Influence of word-based prediction errors
on syntactic priming
Manabu Arai

Franklin Chang

1)

Keywords: Syntactic priming; implicit learning; prediction error; passive structure
Abstract
Syntactic priming is a phenomenon where people tend to repeat syntactic
structures that they have recently experienced. It has been argued to reflect errorbased implicit learning of syntactic structures, where the error signal comes fromword-level predictions in online comprehension. Previous studies, however, used
sentence-level prediction errors measured offline and it is not yet clear whether
syntactic priming indeed reflects word-level prediction errors. By making use of
the ambiguous case marker ni in Japanese, that is used to denote either a goal rolein the dative structure or an agent role in the passive structure, we manipulated
structural expectations at the preverbal argument in prime sentences. Our results
demonstrated a larger effect of syntactic priming when participants previously
experienced the dative structure in filler items than when they did not experienceany dative fillers. The finding is taken as the evidence for an influence of wordlevel prediction errors on syntactic priming.

1．Introduction
People tend to repeat syntactic structures that they have recently experienced.
1) Department of English Studies, Kobe City University of Foreign Studies

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成城・経済研究 第 243 号（2024 年 1 月）

For example, one would be more likely to produce a passive sentence such as The
celebrity was embarrassed by the news anchor after hearing a passive sentence
like The city was attacked by terrorists. This phenomenon is called syntactic
priming and occurs across utterances with different words and meanings (Bock,1986). It has also been observed both in production and comprehension (Arai, vanGompel, & Scheepers, 2007; Branigan, Pickering, & McLean, 2005; Thothathiri &Snedeker, 2008). This suggests that the effect taps into a shared representation ofsyntactic structures that can adapt to recent exposure. Furthermore, it has beenshown that priming can be quite long lasting (Bock & Griffin, 2000; Branigan,
Pickering, Stewart, & McLean, 2000; Hartsuiker, Bernolet, Schoonbaert, Speybroeck, &Vanderelst, 2008) and it is even possible for adults to learn novel sentence structures(Kaschak & Glenberg, 2004).

The persistence of priming has been used to argue that syntactic priming is aform of implicit learning rather than just transient activation of a particularsyntactic structure (e.g., Bock & Griffin, 2000; Chang, Dell, Bock, & Griffin, 2000). To
demonstrate this link, Chang, Dell, and Bock (2006) developed a model ofsyntactic priming using an error-based learning mechanism. The model would
make predictions about the next word in sentences and the difference between themodel’s prediction and the actual next word was called the error signal. The model
was able to use this error signal to change the weights in the model so that themodel was better able to predict the sentences that it experienced. Part of thesechanges involved changes to syntactic representations and this created syntactic
priming if learning was left ON during the processing of the prime sentence.
Support for error-based learning comes from studies that find that magnitude ofpriming is inversely correlated with structural preference of individual verbs(Barnolet & Hartsuiker, 2010, for production; Fine & Jaeger, 2013, for comprehension).

When the verb’s preference mismatches the sentence structure, that mismatch─ 98 ─

Influence of word-based prediction errors on syntactic priming

should create larger prediction errors and yield more weight changes.
The idea of using the mismatch between expectations and actual inputs is a
general idea that is also present in theories of language comprehension. One suchapproach is the surprisal theory (Hale, 2001; Levy, 2008). It assumes that processing
cost is inversely correlated with the surprisal value, which is defined as thenegative logarithm of the probability of a structure (x) given the preceding context
as shown in (1).
(1) 񐁳񐁳񐁵񐁵񐁲񐁲񐁰񐁰񐁲񐁲񐁩񐁩񐁳񐁳񐁡񐁡񐁬񐁬񀀨񀀨񞍸񞍸񀀩񀀩񀀽񀀽

񀀱񀀱񀁬񀁬񀁯񀁯񀁧񀁧 񐁰񐁰񐁲񐁲񐁯񐁯񐁢񐁢񐁡񐁡񐁢񐁢񐁩񐁩񐁬񐁬񐁩񐁩񐁴񐁴񞍹񞍹񀀨񀀨񞍸񞍸񀀩񀀩

The surprisal value for a particular input would be large if it is unlikely on thebasis of preceding input. In the classic example The horse raced past the barn fell,
the surprisal for the word fell is very large because all the input preceding the verb
supports the dominant (most frequent) main clause analysis (Bever, 1970). This
explains the very large processing difficulty on encountering the verb fell, which iscommonly referred to as a garden-path effect.
Surprisal has also been used to explain structural priming in a way that issimilar to error-based learning (Fine & Jaeger, 2013; Fine, Jaeger, Farmer, & Qian, 2013;
Hartsuiker et al., 2008). The main difference is that surprisal is estimated at aparticular level (e.g., words, structure) and the effects of surprisal are also localized
to that level. Surprisal accounts of sentence comprehension often use word-based
surprisal, and surprisal accounts of syntactic priming use estimated surprisal of
sentence structures in a particular corpus or norming study (Fine et al., 2013; Jaeger& Snider, 2013). For example, Fine et al. (2013) conducted a study using a self-

paced reading task to investigate the influence of prediction errors on thesubsequent processing of similar sentences using the sentences such as (2).
(2) The experienced soldiers warned about the dangers conducted the midnight─ 99 ─

成城・経済研究 第 243 号（2024 年 1 月）

raid.
They found that the reading times for the ambiguous relative clause sentences
like (2) decreased over the course of the experiment relative to those for theunambiguous counterpart (The experienced soldiers who were told about the dangers
conducted the midnight raid) as predicted by the surprisal that they calculated. In theirstudy, the surprisal value was estimated based on corpus statistics for individual
verbs (i.e., how often a particular verb occured with the relative clause or the main clausestructure occurs as a whole) as well as also local statistics (i.e., how many relative clausesentences had appeared at a given trial during the experiment). Error-based learning

accounts of priming differ in that they assume that word-based error/surprisal isused to generate error/surprisal at the structural level. On this account, syntactic
priming should be sensitive to word-based surprisal and that is not predicted in apure surprisal account of priming.
Another issue with past studies is that the prediction errors based on the
structural bias of a specific verb are likely to be lexically associated. For example,
Jaeger and Snider (2013), who showed that their estimated surprisal can account forthe priming effect in comprehension, calculated the lexical surprisal. That is, for a
verb give, for example, the surprisal is estimated based on how often this particular
verb appeared in the double object construction and in the prepositional object
construction in the Switchboard corpus (See also Bernolet & Hartsuiker, 2010).
Therefore, learning has to be at least partly lexically specific although the effect ofthe prediction error with a specific verb is generalized to a different verb (See
Kaschak & Borreggine, 2008 for the finding that distributional information of syntactic
structures for a particular verb caused little influence on a long-term structural priming
effect). It is known that a priming effect tends to be stronger when the verb isrepeated between prime and target than when it is not and previous research─ 100 ─

Influence of word-based prediction errors on syntactic priming

suggests that this lexical boost effect is likely to be attributed to explicit memory
rather than error-based learning. In support of this, Hartsuiker et al. (2008) showedthat a lexical boost effect is short-lived whereas an abstract priming effect is longlasting and thus reflects error-based learning.
In this work, we hope to tease apart the word-level surprisal/prediction error
effects in priming. In particular, we would like to see if these effects occur
independently of verbs, which have many links to structural representations.
Japanese provides a useful language to test these issues, because Japanese is averb-final language, where structural processing must occur before the verb isencountered (Kamide, Altmann, & Haywood, 2003; Miyamoto, 2002). Syntactic priming
has been found in both production as well as in comprehension with active/passive
structures independently of the verb (e. g., Ishikawa, Arai, & Hirose, 2014 forproduction; Arai & Mazuka, 2014 for comprehension). To test for word-based prediction

error, we manipulated the ambiguous case marker ni in the passive structure. In
Japanese, the case marker ni is used not only to mark an agent role in the passive
structure as in (3) but also to mark a goal role in the dative structure as in (4).
(3) Otokonoko-ga
boy-NOM

tomodachi-ni

tatak-are-ta.

friend-OBLIQUE

hit-PASSIVE-PAST“The boy was hit by his friend.”(4) Otokonoko-ga

tomodachi-ni

hanashikake-ta.

boy-NOM

friend-DAT

talked to-PAST“The boy talked to his friend.”
This creates structural ambiguity on encountering a noun phrase with the casemarker ni as comprehenders would not know whether it denotes an agent role in─ 101 ─

成城・経済研究 第 243 号（2024 年 1 月）

the passive structure or a goal role in the dative structure. We hypothesized that adative use of the ambiguous case marker ni would increase the probability ofpredicting the dative on the next encounter with the case marker. In this study, wetested syntactic priming by presenting active/passive transitive prime sentences
and seeing if they influenced descriptions of transitive target pictures. We alsocollected reading time measures to provide measures of expectations on theprimes. To manipulate expectations about the case marker ni, we varied the fillers
that were used between the prime-target pairs in three conditions. The All-dative
condition only had dative fillers, the No-dative condition had other structures
without ni arguments, and the Half-dative had half of both of these conditions.
One possibility is that participants will treat dative ni and passive ni as thesame case marker based on their form. In this Form-based account, participants
will have stronger expectations for the case marker ni in the All-dative condition
than the No-dative condition (with Half-dative in between). If priming is due to error
or surprisal, then they should show less syntactic priming of passives when there
are more dative fillers. Thus, the Form-based account predicts priming will bestrongest in the No-dative condition.
Another possibility is that participants treat dative ni and passive ni asdifferent case markers based on their function. This Function-based account argues
that the structures that are primed depend in part on the meaning that they encode.
The case marker ni encodes the agent in the passive and it encode the recipient in
the dative. If this is the case, then the All-dative condition will yield strong
expectations that the case marker ni is marking the dative recipient, and when it isseen in the passive prime, strong error will be generated. Thus, the Function-based
account should predict stronger passive priming in the All-dative condition thanthe No-dative condition.
A final possibility is motivated by theories of syntactic priming that do not─ 102 ─

Influence of word-based prediction errors on syntactic priming

use error/surprisal. In these theories, syntactic priming is due to the residual
activation of structures that is caused by the processing of prime sentences(Pickering & Branigan, 1998). Since passive structures and dative structures aredifferent structures, residual activation does not transfer from one structure to theother. In addition, the residual activation mechanism does not depend on
expectations, so the composition of the fillers should not influence passive
priming. Therefore, this activation account predicts priming should be the sameacross all three conditions.

2．Experiment2.1. Method
2.1.1. Participants
Forty-three participants, recruited from the student community at the University ofTokyo, took part in the experiment for which they received monetarycompensation. All the participants were native speakers of Japanese.
2.1.2. Stimuli
We prepared 18 experimental items, each consisting of a pair of a prime item and atarget item. The prime item was either an active sentence (5a) or a passive sentence(5b).
(5a) Rooba-ga
old lady-NOM

akanboo-osarat-ta.

baby-ACC

abduct-PAST“The old lady abducted the baby.”(5b) Akanboo-ga
baby-NOM

rooba-nisaraw-are-ta.

old lady-DAT

abduct-PASSIVE-PAST“The baby was abducted by the old lady.”─ 103 ─

成城・経済研究 第 243 号（2024 年 1 月）

The target item was made of a set of a verb and a target picture. The verb waspresented prior to the target picture. The target picture was a simple line-drawing
such as Figure 1.
The target picture can be described by either an active sentence (e.g., Keikanga dorobou-o tsukamaeta, “The policeman caught the thief ”) or a passive sentence (e.g.,
Dorobou-ga keikan-ni tsukamaerareta, “The thief was caught by the policeman”). The verb

was never repeated between prime and target2). We included sixty filler sentencesin total. Two to four fillers were inserted between each experimental item. For
those fillers, we manipulated the occurrence of the dative structure at three levels.
In the All-dative condition, the fillers that appeared between experimental items
were always dative sentences. In the Half-dative condition, half of the fillers were
the dative structure and the other half were the unrelated structure. In the Nodative condition, all the fillers were of the unrelated structures. These unrelated
fillers were neither the dative sentences nor the passive sentences. In addition, weFigure 1. An example of target pictures
2)

Previous research showed that repetition of the verb is not required for a priming effectof the passive structure in Japanese (Arai & Mazuka, 2014; See also Arai, 2012 for the
discussion on the influence of head-directionality on syntactic priming).

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Influence of word-based prediction errors on syntactic priming

also included 12 picture fillers for picture description, whose pictures depicted
intransitive events. The independent variables are Prime Type (Active or Passive)
and Filler Type (All-dative, Half-dative, or No-dative), resulting in 2 × 3 design. We
created 6 experimental lists based on Latin square design.
2.1.3. Procedure
Participants were given an identical instruction and signed a consent form. They
were then randomly assigned to one of these experimental conditions. They readand comprehend a sentence that was presented on the monitor and were asked todescribe the picture when a picture was presented, using the verb presented prior to
the picture. In the experimental trails, participants first read a prime sentence,
which was either an active or passive sentence. They next saw a target verb (e.g.,
tsukamaeru, ‘arrest’) and then a target picture, which they described using thepreviously shown verb. Crucially, all the participants saw the same experimental
items and only difference was the structure type of the filler sentences that are
inserted between them. The entire experiment took approximately 30 minutes.
2.2. Results
2.2.1. Reading Times of Prime Sentences
We first analyzed reading times of the sentence-final verb in the prime sentences.
Note that the verb form was different between the active and passive conditions
but identical across the three filler conditions. Figure 2 shows the reading times atthe verb in each condition.
For the data analysis, we first excluded reading times exceeding 5000ms as
such extreme values are likely to reflect some kind of errors. We analyzed theremaining data using Linear Mixed-Effects (LME) models (e.g., Baayen, Davidson, &Bates, 2008). The model included Prime Type (Active or Passive) and Filler Group─ 105 ─

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Figure 2. Reading times at the verb in prime trials for each condition(All-dative, Half-dative, or No-dative) as fixed factors, including their interaction, plus

Trial Order (The order number that the prime trial appeared in the experiment).
Participants and items were included as random factors. Prime Type was centeredwith deviation coding (-0.5, + 0.5) while the filler structure was coded with dummy
variable coding, with the No-dative condition is treated as a baseline. Trial Orderwas centered and normalized. We selected the optimal model using backward
selection approach. We started from the model with the maximal random structure
including slopes for Prime Type and Filler Group as well as their interaction forboth random variables. With the optimal model, we further excluded extreme
values whose residuals exceeded ±3 SD from the model (Baayen, 2008). We reportcoefficients (β), standard errors (SE), t values, and their p values from the best-fit
model. Fixed factors’ p values were computed based on the posterior distribution
computed using Monte Carlo Markov chain sampling. Where the optimal model
included more than one random slope, we computed the p value for each factor
using likelihood ratio (LR) tests. Table 1 showed the results from the optimalmodel.
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Influence of word-based prediction errors on syntactic priming
Table 1. The results from the optimal LME model on reading times at the verb
(Intercept)

Coeff.

SE

t

p

754.5

77.5

9.74

<.001

Prime Type

14.1

35.5

0.40

0.692

Half-dative

173.3

107.7

1.61

0.115

All-dative

89.7

105.9

0.85

0.402

Trial Order

-80.1

17.53

-4.57

<.001

Prime Type × Half-dative

-9.4

50.3

-0.19

0.852

Prime Type × All-dative

99.0

49.5

2.00

0.046

The results showed that there was neither a main effect of Prime Type nor
Filler Group. The main effect of Trial Order was fully significant, suggesting thatthe reading times of the verb decreased over the course of the experiment.
Importantly, we observed a significant interaction between Prime structure and theAll-dative condition. The positive coefficient suggests that the difference inreading times between active and passive prime sentences were significantly larger
in the All-dative condition than in the No-dative condition. Given that ourparticipants read the identical prime sentences, this must be due to the experienceof the dative fillers. Since those in the All-dative condition expected the dative
structure more after reading the dative fillers, they were more surprised to see thepassive verb, resulting in increased reading times in the All-dative condition
compared to those in the No-dative condition. The interaction between Prime Typeand the Half-dative condition was not significant.
2.2.2. Production of passive descriptions
We next analyzed the production of passive descriptions. Figure 3 showed the
proportions of the target descriptions using the passive structure.
We analyzed the data using Generalized Linear Mixed Models (GLMM)(Jaeger, 2008). We selected the optimal model using backward selection approach.

The fixed and random effects included in the model was the same as the one on
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reading times (Prime Type as within-participants and Filler Group as between participants
as well as Trial Order). Table 2 showed the results of the analysis.

There was neither a main effect of Prime Type nor that of Filler Groupalthough the effect of the Half-dative condition was marginally significant. There
was a main effect of Trial Order, suggesting that our participants became lesslikely to produce a passive description over the course of the experiment. This is
likely to suggest that the participants become less surprised on seeing the passive
prime over the course of the experiment. In the experiment as a whole, they were
exposed to the equal production of the active and passive structures in the whole

Figure 3. Proportions of passive completions per condition.
Table 2. The results from the optimal GLMM model on the number of passive descriptions(Intercept)

Coeff.

SE

z

p

-2.56

0.45

-5.68

<.001

Prime Type

0.60

0.41

1.45

0.147

Half-dative

0.99

0.55

1.81

0.070

All-dative

0.14

0.55

0.26

0.793

Trial Order

-0.74

0.22

-3.40

<.001

Prime Type × Half-dative

0.36

0.54

0.66

0.510

Prime Type × All-dative

1.16

0.58

2.00

0.046

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Influence of word-based prediction errors on syntactic priming

experiment, which is different from normal statistics in real life, in which thepassives occur relatively much less frequently than the actives. Importantly, weobserved a significant interaction between Prime Type and All-dative condition.
This demonstrates that the structural priming effect was larger in the All-dative
condition compared to the No-dative condition. We did not find an interaction
between Prime Type and Half-dative condition. The results of the passive
production therefore are consistent with those of reading times at the verb.
2.2.3. Additional Analysis I: Combined Analysis of Reading Times and Passive
Production
We next combined the reading time data in the prime trials and the passive
production data in the target trials for each trial and conducted a combined
analysis. If an effect of structural priming reflects prediction errors at the verb in
the prime trials, we may be able to observe a direct relationship between thereading times and an effect of priming. We replaced Filler Group with the reading
times in prime trials and used the same model as the one on the passive
descriptions. We now have a continuous variable of Prime Reading Time insteadof the three-level categorical factor of Filler Group. Table 3 showed the results of
the analysis.
Figure 4 shows the relationship between the reading times at the verb in theprime trials and the likelihood of the passive production for each prime structure.
Table 3. The results from the optimal GLMM model of the combined analysis
Coeff.

SEzp(Intercept)

-2.16

0.31

-7.02

<.001

Prime Type

1.09

0.24

4.60

<.001

Prime Reading Time

0.12

0.14

0.83

0.405

Trial Order

-0.75

0.22

-3.49

<.001

Prime Type × Prime Reading Time

-0.48

0.22

-2.20

0.028

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Figure 4. Relationship between predicted likelihood of passive target and reading times inverb region of prime sentences

Although there is a numerical trend that the greater reading times are associatedwith the greater likelihood of the passive production, the main effect of Prime
Reading Times was not significant. However, the results showed a significant
interaction between Prime Type and Prime Reading Time.
The pattern of the interaction however does not exactly match what we
predicted. It appears to reflect the positive correlation between the likelihood of
the passive production and the reading times of the active prime sentences rather
than those of the passive prime sentences. Planned comparisons demonstrated thatthe simple effect of Prime Reading Times is not significant with passive primes(β= -0.12, SE = 0.16, z = -0.76, p = 0.445) and it is marginally significant with active

primes (β= 0.35, SE = 0.19, z = 1.86, p = 0.062). Although the effect was not fully
significant, this indicates that the greater reading times at the verb in active prime
sentences were associated with the greater likelihood of the passive production.
We discuss possible interpretation of this finding in the following section.

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Influence of word-based prediction errors on syntactic priming

2.2.4. Additional Analysis II: Interaction between Prime Structure and Trial order
In the above analyses, trial order was included as a control variable without
including an interaction between Prime Type and Trial Order. Since the effect ofword-level prediction errors should vary on a trial-by-trial basis due to adaptation
to the input in the local environment, the influence of trial order on priming of thepassive structure is non-trivial for the model of error-based implicit learning aswell as the surprisal theory. We initially did not include this interaction in theearlier analyses because the models with the interaction failed to converge and thus
yielded no output, which might be because there were not enough data points ineach condition in this complex design.
One different approach is to adopt Helmert coding, which can be applied forordinal variables. With this coding scheme, each level of a variable is compared tolater levels of the variable. Since the Filler Group variable can be treated as an
ordinal variable (All→Half→None), this coding scheme could solve theconvergence issue with the earlier analyses. Specifically, two contrasts are tested
with this coding. The first one is the contrast of No-dative level vs. two dative
levels (All-dative and No-dative conditions merged). The second one is the contrast of
Half-dative level vs. All-dative level (excluding the No-dative level). Inclusion of theinteraction between Prime Type and Filler Group with Helmert coding would tell
us how the differences in these contrasts would differ for each prime structure.
This additional analysis with Helmert coding also included a three-way interaction
between Prime Type, Filler Group, and Trial Order both on the prime reading
times as well as the target passive descriptions. First, Table 4 shows the output
from the optimal LME analysis on the reading times at the verb in the prime trials.
The results showed a significant interaction between Trial Order and theDatives vs. No-dative contrast and also a significant interaction between Trial
Order and Prime Type. Importantly, these two interactions are accounted for by a
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significant three-way interaction between the Datives vs. No dative contrast, Prime
Type, and Trial Order. Figure 5 shows the relationship between the prime reading
times and trial order for each group and prime type. This three-way interaction isdue to the fact that the lines for actives and passives differ in the two dative filler
Table 4. The results from the optimal LME model on reading times with the interactions
with trial order included
Coeff.

SEzp(Intercept)

6.63

0.05

130.76< 0.001

Trial Order

-0.10

0.02

-6.05

< 0.001

Prime Type

0.05

0.02

2.43

0.016

Datives vs. No-dative

0.11

0.07

1.58

0.121

All-dative vs. Half-dative

-0.03

0.12

-0.25

0.807

Datives vs. No-dative × Trial Order

0.04

0.03

2.29

0.023

Prime Type × Trial Order

-0.05

0.02

-2.52

0.012

Prime Type × Datives vs. No-dative

0.02

0.03

0.77

0.444

All-dative vs. Half-dative × Trial Order

-0.03

0.03

-1.17

0.242

All-dative vs. Half-dative × Prime Type

0.12

0.05

2.34

0.020

Datives vs. No-dative × Prime Type × Trial Order

-0.06

0.03

-2.01

0.045

All-dative vs. Half-dative × Prime Type × Trial Order

0.02

0.05

0.38

0.706

Figure 5. Relationship between log-transformed reading times of prime verbs and trial orderfor each prime type

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Influence of word-based prediction errors on syntactic priming

conditions, with the slope for the passive prime being steeper than that for theactive prime, but are almost parallel in the No-dative condition.
This demonstrated the longer reading times for the passive structure at the
beginning of the experiment compared to the active structure in the two dative
conditions, suggesting that the impact of dative fillers on the prediction error with
passive prime sentences was largest at the beginning of the experiment.
Next, Figure 6 shows the relationship between the production of the passive
structure and trial order for each group and each prime type. The pattern looked
surprisingly similar to that of the reading times. Whereas there seems no difference
between the two prime types in the No-dative condition, the likelihood of passive
production in the passive prime condition seems higher at the beginning of theexperiment than that in the active prime condition in the two dative filler
conditions. However, the GLMM with Helmert coding failed to converge and no
output was obtained. The pattern was therefore not statistically confirmed.

Figure 6. Relationship between the likelihood of passive production and trial order for eachprime type

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In sum, the additional analysis of reading times showed that the processing
cost that reflects prediction errors on the passive prime sentences following dative
fillers was largest at the beginning of the experiment and gradually decreased overthe course of the experiment. Consistently, although not statistically confirmed,
the pattern of the results for the production of passive descriptions was consistent
with that of the prime reading times.

3．Discussion
The current study investigated an influence of word-based predictions on
structural priming. We manipulated the structure of filler sentences inserted
between experimental items. Taking advantage of the ambiguity of the casemarker ni, we created an environment in which participants were led to expect a
different structure on encountering the noun phrase case-marked with ni in prime
sentences.
The results of the reading times at the verb in the prime sentences showed thatthe prior experience of dative fillers in the All-dative condition increased thereading times at the verb in the passive prime sentences, suggesting that dative
fillers led to greater expectations of the dative structure and thus resulted in greater
predictor errors on encountering the verb in the passive form. Importantly, primetarget pairs were identical across all the filler group conditions, ensuring that theeffect was due to the processing of filler sentences, particularly, that of the dative
structure with the ambiguous case marker ni.
The results from the production data showed that the size of structural
priming was larger when participants experienced the dative structure in filler
items compared to when they did not experience the dative structure at all. Thisfinding suggests that the experience with the dative fillers, which caused larger
prediction error on encountering the passive structure in prime trials, resulted in─ 114 ─

Influence of word-based prediction errors on syntactic priming

greater syntactic priming. Our results thus provided evidence for the Functionbased account, according to which prediction error is sensitive to the function of
the case marker ni and thematic role information. Specifically, after reading dative
fillers, comprehenders were more likely to predict another dative sentence on
encountering a noun phrase with the case marker ni following the nominativeargument in prime sentences. This caused greater surprisal or prediction error on
encountering the subsequent verb in passive form, which in turn resulted in large
weight changes in the networks representing the active and the passive structures.
This was reflected in the priming effect of the passives. Our results provide someof the first evidence for the word-level prediction errors on structural priming.
In addition, the combined analysis of the reading times of prime sentences
and the production of the passive descriptions in target trials showed a significant
interaction between Prime Type and Prime Reading Time. However, the results
were not the predicted pattern and were likely due to the positive correlation
between the likelihood of the passive production and the reading times of theactive prime sentences rather than those of the passive prime sentences. This prima
facie appears rather odd as the active prime sentences are not supposed to cause
word-level prediction errors. One possibility is that in some prime trials of theactive structure comprehenders may have predicted the passive structure before
reading prime sentences. This sentence-level prediction was disconfirmed by anactive prime sentence, resulting in increased reading times at the verb. Thissentence-level prediction error could have led to the greater likelihood of thepassive production. In fact, the prediction of the passive structure becomes quite
reasonable after encountering a certain number of experimental trials since thefrequency of passive sentences relative to that of active sentences in theexperiment (i.e., 1:1) is so much higher than that in real life. Although this is only apossibility, this may suggest that both word-level prediction errors and the─ 115 ─

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sentence-level prediction errors contribute to the priming effect of the passive
structure in Japanese.
Furthermore, another set of additional analyses was conducted to examine an
influence of trial order on the effects of prime structure and filler group. The
results showed that participants initially found the active easier to process than thepassive (Prime Type), but they came to expect both structures more as theexperiment progressed (Prime Type × Trial Order). But this pattern depended on thefillers in the study as when the fillers had no datives, participants quickly came to
expect both prime structures and they processed them at the same speed (Datives vs.
No-dative × Prime Type × Trial Order). In contrast, in the conditions with dative

fillers, participants processed active and passives differently at different points inthe study, suggesting that the dative ni-arguments made it harder to predict themeaning of the agent ni-argument in the passive prime structure. Although not
fully supported by statistical analyses, the similarity of the pattern for the
production data suggests that greater difficulty in processing (prediction error) was
positively related to greater priming in production, as suggested in error-basedlearning theories.
These results are not compatible with the activation-based model. The
activation-based model assumes that residual activation is left in nodes for theactive and passive structures and this residual activation causes syntactic priming
in the transitive alternation. But the dative structure involves different nodes, so
there is no reason to expect any transfer of residual activation to active and passive
transitive nodes. Previous work has found priming from structures with transitive
verbs to structures with dative verbs (Chang, Tsumura, Minemi, & Hirose, 2022), but
this only occurred when the transitive verb occurred with three arguments (ga/ha +ni + o). In a node-based account, it would be argued that three-argument sentencesused shared structural nodes and this explains why priming transfers from─ 116 ─

Influence of word-based prediction errors on syntactic primingtransitives to datives. But in both the Form-based and Function-based accounts, we
assume that syntactic representations are more distributed with separate elementsfor particles and structures, and these sentence elements may be linked to distinct
thematic roles. Therefore, the previous work that found transitive to dative primingin Japanese is consistent with the present work where dative ni arguments affected
the processing of transitive ni arguments.
This study presented dative filler sentences and the expectations about thesestructures presumably increased over the study. This was, however, not reflected
in reading times of passive sentences, which decreased over the study due to alarge number of experiences of the passive structure. This issue is related to thephenomenon called cumulative priming, where priming of a particular structurebuilds up over the experiment. Several studies have manipulated the participant’s
recent experience with particular structures. For example, Kaschak and colleagues
have varied the number of DO and PO structures that precede a priming phase, andvaried whether they are blocked or not (Kaschak, 2007; Kaschak & Borreggine, 2008).
Some of these studies have not found strong variation in priming due to frequency
or blocking (e.g., Kaschak, Loney, & Borreggine, 2006). Jaeger and Snider (2013) did areanalysis of several of Kaschak’s studies and found that structural surprisal did
predict greater priming when the probability of a structure was changed as eachsentence was processed. Although these studies provide a mixed set of results
about the role of cumulative priming and error-based prediction, one thing toremember is that our study used different structures for fillers and prime trials.
This study therefore provides some of the first evidence that cumulative priming
for one structure (e.g. datives) can influence priming of a different structure (e.g.
transitives).

In summary, being consistent with the error-based model, our results
demonstrated that syntactic priming of the passive structure was caused by the─ 117 ─

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errors in word-level predictions during the incremental processing of the prime
sentences. Furthermore, the influence of word-level prediction errors was observedin the environment in which word-level predictions was not dependent on
particular lexical items. The effect was observed without the repetition of the verb
between prime and target, ensuring that the errors are not lexically associated. Thisis important because our findings are independent of an influence of explicit
memory. The current study thus provided the first evidence for the influence ofword-based prediction errors on lexically independent syntactic priming.
Acknowledgments
We thank Megumi Ishikawa for helping to conduct the experiment and collect datareported in the current paper.

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