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Before the eye-tracking experiment began, each participant was instructed to read for comprehension in a normal manner and had a practice session of seven sentences. Eye movements were recorded using an EyeLink eye-tracker, interfaced with a PC. Subjects were seated 65 cm from the computer screen. Viewing was binocular, but only the right eye was recorded. All sentences were displayed on a single line and were presented in twelve points Arial font. At the beginning of each trial, a dot appeared at the left edge of the screen and after participants fixated on this dot, the sentence appeared.
Participants had to look at the bottom right corner of the screen to indicate they had finished reading. True-or-false statements appeared randomly for half of the stimuli at this point. No feedback was given as to whether the response was correct or not. After reading half of the sentences, participants took a min break.
A calibration procedure was performed at the beginning of the eye-tracking experiment, at the end of the break, and between trials as needed. The appropriate transformation of the dependent variable was determined using the Box-Cox method Box and Cox, ; Kliegl et al.
The log transformation was suggested as the most appropriate transformation. As in Vasishth and Drenhaus , we found effects in the critical regions only in dependent measures related to re-reading; in the spillover region, we found effects only for total fixation time, consistent with Levy and Keller We provide the analysis of regions of interest for first-pass regression probability, re-reading probability and total fixation time.
As defined in Vasishth and Drenhaus , first-pass regression probability at a word is the probability of the eye moving leftwards after this word was fixated at least once; re-reading probability for a word is the probability of revisiting that word after having having made a first-pass.
After inspecting each LMM with total fixation time as dependent variable, we removed 0. Below we report only statistically significant effects. The figure depicts the partial effects on first pass regression probabilities in log-odds scale for the contributing factors condition, WMC, and their interaction; random factors variance and effects due to reading skills were removed from the dependent variable using the remef function Hohenstein and Kliegl, The central finding in the eye-tracking study is that individual differences associated with working memory have an impact in parsing sentences with long-distance dependencies.
That is, locality effects may become antilocality effects when WMC is large enough. This pattern can be explained by a memory account acting together with the expectation account. However, from this pattern alone it is not clear whether DLT or the activation-based model best explain the data. At least for first pass regression probabilities for the critical region, it is unclear where VP3 condition stands: there is no significant facilitation in comparison with VP1 as all the described accounts would predict.
However, the study does provide some evidence for a differential effect that depends on where the extra material is attached, and not just on the linear distance of the dependency as DLT and expectation account would predict. The fact that facilitation occurs only for VP3 condition in comparison with the short dependency condition VP1, and not when the extra material modifies the intermediate VP VP2 , provides some indirect evidence indicating differential facilitation between VP2 and VP3 as predicted by the activation account.
In addition, and in contrast with VP2, the facilitation did not depend on the WMC of the participants. Regarding the differences in the eye-tracking measures and spillover, the effect of adding preverbal material may have been more complex than hypothesized. The preverbal material may have added a new retrieval process at the head and thus overshadowed any facilitation caused by increased expectations. Furthermore, the appearance of the facilitation in different measures can be accounted for by assuming that facilitation due to preactivation, and facilitation due to increased expectations depend on different underlying mechanisms resulting in qualitatively different behavioral consequences in reading Staub, This lack of an effect of WMC on the facilitation might presumable be because of our relatively homogeneous pool of participants, who did not display a big enough variance in their WMC.
This experiment is a replication of Experiment 1 using self-paced reading methodology. Even though eye-tracking experiments provide a more natural setting than self-paced reading, eye-tracking allows participants reading strategies that are absent in self-paced reading, such as skipping words and re-reading. Moreover, since it is possible to calculate many different eye-tracking measures, the chance of getting a false positive a Type I error goes up due to the multiple testing problem.
Thus, one important motivation for the self-paced reading experiment was to determine whether the previous results were robust. A second motivation was to attempt a replication of the eye-tracking result using a different method. The absence of replication has been recognized as a major problem in psychology and related areas Asendorpf et al. Eighty subjects aged between 18—44 years mean age 25 years participated in a self-paced reading experiment in Argentina. The first 34 subjects participated in Buenos Aires and the rest in Mendoza. Partial-credit unit scores for the operation span test measuring WMC of the remaining 79 participants ranged between 0.
The stimuli for this experiment consisted of 36 items similar to the items of Experiment 1, but with an extended spillover region. This extra region was included in case the self-paced reading task may delay the effects seen in the eye-tracking experiment. Similarly to Experiment 1, each participant read the 36 items together with unrelated sentences were experimental items of three unrelated experiments and 56 sentences were filler sentences in an individually randomized order after six practice trials; and the stimuli were presented in a Latin square design.
As in the previous experiment, the statements focused on various aspects of the stimuli, and the proportion of true and false statements was balanced. Subjects were tested individually using a PC. Participants completed the three tasks at their own pace: First, they performed a rapid automatized naming task, second, an operation span task, and finally, a self-paced reading task Just et al. Before the self-paced reading task began, each participant was instructed to read for comprehension in a normal manner and had a practice session of six sentences. In order to read each word of a sentence successively in a moving window display, participants had to press the space bar; then the word seen previously was masked and the next word was shown.
At the end of some of the sentences, participants had to answer whether a certain statement related to the experimental item was true or false. Twice during the self-paced reading task, a screen announced the number of sentences read so far and invited the participants to take a break. The appropriate transformation of the dependent variable according to the Box-Cox method Box and Cox, was the inverse transformation.
We compared reading times at the same three regions of interest as in Experiment 1, using the same successive differences contrast coding. Since the effects appeared in the same regions as in Experiment 1, the added spillover regions were omitted from the analysis. We removed 0. Consistent with the indirect evidence in Experiment 1 recall that for re-reading probabilities, we found significant facilitation in VP3 vs. VP1, but not in VP2 vs. In other words, speedups were seen in low as well as high-capacity readers, but not in medium-capacity readers.
Due to these findings, we also fitted a separate model that included the VP3-VP1 contrast. As expected, subjects with higher reading skills scores tended to have shorter reading times, but we also found an unpredicted interaction of reading skills with VP3-VP2 showing that as the reading skill score increases, reading times at the critical region get increasingly shorter for VP3 in comparison with VP2. For these regions a quadratic term for WMC was not justified, so we report the main findings for the model including only linear terms for WMC and reading skills.
The main results of the self-paced reading study are an inverted U-shaped effect of WMC on reading times for the first critical region for the condition where the extra material modified the VP VP2 in comparison with the condition with the short dependency VP1 , and a speedup at the two critical regions when the extra material modified the VP that contained the subcategorizing verb VP3 in comparison with when it modified the intermediate VP VP2.
The study thus shows that individual differences associated with working memory have an impact in reading strategies for processes associated with build-up of expectations and retrieval. Moreover, this study provides more evidence for a differential effect that depends on whether the VP that contains the head of the dependency is modified, as predicted by the activation-based model, but not by DLT and the expectation account.
We found that when the extra material modifies the VP where the dependency is completed VP3 , participants showed a speedup in comparison with the condition where the extra material modifies the intermediate verb VP2. An analogy to exam-taking may explain how two different underlying causes may lead to a process finishing early: students leave an examination hall early either because they do not have the resources knowledge, skills, etc.
Similarly, there may be two different reasons for the shorter RTs: Low-WMC subjects may read fast because they have done a shallow parse due to not having enough computational resources probably using a good-enough parsing heuristic see: Ferreira et al. Medium-WMC participants, however, may have built a complete parse but either did not have enough resources available for the build-up of predictions of the upcoming head, or the memory-driven locality effect offset the facilitation due to expectations. The difference between this study and the eye-tracking study may be due to the increased task demands of self-paced reading and the impossibility of making regressive saccades.
This difference is also evident from the lower comprehension accuracy in self-paced reading in comparison with eye-tracking 70 vs. As in the previous experiment, the speedup at the critical region depends only on WMC when the dependent-head distance is increased without a modification of the VP that contains the head VP2-VP1 , while the speedup is independent of WMC when distance is increased by a modification of the VP that contains the head VP3-VP1. As it was shown in Figures 1B,F , it is expected that a facilitation that depends on WMC will have a bottom asymptote since the duration of the reading times cannot be zero and presumably there is a minimum time needed for recognizing the word, pressing the space bar, etc.
It should be noted that for the extremely high values of WMC, however, the speedup of VP2 is stronger than of VP3, which is not predicted by the activation-based model and neither by the expectation account or DLT.
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However, this is true for a few subjects, and it may be due to the lack of data for the extreme values of WMC. In addition, the results showed that the facilitation due to preactivation VP3 vs. A major contribution of this paper is the finding that participants' WMC affects the processes involved in the dependency resolution. Even though recent research has shown that in some cases the relevant measure of individual difference to explain reading strategies is related to experience with language rather than memory vocabulary size in Prat, ; reading speed in Traxler et al.
Even though long-distance dependency completion is widely assumed to depend on the available working memory but see Waters and Caplan's approach to working memory: Waters and Caplan, ; Caplan and Waters, ; Waters and Caplan, , this is, to our knowledge, the first study showing that WMC modulates the reading times and regressions at the head of long-distance dependencies, as predicted by both DLT Gibson, and the activation-based model Vasishth and Lewis, The findings are consistent with the recent work of Caplan and Waters In this work, the authors argue that working memory supports retrieval in points of high processing load, which are identified by regressive saccades and longer self-paced reading times that enable better comprehension.
In addition, our results show the added value of analyses that take individual variation into account instead of averaging over the data of participants among others: Underwood, ; Brown and Heathcote, ; Traxler et al. The results of Experiments 1 and 2 together suggest that increasing the distance of the dependency affects the parsing of the head of the dependency in different ways, depending on whether the intervening material modifies the upcoming head or not. As predicted by the activation-based model Vasishth and Lewis, but not by DLT or the expectation account, the facilitation is stronger when the intervening material modifies the upcoming head even when the length of the dependency is the same.
The increase of expectation-based facilitation at the subcategorizing head depends on adding lexical material that helps to sharpen predictions on the location of the upcoming head. However, the increase of lexical material also has its cost in memory processes, so expectation-driven facilitation seems to be noticeable as a speedup or as the decrease of regressions for participants with enough resources to overcome the difficulties caused by adding the extra lexical material at least when the added facilitation due to the preactivation of the subcategorizing VP is absent.
While that was the case for our eye-tracking study Experiment 1 , this interaction was more complex than predicted for the self-paced reading task Experiment 2. Expectation-driven facilitation reduced the probability of regressions depending on the WMC of the participants of our eye-tracking study Experiment 1 , so that locality effects decreased as WMC increased until they became increasing antilocality effects. However, for the participants of the self-paced reading task Experiment 2 , the effect of WMC had an inverted-U shape, showing speedups in comparison with the short dependency condition for both high- and low-capacity readers.
Since WMC predicted better comprehension accuracy, we assume that there are different underlying processes behind these two speedups, and only high-WMC readers are assumed to speed up because their WMC allowed them to parse the sentence and predict the upcoming lexical material. Since locality effects are assumed to be a response to either a memory overload Gibson, , the use of more computational resources Gibson, , or higher retrieval costs Vasishth and Lewis, , theories that predict locality effects would not predict that low-WMC participants would speed up in comparison with mid-WMC readers when the distance between head and argument is increased.
In fact, there is ample evidence that proposes that individual differences in WMC reflect limitations in attention allocation for goals, especially in the face of interference or distraction for a review see Engle, There is independent evidence that high working memory load may lead to faster processing; this comes from the self-paced reading studies of Van Dyke and McElree , who found that when subjects were presented with a memory load a series of words to recall later prior to reading a sentence, reading times were shorter and comprehension accuracy was lower in comparison with the conditions without the memory load.
It seems that when the comprehender is parsing material while being engaged in processes that tax memory, a possible reading strategy is to disengage from the memory load sooner by reading faster. These results are in line with good-enough parsing Ferreira et al. Furthermore, the findings converge with studies showing that low-WMC subjects may take less time when ambiguities are present but they had worst accuracy than high-WMCs MacDonald et al.
Since this speedup for low-WMC readers is hypothesized to be a response to an incomplete parse of the more memory demanding condition, the speedup should appear together with a trade-off in the accuracy of the dependency completion. However, the true-or-false statements used for testing the participants' comprehension accuracy included many aspects of the stimuli in order to verify that they paid attention to the sentences, but they did not target exclusively whether the dependency was understood.
Participants could in principle know whether the statement after the stimulus sentence was true or false, even without a complete understanding of the previous probe sentence. In addition, they could answer wrongly because they misunderstood other aspects of the sentences. The reason for this shortcoming is twofold: First, since most of the previous studies on locality effects examined only on RTs except for McElree et al. Even though neglecting a deeper analysis of the sentence comprehension task is the normal state of affairs in psycholinguistics, it is a long-standing shortcoming in psycholinguistic research but see: Christianson et al.
However, the exact relationship between WMC and expectations remains elusive. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Special thanks to the reviewers Kiel Christianson and Leticia Pablos for their valuable comments and suggestions.
National Center for Biotechnology Information , U. Journal List Front Psychol v. Front Psychol. Published online Mar Author information Article notes Copyright and License information Disclaimer. This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology. Received Nov 14; Accepted Mar 4.
The use, distribution or reproduction in other forums is permitted, provided the original author s or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. This article has been cited by other articles in PMC. Abstract There is a wealth of evidence showing that increasing the distance between an argument and its head leads to more processing effort, namely, locality effects; these are usually associated with constraints in working memory DLT: Gibson, ; activation-based model: Lewis and Vasishth, Keywords: locality, antilocality, working memory capacity, individual differences, Spanish, activation, DLT, expectation.
Introduction Long-distance dependencies also called non-local, filler-gap, or unbounded dependencies have been investigated since Fodor's work on parsing strategies, but many questions remain unanswered or only partially answered. Open in a separate window. Memory-based explanations There is a wealth of evidence showing that increasing the distance between an argument and its head hinders underlying memory processes in some way.
The administrator who the nurse who was from the clinic supervised … In spite of the evidence for locality effects, there is a growing body of evidence showing the opposite effect: antilocality. Expectation-based explanations As in other aspects of cognition, predictions play an important role in language, and evidence from different sources supports the view that language processing does not only depend on bottom-up processes for a review of prediction in language see: Kutas et al.
Individual differences 1. WMC and reading skills Differences in WMC can successfully explain individual differences in comprehension performance Daneman and Carpenter, ; and this measure of individual differences seems to be the right candidate to account for differential effects in processes related to dependency resolution. Experiments The experiments have two main objectives. Table 1 Summary of the conditions. Table 2 Summary of the conditions and predictions for the head of the dependency.
Figure 1. General procedure Participants were tested individually using a PC computer. Rapid automatized naming tasks Working memory-capacity correlates with other reader characteristics, which may in turn account for the variance in participants' reading behavior as well as or better than working memory capacity Traxler et al.
Data analysis The data analysis was conducted in the R programming environment R Core Team, , using either linear mixed-effects model LMM; Pinheiro and Bates, or generalized linear mixed-effects models with a binomial link function to the response data GLMM. Experiment 1 3. Method 3. Participants Seventy-six subjects aged between 17—42 years old mean Stimuli The stimuli for this experiment consisted of 48 items with three conditions place of attachment similar to example 5. Procedure Participants performed the eye-tracking task after having completed a rapid automatized naming task and an operation span task.
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