Studia Linguistica (『ことばの科学』), Volume 28, 2014

Task effects on sentence processing using eye-tracking
MANSBRIDGE Michael 1
HAYAKAWA Kyoko 2
TAMAOKA Katsuo 3

Abstract: In sentence processing literature, researchers have generally overlooked how thesecondary task they utilize affects obtained data. What has been the yardstick for experimental
design up to now is to make the task not too easy and not too difficult, as to ensure participants
read and process the sentence without any unnecessary task demands. Currently, in sentence
processing studies, there are two general experimental techniques, verification and plausibility,
where verification uses comprehension questions to make sure participants understand thesentence, and plausibility uses a sentential decision task to judge if the sentence can exist in thereal world. This study compared the processing strategies of Japanese sentences from two
experimental tasks, a verification task and a plausibility task, to demonstrate how methodology
is a factor in sentence processing. The results indicate that the verification task not only required
increased cognitive workload but also had a different processing strategy compared to theplausibility task. The processing in the verification task focused on the disambiguation ofthematic roles whereas the plausibility task was sensitive to animacy inconsistencies. However,despite each experimental methodology having a task-dependent effect, it was observed thatsyntactic effects are task-independent.
Keywords: task-effects, L2 processing, eye-tracking, verification, plausibility

1

MANSBRIDGE, Michael: Research Student at the Graduate School of Languages andCultures, Nagoya University, Japan. E-mail: ktamaoka@lang.nagoya-u.ac.jp
2
HAYAKAWA, Kyoko: Assistant Professor at the Graduate School of Languages and Cultures,
Nagoya University, Japan. E-mail: ktamaoka@lang.nagoya-u.ac.jp
3
TAMAOKA, Katsuo: Professor at the Graduate School of Languages and Cultures, Nagoya
University, Japan. E-mail: ktamaoka@lang.nagoya-u.ac.jp91『ことばの科学』第 28 号（2014 年 12 月）

1. Introduction
Understanding how the cognitive mechanisms of experimental methodology interact withlanguage processing is crucial knowledge for linguistic researchers. In psycholinguistic research,
there are a countless number of different tasks that can be used to study a vast array of linguistic
phenomena. Sentence processing studies overwhelmingly involve more than simply justreading sentences; accordingly, secondary tasks are given which focus on a certain aspect of thesentence that cannot be accomplished without the reading of the sentence. The two secondarytasks that will be discussed in this study are verification tasks and plausibility tasks which areused to investigate if sentence processing is susceptible to change as a function of secondarytask.
Verification tasks employ post-sentence comprehension probes that questions specificinformation on the previously read sentence. After reading a stimulus sentence, the sentence
would disappear and be replaced with the probe which can be regulated by display times or a
feedback device. The participant would then proceed onto answering the question using a
feedback device such as a gamepad or keyboard. Example: [The guard who the prisoner pushed
was punished severely. Positive probe: Was the guard punished? “Yes or No”. False probe:
Was the prisoner punished? “Yes or No”.] In this example, the positive probe is accurate; theguard was punished, so the correct response would be yes. For the false probe, on the other hand,since we cannot extrapolate that the prisoner was punished within the boundary of the sentence,
the answer would be no.
Contrary to verification, plausibility judgments are a sentential decision on whether thesentence read can exist in the real world for the given language without a paranormalinterpretation; this task can have the decision to be made during or after the display of thesentence. Examples: [Plausible: The thief shot John with the shotgun inside the store.
Implausible: The thief shot John with the banana inside the store.] For the first sentence, thesentence is clearly plausible, as we can easily imagine thieves shooting people with guns;
accordingly the participant would indicate on a feedback device using buttons that correspond to“Yes” or “True” after finishing reading the sentence. In comparison, the implausible sentence
becomes an impossible sentence at the site of “banana” as it not a permissible semantic92

Mansbridge, Hayakawa, & Tamaoka
Task effects on sentence processing using eye-tracking

instrument of the verb shoot. Therefore, the participant would have to select the “No” or “False”
button on the feedback device. Plausibility tasks can serve either of two functions. First, it canbe used to compare the processing of plausible stimuli against implausible stimuli as in theexample above, or it can be used as means to distract participants from the purpose of theexperiment by only using implausible items as fillers/distractors.
Prior studies on the use of verification and plausibility answering strategies have shown thatthe process in which to verify an answer takes longer than to make an inference judgment uponit (Reder, 1982; Reder & Ross, 1983; Reder & Wible, 1984). The reasoning behind this
response time discrepancy has been explained by the fan effect (Anderson, 1974) which is theactivation of a proposition network that concurrently activates linked nodes with related
concepts and spreads out across the network field. Consequently, the activation of the network
increases as more knowledge is obtained on the original proposition and its linked nodes. The
reasoning that this causes a delay in response times is that verification strategies over-activate
the network which indicates that more processing resources are being used than what is required.
In contrast, plausibility judgments are not as susceptible to activating linked concepts and thus
activates only what is required by the task. Another coinciding effect taken from Gordon,
Hendrick, and Johnson (2001) shows a similarity-based interference effect during comprehension
when using multiple nouns of the same noun class type (e.g., general noun, pronoun, or name).
Consequently, during the processing of the sentence both nouns are being activated with similar
semantics which causes ambiguity for theta-role assignment. This effect taken with the fan
effect suggests that during comprehension people use extra cognitive resources for fact-retrieval.
Recently, Caplan, Chen, and Waters (2008) investigated the task-dependent andtask-independent effects of verification, plausibility, and non-word detection using fMRI brain
imagining. For both verification and plausibility it was shown that more complex
object-extracted relative clauses (ORC) were more difficult to process than subject-extracted
relative clauses (SRC). From this, they infer the area responsible for syntactic processing isshared and closely interacts with semantics during theta-assignment at the left posterior inferior
frontal gyrus. However, in their verification task it was argued that additional unique brain
regions being activated were attributed to other possible cognitive functions such as
phonological storage and rehearsal of the more complex sentence type to help disambiguate93『ことばの科学』第 28 号（2014 年 12 月）

between similar theta-role arguments. Though their plausibility tasks had increased brain
activation in comparison to the non-word detection task, Caplan et al. (2008) do not suspectthese activations to be linked with cognitive functions like in the verification task but instead
speculate they are related to plausibility of the theta-assignment in relation to the syntactic
structure. The findings in Caplan et al. (2008) are intriguing in that the results implicate thatverification involves more cognitive work than plausibility judgments. The study, however,does not make a comparison on reading times between experiments.
Another study by Leeser, Brandl, and Weissglass (2011) compared the effects of averification task and a grammaticality judgment task using self-pace reading (c.f., Just,
Carpenter & Wooley, 1982) on second language (L2) leaners of Spanish. In this study, theauthors investigated the sensitivity of grammatical violations in Spanish; more specifically,
gender agreement and subject-verb inversion. For noun-adjective gender agreement, only thegrammaticality task demonstrated effects of gender agreement violations. This was indicated bylonger response times at the adjective for the ungrammatical condition and shorter response
times in the following spillover regions for the same condition. For subject-verb inversion,
though within the grammaticality judgment task there were longer response times for theungrammatical condition, no main effects of grammatically were found. The verification task
did not demonstrate sensitivity to violations for either violation conditions. One interesting result
was that the grammaticality judgment task had consistently longer response times than theverification strategy which highlights the fact different reading mechanisms are involved in eachtask. They contend that the sensitivity to violations found during grammaticality judgments is an
artifact of the task, and learners are relying on explicit knowledge of grammar rather thandrawing upon the implicit knowledge structure involved during comprehension.
In the present study, we use eye-tracking methodology to investigate the processing of averification task against a plausibility task in order to investigate the differences of cognitive
workload on sentence processing in a second language with a purpose of determiningtask-dependent and –independent effects involve for each task. The Leeser et al. (2011) study
was able to demonstrate behavior responses are longer for grammaticality judgment tasks oververification strategies, but the Caplan et al. (2008) study makes no such claim between
plausibility and verification strategies. As a result of using eye-tracking, we are able to measure94

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Task effects on sentence processing using eye-tracking

the differences between the tasks through time course processing stages which elucidates to a
general pattern of processing within the sentence (c.f., Clifton, Staub, & Rayner, 2007; Staub &Rayner, 2007). The current study shares more similarities to the Leeser et al. (2011) study over
Caplan et al. (2008) as we used second language speakers of Japanese rather than native
speakers. The sentence types were also different; instead of using ORCs and SRCs todemonstrate processing difficulty associated with syntactic structure, we used a difference insyntactic voicing with active and passive voiced verbs (c.f., Gordon & Chan, 1995; Gorin,2005). Additionally, we only analyzed semantically plausible sentences. The choice to not
analyze implausible items was due to implausible items not existing in the verification task; themain interest is the difference between real world sentences. If the propositions made by Caplan
et al. (2008) are accurate, we not only expect to see extra processing costs (i.e., increased
reading time) associated with the verification task, but also a clear difference in processing
strategy between the two tasks.

2. Experiment
2.1 Participants
Forty native speakers of Chinese and Korean were recruited for this study (Chinese = 22 &Korean = 18). A Japanese grammar and vocabulary test was given to confirm that theparticipants had a sufficient level of proficiency. It was determined that the mean average of thegrammar test was 89.93% while the vocabulary test had a mean average of 79.48%. No
participant was eliminated for poor Japanese proficiency.

2.2 Materials
All experimental items were designed as three word canonical SOV sentences. There were
only four comparable conditions between the two tasks. (1) An active voiced sentence withinanimate agent and an animate patient (active-IA): [S R1 kaze-ga [VP R2 Junko-o R3
toba-shita]] ‘The wind pushed Junko’. (2) The passive counterpart (passive-AI): [S R1 Junko-ga[VP R2 kaze-ni R3 toba-sare-ta]] ‘Junko was pushed by the wind’. (3) An active sentence withan animate agent and patient (active-AA): [S R1 Taro-ga [VP R2 Junko-o R3 ket-ta] ‘Taro kicked95『ことばの科学』第 28 号（2014 年 12 月）

Junko’. (4) And the passive counterpart: [S R1 Junko-ga [VP R2 Taro-ni R3 ker-are-ta]] ‘Junko
was kicked by Taro’. For each experimental session there were 24 experimental items used withsix of each of the four conditions present for both tasks. Sentences were displayed normallywith a mixture of hiragana and kanji script. For the verification task, each sentence was followedby either a positive or false probe. Positive probe: Taro-ga Junko-o kerimashitaka ‘Did Tarokick Junko’. False probe: Junko-ga Taro-o kerimashitaka ‘Did Junko kick Taro’.

2.3 Procedure
Each participant was seated in front of a computer monitor in a quiet room. Prior to thebeginning of each experimental session, the participant’s right eye was calibrated to theeye-tracking camera using a 9-point calibration and validation method (desktop Eyelink 1000
SR Research Ltd., Ontario, Canada). Before the display of an experimental trial, a drift
correcting mask was presented on the far left center of the screen indicated by the circle “◎”.
Once a participant accurately fixated on the mask, the experimenter then accepted the fixation toallow the presentation of a trial item that replaced the mask. Each stimulus item had a maximum
display of eight seconds. However, each task differed in experimental procedure during thepresentation of the stimulus item. For the verification task, participants were instructed to readthe sentence at their natural pace, and once finished reading and comprehending the sentence,
they were instructed to press any button on the gamepad to replace the sentence with averification question that targeted the assignment of thematic roles. For the plausibility task,
participants were instructed to read the sentence at their natural pace, and decide whether or not
the sentence as a whole was semantically plausible by pressing a corresponding button on the
gamepad. In both tasks, feedback indicated by ‘Correct’ or ‘Incorrect’ in Japanese was given
after they selected their response in the form of a message that was displayed in the middle of a
new screen.

2.4 Analyses
Prior to the analyses, trials with tracker loss were removed from the data set. In addition,
trials that lacked participant feedback were also removed from the data set. Subsequently, allfixations below 80 ms were merged into a neighboring fixation within a one character distance.
96Mansbridge, Hayakawa, & Tamaoka
Task effects on sentence processing using eye-tracking

This was then followed by the removal of all remaining fixations under 80 ms and also fixations
that were over 1000 ms. To analyze each part of the sentence, the sentence was divided into
three aforementioned interest regions to capture reading times and eye-movements at each partof the sentence. Region 1 is always the subject of the sentence, region 2 is the object for active
sentences and the agent for passive sentences, and region 3 is always the verb.
A series of linear mixed effect (LME) modeling (Baayen, Davidson, & Bates, 2008) analyses
were then conducted on the collected reading times and eye-movements using SPSS; subject,items, and L1 groups as random factors. Via eye-tracking, we are able to report the reading
times (RT) for each task within each part of the sentence. Accordingly, we are able to compare
the tasks through different stages of processing. The earliest processing measure we will reportis the first-fixation duration which refers to the first-fixation RT at a specified interest region
within the sentence. This is followed by the first-pass reading which is composed of all fixations
made within an interest region from when it is first entered from the left until it is exited in either
direction. This measure is also an indication of early processing but is nevertheless a later
processing measure compared with the first-fixation duration data as a given word or phrase
may have multiple fixations during its first reading. The late processing measure we will reportin this study is dwell-time which refers to the summation of all fixations that fall within a region.
Additionally, the regression-out percentages will be reported which refers to the likelihood ofmaking an eye-movement out of an interest region into a previous part of the sentence. However,
only the second and third interest regions will be reported as it is not possible to regress-out of
the first region. The total reading time of the sentence and the accuracy percentages will also bereported to analyze sentence as a whole. In the reporting of the results for this experiment,
analyses done within tasks will only be reported if there is first an interaction of the tasks andconditions. Multiple comparison analyses were done using Bonferroni adjustments.

2.5 Results
Please see Tables 1-3 for the means and standard errors of the RTs, regressions, andaccuracies for the task and task conditions. Reading times are in milliseconds.

97『ことばの科学』第 28 号（2014 年 12 月）

2.5.1 Total reading times of sentences and accuracies
In the total reading time of the sentence, a significant main effect of task was found, [F(1, 43)= 115.31, p < .001], which showed that the RTs for the verification task (M = 3,481 ms, SE= 56ms) were significantly longer than the plausibility task (M = 2,256 ms, SE = 32 ms). In addition to
the main effect, an interaction of task:condition was found [F(7, 399) = 30.68, p < .001]. Thus,
each task was then separately analyzed. Within the verification task, a robust effect of sentence
condition was found at the sentence level [F(3, 736) = 15.89, p < .001]. This effect revealed
through multiple comparisons that the passive-AA condition (4) had significantly longer RTs
compared to the other three conditions (4 > 1, 2, 3); additionally, the active-AA condition (3)had significantly longer RTs in comparison to the active-IA (1) condition (3 > 1). Similarly, theplausibility task had a significant effect of condition type, [F(3, 725) = 20.20, p < .001]. The
effect, however, was seemingly reversed; the active-IA (1) condition had significantly longer
RTs than the other three conditions (1 > 2, 3, 4). The passive-AI condition (2) also had longer
RTs than the active-AA (3) condition (2 > 3). For accuracy percentages, the main effect of task
was not significant, [F(1, 1813) = 0.34, p = .561, ns], showing that the overall mean accuraciesof the two tasks were analogous (V: M = 82.94%, SE = 1.23; P: M = 83.99%, SE = 1.21). However,since a significant interaction of task:condition was revealed, [F(7, 1786) = 11.89, p < .001],
each task was then separately analyzed. The verification task produced an effect of condition,[F(3, 922) = 7.87, p < .001], which through multiple comparisons demonstrated that thepassive-AA (4) was significantly less accurate than all other conditions (4 < 1, 2, 3). Likewise,
the plausibility task had an even more robust effect, [F(3, 891) = 19.81, p < .001]. In contrast to
the verification task, the plausibility task produced a different pattern of results in which theactive-IA condition exhibited lower accuracies in comparison to both active-AA andpassive-AA conditions (1 < 3, 4). Additionally, the passive-AI had significantly lower accuracy
than the active-AA and passive-AA conditions (2 < 3, 4).

2.5.2 Subject (R1)
At the subject, there was an immediate main effect of task for first-fixation durations, [F(1,42) = 54.86, p < .001], revealing that at the earliest possible processing measure the verification
task was reliably longer than the plausibility task. Subsequently, we then tested for an interaction98

Mansbridge, Hayakawa, & Tamaoka
Task effects on sentence processing using eye-tracking

Table 1. Total reading times and accuracy ratings for task conditions
Total Reading Time

Accuracy

Means

SE

Means

SE

1

3,187

97

86.86

2.20

2

3,381

112

83.12

2.44

3

3,520

109

87.98

2.13

4

3,900

127

73.71

2.90

1

2,573

83

71.49

3.00

2

2,312

64

79.91

2.68

3

2,063

54

89.13

2.06

4

2,156

53

95.22

1.41

Verification

Plausibility

between the tasks and conditions which the analysis revealed to be significant, [F(7, 1549) =8.35, p < .001]. However, it is unlikely that voice effects would be apparent already as the firsttrue indication of syntactic-voice appears at the verb morphology in the third region. Analyses
were then conducted on both tasks, but neither the verification task, [F(3, 737) = 1.03, p = .378,
ns] nor the plausibility task [F(3, 727) = 0.62, p = .605, ns], produced a significant effectdemonstrating that there were differences between animacy and voice for the four conditions.
Given these points, it is apparent that for first-fixation durations a pattern of results cannot to beseen; as such, there is only a reading time difference between the two tasks at the earliest
processing measure.
Moving onto first-pass RT, a main effect was still observed, [F(1, 41) = 23.45, p < .01].
Analogous to the previous measure, the verification task produced longer reading times than theplausibility task. Again, the follow-up analysis revealed that there was an interaction, [F (7, 430)
= 8.44, p < .001]; thus, further analyses were then conducted on each task. In the verificationtask, there were still no effects found, [F(3, 775) = 0.07, p = .974, ns]. The plausibility task, on
the other hand, did have a robust effect, [F(3, 724) = 16.05, p < .001]. This analysis revealed that
the RTs for the active-IA condition were longer than all other conditions (1 > 2, 3, 4).
99

『ことばの科学』第 28 号（2014 年 12 月）

Table 2. Reading times and regressions for tasks
First-Fixation

First-Pass

Dwell-Time

Regression-Out

Means

SE

Means

SE

Means

SE

Means

SE

Verification

239

3

519

12

1,254

26

-

-

Plausibility

209

2

426

9

775

16

-

-

Verification

244

4

367

7

1,086

25

22.81

1.51

Plausibility

243

3

394

8

721

16

10.48

1.11

Verification

301

5

804

20

1,161

27

95.44

0.75

Plausibility

312

5

630

13

782

16

85.73

1.28

Region 1

Region 2

Region 3

Altogether, at the completion of the reading of the subject, the plausibility task, unlike theverification task, developed a processing pattern.
At later processing measures, i.e. dwell-time, it was shown that there was no attenuation of
the main effect for task [F(1, 42) = 85.75, p < .001]; the difference in reading time had only
increased through processing stages. The succeeding analysis for interaction of task:condition
was still significant, [F(7, 428) = 20.01, p < .001]. However, at this processing stage theverification task was able to produce a significant effect of condition type, [F(3, 736) = 5.77, p< .01], revealing that the RTs for the active-IA condition were shorter than the active-AA andpassive-AA conditions (1 < 3, 4). Comparable to the verification task, the plausibility task also
had a significant effect, [F(3, 724) = 16.77, p < .001]; yet, akin to the first-pass reading measure,the active-IA continued to be significantly longer than other conditions (1 > 2, 3, 4), which is in
direct contrast to the result found in the verification task.
Overall, it is shown that at the subject the verification task induced reading times throughout
all measures with task-dependent results showing up earlier in the plausibility task.

2.5.3 Object/Agent (R2)
For the earliest processing measure (i.e., first-fixation duration), there was no main effect of task, [F(1,100Mansbridge, Hayakawa, & Tamaoka
Task effects on sentence processing using eye-tracking42) = 0.51, p = .480, ns], yet the subsequent analysis on interaction between tasks revealed that atask:condition interaction was nonetheless present, [F(7, 417) = 2.34, p < .05]. When analyzing the tasks
separately it was observed that the verification task did not produced an effect, [F(3, 565) = 0.37, p = .772,ns], but the plausibility task did have a significant effect of sentence condition, [F(3, 724) = 5.51, p < .01].
The analysis from multiple comparisons revealed that the first fixation times on the agent within thepassive-AA condition were significantly shorter than the passive-AI agent and the active-IA object (4 < 1,2). Additionally, the active-AA object was read faster than the passive-AI agent (3 < 2). Despite the tasks
not having a difference between overall means, the plausibility task was able to produce effects between
conditions.
At the first-pass reading measure, there was no main effect, [F(1, 46) = 1.91, p = .174, ns]; and likebefore, an interaction effect was observed, [F(7, 446) = 4.38, p < .001]. Within the verification task a main
effect was not seen, [F(3, 736) = 1.33, p = .265, ns]. The plausibility task, on the other hand, had a main
effect, [F(3, 724) = 8.11, p < .001], demonstrating that the passive-AA condition was faster than theactive-IA and passive-AI conditions (4 < 1, 2).
At the late processing measures, there was an overall main effect of task found for dwell-times, [F (1,41) = 61.85, p < .001], which indicated that the verification task produced reliably longer reading times
than the plausibility task. Moreover, an interaction was observed, [F(7, 431) = 19.78, p < .001], whichallowed for further analyses on each task. The analysis for the verification task revealed that there was aneffect of sentence type, [F(3, 734) = 13.78, p < .001]. This revealed that the RTs for the passive-AI
condition were reliably faster than the other three conditions (2 < 1, 3, 4). Additionally, the active-IA
condition was faster than the passive-AA condition (1 < 4). Incidentally, the plausibility task also had asignificant effect, [F(3, 723) = 11.78, p < .001]. But in contrast to the verification task, it was shown that
the RTs for the active-IA condition were significantly longer than all other conditions (1 > 2, 3, 4).
At the second region, R2, regression-out proportion data was available for analysis. Congruent with the
RT measures, there was a significant main effect of task, [F(1, 1502) = 46.53, p < .001], which revealed
that the verification task was approximately twice as likely to regress out from the direct object, or agent,
into the subject in comparison to the plausibility task. In addition to the main effect, there was aninteraction of task:condition, [F (7, 1456) = 4.90, p < .001], which allowed for further analyses withineach task. Within the verification task, a main effect was found, [F(3, 737) = 4.05, p < .01], demonstratingthat the passive-AI condition regressed out less than the active-AA condition (2 < 3).
101

Region 3

Region 2

Region 1

Verification:

102

298

295

296

2

3

4

237

4

316

243

3

1

250

234

4

2

242

3

247

232

2

1

249

1

Means

9

10

10

10

7

6

8

7

6

8

6

7

SE

First-Fixation

918

718

910

688

351

382

353

378

506

519

517

533

Means

45

38

42

29

16

16

13

1,376

1,037

1,257

1,008

1,223

1,163

907

1,065

1,300

27

13

1,352

1,239

1,134

Means

63

47

60

46

55

56

44

44

57

52

49

48

SE

Dwell-Time

23

23

27

SE

First-Pass

94.74

96.00

94.36

96.52

20.47

28.78

16.92

24.39

-

-

-

-

Means

1.71

1.39

1.66

1.30

3.09

3.17

2.69

3.01

-

-

-

-

SE

Regression-Out

Table 3. Reading times and regression percentage for the task conditions
Plausibility:

307

332

295

313

231

235

256

256

209

209

214

205

Means

9

10

9

12

6

5

7

7

4

4

5

4

SE

First-Fixation

674

535

742

560

341

392

441

418

383

396

424

523

Means

24

24

26

30

13

15

19

19

13

12

18

28

810

617

912

798

632

729

704

848

721

753

704

952

28

27

35

39

28

30

29

39

27

27

30

41

SE

Dwell-Time

SE Means

First-Pass

85.32

87.63

79.33

91.19

5.94

12.20

6.78

18.52

-

-

-

-

Means

2.40

2.37

3.04

2.25

1.60

2.29

1.89

3.06

-

-

-

-

SE

Regression-Out『ことばの科学』第 28 号（2014 年 12 月）

Mansbridge, Hayakawa, & Tamaoka
Task effects on sentence processing using eye-tracking

Within the plausibility task there was a significant effect of sentence condition, [F(3, 759) = 6.53, p< .001], demonstrating that the regression-out proportion ratio for the active-IA condition was
significantly greater than both the passive-AA and passive-AI conditions (1 > 2, 4).

2.5.4 Verb (R3)
The early processing at the verb revealed that there was neither a main effect, [F(1, 57) = 0.83, p = .368,ns], nor was there an interaction, [F(7, 505) = 1.68, p = .113, ns] for the first-fixation durations.
Accordingly, no further analyses were conducted.
During first-pass RT there was a significant main effect of task, [F(1, 85) = 74.26, p < .001],
demonstrating that the RTs for the verification task were reliably longer than the plausibility task at thisprocessing stage. Furthermore, the analysis showed a significant interaction, [F(7, 408) = 16.78, p < .001],
which allowed for further analyses on each task. In the verification task, a significant effect of condition
was observed, [F(3, 726) = 14.49, p < .001], which indicated that the RTs were significantly longer forboth the passive-AI and passive-AA conditions in comparison to the active-IA and active-AA conditions(2, 4 > 1, 3). Within the plausibility task, a significant effect was also seen, [F(3, 711) = 17.54, p < .001].
Congruent with the verification task, the exact same effect was found; the passive conditions weresignificantly longer than the active conditions (2, 4 > 1, 3).
For the dwell-time measures (i.e., late processing) there was also a significant main effect of task, [F(1,40) = 50.06, p < .001], indicating that the plausibility task was significantly faster than the verification task.
Once again, an interaction of task:condition was found, [F(7, 418) = 21.59, p < .001]. Further analyses
showed that within the verification task a main effect of condition was present, [F(3, 726) = 17.65, p< .001]. The effects found here were the same as the first-pass reading; the passive conditions weresignificantly longer than the active conditions (2, 4 > 1, 3). Within the plausibility task, a main effect wasalso found, [F(3, 729) = 17.75, p < .001]; for this task, however, it was indicated that the RTs for theactive-AA condition were significantly faster than other three conditions (3 < 1, 2, 4). Additionally, it was
shown that passive-AI condition had significantly longer RTs than the active-IA condition (2 > 1); it wassuggested through a marginal value that the passive-AI condition was longer than the passive-AA
condition (p = .51).
Congruent with the RT data, a significant main effect of task was found for regression-out proportion,[F(1, 76) = 18.05, p < .001], which as before revealed that the verification task was significantly more103

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reliable to make a regression out of the verb into previous parts of the sentence. Moreover, there was aninteraction found for task:condition, [F(7, 556) = 4.79, p < .001]; however, in further analysis on theverification task, [F (3, 691) = 0.29, p = .831, ns] revealed no effects. The plausibility task, [F (3, 712) =3.92, p < .01], demonstrated that the active-IA condition was more likely to make a regression out of theverb than the passive-AI condition (1 > 2).

3. Discussion
The results of this study conclusively demonstrate that when participants have to use a verification
answering strategy (i.e., fact retrieval) reading times will increase during the processing of the sentence.
One unassuming explanation as to why reading time is a function of task is that participants are preparing
for later fact retrieval. In other words, as they are reading along they are storing the argument structure of
the sentence in working memory (c.f., Baddeley, 1996, 2003, MacDonald, Just, & Carpenter, 1992); andas a result, the extra memory work adds to the overall processing associated with reading the sentence.
Since the reading times associated with a plausibility judgment are much quicker, it becomes evident thatplausibility judgments do not require intricate short term memory input during sentence processing. Thisis congruent with past cognitive studies on verification versus plausibility (c.f., Reder, 1982; Reder &Ross, 1983; Reder & Wible, 1984) which report on the difference of the behavioral response times
between answering strategies rather than sentence processing time; accordingly, these results provide
evidence that fan effects (Anderson, 1974) are first present in the reading of the sentence that supersede
the use of the answering strategy.
This study’s use of plausibility judgments was dissimilar from the type of questions used in the studies
conducted by Reder and colleagues (Reder, 1982; Reder & Ross, 1983; Reder & Wible, 1984), whichtargeted plausibility of thematically related items of studied items and items that did not allow use of fact
retrieval strategies. In contrast, in this study judgments were made within the sentence on its plausibility of
being a sentence in the language (c.f., Caplan et al., 2008; Tamaoka, et al., 2005; Koizumi & Tamaoka,2010). Admittedly, the tasks between these studies are not equivalent, but they are neverthelesscomparable as neither relies on direct retrieval from previous knowledge derived from the study.
But even despite the differences in the methods of the studies, it has been a major argument in thisstudy that the difference in cognitive mechanisms of verification and plausibility also occurs during the104Mansbridge, Hayakawa, & Tamaoka
Task effects on sentence processing using eye-tracking

processing of the sentence. The results clearly show that the differences between the two tasks are not only
representative of a difference in overall reading time. When comparing the pattern of results of each task
against each other it becomes evident that they are indeed different from one another at the majority of thereported reading time measures.

3.1 Task-independent effects
Before digressing into the differences found within in each task, it is important to first note thesimilarities as it demonstrates what effects are task-independent. It has been reported (Koizumi &Tamaoka, 2010; Tamaoka, Asano, Miyaoka, & Yokosawa, 2013; Caplan et al., 2008) that the syntactic
parser is activated during plausibility judgments such that processing difficulty can be observed insyntactically more complex sentence types (e.g., passive vs. active or ORC vs. SRC). Accordingly, this
proposition is validated by our finding that syntactic processing at the verb had processing costs associatedwith the more complex passive voice conditions. This supports the finding proposed by Caplan et al.,(2008) that the syntactic parser is activated in both verification and plausibility tasks and is sensitive tosyntactic complexities regardless of the experimental task. Granted, there were some differences
associated to the voice effect that interacted with animacy in our plausibility task. But even so, bothpassive voice conditions resulted in longer reading times when compared to their active counterparts.

3.2 Plausibility
At the sentence level, it was shown that the verification task’s most difficult condition was the passive
sentence with two animate nouns (4), while in the plausibility task it was the active sentence with aninanimate agent and animate object (1). So, even though the processing during at the verb was the same,the overall processing is not directly associated with syntactic complexity in both tasks.
In the literature, one major argument of plausibility during sentence processing is that it occurs rapidly
at early processing measures for implausible or inconsistent semantics as a function of incremental
processing (Staub, Rayner, Pollatsek, Hyönä, & Majewski, 2007; Murray, 2006; Kamide & Mitchell,1999). In the case of this study, all experimental items were plausible sentences. However, the items withan inanimate agent may be considered as an inconsistent animacy value for agency preferences,
especially in the active condition where the inanimate agent sits in subject position (Traxler, Williams,
Blozis, & Morris, 2005; Ferreira, 1994). With the assumption that an inanimate agent is inconsistent with105

『ことばの科学』第 28 号（2014 年 12 月）

agency expectations, our results are in line with previous studies. For the active-voice condition with aninanimate agent, there was an effect shown during first-pass reading at the position of the inanimate agent
in subject position. In addition to this, for the passive counterpart with an inanimate agent, there was aneffect shown at the agent during first-fixation durations. In both cases, early processing measures reveal aneffect on the agent for inconsistent animacy. During late processing, presumably after the grammatical
function of the verb and thematic roles were established, it is shown that participants also take inconsideration of the plausibility of the sentence as well since the processing difficulty associated with thepassive-AI condition was attenuated within its inconsistent domain and its active counterpart became
significantly more difficult at spec of V, region 2. This effect can be explained by past research on subject
animacy effects where participants will front an animate object to subject position and passivize thesentence to avoid having an inanimate subject (Bock, Loebell, & Morey, 1992). Consequently, since theconditions with both an animate agent and patient lacked any presupposed violation in animacy, they
were free from effects associated with their animacy during plausibility.

3.3 Verification
Moving into comprehension, it has been argued that there is a similarity based interference ofthematic arguments when processing syntactically complex sentences, like ORCs, if the sentence isunbiased to an assumed assignment (Gordon et al., 2001; Gordon, Hendrick, & Levine, 2002; Caplan etal., 2008; Traxler et al., 2005; MacDonald et al., 1992). In line with these studies, the more complex
passive voice condition was more difficult to process when both nouns were animate which is likely
attributed to thematic disambiguation of competing nouns in working memory. Albeit, unlike theplausibility task, these effects were not able to be observed until the processing of the verb which suggeststhat the participants begin to have processing difficulty after grammatical argument is established. Anotherjustification as to why the other passive condition with an inanimate agent failed to induce processing costhas been argued by Mak, Vonk, and Schriefers (2002); thematic roles contrasting in animacy attenuates
the processing costs associated with ambiguity of thematic roles. This result is in contrast with the findingof Rayner, Warren, Juhasz, and Liversedge (2004) which revealed an early effect of an impossible
thematic role assignment using a verification strategy. However, in their study it was impossible toappropriately assign the theta-role to the direct object, and in their condition where it was more or lessinappropriate but possible, effects were not seen immediately. Considering this, our results are consistent106

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Task effects on sentence processing using eye-tracking

with theirs as all of our items were possible utterances.
In addition to working memory ambiguity, the cognitive differences that drive the fan effects may be
attributed to the storage of orthographic and phonetic representations into short term memory (Caplan etal., 2008). Despite the fact that phonological processing can occur during a semantic judgment whenusing tongue-twisters (McCutchen & Perfetti, 1982; Hanson, Goodell, & Perfetti, 1991), work done byCaplan et al. (2008), Caplan and Waters (1999), Daneman, Reingold, and Davidson (1995), and Chen
and Shu (2001) argue that phonological representations are secondary and even optional to lexical andorthographic activation. As such, it can serve to help resolve ambiguity, phonetic interferences, and as wellbe used a strategy to rehearse the sentence in memory for later fact retrieval and overall comprehension(Caplan et al., 2008; Keller, Carpenter, & Just, 2003; McCutchen, Bell, France, & Perfetti, 1991).
Following this assumption, phonological processing during silent reading may be an ancillary cognitive
function that is used in comprehension which contributes to an increase of reading times.

4. Conclusion
The results of this study support the argument made by Caplan et al. (2008) that participants are using
more cognitive processes than that are required to process the sentence in order to complete a verification
task. These extra cognitive functions in turn contribute to an induced reading time of the sentence which issimilar to a fan effect (i.e., over activation). More importantly, each task relies on a different set of thematicheuristics to complete the task which in turn alters processing. Verification is sensitive to similar entities
during thematic disambiguation, and plausibility is sensitive to the semantic requirements assumed by apossible thematic role. Though, both tasks share similarities in syntactic processing with difficulty being
found with passive sentence types at the verb. Yet both tasks are purposeful as verification tasks may be
useful in studies to demonstrate overall comprehension difficulty, and plausibility tasks, can be effective todemonstrate processing difficulty associated with syntactic difficulty free of semantic effects if
experimental items are controlled to be semantically neutral. As a final point, it is important for a
researcher to understand that language use in a laboratory setting is a function of task, and experimental
methodology needs to be appropriately selected to target a particular language or cognitive phenomena.

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----------------------------------MANSBRIDGE Michael (MANSBRIDGE, Michael)
名古屋大学大学院 国際言語文化研究科・研究生
michaelp.mansbridge@gmail.com

早川 杏子(HAYAKAWA, Kyoko)
名古屋大学大学院 国際言語文化研究科 日本言語文化専攻・助教
hayakawa@lang.nagoya-u.ac.jp

玉岡 賀津雄（TAMAOKA, Katsuo）
名古屋大学大学院 国際言語文化研究科・教授
ktamaoka@lang.nagoya-u.ac.jp

110