The Cognitive Modeling of Errors During the Japanese Phonological AwarenessFormation Process
Jumpei Nishikawa (nishikawa.jumpei.16@shizuoka.ac.jp)1
Junya Morita (j-morita@inf.shizuoka.ac.jp)1
1 Faculty of Informatics, Shizuoka University, 3-5-1, Johoku, Naka-ku, Hamamatsu, Shizuoka 432-8011,Japan

Abstract
Both nature and nurture contribute to language development.
In the case of phoneme segmentation, children have the natural ability to recognize a continuous sound in various units,
but as they grow, they only selectively learn to recognize it aspart of a series in the unit that is used in their mother tongue.
This developmental process is supported by an ability calledphonological awareness that allows children to become intentionally aware of units of phonology. It is known that erroneous pronunciation appears during the phonological awareness formation process. In this research, we aim to examine
the factors that induce and reduce such errors. To do so, we
modeled phonological awareness using the cognitive architecture ACT-R and performed simulations that manipulated ACTR parameters that correspond to both nature and nurture factors. As a result, it was confirmed that errors due to a lack ofphonological awareness can be modeled with the innate memory retrieval mechanism. We also observed that such errors
were reduced when learning factors were added to the model.
However, we could not simulate this learning process. In the
future, we will study the interaction task that enables learning
to reduce phonological errors and contribute to the acquisitionof phonological awareness.
Keywords: cognitive modeling; phonological awareness;
Japanese;

Introduction
Language development is influenced by both innate and experiential factors. Newborn infants have an innate foundation for acquiring a variety of languages. Through experiences such as observing and imitating the behavior of familiar
adults (Baron-Cohen, 1997; Tomasello, 1999), they graduallyform a specific mother language structure, based on a general,
innate language foundation. A prominent example of such
convergence can be found in phoneme segmentation, which
is one of the linguistic components. Infants initially have opportunities to divide a sound into various types of segments,
like syllables or morae, though, as they grow up and master their language, they find it difficult to recognize phoneme
segments that do not belong to their mother tongue.
In the fields of developmental psychology and speechlanguage pathology, this developmental process is partly attributed to an ability called phonological awareness, whichenables one to intentionally pay attention to phonologicalaspects, such as phonemes and rhythms, of oral languages(Stahl & Murray, 1994). This ability is usually developed
during the preschool period and relates to the acquisition ofreading and writing skills. Researchers have detected several

phonological errors that seem to occur due to a lack of phonological awareness during the language development process(Kubozono, 1989). In addition, there are reported cases
in which infants with atypical developmental traits, such as
those on the autism spectrum, who experienced an overall
delay in acquiring phonological units, improved these problems with support for enhancing their phonological awareness (Mugitani et al., 2019).
Although psychologists have revealed the age at which
phonological awareness is formed and its role in language development, it has not been clarified that the internal processes,
cognitive functions, and mechanisms behind the occurrence
and suppression of errors relating this ability.
Based on this background, the current study aims to examine error occurrence and suppression factors that are related
to the phonological awareness formation process. We focus
on two language development factors, namely nature and nurture, and construct a model using the cognitive architecture
ACT-R (Anderson, 2007). In addition, we examine the process and mechanism of the phonological awareness formation
process by simulating Japanese language development.
The structure of this paper is as follows. The first section
introduces the research related to this study. The model andthe model-based simulation will then be shown. Finally, asummary of the current status and future issues is presented.

Related Research
In this section, we first introduce previous studies on language
development and the formation of phonological awareness.
Next, we introduce cognitive modeling and cognitive architecture as the method for understanding and explaining human cognitive processes. We also review research on language learning using cognitive models.

Studies on Phonological AwarenessIn the field of phonology, researchers have developed systems
for classifying the sounds that humans perceive and utter as
mental representations of sounds. The most famous systemis the distinctive feature (Chomsky & Halle, 1968), whichis the basic unit that distinguishes phonemes; it is typicallydefined as a binomial variable that takes the value of + or
− by classifying the tongue and throat movements associatedwith vocalization. Table 1 provides the distinctive features
for some phonemes.

Table 1: Distinctive feature examples
consonantal
syllabic
sonorant
hign
back
low
anterior
coronalvoice
continuant
nasal
strident
delayed release
round

a
−
+
+
−
+
+

i
−
+
+
+
−
−

u
−
+
+
+
+
−

e
−
+
+
−
−
−

o
−
+
+
−
+
−

−

−

+

−

+

k
+
−
−
+
+
−
−
−
−
−
−
−
−
−

g
+
−
−
+
+
−
−
−
+
−
−
−
−
−

s
+
−
−
−
−
−
+
+
−
+
−
+
−
−

z
+
−
−
−
−
−
+
+
+
+
−
+
−
−

t
+
−
−
−
−
−
+
+
−
−
−
−
−
−

d
+
−
−
−
−
−
+
+
+
−
−
−
−
−

n
+
−
+
−
−
−
+
+
+
−
+
−
−
−

Around the world, there are varieties of systems that combine these innate phonetic elements as units. According to
Trubetzkoy (1969), such systems fall into two groups, namely“mora languages” and “syllable languages,” based on thesmallest prosodic unit that is used in that language. Amongthe mora languages, Japanese defines the mora as a unit of duration (Bloch, 1950), and each mora is associated with a single kana (Japanese character), which consists of five vowels(a, i, u, e, o) and 59 combinations of consonants and vowels(ka, ki, ku, ke, ko, sa, si, etc.), and other special morae representing the duration or gemination of sounds. In other words,
Japanese phonic elements have a clear connection with written symbols. We think that this characteristic offers an advantage for modeling phonological awareness in a symbolic
cognitive architecture. Therefore, in this study, we focus on
Japanese morae and try to reveal factors that relate to the acquisition process of this ability.
Several Japanese researchers have investigated the formation of phonological awareness. Hara (2001) presented astudy where participants engaged in several phonological manipulation tasks. For example, in the mora deletion task, participants were required to respond with a mora sequence thatinvolved deleting a single mora from an orally presented word(e.g., i-ko is the correct answer for a task that deletes ta fromthe orally presented word ta-i-ko, which means “a drum”).
Similarly, in the mora reversal task, participants were required to respond with a reverse-ordered mora sequence after
an oral word prompt (e.g., ka-i-su for su-i-ka, which means“a watermelon”). In her study, the performance of such tasks
was increased according to participants’ written and reading
language skills.
In a clinical setting, Japanese speech-language-hearing
therapists (ST) reported cases where children confused specific types of morae. For example, it was reported that young
children around 2 to 3 years old tend to confuse morae that include the consonants /r/ and /d/ or /s/ and /sj/, though Japanese
adults can clearly distinguish these (Kobayashi, 2018). Moreover, there have been reports that some children with developmental disorders have difficulty distinguishing a morathat consists of only a vowel as well as other morae that
include that vowel (e.g., confusions between a and ka, sa,
or ta) (Ishida & Ishizaka, 2016). Typically developed chil-dren also exhibited similar erroneous utterances in which theyomitted consonants (e.g., pronouncing a-p-pa instead of rap-pa; trumpet) (Nakamura, Kojima, & Fujiwara, 2015). This
type of confusion suggests the existence of a developmental process that migrates from the innate phonological system(Chomsky & Halle, 1968) to the language-specific mora system.
Furthermore, several researchers have used the popular
Japanese word game “shiritori” as a task for examining adevelopment stage of phonological awareness. In this game,players take turns providing a noun whose first character(mora) is the same as the last character of the previously
given noun. For example, after a player answers “ri-n-go”(meaning: apple), the other player continues with “go-ri-ra”(meaning: gorilla). The game is over when a player provides
a word that ends with /N/, since no Japanese word can begin
with this character (e.g., “ri-n-go” → “go-ri-ra” → “ra-ppa” (trumpet) → “pa-n” (Bread) → game over). To avoid
looping, the game also ends if a player repeats a word that
was already provided as an answer in the game (e.g., “ma-suku” (mask) → “ku-ru-ma” (car) → “ma-su-ku” → · · ·).
Takahashi (1997) examined the conditions for being able
to play shiritori through a cross-sectional developmental psychological experiment involving children with typical development. This research indicated that playing shiritori requires the ability to divide sounds into morae and the mental lexicon indexed by phonemes, and that the acquisitionof kana characters is effective for indexing vocabulary bymorae. These results suggest that playing shiritori requires
phonological awareness, paying attention to a phoneme in
the mother language (mora) in a sound, and that such ability is enhanced by presenting visual aids (kana character) that
correspond to the sound. Furthermore, it has been shown thateven if a child does not have the phonological awareness thatis necessary to play shiritori, s/he can participate with help
from adults, who can provide hints.
Kubozono (2000) also conducted an experiment that examined the shiritori process in a 4-year-old Japanese child inorder to present the phonological awareness formation process. In his experiment, the participant was sometimes confused about the unit of mora. For example, she gave “mon-shi-ro-cho-u” (cabbage butterfly) as an answer for “do-rae-mo-n” (doraemon). She also noted “yo-u-gu-ru-to” (yogurt) after “ta-i-yo-u” (sun) was presented. Based on these
two examples, the participant seems to have recognized theconsonant-vowel-vowel sequence (“mo-n” and “yo-u”) asa single unit, although the Japanese mora system divides
this into two morae, namely “consonant-vowel” and “vowel.”
These reports suggest that the language-specific phonetic system is experientially acquired after childhood, and the misconfiguration of the system can be observed culturally while
playing popular word games.
Based on the above-mentioned previous findings, the current research focuses on the ability to extract the mora at theend of a word from the continuous sounds that correspondto words and the ability to search for words by initial mora,
especially in phonological awareness. In addition, we apply
shiritori as the task set based on Takahashi’s and Kubozono’s
work. In addition, we refer to Chomsky and Halle (1968)’s
definition and use a distinctive feature to express and characterize the mora as a symbol.

shiritori going by taking turns providing words. Boxes in theagent area corresponds to each module of ACT-R. In the following, we show how the shiritori process is realized throughthe ACT-R module structure.

Cognitive Modeling
One of the methods for understanding and explaining themechanisms and processes that are related to human cognition is through the cognitive modeling approach in the fieldof cognitive science. In this approach, a model that approximates a person’s task execution (a cognitive model) is built
as a computer program, and a simulation is done using themodel. By observing the cognitive model’s behavior and internal states during the simulation, we can infer a person’sinternal states and cognitive processes during task execution.
Cognitive architectures have been developed as a basis forcognitive modeling. Using a cognitive architecture, it is possible to construct a model that isolates the factors related totask achievement on a common basis. From among the various cognitive architectures that have been developed, we selected ACT-R (Anderson, 2007) for use in this study. Since
ACT-R is based on psychological experiments on thinking
and memory, it enables us to comprehensively grasp various
phenomena related to human cognition. Furthermore, ACT-R
is a production system with multiple modules. There are various parameters that define the module’s behavior, thus making it easier to model the individual. There are also modules that handle interaction with the external environment,
thus making it possible to predict reaction times and associatethem with experimental data.
There have been many studies on language acquisition using ACT-R. For instance, models related to the acquisitionof irregular verbs in English learning (Taatgen & Anderson, 2002) and models of infants’ noun learning (Van Rij,
Van Rijn, & Hendriks, 2010) have been constructed. In addition, brain dysfunction has also been modeled by ACT-R,
and some studies have explained the errors in sentence comprehension in aphasia using ACT-R parameters (Mätzig, Vasishth, Engelmann, Caplan, & Burchert, 2018).
In this study, we aim to model the language development
process by mapping phonological awareness from the memory retrieval mechanism of the ACT-R declarative module.
In particular, we focus on parameters that express knowledge
similarity and the effect of learning, assuming that changes
in these parameters through simulation correspond to innate,experiential language development factors.

Model
In this section, we describe the model of errors in theJapanese phonological awareness formation process. The
model executes shiritori to exhibit reported errors anddemonstrate the factors that suppress such errors.
An overview of the model is shown in Figure 1. This model
includes two agents (dashed line area) who keep a game of

Figure 1: An overview of the model

Module Structure
Declarative Module The declarative module of ACT-R
contains knowledge that is required for task execution in theform of chunks. The model in this study retains three typesof chunks that relate to word (vocabulary) and phonologicalknowledge, and the association between words and phonological knowledge (Table 2). The model also has chunks that
store the words that have already been provided in the current
shiritori trial. These chunks do not exist in the declarative
module at the beginning of the trial; rather, they are generated and stored as the trial progresses using imaginal module.
Table 2: The model’s declarative memory(a) Word knowledge
word
ringo
gorirakuri···(b) Phonological knowledge

sound“ringo”“gorira”“kuri”
···

mora
/ri/
/go/
/ku/
···

sound
“ri”
“go”
“ku”
···

(c) The word–mora relationship
word
ringo
goriragorira
···

mora
/ri/
/go/
/go/
···

position
head
tail
head
···

Production Module The production module selects andapplies rules, and operates the module, while using information and states that are held by other modules. In the modelof this research, when word information is received as a partner’s answer, the novel word is retrieved and provided as ananswer according to the rules of shiritori that were presented
in the previous section.
Figure 2 summarizes this process. Using the word chunk(chunk type b in Table 2) acquired by the aural module, themodel retrieves a chunk that connects the word and the ending mora (chunk type c in Table 2). Phonological knowledge

as in Eq. 1:
Ai = Bi + Si + Pi + εi

(1)

If more than one chunk matches the search request fromthe production module, the chunk with the highest activationis selected. In our model, among the activation elements, we
focus on the similarity Pi and the base level Bi .
Similarity The similarity term Pi of the activation assigned
to chunk i is computed using Eq. 2:
Pi = ∑ PMki

(2)kFigure 2: The answer processThis value is computed as the summation of the weighted
degree of similarity Mki for each retrieval request k to the
chunk i. Mki usually takes a negative value, and P serves as apenalty in the effect of similarity retrieval. Introducing similarity effects into the model’s knowledge allows for the use of
a mechanism called partial matching. For production modulesearch requests, it is possible to search for chunks that do nothave an exact match, thus allowing for flexible selection andthe reproduction of certain errors.
In this study, for each phonological knowledge combination, we set Mki based on the distinctive feature (Chomsky &Halle, 1968). That is, we assumed that the similarity between
two phonemes can be defined as the overlap of distinctive
features. In the simulation that is reported in the next section,
we treat this parameter as the innate factor of language development, with the expectation of frequently observing errors
involving the confusion of similar morae in the early phase ofdevelopment.

(chunk type b in Table 2) is activated using this chunk. Activated phonological knowledge guides the retrieval of a chunkwhere the same mora is connected with a new word in an initial position (chunk type c in Table 2). When successful, theword knowledge in the retrieved chunk is stored in the goal
module as an answer candidate.
After this, the model checks that the stored answer candidate is valid according to the rules of shiritori, such as notincluding /N/ as the end of a mora and not having been answered before in the current shiritori trial. If the current candidate violates these rules, the model re-searches for the answer candidate.
When the candidate word is confirmed as valid, the model
stores it in the declarative module as an answered word andoutputs the word through the speech module. During one
shiritori task trial, the two agents alternately execute this procedure up to the given time limit.

The value is computed from the number of presentations
for chunk i (n) and the elapsed time since the chunk was referenced (t j ). d indicates the decay rate, and βi is the offset parameter that can be modulated according to the simulation’s aim. In this study, the base level is introduced into themodel to examine how the experiential factor influences thedevelopment of phonological awareness. It is assumed thatthe innate factor’s (similarity) relative importance decreases
as base level activation increases and that errors related tophonological awareness are suppressed.

Model Parameters

Learning Interactions

An important parameter in this study is related to chunk retrieval in the declarative module (activation). An activation is
assigned to each chunk that is stored in the declarative module and affects the success or failure of the retrieval and thetime required for the retrieval. The activity value Ai that is assigned to chunk i is defined as the addition of multiple terms

Children with inadequate phonological awareness need assistance from adults to play shiritori (Takahashi, 1997). In thisstudy, we introduce assistance that encourages undeveloped
agents to continue the shiritori task. We also assume asymmetric interactions, such as those between children and parents, setting the similarity parameter for only one of the two

Base Level The base level is the basic element of the activity value that corresponds to learning and forgetting. It isrepresented by Eq. 3:!
nBi = ln∑ t −d
j

+ βi

(3)

j=1

agents. For the other agent, we incorporate the process of presenting the earlier answer again when the wrong answer has
been received (for the wrong answer of “o-ka-si” (sweets)
to “ri-n-go”, the agent presented “ri-n-go” again). Since
ACT-R learning is calculated according to the frequency ofusing a chunk, it is expected that presenting correct answers
increases activation of the correct association between words
and morae.

Simulation
In this section, we describe three simulations using the model.

Error Occurrence Factors
Simulation Settings First, we test whether it is possible tomodel phonological errors using the ACT-R knowledge similarity function. Specifically, we perform simulations underthe varying conditions of the ACT-R parameters (:mp), corresponding to the similarity weights P in Eq. 2.
The value of P was set to the following eight conditions: 1,
10, 20, 30, 40, 50, 60, and 70. Word knowledge in the model
was selected from those listed in Amano and Kobayashi(2008)’s Japanese word database. Based on the rules of shiritori, we took out 20,544 nouns, excluding homonym duplications, and words consisting of only one mora, such as “ro”
(furnace) and “wa” (ring). Next, referring to the child’s associative vocabulary survey (for Japanese Language & Linguistics, 1981), 2,054 words were randomly taken out and used asmodel knowledge. For phonological knowledge, 103 piecesof knowledge were defined based on Japanese morae.
In this research, we use a unit called a “chain,” which is thenumber of shiritori continuations. A trial in the simulation is
terminated when the model achieves 100 chains or after 3,600
seconds have passed from the beginning of the trial. A totalof 100 trials were simulated for each condition. Phonologicalknowledge similarity was calculated as the cosine similarity
with the distinctive feature column of one mora as a vector,
based on Chomsky’s table of distinctive features (Chomsky& Halle, 1968).
Results and Discussion Figure 3 shows the types and numbers of errors that appeared during the simulation. In thisgraph, the horizontal axis indicates a phonological knowledge pair (morae) arranged in order from left to right basedon similarity. The vertical axis shows the number of errorsthat occurred during the simulation using the pair of morae.
For example, an incorrect answer of “ri-n-go” → “o-ka-si”
counts as a pair of “go – o.”In the graph, the total number of errors is smaller in thecondition where the value of P is larger, and the majority oferrors is concentrated on the left side of the figure. Fromthese results, it can be confirmed that phonological awareness errors are actually invoked by incorporating knowledge
similarity. In addition, it can be observed that a larger similarity weight reduces the number of errors, and that errors areless likely to occur in low-similarity pairs. This is thought
to correspond to the phenomenon that was confirmed in the

Figure 3: A comparison of similarity differences and errors

child development process where the “ra” sound is confusedwith a “da” sound (Kobayashi, 2018).

Error Suppression Factors
Simulation Settings Following the simulations that were
described in the previous section, we will now examine thefactors that suppress phonological errors. Here, we hypothesize that the impact of similarity is relatively reduced by theeffect of learning. To test this hypothesis, we simulate a condition in which the ACT-R parameter (:blc) corresponding to
the value of the offset βi in Eq. 3 is changed to the following six conditions: 0.1, 1, 5, 10, 15, and 20, while keeping
the same setting in the other parameters, as in the previous
section’s simulation.
In addition, we examine whether learning occurs as a resultof the execution of the tasks that were set in this study. We
set the condition that the offset βi is set to 0 and observe thetime-series change through the execution of the task.
Results and Discussion Figure 4 shows the change in therate of correct answers due to the manipulation of the offset
value βi in Eq. 3. The horizontal and vertical axes, respectively, represent the value of βi and the rate of correct answers
defined as the ratio of words suitable for the shiritori rulefrom among all the words that were provided during the task.
The graph shows an increase in correct answers as the baselevel constant βi increases, suggesting that the effect of learning Bi increased and the similarity effect Pi decreased relatively with respect to the ACT-R activation calculation (Eq.
1). In other words, the result indicates that the learning effectcan suppress the phonological errors that the similarity effectcauses.
Figure 5 shows the change in the rate of correct answers
given during the execution of the task. The horizontal axis
shows the time spent completing the task, divided into fourintervals, while the vertical axis shows the percentage of cor-

Figure 4: The change in the correct answer rate, as indicatedby βi (error bar is ± SD
5 )rect answers in each interval. In the graph, there is no condition showing that the rate of correct answers is increasing, insome conditions, it is decreasing. This indicates that learning
does not occur in the task that was set in this study.

chitecture ACT-R and performed simulations that manipulated ACT-R parameters corresponding to both innate and experiential factors. As a result, it is suggested that phonological awareness and the errors that occur during its formation
process can be modeled through the innate memory retrieval
mechanism. Furthermore, the effect of learning may reduce
errors by suppressing innate factors. However, the results did
not show that learning occurred while performing the tasks
set in this study.
This study leaves a number of issues to be addressed. In
its simulations, the authors arbitrarily determined values foreach parameter. Although we were able to observe a variationin the correct answer rate, the threshold and maximum values need to be verified using further simulation. In addition,
this study only showed that the correct answer rate was improved by setting the parameters in advance; it did not showthat learning occurred or that the correct answer rate was improved through task performance. It is therefore necessary
to further examine the interaction in which the learning effect can be expected to operate. Consideration should also begiven to non-phonological aids, such as letters, pictures, andword meanings. In addition, evaluating the constructed modelis essential. To ensure that the model’s task-related learning
process explains the child’s language development, it is crucial to correlate it with experimental data from humans. We
believe that after setting the learning task, it is possible toexamine this by comparing it with the existing human experimental data (Hara, 2001).

Acknowledgment
Figure 5: Changes in the rate of correct answers throughout
the task (error bar is ± SD
5 )In this regard, it is suggested that implemented assistance(repeating the previous answer) has no effect on learning, andwe need to explore other forms of support to enhance phonological awareness in the shiritori task. In Takahashi (1997)’s
study, the most effective assistance came in the form of offering hints that activate semantic knowledge pertaining to thecorrect answer.
In addition, through individual case observation, the number of times that shiritori has continued stands at about 10,
and it is possible that a sufficient number of presentations ofknowledge for learning may not have been secured. Furthermore, due to the research process’ effect with regard to avoiding words that were already provided as answers and words
that end with /N/, a problem can be assumed in which only
the same words are searched and it is impossible to proceed
to the next answer.

Summary and Future Work
The purpose of this study was to investigate the factors that
contribute to the occurrence and suppression of errors during
the formation of phonological awareness. For this purpose,
we modeled phonological awareness using the cognitive ar-This research was supported by JSPS KAKENHI Grant Numbers 18H05068, 20H05560, 20H04996.

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