versus other formulaic sequences
Eye movement results of the present study stand in contrast with Siyanova-Chanturia et al.'s (Reference Siyanova-Chanturia, Conklin and van Heuven2011b) finding of frequency effects on both early and late processing of fixed binominals. This discrepancy can be attributed to (and essentially reinforces) the inherent difference between collocations and other fixed categories of formulaic sequences. Natives and non-natives are able to recover quickly from difficulties when reading an infrequent collocation (or even unattested one) but cannot that easily cope with a very marked binominal in the reversed form. It seems that, while reading naturally, language users are more likely to accept deviations from typical collocations than those invovling the fixed configuration of binominals. Altering the adjective most typically used with a given noun is likely to cause only initial reading difficulty. However, a change in the most distinctive word order in binominals can cause initial processing disadvantage that persists. Collocations are not as fixed as other types of formulaic sequences, and thus language users might be more tolerant of alterations in their structure.
This conclusion is further supported by the off-line results of the present study where the average rating scores for non-collocations (1st and 2nd quantiles) by both natives and non-natives were above 3.00 on a 6-point scale (3.36 and 3.44, respectively: see Table 6). These relatively high ratings for non-collocations seem to emphasize the “fluidness” of collocations in comparison with other types of formaulic sequences (see Introduction section). Wolter and Gyllstad (Reference Wolter and Gyllstad2013) also touched upon this point given the finding that error rates in their off-line collocational decision task were significantly higher for non-collocates than for real collocations (i.e., more YES responses to non-collocates than NO responses to real collocations) both for natives and non-natives. They explained this finding in light of the usage-based notion of “schemata” (recurrent, generalized patterns abstracted from frequent experience) leading to various adjective + noun pairs (even the unattested ones) being judged as common (this point will be revisited below upon discussing implications of the present study to usage-based models).
L2 proficiency and on-line/off-line performance
As for the effect of proficiency, it is intereseting to note that natives and non-natives do not differ in the way they respond to variation in collocational frequency during real time reading. Both groups showed an on-line processing advantage for highly frequent collocations over less frequent ones initially but recovered later. Off-line, however, proficiency had a positive modulating effect on the reaction to collocational frequency; participants manifested bigger differences across frequency levels as proficiency increased.
Based on this distinction between off-line and on-line results, it might be concluded that non-natives develop implicit native-like sensitivity to collocational frequency through exposure but cannot as easily develop explicit native-like sensitivity as evident in rating scores. This result seems to support McLaughlin, Osterhout and Kim's (Reference McLaughlin, Osterhout and Kim2004) finding that non-native learners develop implicit sensitivity to lexical aspects of language prior to their ability to explicitly judge them. Thus, researchers have to be careful when employing off-line behavioural measures of lexical (in particular, collocational) knowledge as these might underestimate non-natives’ actual knowledge.
Another important point to note here is related to the false discrepancy between the current on-line, eye-tracking, results and those reported in Siyanova-Chanturia et al. (Reference Siyanova-Chanturia, Conklin and van Heuven2011b) when it comes to effects of proficiency. One might misleadingly argue that results of that study manifest a modulating effect of proficiency on non-natives’ on-line sensitivity to frequency while the present study does not. It should be noted, however, (as pointed out earlier) that the modulating effect of proficiency reported in that study is more related to sensitivity to binominals’ word order (one of their special features) than to the raw corpus-derived frequency. Accordingly, it might be argued that proficiency had no modulating effect on sensitivity to frequency in either study. Although this finding might be viwed as going against predictions of usage-based theories (i.e., more exposure should lead to entrenchment of connections), it should be noted that both studies tested non-native participants of a fairly advanced level (passing the language requirements of a master’s/Ph.D course in an English-medium university) and with little variation. It might be claimed that, at this advanced level, natives and non-natives are equally sensitive to the distibutional properties of formulaic sequences (at least on-line while reading).
Speaking about non-native participants in the present study and in Siyanova-Chanturia et al. (Reference Siyanova-Chanturia, Conklin and van Heuven2011b), an important point to note is that L2 users in both studies came from a wide variety of L1 backgrounds. Recent research on collocations has revealed L1 congruence as a significant factor during both off-line (Yamashita & Jiang, Reference Yamashita and Jiang2010) and on-line (Wolter & Gyllstad, Reference Wolter and Gyllstad2011) processing. Wolter and Gyllstad (Reference Wolter and Gyllstad2013) did test the relationship between L1 congruence and off-line sensitivity to collocational frequency and did not find any interaction. However, a totally different picture might emerge for on-line measures and/or for lower-level non-natives (shown to be more sensitive to L1 congurence than higher-level non-natives in Yamashita and Jiang (Reference Yamashita and Jiang2010)).
Usage-based theories of language acquisition
Results of the present study are incompatible with the words-and-rules approach to language processing/acquisition (Pinker, Reference Pinker1999). Natives and advanced non-natives are not only sensitive to the frequency of memorized individual lexical items in the language (as established by earlier research), but are also sensitive to the distributional properties of longer constructions (here collocations).
In contrast with the words-and-rules approach, usage-based theories (Bybee, Reference Bybee2001, Reference Bybee2006; Langacker, Reference Langacker, Barlow and Kemmer2000) assume that the processing of any linguistic unit (from the smallest to the largest) is influenced by the frequency of exposure to/experience with that unit in the language. These assumptions are supported by results of the present study. Corpus-derived frequency plays an important role in natives’ and non-natives’ processing of collocations both off-line (while making explicit judgements) and on-line (while reading for comprehension). This effect was clearer off-line than on-line, however, where the effect was found only early during processing. The fact that frequency effects disappeared later might be related, as pointed out above, to the non-exclusive nature of collocations. Usage-based theories are capable of accounting for this lack of effect in late reading times through the notion of “schemata” (see Tomasello, Reference Tomasello2000). It might be argued that, upon encountering an unattested (non-collocate) pair like extreme mistake during reading, the language user (whether native or non-native) will intuitively respond by spending some time to try to tackle the unnaturalness. However, given the fact that collocations are composed of two open-class lemmas filling in slots in a common abstract pattern/”schema” (adjective+noun), language users will quickly cope with the abnormality. They might be able to quickly generalize the common “schema” to the novel (unattested) pair.
Although the present research has provided interesting insights into usage-based models of language processing both off-line and on-line, the study is limited in a number of ways. First, an important assumption of usage-based models is that more exposure to the language should lead to deeper entrenchment of a unit's memory. It should be noted, however, that all participants in the present study (natives and non-natives) had high levels of academic achievement (B.A and master’s/Ph.D students, respectively) and should, thus, have been exposed to a large repertoire of texts in English. Second, as pointed out above, the non-natives in the present study had little variation in their language ability and came from a variety of L1 backgrounds. Finally, the present study did not consider the influence of working memory on the retention of frequency-related information from L1/L2 exposure (see Martin & Ellis, Reference Martin and Ellis2012). All of these factors might have affected sensitivity to collocational frequency both off-line and on-line. In order to fully test assumptions of usage-based theories, future research will need to consider a wider range of variation not only in language users’ academic attainment and working memory resources but also in their language proficiency (for non-natives). It should also consider the effect of resemblance/difference between L1 and L2 as a potential factor influencing sensitivity to the distributional properties of collocations.
Conclusion
The present study was conducted in an attempt to gain greater understanding of the emerging issue of frequency effects on the processing of formulaic sequences. The study dealt with limitations of the previous research through combining off-line and on-line measures in testing both natives and non-natives. Results showed clear off-line and early on-line sensitivity to frequency for both natives and non-natives with an effect of proficiency on the former measure only. Future research in this area will need to test a homogenous group of bilinguals in order to detect any congruence effects (L1 = L2 or L1≠ L2) on sensitivity to collocational frequency. Moreover, the potential contribution of factors such as level of proficiency, working memory resources, and level of academic attainment should also be considered (along with the interaction among them) in order to arrive at a deeper understanding of usage-based models of language acquisition/processing both in L1 and L2.
Appendix: Target Items
