| Research Ar cle

Received: 4 March 2020 | Accepted: 23 May 2020
DOI: 10.37716/HBAB.2020010203

Trade-oﬀ eﬀect in the processing of Korean case-drop sentences: An
eye tracking inves ga on
Shaoyun Yu¹*, Katsuo Tamaoka¹
Nomina ve and accusa ve case markers are frequently dropped in colloquial Korean. Prior studies demonstrate a nomina ve-accusa ve
asymmetry in frequency and acceptability of case-drop sentences, which correlates well with the implica ons of the actor iden ﬁca on
strategy (AIS) of the extended argument dependency model (eADM). This study inves gated the reading of case-drop sentences by na ve
Korean speakers using the eye tracking methodology. While the nomina ve-accusa ve asymmetry was only par ally observed in the reading
me data, our results showed an important trade-oﬀ eﬀect in the processing of case-drop sentences: a decrease in reading me on the barenoun phrase (NP) was balanced by an increase in reading me on the case-marked NP. A cri cal excep on to this trade-oﬀ eﬀect suggested
that extra processing costs can be avoided when the AIS works in an op mal condi on.
Keywords: case marking, nomina ve-accusa ve asymmetry, Korean, eye tracking, word order, animacy

INTRODUCTION
Although a majority of subject-object-verb (SOV) languages
exhibit a case marking system (1), universally overt case marking isrelatively uncommon among case marking languages (2). In manydifferential case marking (DCM) languages, for example, the use
or non-use of overt case marking is strongly modulated by the
animacy and/or definiteness of the noun (3–5).
The nominative (marks the subject) and accusative (marks theobject) case markers are frequently dropped or omitted in colloquial
Korean and Japanese. However, unlike differential case marking(DCM) languages, case marker drop is far less predictable in these
two languages. Case drop in Korean and Japanese is considered
to be “a multi-factor phenomenon that is affected by a variety ofpragmatic, semantic, syntactic, and phonological factors” (4).
Case markers in Korean are attached after noun phrases (NPs).
The nominative case marker is -ka or -i; the accusative case marker
is -lul or -ul. Below are two sets of examples of case drop in Korean.
Sentence (2) is an example of nominative case-drop, where thenominative case marker -ka for the subject Cinwu (a person) is
dropped. Sentence (4) is an example of accusative case-drop, where
the accusative case marker -lul for the object Mincay (a person) is
dropped. Although sentences in (2) and (4) each have one missing
case marker, both sentences are acceptable.
(1) Cinwu-ka khwukhi-lul mek-ess-ta
Cinwu-NOM cookie-ACC eat-PST-IND
Cinwu ate the cookie.
(2) Cinwu-∅ khwukhi-lul mek-ess-ta
Cinwu-∅ cookie-ACC eat-PST-IND(3) Senho-ka Mincay-lul mil-ess-ta
Senho-NOM Mincay-ACC push-PST-IND
Senho pushed Mincay.
(4) Senho-ka Mincay-∅ mil-ess-ta
Senho-NOM Mincay-∅ push-PST-IND
Theoretical studies have argued that nominative case-drop canbe more restricted in usage than accusative case-drop and theformer can lead to ungrammaticality in Korean and Japanese (6–8). Previous corpus-based studies have shown that such nominativeaccusative asymmetry does exist, and that nominative case-drop (forsubjects) is less frequent than accusative case-drop (for objects)
in both Korean and Japanese. Lee (9) reported that in colloquial¹Graduate School of Humani es, Nagoya University, Japan.
*Corresponding author. Email: s.yu@nagoya-u.jp (S.Y.)

Korean, the accusative case marker is dropped 65% of the time
while the nominative case marker is dropped only 41% of the time.
Furthermore, based on survey results, Yu and Tamaoka (10) foundthat while both accusative and nominative case drops were lessacceptable than fully case-marked sentences, accusative case drop
was generally more acceptable than nominative case drop, in bothsubject-object-verb (SOV) and object-subject-verb (OSV) word
orders (see Fig. 1). Given that the presence of either nominativeor accusative case markers allows unambiguous identification of thesubject and object in a transitive sentence (11, 12), it is an intriguingquestion of how such nominative-accusative asymmetry came into
existence.

Fig. 1. Es mated acceptability of Korean case-drop sentences (10)The possible underlying cause for the nominative-accusative
asymmetry in case drop sentences may be the fundamental asymmetry in the identification of the subject and object during language comprehension (13–15). In their neurolinguistic model, theargument dependency model (eADM), Bornkessel-Schlesewsky
and Schlesewsky propose the “actor identification strategy” (AIS)
as one principal strategy employed by the language processing
system to determine the thematic (e.g., actor or undergoer) andsyntactic roles (e.g., subject or object) of the arguments in thesentence. The AIS contends that the identification of arguments isfundamentally asymmetrical in that the process is actor-centered(the notions of “actor” and “subject” are equivalent in the context
of the present study): the parser seeks to “identify the actor
role as quickly and unambiguously as possible” (14). Support for

Copyright © 2020 Interna onal Society of Neuroscience Publishing Company Ltd. All rights reserved.
Vol.1(2), 2020www.humanbab.net | 49

| Research Ar cle
the AIS has been drawn from experiments on a wide range oflanguages, including English (16), German (17–19), Turkish (20),and Mandarin Chinese (21, 22).
According to the AIS and the eADM, identification of the actor(subject) is guided by the analysis of an array of “prominencefeatures” (14, 15) which can be regarded as a set of features thata typical actor is expected to possess. For example, a typical actor(subject) is expected to be a human or an animate noun (i.e., a living
creature) rather than an inanimate noun; this property of the actor isdescribed as the “+human” or “+animate” feature. The actor (subject) is also preferred to come as the first argument of the sentence
rather than appearing after other arguments (e.g., after the object),
and this is referred to as “+1st (argument) position”. Furthermore,
and importantly, in nominative-accusative case marking languages
such as Korean, a typical actor (subject) is expected to have thenominative case (e.g., marked by -ka or -i in Korean), carrying
the “+nominative” feature. This expectation of the +nominative
feature has a direct implication on the case drop phenomenon in
Korean. Because the argument identification process focuses on
identifying the actor (subject) both unambiguously and efficiently,
and because nominative case marking is one of the critical featuresthat the parser leverages to determine the subject, the lack ofovert nominative case marking should pose uncertainty about the
subject role of the argument to the parser, and therefore should
induce greater processing difficulties. By contrast, accusative casedrop should have less impact on sentence processing since thenominative case marker is preserved, enabling the parser to identify
the subject efficiently. The asymmetry of nominative and accusative
case drops in usage frequency and acceptability in the Korean
language may be a result of the intrinsic difference in the case drops
in terms of processing difficulty or efficiency.
Few studies have explored the online processing of case-drop
sentences in Korean. While the impact of case drop on sentence
processing can be deduced from the AIS of the eADM, to ourknowledge, the effects of missing the +nominative feature have notyet been examined as extensively as have with other prominencefeatures in the line of eADM-based studies. This could be partially
due to the difficulty of manipulating the presence or absenceof nominative and accusative case marking independently andsystematically in many case marking languages such as German(where case marking information is embedded in articles, and theform varies with noun gender and number) and Turkish (where there
is no explicit case marker for the nominative case). The optionalityof the nominative and accusative case markers in Korean makes it
possible to study the effects of the lack of overt case marking inhighly controlled conditions.
The present study investigates the reading of case-drop sentencesby native Korean speakers with the eye tracking methodology. We
would expect a correlation between the reading time and the resultsfrom previous corpus and acceptability studies (9, 10). First, bothnominative and accusatives case-drop sentences should be moredifficult to process than normal, fully case-marked sentences as
the former are less acceptable (processing difficulties should bereflected as relative increases in reaction time or reading time).
Second, nominative case-drop sentences should be more difficult toprocess than their accusative case-drop counterparts as the former
are both less frequent and less acceptable than the latter; suchprediction is also implied by the AIS. The experimental sentencesof this study were simple active sentences that omitted either thenominative case marker or the accusative case marker. Fully casemarked sentences were used as the baseline. The stimuli also varied

Vol.1(2), 2020

Yu and Tamaoka

in object animacy (inanimate and human) and word order (SOV andOSV).
RESULTSWe report the Bayesian analysis of reaction time and eye-tracking
reading measures (first fixation duration, gaze duration, and total
duration) in the following sections, with a focus on the effects ofcase marking.
For each unique condition formed by the combination of thecategorical predictors, we computed the estimated posterior values
with the population-level coefficients (the covariate trials was
fixed to its mean). Furthermore, we back-transformed the values
to the response scale (in milliseconds) to allow more intuitive
interpretation. The back-transformed values for reaction time andthe fixation duration measures corresponded to the median on
the response scale (by taking the exponential of µ : eµ ). For
contrasts between conditions, however, we calculated the posteriordifferences (denoted by ∆) in the scale of the model coefficients for
simplicity (i.e., the logarithmic scale).
Statistical inference was performed based on posterior summaries. Medians of posterior distributions were used as point
estimates. 95% equal-tailed credible intervals (CrI) served asinterval estimates (50% CrIs were also illustrated in figures). The
posterior probability of an effect being greater or less than zero,denoted as P (∆ > 0) or P (∆ < 0), was jointly utilized to determine
the existence of the effect. Interpretation of the results is intuitive:
evidence for an effect can be concluded if the 95% CrI excluded
zero; weak evidence for an effect can be supposed if the 95% CrI
covered zero but P (∆ > 0) or P (∆ < 0) was considerably high, e.g.,
being above 95% (23–26).
Reac on TimeAn overview of the estimated median reaction time (RT) for eachcondition is shown in Fig. 2. To evaluate the effects of casemarking, we compared the estimates of the three case marking
conditions while holding other categorical predictors at fixed levels.
Fig. 3 summarizes the results of the comparison by presenting thepoint and interval estimates both visually and numerically; it also
provides values of P (∆ > 0). The posterior probability densities
are plotted as colored areas to give a visualization of the posterior
distributions.

Fig. 2. Es mated median reac on me (ms). Reac on me is back-transformed
as medians on the response scale. Points are posterior medians; outer and innerintervals indicate 95% and 50% equal-tailed credible intervals.

As displayed in the top-left quarter of Fig. 3, for SOV inanimateobject sentences, the estimated RT in the accusative case-drop
condition (ACC drop) was faster than the nominative case-drop50

| Research Ar cle

Yu and Tamaoka

Fig. 3. Es mated diﬀerences of case marking condi ons in reac on me (log scale). Points are posterior medians; outer and inner intervals indicate 95% and 50% equal-tailed
credible intervals (CrI). Numbers over the intervals summarize (1) the posterior median and the 95% CrI, (2) the posterior probability of the diﬀerence being greater than zero,as denoted by P (∆ > 0). Probabili es are in bold if P (∆ > 0) ≥ 95% or P (∆ > 0) ≤ 5%.

condition (NOM drop), ∆ = 0.07, 95% CrI [0.01, 0.14], P(NOM drop
> ACC drop) = 98.7%. Moreover, there was also weak evidence that
RT in the ACC drop condition was faster than the fully case-marked
condition (Full), ∆ = 0.06, 95% CrI [-0.01, 0.12], P(Full > ACCdrop) = 96.4%. For reference, the estimated median RT was 932ms,
95% CrI [778, 1109] for ACC drop; 985 ms, 95% CrI [822, 1176]
for Full; and 1003 ms, 95% CrI [835, 1199] for NOM drop.
While ACC drop was expected to be processed faster than NOM
drop, it was somewhat surprising that ACC drop was processed
even faster than the Full condition. It was also unexpected thatdifferences between the case marking conditions were only foundfor SOV inanimate-object sentences. More details on participants’reading behavior were revealed by the reading measures, which
could offer explanations for this outcome.
Reading measures
We examine three fixation duration measures: 1. First fixation
duration, which is the duration of the very first fixation on anarea of interest (AOI) in progressive reading; 2. Gaze duration,or first-pass reading time, which is the accumulated duration of
all fixations on an AOI in progressive reading; 3. Total duration,
which is the summed duration of all fixations on an AOI during
the entire reading period. First fixation duration and gaze duration
are commonly regarded as early measures of sentence processing,while total duration is regarded as a late measure (27, 28).
First ﬁxa on dura on
Case-marker drop appeared to have negligible effects on first
fixation duration as can be seen from Supplementary Fig. 1.
Although the case-marked nouns are one character longer thanthe bare nouns, reading time seemed to be comparable for thecase-marked and bare nouns, regardless of word order and object
animacy configurations. Summaries of the posterior differences(Supplementary Fig. 2) confirmed the observation that no reliable
differences existed between the case marking conditions at thefirst noun phrase (NP1) and the second noun phrase (NP2). Therewas weak evidence that for OSV human-object sentences, fixation
duration on the verb in the ACC drop condition might be shorter
than that in the Full condition (see the bottom-right portion
Vol.1(2), 2020of Supplementary Fig. 2). However, we refrain from further
interpretation of this result as it was not found in other fixation
duration measures.
The result that NPs had similar first fixation duration whether
the case marker was presented or not indicates that case-marking
information is not taken into account at the very earliest stage ofreading. This is understandable as first fixation duration is mainly
associated with word recognition (28).
Gaze dura on
As identified in Fig. 4, a particular pattern of reading behaviorbegan to emerge in gaze duration. Using the Full condition asa baseline, one can observe that in either the ACC drop or the NOM
drop condition, one of the two NPs tended to receive shorter
fixations than the baseline while the other tended to receive longer
fixations. More specifically, it was the bare noun that tended to be
fixated shorter while the case-marked noun tended to be fixated
longer. As an example, refer to the bottom-left quarter of Fig. 4,
which corresponds to the results of SOV human-object sentences.
The NP1 in the ACC drop condition was nominative case-marked
and its estimated gaze duration was longer than the baseline;
meanwhile, the NP2 was a bare noun with the accusative casemarker dropped and its estimated gaze duration was shorter thanthe baseline. This observation can be verified with the posterior
summaries by referring to the ACC drop−Full and NOM drop−Fullcontrasts in Supplementary Fig. 3 (the first two rows in each small
panel).
This particular pattern was not always clearly manifested in gaze
duration when compared to results for total duration (especially for
OSV sentences). We examine the pattern in greater detail in the next
section.
Total dura on
The previously mentioned pattern became more evident in total
duration (Fig. 5). A “trade-off” in reading time can generally befound between the two NPs in the case-drop conditions: the bare
NP was read longer while the case-marked NP was read shorter, incomparison to the baseline (Full). The same results in Fig. 5 arealso visualized from an alternative perspective in Supplementary51

| Research Ar cle

Fig. 4. Es mated median gaze dura on (ms). Dura on is back-transformed as
medians on the response scale. Points are posterior medians; outer and innerintervals indicate 95% and 50% equal-tailed credible intervals.

Fig. 4, where the case marking conditions are used as the x-axes,
and the SOV and OSV word orders are compared side-by-side foreach of the AOIs (NP1, NP2, Verb). The x-axes of SOV and OSV
are matched vertically according to the presence or non-presence ofcase marker; for both SOV and OSV, the x-axis order is (1) Full, (2)NP1-CASE NP2-∅, and (3) NP1-∅ NP2-CASE. Supplementary
Fig. 4 clearly demonstrates that in the case-drop conditions, a nounphrase tended to be read longer when it was case-marked, andit tended to be read shorter when the case marker was dropped,
irrespective of the word order.

Fig. 5. Es mated median total dura on (ms). Dura on is back-transformed as
medians on the response scale. Points are posterior medians; outer and innerintervals indicate 95% and 50% equal-tailed credible intervals.

There was, however, one obvious exception to this tradeoff pattern. As can be seen from the top-left quarter of Fig.
5 (corresponding to SOV inanimate-object sentences), the total
duration of the NP1 in the ACC drop condition did not appear to
deviate from the baseline Full condition. Because the NP1 wasnominative cased-marked and the NP2 was a bare noun in the
ACC drop condition for these sentences (with an “NP1- NOM NP2∅ Verb” configuration), the NP1 should have been fixated longer
relative to the baseline if the trade-off effect existed. However, theestimated total duration of the NP1 in ACC drop was 394 ms, 95%
CrI [335, 462], which was not reliably different from the baseline(383 ms, 95% CrI [326, 450]), ∆= 0.03, 95% CrI [-0.06, 0.12],
P(ACC drop > Full) = 72.7%. By contrast, the estimated durationof the NP2 in ACC drop (285 ms, 95% CrI [236, 345]) was indeed
shorter than the baseline (332 ms, 95% CrI [276, 402]), ∆ = 0.15,
Vol.1(2), 2020

Yu and Tamaoka

95% CrI [0.06, 0.25], P(Full > ACC drop) = 99.9%. Therefore, it canbe concluded that the trade-off effect was absent for the NP1 in the
ACC drop condition of the SOV inanimate-object sentences. Thisfinding has a direct link with the results of the reaction time data.
Recall that for SOV inanimate-object sentences, the reaction time of
the ACC drop condition was faster than the normal Full condition.
The explanation for this outcome could be attributed the fact that
the NP2 in the ACC drop condition was read shorter than Full, while
the reading time on the NP1 was not inflated, leading to an overall
reduction in reaction time.
Fig. 6 details the comparison of the case marking conditions.
Focusing on the ACC drop−Full and NOM drop−Full contrasts(the first two rows in each small panel) for the NP1 and NP2, we
can confirm that the trade-off effect was generally true in mostcases except for the aforementioned ACC drop condition of the
SOV inanimate-object sentences. It should be noted that there werealso two minor “exceptions”. The trade-off effect was somewhat
weaker for the NP1 in the NOM drop condition of OSV humanobject sentences (with an “NP1-ACC NP2-∅ Verb” configuration);
the NP1 here should be read longer than the baseline but the effectwas weak, ∆ = 0.08, 95% CrI [-0.02, 0.17], P(∆ > 0) = 94.2%(see the second panel from the top-right of Fig. 6). The trade-off
effect was also somewhat weaker for the NP2 in the NOM drop
condition of OSV inanimate-object sentences (with an “NP1-ACC
NP2-∅ Verb” configuration); the NP2 here should be read shorter
than the baseline but the effect was weak, ∆ = -0.08, 95% CrI [-0.18,
0.03], P(∆ > 0) = 7.2% or P(∆ < 0) = 92.8% (see the third panel
from the top-right of Fig. 6). Because the overall tendency was stillvisible and the values of P(∆ > 0) or P(∆ < 0) were still reasonably
high, we assume that the trade-off effect was weakly maintained inthose two cases.
As with the results of gaze duration (Supplementary Fig. 3), no
credible difference in reading time can be found between the casemarking conditions for the final verb.

DISCUSSION
The trade-oﬀ eﬀect
We first recap the prevalent pattern in our results before discussing
the theoretical implications. The present study revealed a trade-off
effect in the processing of Korean case-drop sentences by native
Korean speakers. Results of total fixation duration demonstrated
that reading time was generally reduced for the bare noun phrase(NP) and increased for the case-marked NP (relative to the fully
case-marked condition) during the whole reading period (see Fig.
5 and Supplementary Fig. 4). For sentences with an “NP1-ACC
NP2-∅ Verb” or “NP1-NOM NP2-∅ Verb” configuration, the casemarked NP1 was generally fixated longer than in the fully casemarked condition, and the case-less NP2 was fixated shorter. In
the same vein, for sentences with an “NP1-∅ NP2-ACC Verb” or“NP1-∅ NP2-NOM Verb” configuration, the case-marked NP2 wasgenerally fixated longer than in the fully case-marked condition,and the case-less NP1 was fixated shorter. This trade-off effectwas detectable at an early stage of processing (in gaze duration),
although the effect in OSV sentences became more evident only
when it was measured at a later stage (in total duration). The tradeoff effect also explains why reaction time did not reliably differ
between case-drop sentences and fully case-marked sentences inmost situations: a decrease in reading time on the bare NP wasbalanced by an increase in reading time on the case-marked NP.
However, there was a critical exception to the trade-off effect.
52

| Research Ar cle

Yu and Tamaoka

Fig. 6. Es mated diﬀerences of case marking condi ons in total dura on (log scale). Points are posterior medians; outer and inner intervals indicate 95% and 50% equal-tailed
credible intervals (CrI). Numbers over the intervals summarize (1) the posterior median and the 95% CrI, (2) the posterior probability of the diﬀerence being greater than zero,as denoted by P (∆ > 0). Probabili es are in bold if P (∆ > 0) ≥ 95% or P (∆ > 0) ≤ 5%.

For SOV inanimate-object sentences, accusative case-drop (sentence configuration: NP1[human]-NOM NP2[inanimate]-∅ Verb)
caused decreased reading time on the case-less NP2 while reading
time on the case-marked NP1 was not increased accordingly. This
unusual absence of processing trade-off was correlated with areduction in overall reaction time: SOV inanimate-object sentencesin the accusative case-drop condition tended to have slightly
faster reaction time than in the fully case-marked condition. The
accusative case-drop condition also had reasonably faster reaction
time than the nominative case-drop condition.
Role iden ﬁca on strategies in the reading of case-drop
sentences
Only a very sparse correlation was found between the resultsof the present study and previous findings in frequency andacceptability (9, 10). While case-drop sentences were expected
to be processed more slowly than fully case-marked sentences
because of their inferior acceptability, it turned out that in mostcases, the overall reaction time of the case-drop sentences andfully case-marked sentences did not differ reliably (due to thetrade-off effect summarized above). Furthermore, we expected that
nominative case-drop sentences should be more difficult to process
Vol.1(2), 2020

than their accusative case-drop counterparts. This prediction wason the one hand based on the previous frequency and acceptability
data, and on the other hand based on the assumption of theactor identification strategy (AIS) (13–15) that the identification ofarguments is fundamentally centered on the actor (subject) rather
than on the undergoer (object). Despite the convergence of the AIS
and previous empirical data, our second prediction was only attested
in a single scenario, namely, the case of SOV inanimate-object
sentences. As discussed below, we interpret the trade-off effectand its crucial exception as reflecting some important strategies forthe online comprehension of case-drop sentences, although some
aspects of the observations may be specific to reading.
The trade-off effect suggests that the uncertainty in identifying
the role of the bare NP (subject or object) can be compensated forby further referring to the case-marked NP. Case marking can assist
the parser to determine the thematic and syntactic roles of the NPs
before encountering the verb (13, 29–31). In Korean, case marking
information is reported to be the predominant factor in resolving
the roles of the arguments in transitive sentences (12). The bare NP
in the case-drop sentences lacked explicit case marking, therefore it
could give rise to uncertainty about the NP’s role. Additionally, thebare NP was consistently estimated to be read for less time than its

53

| Research Ar cle
case-marked counterpart in the fully case-marked condition. Such
difference in reading time is probably a reflection of the cost forprocessing the case information carried by the case marker. The
increase in reading time on the case-marked NP of the case-drop
sentences should reflect a compensating strategy: the uncertainty
about the role of the bare NP was resolved by further referring to
the case marking information of the other NP (and the NP’s roleas indicated by the case information). Because there are only twoarguments (subject and object) in a simple transitive sentence, therole of one argument can always be deduced from the role of theother. Our results suggest that the parser’s strategy to consolidate
its decision on the role of the bare NP is approached by referring toand reconfirming the role of the case-marked NP.
Surprisingly, the processing trade-offs caused by nominative andaccusative case drops during real-time reading were apparently
symmetric in that the two types of case drop eventually resulted
in comparable overall reaction time in most cases. However, it
would be too early to conclude that the prevalence of the trade-off
effect might be a challenge to the actor identification strategy (AIS),
which assumes a fundamental asymmetry in the identification of
the subject and object roles. To appropriately interpret our results,
we need to further discuss the crucial exception to the trade-off
effect where a nominative-accusative asymmetry did exist (in SOV
inanimate-object sentences).
In and only in SOV inanimate-object sentences, the trade-off
effect was absent for the accusative case-drop condition, which
had an “NP1[human]-NOM NP2[inanimate]-∅ Verb” configuration(see the top-left quarter of Fig. 5). On the other hand, the trade-off
effect was evident for the nominative case drop counterpart, which
had an “NP1[human]-∅ NP2[inanimate]-ACC Verb” configuration.
With the word order and animacy factors being held constantand case marking being the only variable, we consider this sharp
contrast of nominative case drop and accusative case drop as amanifestation of the subject-object (or actor-undergoer) asymmetry
in argument role identification (13–15). The absence of the tradeoff effect in the accusative case-drop condition indicates that theargument identification process in this situation was so efficient thatthe compensating strategy that would cost extra reading time on
the case-marked NP (here, the nominative case-marked NP1) wascompletely avoided.
This processing advantage of accusative case drop over nominative case drop in the SOV inanimate-object sentences canbe explained by the fact that the accusative case-drop condition(NP1[human]-NOM NP2[inanimate]-∅ Verb) can offer an optimal
configuration for the actor identification strategy (AIS) to work.
The AIS aims to identify the actor (subject) as quickly andunambiguously as possible, and one crucial corollary of the AIS
is that the two arguments would compete for the actor (subject)role if both of them carry some prominence features, leading to
a decline of processing efficiency (13–15). The accusative casedrop condition of the SOV inanimate-object sentences had two
key advantages that allowed the AIS to work in the most efficient
way among all the case-drop sentences. Recall the sentence configuration of this condition: “NP1[human]-NOM NP2[inanimate]∅ Verb”. First, the subject (i.e., NP1) in this particular conditionis highly typical, bearing major prominence features that a typicalsubject (or actor) is supposed to have: +1st argument position,+human, and +nominative (the subject appears as the first argumentof the sentence, is a human noun, and is nominative case-marked).
Second, and importantly, there is no competition from the otherNP (i.e., NP2). The NP2 (the object) in this particular condition

Vol.1(2), 2020

Yu and Tamaoka

possesses exactly none of the prominence features that would signal
the subject: it is a case-less inanimate noun that comes as thesecond argument of the sentence. Therefore, because the NP1 washighly typical for the subject, and because there was entirely nocompetition for the subject role from the other NP (i.e., NP2), theparser must enjoy extremely high confidence about the subject roleof the NP1. After encountering the NP2, the parser must have also
deduced the object role of the NP2 with great certainty, and it
thus bypassed the now-superfluous procedure of further referring to
the case-marked NP, eliminating the trade-off effect. As a striking
result, the lack of accusative case marking of the inanimate NP2
did not hinder sentence processing at all, and it even suggested a
possibly faster reaction time than the fully case-marked baseline.
By contrast, the nominative case-drop condition of the
SOV inanimate-object sentences (with an “NP1[human]∅ NP2[inanimate]-ACC Verb” configuration) was obviously
suboptimal for the AIS to achieve its aim of efficiently identifying
the subject. Although there was no competition from the NP2 (asit did not carry any prominence features), NP1 deviated from atypical subject in a significant fashion: lacking the +nominative
feature. To resolve the uncertainty about the subject role of theNP1, reading time on the NP2 was increased (the trade-off effect).
The asymmetry of the nominative case-drop condition and theaccusative case-drop condition provides evidence that the lackof overt nominative case marking (for the subject) would cause
greater processing difficulties to the parser than the lack of overt
accusative case marking (for the object), which echoes the actor(subject)-centered assumption of the AIS.
Now we consider why the effects of nominative and accusative
case drops were apparently symmetric in other parts of our results(i.e., all OSV sentences, and the SOV human-object sentences).
We would like to propose that these results might not necessarily
be counterexamples to the actor identification strategy (AIS),
especially regarding the potential methodology-specific issues. We
speculate that the trade-off effect might take over whenever the
AIS was not allowed to work at its best efficiency. For example,
the non-canonical OSV sentences are not optimal, as the object
NP always occupies the first argument position, and accordingly,
the subject always lacks the “+1st argument position” feature. The
SOV sentences with human objects are suboptimal as well because
the objects always bear the “+human” feature, which would cause
competition for the subject (actor) role. We further speculate thatthe trade-off effect might be specific to the reading paradigm of
our eye tracking experiment, in which participants can preview
contiguous regions (27) and are free to re-read any part of thesentence. The trade-off effect would not be possible if the sentence
is presented word-by-word as with the self-paced reading paradigm
and many event-related potential (ERP) studies. When reading
case-drop sentences with a suboptimal configuration, the parser
might adopt a straightforward strategy to benefit from free-reading:just look more at the case-marked NP whose role is promised
to be identifiable, and then the uncertainty about the bare NP’srole should be resolved (the compensating strategy). The trade-off
effect by this straightforward strategy might have overridden the
more fine-grained nominative-accusative asymmetry in case-drop
sentences. This speculation is admittedly rudimentary and tentative,and definitely requires further validation.
Future research
The actor identification strategy’s (AIS’s) assumption that theargument role identification process is fundamentally asymmetric54

| Research Ar cle
between the actor (subject) and the undergoer (object) is consistent
with usage frequency and acceptability data of Korean casedrop sentences. Although the present study revealed important
processing strategies in the reading of Korean case-drop sentences,
our prediction that nominative case-drop sentences would be moredifficult to process than accusative case-drop sentences was only
partially supported. Methodology-specific strategies used by theparticipants might be responsible for the inconsistencies between
the reading data and previous findings. Our results therefore
call for validation with different experimental paradigms. As the
majority of empirical evidence for the AIS is accumulated fromelectrophysiological experiments based on the eADM (13–15, 32),
further work is planned to investigate the processing of Korean casedrop sentences with ERP experiments.
MATERIALS AND METHODS
Experimental Design
The present experiment was designed to investigate the effects ofnominative and accusative case-drops on Korean sentence processing. The experimental items were comprised of 96 stimuli and 96
filler sentences. All stimuli were simple active sentences consistedof two nouns and one verb. Three areas of interest (AOIs) were
defined for each stimulus sentence: region of the first noun phrase(NP1), region of the second noun phrase (NP2), and region of the
final verb (Verb).
The stimuli were derived from 16 original SOV sentences bymanipulating case marking and word order. All subjects of theoriginal sentences were commonly used Korean given names.
The objects for half (8) of the original sentences were inanimate
nouns, and for the other half were human given names. Three casemarking conditions (including the original sentence) were createdby dropping either the nominative or the accusative case marker:(1) the fully case-marked condition (Full), which was also thebaseline; (2) the accusative case-drop condition (ACC drop); and(3) the nominative case-drop condition (NOM drop). OSV sentenceswere derived from the original SOV sentences. As above, the 96
stimuli can be cross-classified by three factors: object animacy(2 conditions), case marking (3 conditions), and word order (2conditions).
Additionally, the nouns in the stimuli all ended with vowels;
therefore, the nominative and accusative case markers appeared
only in the forms of –ka (nominative) and –lul (accusative). The
phonological form of the nominative and accusative case markers in
Korean depends on the last phoneme of the noun phrase preceding
the case marker. The forms –ka (nominative) and –lul (accusative)
are used after vowels, while –i (nominative) and – ul (accusative)
are used after consonants.
The length of the nouns was controlled so that only two-hangul(or two-character) words were used (e.g., 쿠키 khwukhi “cookie”).
The verbs in the stimuli were in the past tense, and all had a lengthof three characters (e.g., 팔았다 phal-ass-ta “sold”).
Procedure
Participants’ eye-movements during reading were recorded by
an SR Research EyeLink 1000 eye-tracker (right-eye monocular
tracking, 1000Hz sampling rate). A total of 192 items were divided
into six blocks, each containing 16 stimuli and 16 fillers. The
presentation order of blocks and trials was randomized, and variantsof the same original sentence never appeared in the same block.
Rest breaks were provided between blocks. A nine-point calibration
was performed before each block. Drift-checking was performed
Vol.1(2), 2020

Yu and Tamaokaprior to each trial, during which participants’ gaze was directed to a
fixation dot located at the beginning of the upcoming sentence.
For each trial, participants read the whole sentence silently attheir own pace and pressed the “Yes” button on a gamepad whenfinished (with a timeout after 10 seconds). To keep the participants
focused, a comprehension task was given after 1/4 of the trials at
random intervals. In the task, a verification sentence was displayed,and participants were required to judge whether it agreed with thepreviously read sentence by pressing the “Yes” or “No” button on
the gamepad. For example, the sentence Hyenci put on the coat was
followed by an incorrect verification sentence Hyenci took off thecoat. Half of the verification sentences were incorrect, and the locus
of disagreement was either the subject, the object, or the verb (or anadverbial expression in some of the filler sentences). Participants
went through a practice session of 8 trials before the experiment.
The experiment was presented on a 21.5-inch LCD monitor at arefresh rate of 120Hz. The screen resolution was set to 1024×768
pixels. All experimental items were displayed on a single line, in
Malgun Gothic font with a 30-pixel size. Each item was left-alignedwith an 80-pixel offset and was centered vertically. Participants
were seated at a distance of approximately 80 cm from the monitor,
with their head stabilized by a chin rest. One hangul (Korean
character) corresponded to approximately 0.75-degree of visual
angle under the above settings.
Par cipants
Thirty native Korean speakers (20 males and 10 females) participated in the experiment and were included in the final data analysis.
The participants all had normal or corrected-to-normal vision. Theirmean age was 26 years, ranging from 20.0 to 38.2 years (SD =4.4). At the time of the experiment, most of the participants werestudents at Nagoya University, Japan; two participants were nonstudent residents in Nagoya city. Participants were informed of theexperimental procedure, and written consent was obtained before
the experiment.
Four additional participants took part in the experiment but were
excluded from data analysis. One participant was rejected due torelatively poor accuracy in the comprehension task (81%). The
other three participants were rejected because they skipped thefirst noun phrase (NP1) at very high frequencies during first-passreading (33%, 43%, and 69% of the time, respectively).
Sta s cal Analysis
Data cleaning
Fixation data cleaning was performed prior to analysis. Fixations
shorter than 80 ms were merged to a directly adjacent fixation if
the adjacent fixation was longer than 80 ms and their horizontal
distance was within 0.5-degree visual angle. After the merging, allfixations shorter than 80 ms or longer than 800 ms were removed(1,801 or 9.8% of the original 18,341 data points).
Reaction time was analyzed in addition to the eye tracking data.
Outliers that exceeded three median absolute deviations (33) inlogarithmic scale for each unique condition were excluded (18 or
0.6% of the total 2,880 data points).
Hierarchical regression modeling
The data of reaction time, first fixation duration, gaze duration, andtotal duration were analyzed using hierarchical Bayesian regression
models (34) using Stan (35, 36) and R. For the fixation duration
measures, the population-level predictors included four categoricalpredictors: AOI, case marking, object animacy, word order, as
55

| Research Ar cle
well as all their interactions, resulting in a 3 × 3 × 2 × 2 design.
Furthermore, the number of preceding trials (trials) was included
as a covariate. Regressors for the categorical predictors and theirinteractions were created with treatment coding. The numeric
regressor trials were centered and scaled to have a mean of zeroand a standard deviation (SD) of 0.5 (37). The intercept and theslopes for all regressors were allowed to vary by participants; theintercept and the slopes for AOI’s regressors were also allowed
to vary by items (i.e., stimuli). This model structure corresponds
to the maximal random effects structure proposed by Barr, Levy,
Scheepers & Tily (38). For the analysis of reaction time, the samemodel structure was applied except that AOI was not included.
Likelihoods, priors, and posterior sampling
Visual inspection showed that the distributions of reaction time andthe various fixation duration measures were heavily right-skewed.
Log-normal likelihoods were used in the models as the Box-Cox
procedure suggested logarithmic transformation of these data toattain approximate normality (39).
Weakly informative priors were specified for the model parameters (37, 40). Internal to the models, a population-level grandmean intercept was estimated with centered regressors, and the realintercept was restored afterward. The grand-mean intercept wasgiven a Student’s t prior centered at the mean of the response
variable, with ν = 3 and a scale of 10 times of the data SD (in log
scale). The population-level slopes were given Student’s t priors
centered at zero, with ν = 3 and a scale of 2.5 times of the data SD(in log scale). For the by-participant and by-item varying intercepts
and slopes, multivariate normal priors centered at zero were
specified to allow for possible correlations between the varying
coefficients. The variance-covariance matrices were decomposed
into a vector of scale parameters and a correlation matrix. The
Cholesky factors of the correlation matrices were given LKJ priors
with η = 2, which moderately favor lower correlations a priori(41). Finally, the scale parameters of the multivariate normal priors
and the log-normal likelihood were given exponential priors with arate (λ ) being the reciprocal of the data SD (in log scale).
For each model, the joint posterior distribution was sampled byrunning eight Monte Carlo Markov chains, each with 3,000
iterations (including 1,000 warm-ups). All fitted results were successfully validated with Rb (42) and diagnostics specific to Stan’s
Hamiltonian Monte Carlo sampler (43).
SUPPLEMENTARY MATERIALSSupplementary materials for this article are available at http://www.humanbab.net/qfycontent/uploads/2020/06/2020010203SM.pdf
Supplementary Fig. 1. Es mated median ﬁrst ﬁxa on dura on (ms).
Supplementary Fig. 2. Es mated diﬀerences of case marking condi ons in ﬁrst ﬁxa on dura on(log scale).
Supplementary Fig. 3. Es mated diﬀerences of case marking condi ons in gaze dura on (logscale).
Supplementary Fig. 4. Es mated median total dura on (ms), alterna ve perspec ve.

REFERENCES
1. M. S. Dryer, Case dis nc ons, rich verb agreement, and word order type (Comments on
Hawkins' paper). Theor. Linguist. 28, 151-157 (2002).
2. K. Sinnemäki, A typological perspec ve on diﬀeren al object marking. Linguis cs 52, 281-313(2014).
3. J. Aissen, Diﬀeren al object marking: Iconicity vs economy. Nat. Lang. Linguist. Theory 21,
435-483 (2003).
4. H. D. Hoop, P. D. Swart, Diﬀeren al Subject Marking, H. D. Hoop, P. D. Swart, eds. (Springer,2009), pp. 1-16.
5. G. Iemmolo, G. Klumpp, Introduc on to the special issue `Diﬀeren al object marking:
Theore cal and empirical issues'. Linguis cs 52, 271-279 (2014).
6. H. D. Ahn, S. Cho, Unpronounced case markers and apparent subject-object asymmetry. Stud.
Mod. Gramm. 43, 55-80 (2006).

Vol.1(2), 2020

Yu and Tamaoka

7. M. Fukuda, Head government and case marker drop in Japanese. Linguist. Inq. 24, 168-172(1993).
8. M. Saito, Case and government in Japanese. WCCFL 2: Proceedings of the West Coast
Conference on Formal Linguis cs pp. 247-259 (1983).
9. H. Lee, Diﬀeren al Subject Marking, H. de Hoop, P. de Swart, eds. (Springer, 2009), pp. 41-61.
10. S. Yu, K. Tamaoka, Proceedings of the 157th Mee ng of the Linguis c Society of Japan(Linguis c Society of Japan, 2018), pp. 300-305.
11. H. de Hoop, A. L. Malchukov, Case-Marking Strategies. Linguist. Inq. 39, 565-587 (2008).
12. T. Ito, S. Tahara, W. Park, Bun no rikai ni hatasu joshi no hataraki: nihongo to kankokugo
o chuushin ni [The func on of case markers in sentence comprehension: With a focus on
Japanese and Korean] (Kazama Shobou, 1993).
13. I. Bornkessel-Schlesewsky, M. Schlesewsky, The role of prominence informa on in the realme comprehension of transi ve construc ons: A cross-linguis c approach. Linguist. Lang.
Compass 3, 19-58 (2009).
14. I. Bornkessel-Schlesewsky, M. Schlesewsky, Language Down the Garden Path: The Cogni ve
and Biological Basis for Linguis c Structures, M. Sanz, I. Laka, M. K. Tanenhaus, eds. (Oxford
University Press, 2013), pp. 241-252.
15. I. Bornkessel-Schlesewsky, M. Schlesewsky, Compe ng Mo va ons in Grammar and Usage, B. MacWhinney, A. Malchukov, E. Moravcsik, eds. (Oxford University Press, 2014), pp. 107126.
16. J. Weckerly, M. Kutas, An electrophysiological analysis of animacy eﬀects in the processing ofobject rela ve sentences. Psychophysiology 36, 559-570 (1999).
17. D. Roehm, M. Schlesewsky, I. Bornkessel, S. Frisch, H. Haider, Frac ona ng language
comprehension via frequency characteris cs of the human EEG. NeuroReport 15, 409-412(2004).
18. I. Bornkessel, B. McElree, M. Schlesewsky, A. D. Friederici, Mul -dimensional contribu ons togarden path strength: Dissocia ng phrase structure from case marking. Journal of Memory
and Language 51, 495-522 (2004).
19. M. Schlesewsky, I. Bornkessel, On incremental interpreta on: Degrees of meaning accessed
during sentence comprehension. Lingua 114, 1213-1234 (2004).
20. Ş. B. Demiral, M. Schlesewsky, I. Bornkessel-Schlesewsky, On the universality of language
comprehension strategies: Evidence from Turkish. Cogni on 106, 484-500 (2008).
21. M. Philipp, I. Bornkessel-Schlesewsky, W. Bisang, M. Schlesewsky, The role of animacy in thereal me comprehension of Mandarin Chinese: Evidence from auditory event-related brain
poten als. Brain Lang. 105, 112-133 (2008).
22. L. Wang, M. Schlesewsky, B. Bickel, I. Bornkessel-Schlesewsky, Bickel & I. BornkesselSchlesewsky. Exploring the nature of the `subject'-preference: Evidence from the online
comprehension of simple sentences in Mandarin Chinese. Lang. Cogn. Process 24, 1180-1226(2009).
23. B. Nicenboim, S. Vasishth, Sta s cal methods for linguis c research: Founda onal Ideas-Part
II. Linguist. Lang. Compass 10, 591-613 (2016).
24. B. Nicenboim, P. Logačev, C. Ga ei, S. Vasishth, When high-capacity readers slow down andlow-capacity readers speed up: Working memory and locality eﬀects. Front. Psychol. 7 (2016).
25. D. Paape, B. Hemforth, S. Vasishth, Processing of ellipsis with garden-path antecedents in
French and German: Evidence from eye tracking. PLOS ONE 13, e0198620 (2018).
26. B. Nicenboim, S. Vasishth, F. Engelmann, K. Suckow, Exploratory and conﬁrmatory analyses insentence processing: A case study of number interference in German. Cogn. Sci. 42, 10751100 (2018).
27. K. Rayner, A. Pollatsek, Handbook of Psycholinguis cs, M. Traxler, M. Gernsbacher, eds.
(Academic Press, 2006), pp. 613-657.
28. A. Staub, K. Rayner, The Oxford Handbook of Psycholinguis cs , M. G. Gaskell, ed. (Oxford
University Press, 2007), pp. 327-342.
29. B. VanPa en, M. Smith, Word-order typology and the acquisi on of case marking: A selfpaced reading study in La n as a second language. Second Lang. Res. 35, 397-420 (2019).
30. M. Ueno, M. Polinsky, Does headedness aﬀect processing? A new look at the VO–OV contrast.
J. Linguist. 45, 675-710 (2009).
31. E. T. Miyamoto, Case markers as clause boundary inducers in Japanese. J. Psycholinguist. Res.
31, 307-347 (2002).
32. I. Bornkessel, M. Schlesewsky, The extended argument dependency model: A neurocogni ve
approach to sentence comprehension across languages. Psychol. Rev. 113, 787-821 (2006).
33. C. Leys, C. Ley, O. Klein, P. Bernard, L. Licata, Detec ng outliers: Do not use standard devia onaround the mean, use absolute devia on around the median. J. Exp. Soc. Psychol. 49, 764-766(2013).
34. A. Gelman, J. Hill, Data analysis using regression and mul level/hierarchical models(Cambridge University Press, 2007).
35. B. Carpenter, A. Gelman, M. D. Hoﬀman, D. Lee, B. Goodrich, M. Betancourt, M. Brubaker,
J. Guo, P. Li, A. Riddell, Stan: A probabilis c programming language. J. Stat. So w. 76 (2017).
36. T. Sorensen, S. Hohenstein, S. Vasishth, Bayesian linear mixed models using Stan: A tutorial
for psychologists, linguists, and cogni ve scien sts. Quant. Methods Psychol. 12, 175-200(2016).
37. A. Gelman, A. Jakulin, M. G. Pi au, Y.-S. Su, A weakly informa ve default prior distribu on forlogis c and other regression models. Ann. Appl. Stat. 2, 1360-1383 (2008).
38. D. J. Barr, R. Levy, C. Scheepers, H. J. Tily, Random eﬀects structure for conﬁrmatory
hypothesis tes ng: Keep it maximal. J. Mem. Lang. 68, 255-278 (2013).
39. G. E. P. Box, D. R. Cox, An analysis of transforma ons. J. R. Stat. Soc. Ser. B Methodol. 26,
211-243 (1964).
40. Stan Development Team. Prior Choice Recommenda ons. GitHub (2019).
41. D. Lewandowski, D. Kurowicka, H. Joe, Genera ng random correla on matrices based onvines and extended onion method. J. Mul var. Anal. 100, 1989-2001 (1989).
42. A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari, D. Rubin, Bayesian data analysis (CRC
Press, 2013).
43. M. Betancourt, A conceptual introduction to Hamiltonian Monte Carlo, preprint at https://arxiv.
org/abs/1701.02434v2 (2018).

56

| Research Ar cle

Yu and Tamaoka

Acknowledgements: We would like to thank Jeeseon Kim for helping us in preparing theexperimental materials and recrui ng par cipants. Funding: This research was self funded. Author
contribu ons: S.Y. and K.T. designed the study and developed the experimental materials. S.Y.
conducted the experiment, analyzed and interpreted the data, and dra ed the manuscript. K.T.
provided experimental equipment, aided in interpre ng the results and worked on the manuscript.
Compe ng interests: The authors declare that they have no compe ng interests. Data andmaterials availability: All data and materials are available upon request to the corresponding
author.

Vol.1(2), 202057