RUNNING HEAD: The development of verb argument structure

When do children develop knowledge of verb argument structure? Evidence from

verb bias effects in a structural priming task

Michelle Peter1a

Caroline F. Rowland1

Franklin Chang1

University of Liverpool

Ryan Blything2

University of Manchester

1Department of Psychological Sciences, Institute of Psychology, Health & Society,

University of Liverpool, Eleanor Rathbone Building, Liverpool, L69 7ZA, UK.

2Max Plank Child Study Centre, School of Psychological Sciences, University of

Manchester, Manchester, M13 9PL, UK.

a Corresponding author: Michelle Peter, Department of Psychological Sciences,

Institute of Psychology, Health & Society, University of Liverpool, Eleanor Rathbone

Building, Liverpool, L69 7ZA, UK. Email: Michelle.Peter@liv.ac.uk. Telephone: +44

151 794 1401.

Abstract

The structural priming paradigm is a powerful way of constraining theories about

how children’s knowledge of verb-argument structure develops. Using a priming task

that capitalises on verb bias effects and on a phenomenon called prime surprisal, we

tested two accounts of syntactic development: one by Thothathiri & Snedeker

[Thothathiri, M. & Snedeker, J. (2008a). Syntactic priming during language

comprehension in three- and four-year-old children. Journal of Memory and

Language, 58, 188-213] that claims that children have abstract knowledge about

syntax before they learn the lexical links to these structures from verbs, and another

by Chang, Dell, and Bock [Chang, F., Dell, G. S., & Bock, K. (2006). Becoming

syntactic. Psychological Review, 113, 234-272] that proposes that children acquire

abstract and lexical knowledge simultaneously. We assessed structural priming and

the lexical boost across development. We also investigated whether the bias of the

target verb influences structural priming across development, and whether the

priming effect is stronger when there is a mismatch between prime verb bias and

prime structure (prime surprisal).Children (aged 3-4 years and 5-6 years old) and

adults heard and produced double-object dative (DOD) and prepositional-object

dative (PD) primes with DOD- and PD-biased verbs. All participants heard both the

same verb in the prime and target (e.g., gave-gave), and also a different verb (e.g.,

gave-sent). All age groups demonstrated significant evidence of structural priming,

but only adults showed increased priming when the prime and target shared a verb

(the lexical boost). Both children and adults produced more DOD responses with

DOD-biased verbs (e.g., give) than with PD-biased verbs (e.g., bring), and priming

was stronger when there was a mismatch between the prime verb’s bias and its

structure (prime surprisal). These results show that, like adults, children’s knowledge

of verb syntactic preferences influences their structure choice. We conclude that

early syntax is lexically-grounded and that the lexical boost is not a reliable measure

of verb-structure links.

Keywords: structural priming, syntactic development, verb bias, lexical boost, prime

surprisal.

When do children develop knowledge of verb argument structure? Evidence

from verb bias effects in a structural priming task

Structural priming is a powerful way of measuring what adults know about

syntactic structure. A number of priming studies have shown that, despite having the

ability to be linguistically creative, adult speakers tend to repeat the syntactic

structure of the sentences that they have recently encountered (Bock, 1986b). This

effect is not contingent on non-syntactic factors such as the prosody of sentences or

the thematic roles played by arguments, nor does it rely on the repetition of words

across sentences - although the priming effect is often larger when prime and target

sentences share a verb (the lexical boost; Pickering & Branigan, 1998). Thus, the

phenomenon of structural priming is widely interpreted as evidence that adults have

abstract representations of syntax that are stored independently of lexical items

(e.g., Bock & Loebell, 1990; Cleland & Pickering, 2006; Noppeney & Price, 2004).

Recently, the priming paradigm has helped us to learn more about children’s

syntactic representations. These studies have been particularly useful in informing

us about the way in which children’s knowledge of syntactic structure is similar to

(and differs from) that of adults. For example, Shimpi, Gámez, Huttenlocher, and

Vasilyeva (2007) found that children as young as three years old, who heard and

repeated passive prime sentences, were more likely to describe target pictures using

a passive rather than an active structure. Similar results were reported in a more

recent study by Messenger, Branigan, McLean, and Sorace (2012). Evidence of

abstract structural priming in children has also been demonstrated in language

comprehension; Thothathiri and Snedeker (2008a) reported that children’s

interpretation of target dative sentences was influenced by prior processing of dative

prime sentences that contained a different verb. The fact that these finding hold

even when prime and target do not share any lexical items, suggests that, like

adults, children as young as three years old possess verb-general abstract syntactic

representations that they employ during both production and comprehension. Thus,

the results from the child literature are fairly consistent: children appear to have

abstract knowledge of syntax from early on in development.

However, priming studies in the acquisition literature tend to focus only on this

question of what type of syntactic knowledge children have at the early stages of the

acquisition process. While this approach is a useful way of distinguishing between

early abstraction and item-specific theories (e.g., Fisher, 2002a; Pinker 1984; Valian,

1986 vs. Pine, Lieven & Rowland, 1998; Tomasello, 1992; 2000), it does not tell us

much about how children’s knowledge of syntactic structure develops. Given that

children cannot start with an adultlike knowledge of the particular syntactic structures

used in their language, all theories need to incorporate acquisition mechanisms that

enable children to interpret their own particular input to build knowledge of syntax.

Studying how children’s responses to priming tasks change over development allows

us to examine not only what knowledge children bring to the language learning task,

but also how acquisition mechanisms interact with the input to build mature linguistic

knowledge.

Recently, Rowland, Chang, Ambridge, Pine, and Lieven (2012) used a structural

priming task to investigate just this. Using a paradigm designed to test structural

priming across development, they reported significant structural priming effects in

both children (aged 3-4 years and 5-6 years) and adults. All age group produced

more double-object dative (DOD) responses after a DOD prime than after a

prepositional-object dative (PD) prime even when the prime and the target included

different verbs (e.g., give-send). From the fact that there were no significant

differences across development, the authors concluded that three year olds, like

older children and adults, have already built abstract syntactic representations of

English dative constructions.

However, when prime and target sentences shared a verb, the pattern of priming

differed across development; unlike adults, children did not show a lexical boost

effect. The adults demonstrated a substantially larger priming effect when there was

verb overlap compared to when the prime and target verbs were different (a

significant lexical boost of 34%). However, the effect was only marginal in the 5-6

year olds (10%) and non-existent in the 3-4 year olds. In other words, the repetition

of verbs across sentences did not act to enhance the priming effect for the youngest

age group in the same way that it did for adults.

These results have implications for our understanding about the relationship

between abstract and lexical knowledge across development. One possible

explanation is that the lexical boost is absent in the youngest children because

adults and children differ in the way in which they have linked the verb lexicon to

knowledge of syntactic structure. For the adults in Rowland et al.’s (2012) study, the

presence of the lexical boost suggests that syntactic structure choice is influenced,

not only by the activation of the prime structure, but by activation of the verb used in

the prime. This occurs because, in adult representations, there are connections

between verbs and syntactic structure in the form of argument structure links that

specify the syntactic structures in which verbs can occur (see Pickering & Branigan,

1998). However, in three-year olds, these verb-structure links may not exist, or, at

least, may not be strong enough yet to influence syntactic choices in a priming

paradigm. Thothathiri and Snedeker (2008a) have suggested that children may first

build abstract representations of syntactic structures (e.g., the dative structure), and

only later establish links between these representations and individual verbs,

building knowledge of the argument structure constraints of verbs slowly as

experience with each individual verb accumulates. On this view, children’s syntactic

representations are initially wholly abstract, and the lack of a lexical boost in the

youngest age group is due to an absence of any links between verbs and structures.

An alternative explanation, however, is that the lexical boost is the wrong tool to

make inferences about the relationship between verbs and syntactic structure. On

this model, the lexical boost does not stem from a lack of knowledge of verb

argument structure but from a limitation in explicit memory.. According to Chang,

Dell, and Bock (2006), structural priming occurs as a consequence of an error-based

learning mechanism. In their Dual-path model, which provides an account of

structural priming, the difference (or error) between predicted and actual sentences

is used to makes gradual weight changes in the connections between syntactic

representations. Because the model conceptualizes structural priming in terms of the

same learning mechanism that drives syntax acquisition, these weight changes

enable the model to learn syntactic structure whilst developing representations

based on the structural properties of verbs. In this way, Chang et al. predict that

verb-structure links develop in parallel with the development of syntactic knowledge.

Lexical boost effects, however, are too large to result from these types of changes;

they result, instead from the speaker’s explicit awareness of the repetition of lexical

items across prime and target sentences (Chang, Janciauskas, & Fitz, 2012). On this

model, lexical overlap simply acts as a cue in the retrieval of the explicit memory of

the prime structure and, given that explicit memory increases with age (Naito, 1990;

Sprondel, Kipp, & Mecklinger, 2011), the boost increases in line with the ability to

form, store, and retrieve explicit memories. Thus, the lexical boost is small (or even

absent) in young children not because children do not have verb-structure links, but

because they are less efficient at retrieving an explicit memory trace of the prime

sentence.

In sum, there are two possible reasons for why children and adults respond

differently in lexical boost paradigms; one that suggests that children’s knowledge of

verb-structure links has yet to develop, and one that suggest that it develops in

parallel with knowledge of syntactic structure, but that the lexical boost is the wrong

place to look for these links in children. In order to distinguish between these two

theories, and determine what children know about verb argument structure, we thus

need to use a different method to establish whether children’s and adults’ knowledge

of verb argument structure influences their syntactic structure choice. In the current

study, we did this using a modified structural priming method; one that capitalises on

verb bias effects in priming studies and on a phenomenon called prime surprisal.

Verb bias and prime surprisal

Although many dative verbs can occur in both prepositional and double object

datives (e.g., I gave him a cake/I gave a cake to him), most of these verbs will tend

to occur more often in one structure than another. For example, give tends to occur

more often in double object than prepositional dative structures (Campbell &

Tomasello, 2001; Gries & Stefanowitsch , 2004a). These syntactic preferences (or

preferred argument structure constraints) affect how adults behave in priming

studies. For example, in a corpus analysis of English dative verbs, Gries (2005)

found that target verbs strongly associated with one structure resisted being primed

into another structure. In another study by Coyle and Kaschak (2008), priming

effects were found to be larger when the target verb was not strongly associated with

one structure (i.e., when they were equi-biased). In other words, an adult’s

knowledge of a verb’s preferred argument structure (i.e., whether this verb occurs

more often in a DOD or a PD structure) influences how easily it is to prime that adult

to produce the verb in that structure.

Studying the effect of verb bias on priming provides us with a different way of

tapping into a participant’s knowledge of the links between verbs in the lexicon and

syntactic structure. We know that adults’ knowledge of a verb’s preferred argument

structure influences their structure choice during a priming task. If children have also

established these links (as predicted by Chang et al, 2006), then we should expect to

see evidence of target verb bias effects across development. However, if children

have not yet linked verbs with their argument structure preferences (as predicted by

Thothathiri and Snedeker, 2008a), then only adults, and perhaps older children,

should demonstrate target verb bias effects during a structural priming task.

In addition, related research has suggested that the identity of the prime verb

plays an important role in the size of the priming effect, such that priming is stronger

when the bias of the prime verb makes its occurrence in a particular prime structure

unexpected – an effect termed prime surprisal (Chang et al., 2006). For example,

Jaeger and Snider (2007) found that adults were more strongly primed when DOD-

biased prime verbs were presented in a PD prime structure and Bernolet and

Hartsuiker (2010) reported stronger priming when primes with PD-biased verbs were

presented in a DOD-structure in Dutch. In other words, the more unexpected (or

surprising) a verb is in a prime sentence, the more likely participants are to be

primed.

Not only do these results demonstrate that adult speakers store information about

verb syntactic preferences, but they also suggest that adults make predictions about

prime sentences based on their knowledge of these preferences. When these

predictions are not met (i.e., when a verb is presented in an unexpected structure),

prime surprisal works to boost the priming effect (Chang et al., 2006).We can exploit

this to assess children’s knowledge of verb-structure links. If, like adults, children

have created links between verbs and syntactic structure, then verb-structure

mismatches during a structural priming task should also lead to prime surprisal

effects in children. But, if they have not yet formed these verb-structure links, then

we should only expect to see evidence of prime surprisal effects in adults.

The current study

In this study, we capitalise on verb bias and prime surprisal effects to test two

accounts of how children develop knowledge of verb argument structure. One

possibility is that children have abstract knowledge about syntax before they learn

verb argument structure links between syntactic structures and individual verbs

(Thothathiri & Snedeker, 2008a). On this view, we should see abstract structural

priming across development but, because children have not yet created the links

between verbs and the structures in which these verbs can occur, only adults should

demonstrate evidence of lexical boost, target verb bias effects and prime surprisal.

Another possibility is that children acquire abstract and lexical knowledge

simultaneously, but that the lexical boost is not a reliable measure of verb-structure

links (Chang et al., 2006). This account also predicts abstract structural priming

across development and a lexical boost that increases with age. However, on this

account, we should see target verb bias effects and prime surprisal across

development since this account predicts that children acquire both abstract syntactic

representations and knowledge of verb argument structure simultaneously from early

on.

The current study used a modified version of the priming paradigm used by

Rowland et al. to test for structural and verb-specific priming effects in young

children (3-4 years), older children (5-6 years), and adults. Children and adults heard

and repeated DOD and PD prime sentences containing DOD- (give and show) and

PD-biased verbs (bring and send) before producing target sentences with these

verbs. First, we assessed whether we could replicate the findings of Rowland et al.

by examining structural priming and the lexical boost across development. To do

this, we observed whether participants produced more DOD- target sentences after

DOD primes than after PD primes when verbs in the prime in the target were

different (e.g., give-send), and then observed whether this priming effect increased

when the prime and target verb were the same (e.g., give-give). Second, we tested

whether children, like adults, show verb bias effects in priming tasks by exploring

whether the size of the priming effect was influenced by the bias of the target verb.

Third, we assessed whether children, like adults, demonstrate evidence of prime

surprisal. To do this we explored the priming effect was stronger when prime verb

bias and prime syntactic structure were mismatched (e.g., DOD-biased verb in a PD

structure).

Method

Participants

A total of 183 participants were tested. One hundred and twenty-three

monolingual English-speaking children were recruited from nurseries and schools in

the Liverpool area. Sixty-three of these children (32 females) were aged between

five and six years old (mean age 5;8, age range 5;0-6;11) and 55 children (33

females) were aged between three and four years old (mean age 4;0, age range 3;0-

4;11). An additional five children from the 3-4 year old age group were tested but

produced eight or more (over half) non-target responses during the task and so were

excluded from the final analysis. A further 60 monolingual English-speaking adults

(42 females) were recruited from the University of Liverpool student participation

pool. Participants were tested individually in either their classroom/nursery or in the

language development laboratory at the University of Liverpool.

Design and Materials

Design. The study was presented in the form of a bingo game and used a 3 x

2 x 2 x 2 x 2 mixed design. Age (3-4 year olds/5-6 year old/adults) was the between-

subjects variable1. The four within-subjects variables were Prime Type (DOD and

PD), Verb Match (same verb and different verb), Prime Bias (DOD- and PD-biased)

and Target Bias (DOD- and PD-biased). The dependent variable was the proportion

1 Although descriptive analyses are presented by age-group, age was coded in months as a continuous variable (3.5 years/5.5 years/20 years) since it is not possible to compare three categorical factors within a mixed effects model.

of dative responses that were DODs – a ratio of DOD responses over the sum of

DOD and PD target responses.

Visual stimuli. Sixty-four video cartoon animations were created in Anime Studio

Pro and were presented in E-Prime 2.0. The cartoons included three pairs of donor

and recipient characters that are familiar to young British children and have proper

noun names: Tigger and Piglet, Dora (the Explorer) and Boots, and Bob (the Builder)

and Wendy. A further three pairs of donor and recipient characters were referred to

with determiner+noun NPs: the prince and the princess, the king and the queen, the

boy and the girl. Donor and recipient characters were always paired together (e.g.,

Wendy was always paired with Bob, and the prince was always paired with the

princess). A further five characters acted as objects and were referred to with non-

definite determiner+noun NPs: a cat, a baby, a fish, a puppy, a rabbit. All of the

characters were animate to prevent animacy contrasts between the object and

recipient influencing syntactic structure, since DOD sentences tend to occur with

animate recipients and inanimate objects.

Thirty-two of the animations depicted transfer actions that can be described

with dative sentences. Thirty-two others were used as fillers and depicted non-

causal actions that can be described with an intransitive sentence. Eight of the

animations that were used as fillers were also used in a practice session. Each prime

picture was always paired with a target picture that included different characters from

those in the prime. Animations also depicted the direction of motion of transfer

actions from right-to-left equally as often as they were transferred from left-to-right to

control for the possibility that direction of transfer could influence structure choice.

Bingo cards were created to match each video cartoon animation. Four bingo

boards were created on which to place the cards during the ‘game’. Two of these

boards included a grid of four squares that were used in a practice session before

the actual experiment. The other two boards were used in the experiment and

contained nine squares.

Sentence stimuli. The four verbs used in this study – give, show, send, and

bring are dative verbs that have been shown to be familiar in both structures to

young children in the DOD and PD structure (Campbell & Tomasello, 2001; Gropen

et al., 1989).

Ninety-six different sentences and 32 verb stems were created to describe the

64 video cartoon animations. Thirty-two of these sentences described thirty-two

different cartoon animations (eight DOD sentences for each verb). These were used

as primes and depicted transfer actions using a DOD structure (e.g., Dora gave

Boots a rabbit). A further thirty-two prime sentences described the same transfer

actions but used the PD structure (e.g., Dora gave a rabbit to Boots). Thirty-two

target verb-stems (eight verb-stems for each verb) were created (e.g., the boy

brought...) in addition to thirty-two filler sentences which used an intransitive

structure (e.g., the princess jumped).

Each verb was presented eight times per participant: four times in the prime

sentence (twice in the PD structure and twice in the DOD structure) and four times in

the target verb-stem. Each participant was exposed to 16 prime-target pairs,

interspersed with 16 filler-filler pairs. Fillers were used to minimize priming effects

between pairs.

Overall each participant was presented with, and produced, 64 sentences in

total. No participant was asked to produce the same prime sentence twice and all

participants were exposed to an equal number of prime-target pairs from each of the

prime conditions.

We explored both lexically-specific and lexically-independent priming as a

within-subjects variable. Therefore participants were exposed to sentences in which

verbs were repeated across primes and targets and also sentences where the verbs

in primes and targets were different. Pairs of characters appeared equally as often in

prime and target sentences and to avoid lexical overlap (other than that of the verb),

characters in primes were always different to the characters in the targets with which

they were paired. Furthermore, primes that contained determiner noun phrases (e.g.,

the princess) were always followed by targets with proper noun phrases (e.g.,

Wendy) and vice versa. Sentences were always presented in the past tense to avoid

phonological overlap of the regular progressive (–ing) ending. Three of the verbs

were irregular (gave, sent, brought) to avoid repetition of the regular past tense (-ed)

ending.

To control for sentence-specific preferences, 12 counterbalance groups were

created to ensure that: 1) sentences that appeared as a DOD prime in one

counterbalance group appeared as a PD prime in another and vice versa; 2) all

characters in both prime and target sentences appeared equally as often with each

verb, and 3) each prime verb was paired with itself and the three remaining verbs

equally often in target sentences. All sentences across counterbalanced groups were

presented semi-randomly to ensure that participants could not predict the structure

of consecutive prime sentences. This also enabled us to ensure that characters

appearing in a filler-filler pair would not have appeared in the preceding target

sentence or in the following prime sentence.

Procedure

Children. The experiment used a confederate-scripting dialogue method

adapted from Pickering and Branigan (1998) by Rowland et al. (2012). The

experiment was conducted in the form of a bingo game in which the experimenter

and the child took it in turns to describe cartoon animations on a laptop computer to

a confederate. The experimenter introduced all of the characters involved in the task

to the child by showing them a selection of bingo cards on which these characters

appeared. Once happy that the child knew the names of the characters, the

experimenter explained the game to the child.

The experimenter and child sat in front of the computer side-by-side, while the

confederate sat opposite. The confederate was unable to see the cartoons on the

screen. The experimenter’s description of the cartoon on the left-hand side of the

screen served as a prime for the child. The child was asked to repeat the prime

sentence, addressing a hand puppet held by the confederate. The experimenter

explained that the puppet could not see the screen and needed the child to tell them

what was happening. The child was next instructed to produce a target sentence by

describing a cartoon animation on the right-hand side of the screen. A stem-

completion technique was used to ensure that the target sentence contained the

target verb but the child was instructed to produce the entire target sentence. For

example, after describing a prime animation using the verb gave (e.g., The king gave

a baby to the queen) and having the child repeat this sentence, the experimenter

would, then say “Wendy showed...”, encouraging the child to produce either, “Wendy

showed a rabbit to Bob” or “Wendy showed Bob a rabbit”. After each sentence the

confederate looked to see if he/she had the bingo card corresponding to that

cartoon. If he/she did, the correct bingo card was given to the experimenter or child

as appropriate. Each dative prime-target pair was immediately followed by an

intransitive filler-filler pair. The first person to fill the bingo grid with bingo cards was

the winner of the game and the experiment was designed so that the participant

always won.

Before running the experiment using the nine-squared bingo board, a practice

session using the four-squared board was carried out to ensure that the children

understood the task.

Adults. The procedure for adult participants was identical to the procedure for

child participants except that adults were informed that they were serving as controls

in an experiment designed for children. Additionally, they received explicit

instructions to repeat the prime and produce the entire target sentence and did not

have to direct their speech towards a hand puppet.

Coding

Target responses were recorded by the experimenter using the response

coding function of E-Prime 2.0. The experiment was also audio-taped, allowing the

first author to listen back to and then transcribe and code the data. Many of the

young children and some of the older children needed prompting from the

experimenter to produce the prime and the entire target sentence correctly. Some of

them, however, only produced partial target responses (e.g., they completed the

stem without including the target verb). In order to capture these partial target

responses, we employed three levels of coding: lax, intermediate, and strict. To

qualify for lax coding, the prime sentence had to be repeated correctly but the

participant might have received help to do this. In addition, the participant may not

have produced the target verb or the entire target utterance, but instead just

completed the target stem. To qualify for intermediate coding, the prime sentence

had to be repeated correctly with the participant needing minimal help to do this. In

addition, the entire target utterance was produced, but prompting to do this was

needed more than once. To qualify for strict coding, the prime and target sentence

had to be produced correctly with no more than one prompt. A target response was

considered a DOD if it contained the correct target verb followed by two noun

phrases, and a PD if it contained the correct target verb followed by a noun phrase

and a prepositional phrase headed by ‘to’. Responses coded as ‘other’ were those

where, a) the participant failed to repeat the prime correctly (even after help), b) the

participant produced the incorrect target verb and, c) the target sentence included

the preposition ‘at’ rather than ‘to’. Preliminary analysis revealed that all coding

schemes produced a virtually identical pattern of results, and so the following

analyses are reported only on the strictly-coded data.2.

2 Under the lax coding scheme, 3-4-year olds, 5-6-year olds, and adults produced 0.13%, 0.03%, and 0.01% ‘other’ responses respectively. Under the intermediate coding scheme, 3-4-year olds, 5-6-year olds, and adults produced 0.22%, 0.05%, and 0.03% ‘other’ responses respectively. Under the strict coding scheme, 3-4-year olds, 5-6-year olds, and adults produced 0.24%, 0.07%, and 0.03% ‘other’ responses respectively.

Results

The aims of the present study were: 1) to replicate Rowland et al. (2012) to assess

structural priming and the lexical boost across development, 2) to explore whether

the bias of the target verb influences structural priming across development, and 3)

to investigate whether a mismatch between prime verb bias and prime structure

influences structural priming (prime surprisal) across development.

1. Structural priming and the lexical boost across development

Our first aim was to investigate whether we would find abstract structural priming and

a lexical boost across all three age groups. Using a production-to-production priming

paradigm, we tested lexically-independent and lexically-specific priming. The mean

proportion of DOD responses after DOD and PD primes in the same verb and the

different verb conditions was calculated. Structural priming was demonstrated by a

greater proportion of DOD responses after DOD primes than after PD primes.

Evidence of a lexical boost would be demonstrated by a bigger priming effect in the

same verb condition than in the different verb condition.

Fig. 1.Mean proportion of DOD responses after DOD and PD primes when primes and targets were different (different verb) and the same (same verb) (error bars indicate standard error)

3-4 5-6 Adults 3-4 5-6 AdultsDifferent verb Same verb

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

After DOD

After PD

% o

f dat

ive

targ

et re

spon

ses

that

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e D

OD

s

Figure 1 demonstrates the mean proportion of dative responses that were DODs

after PD and DOD primes for each age group and verb condition (same/different).

Table 1 reports the size of the priming effect for each age group and verb match

condition (calculated as a percentage of DOD responses produced after DOD primes

minus the percentage of DOD responses produced after PD primes) and also as

Cohen’s d. These data indicate that the structural priming effect was largest in the

adults (27%). Comparison of the same and different verb conditions shows that verb

overlap boosted the structural priming effect (by 23%) for the adults, but not at all for

the children.

Table 1.Size of priming effect – calculated both as the percentage of DODs produced in each prime condition (difference score) and effect size (Cohen’s d)

Age Different verb Same verb

DifferenceScore

Standard error

Cohen'sd

Difference score

Standarderror

Cohen's d

3-4 0.15 0.049 0.45 0.03 0.041 0.085-6 0.14 0.030 0.42 0.06 0.023 0.17Adults 0.27 0.048 0.90 0.50 0.044 1.86

The results were analysed with a logit mixed effect (maximal) model as these models

are well suited to analyzing binary dependent measures and allow us to model both

random subject and item effects together (Baayen, Davidson, & Bates, 2008; Jaeger,

2008). The model included as fixed effects: a) Verb match condition (same/different

verb); b) Age (3-4 year olds/5-6 year olds/adults) and c) Prime type (DOD, PD). Age

was centred to reduce multi-collinearity (Neter, Wasserman, & Kutner, 1985) and

other factors were effect/sum coded (Wendorf, 2004). Participant was included as a

random effect. The dependent variable was the proportion of DOD structures

produced – a ratio of DOD responses over the sum of DOD and PD target

responses.

The results revealed a main effect of Prime Type; β = 1.26 (SE = 0.11), z =

11.51, p < .001, indicating that the participants produced more DOD responses after

DOD primes than after PD primes. There was also a main effect of Age; β = 0.11

(SE = 0.02), z = 6.37, p < .001, so the likelihood of producing DOD targets increased

with age. An interaction between Prime Type and Age; β = 0.09 (SE = 0.01), z =

6.24, p < .001, showed that, overall, the size of the priming effect increased with age.

There was also a main effect of Verb match condition; β = -0.21 (SE = 0.11), z = -

1.99 p < .01, indicating that more DOD responses were produced in the same verb

condition than in the different verb condition. There was no interaction between

Prime type and Verb match condition, suggesting that the priming effect did not

increase when verbs in the prime and target were the same. However, these results

should be considered in the light of the three-way interaction found between Prime

type, Age and Verb match condition, β = 0.12 (SE = 0.03), z = 4.18, p < .001. This

finding indicates that while, on the whole, there was no lexical boost, the priming

effect was greater when there was verb overlap for some of the age groups. To

explore the interaction further, we conducted three models on each age group.

1.1. Analyses by age

Three separate logit mixed effect models were run on each age-group. The 3-4 year

olds demonstrated a main effect of Prime type only, β = 0.77 (SE = 0.21), z = 3.68, p

< .001, no effect of Verb match condition and no interaction. There was no significant

difference between the proportion of DOD responses after DOD primes produced by

3-4 year olds in the same (31%) and different (35%) verb condition. In other words,

they did not demonstrate a lexical boost. Likewise, the 5-6 year olds showed a main

effect of Prime type only, β = 0.85 (SE = 0.19), z = 4.54, p < .001, and no effect of

Verb match condition or interaction; they too showed no lexical boost. In contrast,

analysis of the adults’ data revealed a main effect of Prime type; β = 2.02 (SE =

0.16), z = 12.49, p < .001, Verb match condition; β = -0.32 (SE = 0.16), z = -2.07, p =

.04, and also a significant interaction between Prime type and Verb match condition;

β = 1.30 (SE = 0.31), z = 4.13, p < .001. This means that, unlike the children, the

adults produced significantly more DOD responses after DOD primes in the same

(76%) verb condition than in the different (70%) verb condition. Thus, there was a

large lexical boost effect in the adults.

2. The effect of target verb bias on structural priming across development

Our second aim was to investigate both how the target verb bias influenced the

participants’ choice of syntactic structure overall, and whether it influenced the size

of structural priming across development.

Fig. 2. Mean proportion of DOD responses when prime and target verbs were and the same with (a) DOD-biased target verbs and (b) PD-biased target verbs (error bars indicate standard error)

(a)

3-4 5-6 Adults 3-4 5-6 AdultsDifferent verb, DOD-biased target Same verb, DOD-biased target

0

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(b)

3-4 5-6 Adults 3-4 5-6 AdultsDifferent verb, PD-biased target Same verb, PD-biased target

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As before, we ran a logit mixed effect model to analyse the data. This time the model

included as fixed effects: a) Verb match condition (same/different verb); b) Age (3-4

year olds/5-6 year olds/adults); c) Prime type (DOD, PD) and d) Target bias (DOD-

biased, PD-biased). Again, age was centred to reduce multi-collinearity. There were

no interactions between Prime type and Target bias, suggesting that the amount of

priming was not affected by the bias of the target verb. However, the bias of the

target verb did affect the participants’ choice of syntactic structure overall. The

results revealed a main effect of Target bias; β = -1.10 (SE = 0.11), z = -9.80, p

< .001, which indicates that more DOD responses were produced with DOD-biased

target verbs than with PD-biased target verbs overall. There was also an interaction

between Target bias and Age; β = -0.03 (SE = 0.01), z = -1.84, p = .07, caused by

the fact that the effect of target verb bias increased with age. There was a three-way

interaction between Target bias, Age and Verb match condition, β = 0.07 (SE =

0.03), z = 2.48, p = .01. This suggests that more DOD responses were produced

with DOD-biased target verbs than with PD-biased target verbs and this effect varied

across both age groups and verb conditions.

2.1. Analyses by age

To analyse the three-way interaction further, three separate logit mixed effect models

were run on each age-group. The 3-4-year olds demonstrated a main effect of

Prime type, β = 1.02 (SE = 0.26), z = 3.86, p < .001, and a main effect of Target bias

only; β = -2.03 (SE = 0.40), z = -5.06, p < .001. So, while the size of the priming

effect was not influenced by the bias of the target verb, 3-4-year olds produced more

DOD responses with DOD-biased target verbs- than with PD-biased target verbs

overall. Likewise, 5-6-year olds showed a main effect of Prime type, β = 0.95 (SE =

0.20), z = 4.67, p < .001, and a main effect of Target bias; β = -1.44 (SE = 0.23), z =

-6.23, p < .001 (see Figure 3). They also showed a main effect of Verb match

condition; β = -0.44 (SE = 0.22), z = -2.02, p < .05, indicating that more DOD

responses were produced when there was no verb overlap (since this is not relevant

to our predictions, we do not consider it further). In the adults, there was a main

effect of Prime type, β = 2.53 (SE = 0.24), z = 10.62, p < .001, of Target bias; β = -

1.59 (SE = 0.22), z = -7.38, p< .001, and of Verb match condition, β = -0.47 (SE =

0.19), z = -2.40, p < .01. There was also an interaction between Target bias and

Verb match condition, β = 1.44 (SE = 0.38), z = 3.83, p< .001 and between Prime

type and Verb match condition, β = 1.18 (SE = 0.45), z = 2.64, p< .001. However, as

with the children, there was no interaction between Target bias and Prime type,

indicating that Target verb bias affected the number of DODs produced overall, but

not the size of the priming effect. In sum, children and adults were more likely to

produce DODs with DOD-biased verbs (and thus more likely to produce PDs with PD

biased target verbs). However, the bias of the target verb did not affect the size of

the priming effect.

3. The effect of prime verb bias on structural priming across development

Lastly, we investigated the effect of prime verb bias on structural priming.

Specifically, we explored whether children and adults are sensitive to prime surprisal

by testing whether the priming effect was bigger when there was a mismatch

between prime verb bias and prime structure.

Fig. 3.Mean proportions of DOD responses when prime verb bias and prime structure were the same (match), and when prime verb bias and prime structure were different (mismatch) with (a) DOD-biased target verbs and (b) PD-biased target verbs (error bars indicate standard error)

(a)

3-4 5-6 Adults 3-4 5-6 AdultsMatch, DOD-biased target Mismatch, DOD-biased target

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3-4 5-6 Adults 3-4 5-6 AdultsMatch, PD-biased target Mismatch, PD-biased target

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To analyse the data, two variables were created: ‘mismatch’, in which the prime verb

occurred in an unexpected structure (e.g. a DOD biased verb in a PD structure), and

‘match’, in which the prime verb occurred in a match structure (a DOD biased verb in

a DOD structure).

Using a logit mixed effect model, analyses were conducted on a subset of the

dataset that only included data from the different verb condition. Data from the same

verb condition was not included because the ‘match’ effect would be confounded by

a lexical boost effect. For example, in the ‘match’ condition, when the prime verb is

PD-biased, the target verb will always be a PD-biased verb (sent or brought) which

could increase the priming effect. The logit mixed effect model included as fixed

effects: a) Age (3-4 year olds/5-6 year olds/adults); b) Prime type (DOD, PD); c)

Prime bias match (match, mismatch), and d) Target bias (DOD-biased, PD-biased).

Again, age was centred to reduce multi-collinearity.

The results revealed main effects of Prime type; β = 1.61 (SE = 0.23), z = 6.98, p

< .001, Age; β = 0.14 (SE = 0.02), z = 6.26, p < .001, and Target bias; β = -1.43 (SE

= 0.21), z = -6.71, p < .001. There was also a significant interaction between Prime

type and Prime bias match; β = 0.88 (SE = 0.43), z = 2.06, p < .05, indicating that,

overall, structural priming was greater when the prime verb bias and prime structure

were mismatched (i.e., prime surprisal increased the priming effect). There was no

interaction between Prime type, Prime bias mismatch and Target bias, indicating that

the bias of the target verb did not influence prime surprisal. However, there was a

three-way interaction between Prime type, Age and Prime bias match; β = -0.09 (SE

= 0.04), z = -2.08, p = 0.037. Inspection of Figure 3 (a and b) shows that this three-

way interaction resulted from the prime surprisal effect decreasing with increasing

age. In other words, a mismatch between prime bias and structure was less

surprising for adults than for children.

3.1. Analyses by age

Three separate analyses were run to further explore the interaction between Prime

type, Age and Prime bias match. For the 3-4 year olds, there was a significant

interaction between Prime type and Prime bias match; β = 3.50 (SE = 1.45), z =

2.41, p < .05, indicating prime surprisal increased the size of the structural priming

effect for this age-group. There was also a significant interaction between Prime type

and Prime bias match for the 5-6 year olds; β = 2.28 (SE = 0.77), z = 2.98, p < .001,

so prime surprisal also increased the amount of structural priming for this age-group.

However, the adult data revealed only marginal interaction between Prime type and

Prime bias match; β = -1.41 (SE = 0.73), z = -1.94, p < .1, showing that the

strengthening effect of prime surprisal on structural priming decreased with age. In

other words, adults were less sensitive to prime surprisal than children.

Results summary

Both children and adults demonstrated significant structural priming; they all

produced more DOD sentences after DOD primes than after PD primes when prime

and target verbs were different. However, only adults showed increased priming

when prime and target sentences shared a verb. Thus, consistent with recent

findings (Rowland et al., 2012), the lexical boost increased with development. The

results also revealed verb bias effects across development: 1) both children and

adults produced more DOD responses with DOD-biased target verbs (e.g., give)

than with PD-biased target verbs (e.g., bring) and this effect was larger in adults than

in children, 2) priming was stronger when there was a mismatch between the prime

verb’s bias and its structure (prime surprisal), with this effect decreasing across

development.

Discussion

In this study, we used a priming paradigm to test for structural and verb-specific

priming effects in young children (3-4 years), older children (5-6 years), and adults.

Our aim was to test two accounts of how children develop knowledge of verb

argument structure. One is that children develop abstract knowledge about syntactic

structure first, and then learn the lexical links between these structures and verbs

that constitute verb argument structure knowledge (Thothathiri & Snedeker, 2008a).

On this view, we predict abstract structural priming across development but a

growing lexical boost with age. Crucially, we also predicted that only adults should

be affected by the argument structure preferences of particular verbs (target verb

bias) and by prime surprisal. An alternative account, however, proposes that children

acquire abstract and lexical knowledge simultaneously, but that the lexical boost is

not a reliable measure of verb-structure links (Chang et al., 2006). This account also

predicts abstract structural priming across development and a lexical boost that

increases with age. However, it also predicts that, once we see structural priming

effects, we should also see target verb bias and prime surprisal effects since children

acquire both abstract and verb-specific knowledge together from early on.

To test these accounts, we first assessed whether we could replicate the

findings of Rowland et al. (2012) that showed structural priming effects are present

across development, but that a lexical boost that grew with age. Second, we tested

whether children, like adults, are influenced by the argument structure bias of the

target verb. Third, we examined whether children, like adults, demonstrate evidence

of prime surprisal.

In our study we replicated the findings of Rowland et al. (2012). We found

evidence for structural priming across development, in that young children, older

children, and adults were significantly more likely to produce DOD sentences (e.g.,

Wendy gave Bob a rabbit) after hearing and repeating DOD primes (e.g., The boy

sent the girl a fish) than after PD primes (e.g., The boy sent a fish to the girl). These

results support Rowland et al.’s conclusion that, by the age of three, young children

have built some form of abstract syntactic representations for the English dative

constructions, which enable them to generalise across similarly-structured

sentences. Our findings also fit with other studies reporting evidence for abstract

priming in three-year-olds (e.g., Bencini & Valian, 2008; Messenger et al., 2012;

Shimpi et al., 2007). Like Rowland et al., we also observed a lexical boost that

increased with development: adults showed increased priming (23%) when the verb

in the prime matched the verb in the target, but 3-4-year old and 5-6-year old

children did not. In other words, although structural priming effects were apparent

across development from age three to adulthood, the lexical boost was only

apparent in adults.

Given that we replicated the results of Rowland et al. (2012), our second aim

was to compare the predictions of two differing explanations of how children develop

knowledge of verb argument structure. We did this in two ways. First we tested

whether participants’ responses were influenced by the bias of the target verb across

all three ages. We found that they were. Although the target verb bias did not affect

the size of the priming effect for either children or adults, all age-groups produced

more DOD responses with DOD-biased target verbs (give, show) than with PD-

biased target verbs (bring, send). In other words, children as young as three years

were influenced by the alternation biases of the four dative verbs used in this study.

Second, we tested whether children and adults were influenced by prime

surprisal. Again, we found significant effects across all three age group. Priming

effects were stronger when there was a mismatch between the prime verb’s bias and

the prime structure (prime surprisal). In other words, the bias of the prime verb had

an effect on the size of the priming effect across all three ages.

Both of these findings are difficult to explain in terms of a theory that assumes

that the acquisition of abstract syntactic knowledge happens prior to, and in isolation

from, the acquisition of verb’s argument structure constraints (e.g., Thothathiri &

Snedeker, 2008a). Instead, our results the results support a model in which children

develop knowledge about syntactic structure at the same time as they learn about

the argument structure preferences of particular verbs.

This model is the connectionist model of syntactic processing and

development called the Dual-path model (Chang, Dell, & Bock, 2006). The model

has a dual-pathway architecture which separates conceptual-lexical and syntactic

learning into separate meaning and sequencing pathways. This architecture is

argued to be critical for learning isolable syntactic structures (Chang, 2002).

Syntactic knowledge is learned within the sequencing pathway through an error-

based learning algorithm that is applied to next-word prediction. For example, the

model might predict that after the word the, the next word should be boy. But, if the

next word is actually girl, then the mismatch between the prediction and the actual

next word (the error) is used to change the weights in the model so that it better

predicts girl after the. Since the weights are initially random, at first, the model might

not predict the next word correctly. With time, the model learns syntactic categories

which subsequently combine into syntactic structures that allow it to predict sets of

words (e.g., ‘the’ is followed by a noun: ‘boy’, ‘girl’, ‘cat’...). Because the model is

building syntactic knowledge from individual words, knowledge of which verbs can

occur in which structure (verb argument structure) develops at the same time as

knowledge of syntactic structure. In other words, the model develops knowledge

about verb argument structure using the same error-based learning mechanism that

it uses to develop knowledge of syntactic structure.

As a result, the model predicts that effects of target verb bias will be seen as

soon as we see evidence of structural priming, because children acquire syntactic

information about the argument preference of particular verb at the same time as

they are building up knowledge of syntactic structure. In addition, a mismatch

between the prime verb and the prime structure (e.g., a PD-biased verb in a DOD

biased structure) leads to more error in prediction and thus a greater weight change

in the model’s representation for that syntactic structure (e.g., the DOD). A greater

weight change means a greater likelihood of the model choosing the same syntactic

structure for its own subsequent production, which leads to an increased priming

effect.

Explaining the developmental pattern of target verb bias and prime surprisal

In sum, our findings support an account like that of Chang et al. (2006) in which it is

assumed that early syntactic representations are lexically-grounded. However, there

are two findings in our data that remain to be explained. First, the effect of target

verb bias increased over development: adults were more strongly influenced by the

bias of the target verb than children. Conversely, the effect of prime surprisal

decreased over development; adults were less influenced by the bias of the prime

verb than children. This apparent contradiction is difficult to explain because both

effects are assumed to stem from the same source; the participants’ knowledge of a

verb’s preferred argument structure, which both influences the choice in production

of the target and generates the expectation that creates primes surprisal.

We speculate here that both results could stem from the dynamics of the learning

process. The growth of the target verb bias over development can be explained by

the way that children use verb-structure pairs in the input to adjust their internal verb-

structure links in their language network. If children learn verb biases over time, their

representations moment-by-moment represents the evidence they have so far

accumulated from the input. However, daily probabilities of verb-structure pairs do

not always match the long term probabilities. This means that early on in the

learning process, while the child is building up evidence, her representations are

likely to be less stable. For example, although the verb throw may have an overall

bias towards the PD frame, it is likely that there will be days in which children only

hear throw in the DOD. Early on in the learning process, these instances would

weaken the PD bias of her representation of the verb throw. Thus, learning target

verb biases is a gradual process of accumulating evidence over time which can be

temporarily derailed by random fluctuations in the input. This can explain why target

verb bias increases with development in this study.

The decrease in prime surprisal over development can be explained by changes

in the magnitude of the learning rate. Priming in the Chang et al. model is due to

error-based learning, which is influenced by a learning rate parameter. The error

between the predicted next word and the actual next word is the error signal (akin to

surprisal) and the influence of this error signal on the model’s representations is

modulated by the learning rate. Thus if the learning rate is higher early in

development, the effect of surprisal on priming will be larger earlier in development.

In other words, we might see greater prime surprisal early in development because

the child is learning at a faster rate than the adult.

There is independent motivation for using higher learning rates early on in

connectionist models (all of the versions of the Dual-path model use higher learning

rates early in development; Chang, 2002; 2009; Chang et al. 2006). In connectionist

models, weights are initially randomized, and a small learning rate means that

learning makes only small changes to these weights. The result is that models

learning a complex set of representations tend to fail to converge on the target

representations (e.g., sequences of abstract syntactic categories) within an

appropriate amount of time. Using large, initial learning rates and reducing them

over development allows the model to escape from these local minima and discover

the right representations. For example, Chang et al. (2006) started his model with a

learning rate of 0.25 for 10,000 patterns and then gradually lowered this learning rate

to 0.05 after another 30,000 patterns. In Chang et al.’s model, if we test priming

early on when the learning rate is 0.25, the effect of surprisal will be larger than if we

test it later on when the learning rate is 0.5. In this way, a decrease in learning rate

over development leads to a decrease in prime surprisal effects over development.

To clarify how these two effects could arise within a developmental account of

language acquisition, we developed a simple dynamic systems model of verb bias

acquisition. In this model, we assumed that children get 100 inputs for two verbs (A,

B), which were coded as 1 for PD and 0 for DOD (variable pd). Verb A occurs in PD

80% of the time and verb B occurs in PD 20% of the time. We then set the learning

rate parameter to start at 0.1 and reduce to 0.01 in 100 steps. The model is

governed by four equations. The first equation updates the PD bias for the verb V

that is inputted by adjusting the previous PD bias for that verb by the error between

the previous bias and the present input (PD = 1, DO = 0), multiplied by the learning

rate. The second equation updates the bias for the other verb to be unchanged from

the previous time step. The third equation set the initial bias to a uniform random

number between 0 and 1. The fourth equation sets the PDCHOICE variable to be

set to the bias of the verb that is being used at each point in time. These equations

were used to generate each random child:

1) BIASV,T = BIASV,T-1 + ( PDINPUTT - BIASV,T-1 ) * LRATET if verb matches V

2) BIASV,T = BIASV,T-1 if verb mismatches V

3) BIASV,0 = UniformRandom (0, 1)

4) PDCHOICET = BIASV,T if verb is V

To see how this model would explain the results in this study, we generated two

random children (child 1 and child 2). The initial bias (at time 0) is a random number

between 0 and 1 and hence child 2 has an initial bias of 0.85 for verb B, which is a

bias for the DOD. But since verb B occurs in the PD 20% on average, over

development, the bias gradually decreases until the child matches the adult’s

baseline bias. On the other hand, verb B in child 1 is already close to the adult level

(0.3) at the start of development. However, due to the random run of PDs early in

development, the bias is moved first towards PD before returning to the adult level.

Fig. 4. Development of target verb bias for two verbs (A and B) over 100 exemplars for two children

Figure 4 shows that early verb bias might not match adult biases and that the

gradual learning of adult biases can explain the growth of the target bias in our

study. The figure also demonstrates effects of the learning rate. The learning rate

starts off high and reduces over development, which means that early verb biases

are affected more substantially by the identity of each prime structure (i.e., vertical

displacement is greater). For example, for Verb A, Child 2 starts off with a bias of

0.98 but, once the first DOD is experienced, the bias drops by 0.08 because the

learning rate is high. Later in development, biases change by only 0.01 because the

learning rate is lower. This explains why prime surprisal effects are larger earlier in

development.

The dynamic systems model helps us understand how a model that allows verb-

structure representations to change over development could provide an explicit

account of the behavioural changes in this study. Although models which

incorporate changes in learning rate over development are generally motivated by

model-specific assumptions (e.g., avoiding local minima), we can link these

assumptions to more general issues with the literature on the critical/sensitive period.

For example, language learning ability is strongly associated with the age that

language learning starts, even when the amount of input is controlled (Johnson &

Newport, 1989; Lenneberg, 1967; Mayberry, 2007), which supports the view that the

learning rate may change over age. Furthermore, there are many different

phenomena in vision, auditory, and general cognition that have different sensitive

periods (e.g., Crawford, Harwerth, Smith, & von Noorden, 1996; Daw, 1994; Katz

and Shatz, 1996; Weisel & Hubel, 1965; Kral & Sharma, 2012), which suggests that

the learning rate changes may be governed by neural mechanisms that are present

across multiple brain areas (e.g., myelination, axon elaboration, synapse elimination,

Knudsen, 2004). Since a complete model of language must be able to explain better

learning abilities early in development, the same mechanisms that support that

phenomenon could also explain the larger surprisal effects early in development in

the present study.

Conclusion

Exploring the effect of verb syntactic preferences on priming in three age groups has

allowed us to begin to build a theory of verb-syntactic development in which the role

of the input as well as prior linguistic knowledge is considered. We observed

evidence of abstract structural priming across development and, although only adults

showed evidence of a lexical boost, both children and adults were sensitive to verb

bias effects in the form of target verb bias and prime surprisal effects. We suggest

that the lexical boost is not a reliable tool for making inferences about verb-syntactic

development and propose that children acquire abstract knowledge of structure

whilst also learning the lexical links to these structures from verbs. We also

tentatively suggest that this process is best explained by a model that uses an error-

based learning mechansim with a variable learning rate.

Acknowledgements

Financial support for this research was supported by a faculty-funded doctoral

studentship awarded to Michelle Peter by the University of Liverpool. We wish to

thank the schools and children who took part in this study, and to the students who

helped to collect the data. Thanks also go to the audiences at the AMLaP

conference (Riva del Garda, Italy) in September, 2012 and at BUCLD in November,

2012 for their helpful comments on an earlier version of this paper.

Appendix A

A1. Experimental items

Prime sentences before the slash are presented in the DOD structure; those after the slash are in the PD structure. The four prime verbs are given in parentheses. Target stems used the same combination of agent, verb, beneficiary, and theme as prime sentences (e.g., the target stem, “Piglet GAVE...” was designed to elicit either Piglet gave Tigger a puppy or Piglet gave a puppy to Tigger).

1. The king (GAVE/ SHOWED/ BROUGHT/ SENT) the queen a baby/ a baby to the queen

2. The boy (GAVE/ SHOWED/ BROUGHT/ SENT) the girl a fish/ a fish to the girl3. Wendy (GAVE/ SHOWED/ BROUGHT/ SENT) Bob a puppy/ a puppy to Bob4. Dora (GAVE/ SHOWED/ BROUGHT/ SENT) Boots a rabbit/ a rabbit to Boots5. The prince (GAVE/ SHOWED/ BROUGHT/ SENT) the princess a baby/ a

baby to the princess6. The king (GAVE/ SHOWED/ BROUGHT/ SENT) the queen a cat/ a cat to the

queen7. Piglet (GAVE/ SHOWED/ BROUGHT/ SENT) Tigger a puppy/ a puppy to

Tigger8. Wendy (GAVE/ SHOWED/ BROUGHT/ SENT) Bob a rabbit/ a rabbit to Bob

A2. Filler items

1. Boots was flying2. The princess jumped3. Piglet and Tigger bounced4. The king and queen waved5. Tigger was washing6. The prince was rocking7. Piglet waved8. The cat was swinging9. Dora was flying10.Bob was swinging11.The princess and the cat were rocking12.Dora and Boots waved13.Bob was flying14.The prince jumped

15.Tigger was rocking16.The queen waved17.The king and queen bounced18.Piglet jumped19.Wendy was flying20.Dora was washing21.The boy waved22.Boots pointed at Dora23.Wendy and Bob jumped24. Dora was swinging25.The girl waved26.Wendy pointed at Bob27.Boots was washing28.Piglet was rocking29.The cat bounced30.Bob jumped31.Boots waved at Dora and the baby32.The king pointed at the queen

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