# A hierarchical Bayesian model for syntactic priming

Weijie Xu<EMAIL_ADDRESS>
Department of Language Science, 3151 Social Science Plaza
Irvine, CA 92697 USA Richard Futrell<EMAIL_ADDRESS>
Department of Language Science, 3151 Social Science Plaza
Irvine, CA 92697 USA

###### Abstract

The effect of syntactic priming exhibits three well-documented empirical
properties: the lexical boost, the inverse frequency effect, and the
asymmetrical decay. We aim to show how these three empirical phenomena can be
reconciled in a general learning framework, the hierarchical Bayesian model
(HBM). The model represents syntactic knowledge in a hierarchical structure of
syntactic statistics, where a lower level represents the verb-specific biases
of syntactic decisions, and a higher level represents the abstract bias as an
aggregation of verb-specific biases. This knowledge is updated in response to
experience by Bayesian inference. In simulations, we show that the HBM
captures the above-mentioned properties of syntactic priming. The results
indicate that some properties of priming which are usually explained by a
residual activation account can also be explained by an implicit learning
account. We also discuss the model’s implications for the lexical basis of
syntactic priming.

Keywords: syntactic priming; hierarchical Bayesian model; lexical boost;
inverse frequency; asymmetrical decay; implicit learning; lexical-based
priming

## Introduction

Syntactic priming refers to an adaptive behavior where exposure to a specific
syntactic structure influences a language user’s subsequent processing of
similar structures. From the production side, speakers tend to repeat the
syntactic structure they have recently been exposed to (?, ?, ?, ?, ?). For
example, after a person produces or perceives a sentence such as “the boy gave
the girl the ball” (called the prime), there is a reliable increase in the
probability that their description of a subsequent ditransitive event (called
the target) will use the same syntactic form, in this case using the double-
object construction (DO; as in “give $X$ $Y$”) rather than a prepositional-
object construction (PO; as in “give $X$ to $Y$”).

### Empirical Background

A large body of work has examined factors that modulate the strength of
syntactic priming (?, ?). In (1) we summarize three major findings from this
literature, and review them below.

  1. (1)

Three empirical properties of syntactic priming

    1. a.

Lexical Boost: Lexical alignment, especially verb overlap, between the prime
and the target enhances the priming effect size.

    2. b.

Inverse Frequency: Less frequent prime constructions elicit stronger priming
effect.

    3. c.

Asymmetrical Decay: Lexical boost decays faster than the abstract verb-
independent priming effect.

The lexical boost refers to an increase in the effect size of syntactic
priming when the prime sentence and the target sentence share critical lexical
items (?, ?, ?). For example, if both the prime and the target use the same
verb “give”, then priming of the DO construction is stronger than if the prime
and target involved different verbs. In comprehension, the alignment of verb
lexical items has been found to be necessary to observe a reliable priming
effect (?, ?).

Another well-documented property of syntactic priming is the inverse frequency
effect, where constructions that are less frequent in language experience are
susceptible to a stronger priming effect (?, ?, ?, ?, ?, ?). For example, in
English usage, PO constructions are overall rarer than DO; consequently, a PO
prime causes a larger increase in the probability of a PO target than a DO
prime does for a DO target. A more precise characterization of the inverse
frequency effect has been put forward by ? (?), who claim that the effect size
of priming is directly proportional to the prediction error during processing,
which in turn depends on statistical regularities in the language experience.
In that case, infrequent constructions or co-occurrences cause larger
prediction errors and thus stronger priming effect.

Our last phenomenon of interest has to do with the decay of the size of the
priming effect as a function of time between the prime and target. Decay of
syntactic priming has been observed in both the short-term (?, ?, ?) and the
long-term (?, ?, ?, ?), in a way that depends on lexical alignment between the
prime and target. In particular, asymmetrical decay refers to the fact that
the lexical boost decays more rapidly than the abstract priming effect, where
there is no lexical alignment (?, ?, ?).

### Related Work

Two lines of theories are proposed in the literature, the residual activation
account and the implicit learning account. Each account captures some but not
all the empirical properties mentioned above.

The residual activation account of priming holds that production or
comprehension of a syntactic form is influenced by its level of _activation_.
Upon encountering a sentence, activation is boosted for the representation of
both the lexical items and the syntactic structure, facilitating future reuse
(?, ?). Based on a spreading activation model of lemma retrieval (?, ?), ? (?)
specify a representational architecture where nodes representing verb lemmas
are connected to the combinatorial nodes representing syntactic structures. In
this model, a boost in activation on a certain verb lemma spreads outward to
the combinatorial nodes and to other verb lemmas, which in turn further
contribute to the activation of the combinatorial nodes. This residual
activation account predicts the the lexical boost effect, since the the verb
lemma used in the prime is directly activated and receives the strongest
activation boost. However, the residual activation account has difficulty
explaining long-term priming, since all activation is posited to decay over
time.

The implicit learning account proposes that syntactic priming is a kind of
syntactic learning. This idea was originally implemented in a connectionist
model that predicts the next word based on the previously heard word (?, ?).
In this model, prediction error serves as an explicit learning signal, based
on which the weights between nodes are updated via backpropagation. The
implicit learning account predicts that the priming effect size is
proportional to the prediction error when processing the prime, thus
accounting for the inverse frequency effect (?, ?). This error-based effect is
also reproduced in learning models that do not include an explicit error
signal, such as the Bayesian belief-update (?, ?, ?, ?). However, compared to
the residual activation account, the implicit learning account struggles to
capture the observation that the syntactic priming effect can sometimes be
short-lived.

Since neither account simultaneously explains all the three properties in (1),
some studies propose a hybrid account, holding that both residual activation
and implicit learning play a role. This amalgamation is also motivated by the
observation of asymmetrical decay. In particular, residual activation explains
the rapid decay of lexical boost, whereas long-lived abstract priming is
mostly driven by implicit learning (?, ?, ?). In line with this hybrid
mechanism, ? (?) propose an activation-based model in the ACT-R framework (?,
?), where lexical chunks and syntactic chunks are represented separately in
the declarative memory. The priming effect results both from an increase of
the base-level activation of the syntactic chunk and from the spreading
activation from lexical chunks. Similar to ? (?), the spreading activation
mechanism allows the model to capture lexical boost. The model also captures
the inverse frequency effect because of the diminished base-level learning for
high-frequency chunks. Moreover, the model captures the asymmetrical decay
since the spreading activation in ACT-R only goes from chunks that are
temporarily held in the working memory buffer to chunks in long-term memory.
Therefore, when lexical chunks are evacuated from working memory, they cannot
spread activation anymore.

### The Current Study

We develop a computational-level theory of syntactic priming in terms of a
hierarchical Bayesian model (HBM) of syntactic knowledge. Our HBM extends the
basic Bayesian belief-update model by representing syntactic knowledge at
multiple levels of abstraction (?, ?). As a general learning framework, HBMs
have been broadly applied to speech perception (?, ?), the emergence of
communicative systems (?, ?), and human cognition in general (?, ?). We show
how this general learning framework can capture the three empirical properties
of priming in (1), namely the lexical boost effect, the inverse frequency
effect, and asymmetrical decay.

## Modeling Framework

### Representing Syntactic Knowledge

The core of the priming effect is a decision-making problem: the decision
between multiple syntactic frames for the same message in production, or
between multiple parses for an ambiguous input in comprehension. The decision-
making can be characterized by a probability distribution over the syntactic
alternatives of interest. In the production of ditransitives, for instance,
the decision between dative object (DO) and prepositional object (PO) can be
captured by a Bernoulli distribution, whose parameter represents the
production probability for DO versus PO. We aim to characterize this
probability distribution and how it changes in response to experience.

Following previous work, we represent a person’s syntactic knowledge as
syntactic statistics: counts of how often one expects to experience different
syntactic constructions (?, ?, ?), represented at different levels of
abstraction. We posit a hierarchical structure for syntactic statistics, as
illustrated in Figure 1. We specify two levels of abstraction. The lower level
captures verb-specific statistics $\phi_{v}$, such that each verb imposes its
own bias (?, ?). The verb-specific statistics echo the lexicalist view of
sentence processing, where syntactic knowledge is accessible via lexical
representations (?, ?, ?, ?). The higher-level abstraction $\Theta$ is an
aggregation over all the lower-level statistics and represents the general
decision bias across all the verbs.

Taking ditransitive priming as an example, the variables in Figure 1 would
represent counts over DO constructions and PO constructions, conditional on
specific verbs (the $\phi_{v}$) or aggregating across verbs (the high-level
$\Theta$). However, this hierarchical structure of syntactic statistics is not
limited to any specific construction (e.g. ditransitives). The idea can be
applied to any scenario that includes processing decisions, such as ambiguity
resolution (for example, garden-path sentences) in comprehension and the
choice of syntactic frames in production (for example, passive vs. active for
transitives).

Figure 1: Hierarchical representation of syntactic statistics.

### Updating Syntactic Knowledge

We posit that syntactic learning takes the form of Bayesian updates to the
hierarchical model in Figure 1. We explain how this works first using a
familiar non-hierarchical Beta-Binomial model, and then the full hierarchical
model.

#### Beta-Binomial Process.

The Beta-Binomial model is a simple version of Bayesian belief updating. It
postulates a generative model where a set of decisions $x$ between two
outcomes are sampled from a Binomial distribution parameterized by variable
$\Theta$, which in turn is sampled from a prior Beta distribution with
hyperparameters $\alpha,\beta>0$:

$\displaystyle\Theta$ $\displaystyle\sim\mathrm{Beta}(\alpha,\beta)$ (1)
$\displaystyle x$ $\displaystyle\sim\mathrm{Binomial}(\Theta).$ (2)

Applied to syntactic priming, the variable $\Theta$ represents the probability
of the DO form versus the PO form, and the hyperparameters $\alpha,\beta$ in
the Beta distribution represent prior expectations for frequencies of the DO
and PO outcome respectively. After observing an outcome $x$, the variable
$\Theta$ updates by Bayesian inference, following the new distribution

$p(\Theta\mid x)\propto p(x\mid\Theta)p(\Theta).$ (3)

This updated distribution takes a simple form. If one observes $N$ datapoints
containing $x$ examples of a DO outcome, the updated decision probabilities
follow

$\Theta\mid x\sim\text{Beta}(\alpha+x,\beta+N-x).$ (4)

Thus observing many DO outcomes will increase the probability of future DO
outcomes. Having demonstrated the logic of Bayesian belief updating in a
simple setting, we now turn to our hierarchical model.

#### Hierarchical Bayesian Model.

As an extension of the Beta-Binomial process, we specify a hierarchical
structure for syntactic statistics. Instead of having a single parameter
$\Theta$ giving the decision bias, we posit that each verb has an individual
decision bias $\phi_{v}$, which is in turn sampled from a global decision bias
variable $\Theta$ that ties the individual verb biases together. Specifically,
we posit a generative model

$\displaystyle\Theta$ $\displaystyle\sim\mathrm{Beta}(1,1)$ (5)
$\displaystyle\phi_{v}$
$\displaystyle\sim\mathrm{Beta}(\alpha\Theta,\alpha(1-\Theta))$ (6)
$\displaystyle x_{v}$ $\displaystyle\sim\mathrm{Binomial}(\phi_{v}).$ (7)

In this generative model, a global decision bias variable $\Theta$ is sampled
from a distribution $\text{Beta}(1,1)$ (which is uniform on the interval
$[0,1]$), then for each verb $v$, an individual decision bias $\phi_{v}$ is
sampled from a Beta distribution parameterized by $\Theta$. Here the parameter
$\alpha>0$ represents the relative importance of the global prior $\Theta$ in
determining the verb-specific biases $\phi_{v}$.

The hierarchical structure of the generative model means that experience with
one verb can cause changes not only to that verb’s decision bias, but also to
the global decision bias, which in turn affects other verbs. Given counts of
$x_{v}$ DO outcomes for a specific verb $v$, when we are considering the
update to the statistics for the currently-observed verb $v$, then the update
is given by Bayes’ rule applied to $\phi_{v}$ alone:

$\displaystyle p(\phi_{v}\mid x_{v})$ $\displaystyle\propto
p(x_{v}\mid\phi_{v})p(\phi_{v}).$ (8)

When we are considering the update to the variable $\phi_{w}$ for a
_different_ verb $w\neq v$, then the update is mediated through the updated
global statistics $\Theta$:

$\displaystyle p(\phi_{w}\mid x_{v})$ $\displaystyle=\int
p(\phi_{w}\mid\Theta)p(\Theta\mid x_{v})\text{ d}\Theta.$ (9)

Therefore, information flows both bottom-up and top-down in the HBM. When the
new data $x_{v}$ for verb $v$ is encountered, it impacts the verb-specific
parameter $\phi_{v}$ and the effect goes bottom up to impact the higher-level
$\Theta$. Since this abstract $\Theta$ governs and constrain all the verb-
specific parameters, as a top-down process, the effect on $\Theta$ in turn
influences other $\phi_{w}$. In this way, learning from the data of one verb
can be generalized to others. We implement the learning of HBM using WebPPL
(?, ?).

Figure 2: Results of Simulation 1. Panel A: Model-estimated average prior and
posterior probability of DO for the target verbs in ? (?); Same refers to the
condition with verb overlap between the prime and the target; Different refers
to the condition without verb overlap. Panel B: Model-predicted priming effect
size, calculated as the difference of log-odds between the posterior and the
prior for DO.

## Model Evaluation

In two simulations, we evaluate our model on the experimental materials of ?
(?)’s Experiment 1, aiming to capture the three empirical properties of
syntactic priming outlined earlier. The original experiment is in a trial-to-
trial production priming paradigm using English ditransitives. There are 32
items, each consisting of a prime and a target as in (2) below. At the prime,
participants are presented with the partial input of a ditransitive sentence
until the first post-verbal object noun phrase, and are asked to complete the
sentence. At the target, participants are asked to complete another partial
sentence input that includes a subject noun phrase and a ditransitive verb.
The prime sentences are manipulated in a 2$\times$2 design. First, the prime
is biased towards either DO or PO structure. Second, verb overlap is
manipulated such that the prime either shares or does not share the same verb
as the target.

  1. (2)

Sample stimuli from ? (?)

    1. a.

Prime:
The racing driver showed the torn overall…
The racing driver showed the helpful mechanic…
The racing driver gave the torn overall…
The racing driver gave the helpful mechanic…

    2. b.

Target: The patient showed…

### Simulation 1: Lexical Boost and Inverse Frequency

This first simulation aims to capture both the lexical boost and the inverse
frequency effect. First, we expect the materials with verb overlap in ? (?) to
generate a stronger priming effect in the model output, compared to the
condition without verb overlap. Second, we expect the model to generate
stronger priming effect for the ditransitive structure that is favored _a
priori_ before priming.

#### Estimating the prior.

We first construct a dataset to represent the participant’s language
experience prior to the priming experiment.111In Bayesian learning, this prior
dataset corresponds to pseudocounts. The larger the size of the prior data,
the smaller the priming effect for the same amount of exposure data. As
summarized in Table 1, this dataset contains 100 datapoints distributed across
the nine verbs used in the original experiment. Each data point represents an
instance of verb use in one of the two ditransitive structures (e.g.
<give:DO>, <show:PO>). The frequency of each verb and their relative use of
DO/PO are specified based on counts from the British National Corpus (?, ?,
?).222The verb frequencies in Table 1 are sampled from a multinomial
distribution with the parameter $N=100$ and the parameter $\mathbf{p}$
corresponding to the verb frequencies in the corpus. The DO/PO frequency for
each verb is generated in a similar way but from a binomial distribution. The
model starts with the global bias $\Theta$ and verb-specific biases $\phi_{v}$
derived from this dataset following the procedures above. This initial state
is the prior distribution which will be compared with the updated posterior
distribution after exposure to the priming data.

Verb | DO Freq. | PO Freq. | Verb Freq.
---|---|---|---
give | 51 | 20 | 71
show | 1 | 3 | 4
send | 5 | 8 | 13
lend | 1 | 0 | 1
hand | 0 | 3 | 3
loan | 0 | 0 | 0
offer | 2 | 4 | 6
sell | 0 | 2 | 2
post | 0 | 0 | 0
in total | 60 | 40 | 100
Table 1: Verb frequencies and counts of DO/PO ditransitives used to form the
model’s prior distribution.

#### Inferring the posterior.

Starting with the prior defined above, the model infers a posterior based on
the exposure data presented in the primes in ? (?). The posterior learned by
the model is a joint distribution for both the global and verb-specific
decision biases. However, the parameters that can be directly measured from
the behavioral data are the verb-specific parameters, that is, the proportion
of DO/PO use associated with the specific verbs in the target sentence of each
item. Therefore, for the target verb in each item, we obtain the verb-specific
posterior through marginalization as given in Eq. 9.

#### Result.

Figure 2A shows the average DO probability for the target verbs in each
condition. The control condition shows the prior DO probability before
priming. Although there are more DOs in the prior data based on the raw
frequency in Table 1, the model infers a global bias slightly against DO (i.e.
$p(\text{DO})<0.5$), because the DO preference in the raw frequency is mainly
driven by one single frequent verb _give_ in the prior dataset. After updating
based on the prime data, we find a priming effect in the form of an increase
in DO probability with DO primes and a decrease in DO probability with PO
primes. Figure 2B shows the priming effect size more directly by calculating
the difference of log-odds between the prior and the posterior of DO for each
target verb.

Consistent with lexical boost, the model predicts a stronger priming effect
when the prime and the target share the same verb. Consistent with the inverse
frequency effect, the model predicts that DO primes—which is the structure
less preferred in the prior—should elicit a stronger priming effect.

#### Discussion.

The model’s ability to capture the inverse frequency effect comes with
Bayesian learning in general, where events that are more surprising induce
larger updates (?, ?). More crucially, the model is also able to capture
lexical boost. In fact, in the Bayesian framework, a better way to interpret
the effect of verb (non-)overlap is _lexical transfer_ , where the learning
effect on one verb is generalized to others. It is worth noting that although
our model successfully captures the qualitative conclusions in ? (?), it is
difficult to directly compare the model-predicted results with the empirical
data, because experimental work rarely reports the baseline pre-priming DO
probability.

### Simulation 2: Asymmetrical Decay

In Simulation 2, we aim to model the asymmetrical decay in syntactic priming,
where lexical boost decays more rapidly than the abstract priming effect
without verb overlap. The prior estimation follows the procedure identical to
Simulation 1, using the prior data in Table 1.

The simulation is conducted in two groups, an exposure and a control group.
For the exposure group, the model is first conditioned on the prime sentence.
For the control group, there is no conditioning on prime sentences. Then, both
groups are exposed to two batches of additional data, each containing 100
samples from the frequency distribution in Table 1. These post-priming data
reflect the average effect of post-priming trials and experience. The priming
effect size is then calculated as the difference of log-odds for DO between
the model-inferred posterior in the exposure group and the one in the control
group.

Figure 3: Simulation 2 model-predicted priming effect size as a function of
the number of additional batches of post-priming data. Effect size calculated
as in Simulation 1.

#### Result.

Figure 3 shows the model-predicted priming effect size as a function of the
number of batches of post-priming data. The result with zero batch of post-
priming data is the same as the result in Simulation 1 (Figure 2B), and is
repeated here for the convenience of comparison. First, as in Simulation 1,
the model reproduces the lexical boost and the inverse frequency effect with
additional post-priming data. Second, the result indicates a decay of the
priming effect in general for both conditions of verb overlap: The priming
effect size decreases as more and more post-priming data are taken by the
model. Critically, the model also demonstrates an asymmetry of decay: The
priming effect size diminishes more than twice as rapidly in the condition
with verb overlap compared to the condition without verb overlap.

#### Discussion.

As expected, our model successfully captures the asymmetrical longevity of
syntactic priming, such that the lexical boost effect decays more rapidly than
the priming effect without verb overlap. To our knowledge, the decay of
syntactic priming and the distinction between long- and short-term priming has
rarely been explained from the perspective of implicit learning, and has
actually been considered one of the major limitations of the implicit learning
account. However, the result of the current simulation demonstrates that at
least some aspects of the long-/short-term distinction in priming can be
captured by the implicit learning account in principle. We discuss the
potential mechanism of how the HBM can capture the asymmetrical decay of
priming below in General Discussion.

## General Discussion

To sum up, in two simulations, we evaluate HBM against the materials from ?
(?)’s priming experiment in English ditransitives. The model successfully
captures lexical boost, the inverse frequency effect, and asymmetrical decay.

### How abstract is syntactic priming?

One of the major questions under debate is: How abstract is syntactic priming?
Previous studies observe that priming can be somewhat context-independent, for
example, without the alignment of semantic features (?, ?, ?) or the specific
phonetic content of the morphosyntactic markers (?, ?, ?). However, in terms
of whether priming is verb-independent, the empirical evidence presents a
mixed picture. Although there is some evidence for verb-independent syntactic
priming from the production side, studies focused on comprehension tend to
find that verb overlap is required for a reliable priming effect (?, ?).

In the current study, by positing a verb-specific level of statistics directly
matching the structure of verb-specific input data (i.e. <give:DO>), our model
assumes that the major locus of syntactic priming lies in the verb-specific
biases. At least for the scenario of ditransitives in the current study, this
means that what has been directly learned from the input data is the verb-
specific statistics, and that there is a lexical basis of syntactic priming
where it is the syntactic knowledge stored in the specific verb items that is
directly primed. The abstract higher-level priming, under the framework of
HBM, can be viewed as an emergent effect that is driven by the verb-specific
effects. This idea of lexical-driven syntactic priming is in fact similar to
the assumption held by the activation-based mechanism proposed in ? (?), where
the activation of verb lemmas is first boosted by the prime data, and the
activation then spreads to the combinatorial nodes that represent syntactic
structures.

Given the assumption that syntactic priming is lexical-driven, lexical boost
can be reinterpreted as lexical transfer by reconsidering the role of verb
overlap. If priming is assumed to be operated at an abstract level, it would
be reasonable to interpret the role of verb overlap as providing additional
processing cues to facilitate the selection of an abstract syntactic structure
(?, ?), resulting in a lexical boost. However, if priming is lexical-driven,
then the effect of lexical boost, as another side of the same coin, can be
interpreted as a weakening effect of verb misalignment. This reinterpretation
naturally fits the mechanism of knowledge generalization in HBM: The verb-
specific input data not only impacts the verb-specific knowledge, but also
goes bottom-up to impact the abstract verb-independent knowledge, which in
turn influences other verb-specific knowledge. The magnitude of this
generalized priming should be much smaller, not only because the bottom-up
effect on the higher-level statistics is counterbalanced by other lower-level
statistics, but also because other verb-specific statistics have their own
biases.

### Decay as interference-based unlearning

What is the mechanism for the decay of priming? In the activation-based
account, decay is temporal and in a radioactive fashion. In the implicit
learning account, decay is simply underspecified, with no mechanism to unlearn
the prime data without any additional input. As intuitive as the time-related
decay seems to be, it is empirically difficult to tease apart from another
mechanism of decay, which is the interference-based decay. In fact, although
the learning literature does not pay much attention to the interference-based
decay, studies of working memory has been gradually converged on the claim
that memory interference plays a much more important role than temporal decay
for many memory mechanisms, such as memory retrieval (?, ?, ?).

The interference-based mechanism in fact can be a potential way to “unlearn”
the primed knowledge for implicit learning. In line with the idea that
statistical learning spans over life time (?, ?), the cognitive system keeps
self-updating for the incoming data received after the primes. That being
said, for participants in a priming experiment, learning is still ongoing
during filler items (?, ?) or in the naturalistic communication environment
between experiment sessions (?, ?). This post-priming learning can wash out
the original priming effect, manifested as a decay over time.

This interference-based unlearning naturally provides a mechanism for
asymmetrical decay in HBM. The lower-level statistics, due to the sparsity of
data, can be fairly susceptible to a small amount of incoming data. The
higher-level statistics, in contrast, is much more stable as it collects
information from all the lower-level statistics. Since the verb-independent
priming results from the top-down processing governed by the higher-level
statistics, its effect is more stable and less susceptible to the interference
from the post-priming data. In another word, although the abstract verb-
independent knowledge is more difficult to prime, once it has indeed been
primed, it is also more difficult to unprime.

Although we show that HBM can demonstrate the decay of priming in principle,
there are still some aspects of decay that can be hardly explained purely from
the perspective of implicit learning as a computational theory. First,
although HBM captures the qualitative pattern that lexical boost decays faster
than verb-independent priming, many studies find that lexical alignment can
completely lose its benefit after decay (?, ?). This pattern cannot be
reproduced by HBM, where there is always a stronger priming effect with verb
overlap. Second, sampling from prior data in the post-priming stage also has
its limitation, especially given that the intervening filler items in the
experiments of some studies are in a construction differing from the one
represented by the statistics in our model. Model architectures that
explicitly include a network of constructions as part of the structure of
syntactic knowledge, in which (for example) transitive sentences have some
relation to ditransitives, may account for the observed decay of syntactic
priming when post-priming trials involve sentences in apparently unrelated
constructions.

## Conclusion

In the current study, we propose a hierarchical Bayesian model for syntactic
priming. The model represents syntactic knowledge as hierarchical syntactic
statistics, with two levels of abstraction to represent the verb-specific
biases and the abstract verb-independent bias for syntactic decisions. As a
learning model, HBM is able to capture the three properties of syntactic
priming: the lexical boost, the inverse frequency effect, and the asymmetrical
decay. This result expands the explanatory power of the implicit learning
account to cover more empirical properties of syntactic priming that tend to
be accounted for through residual activation, such as the lexical boost and at
least some aspects of the short-term priming.

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