Relationship between Mood Improvement andQuestioning to Evaluate Automatic Thoughts in
Cognitive Restructuring with a Virtual Agent
Kazuhiro Shidara
NAIST, Japanshidara.kazuhiro.sc5@is.naist.jp

Hiroki Tanaka

Hiroyoshi Adachi

Daisuke Kanayama

NAIST, Japan
Osaka University, Japan
Osaka University, Japan
hiroki-tan@is.naist.jp hadachi@psy.med.osaka-u.ac.jp kanayama@psy.med.osaka-u.ac.jp

Yukako Sakagami

Takashi Kudo

Satoshi Nakamura

Osaka University, Japansakagamiyukako@wellness.hss.osaka-u.ac.jp

Osaka University, Japan
kudo@psy.med.osaka-u.ac.jp

NAIST, Japans-nakamura@is.naist.jp

Abstract—Cognitive restructuring is a therapeutic technique of
cognitive behavior therapy that helps convert negative automatic
thoughts to balanced thoughts. Automatic thoughts can be converted by the patient/participant’s own objective evaluation froma different perspective. Human therapists ask questions intending
to guide the evaluation of automatic thoughts more effectively.
Virtual agents, as therapists, have a great potential to support
cognitive restructuring. We investigated how a virtual agent could
affect the participant’s mood when asked questions to evaluate
automatic thoughts in cognitive restructuring. We implemented
a virtual agent that performs scenario-based dialogue with two
types of dialogue scenarios: with and without questions to evaluate automatic thoughts. We conducted a dialogue experiment with
20 healthy graduate students and divided them into two groupsof ten, finding that the participant’s negative mood significantly
improved when the virtual agent asked questions to evaluate
the automatic thoughts. Furthermore, the number of helpfulquestions was significantly correlated with the degree of moodchange (ρ=0.81). The results suggest that it is important toprovide appropriate questions for cognitive restructuring andthat the number of helpful questions reflects the dialogue’s
effectiveness.
Index Terms—cognitive restructuring, automatic thought, wellbeing, virtual agent

I. I NTRODUCTION
Cognitive behavior therapy (CBT) examines biased unhelpful thoughts and seeks solutions to current problems [1]. The
effectiveness of the CBT conducted by virtual agents havebeen confirmed in previous researches [2]. Such empathic
systems could be applied in various fields of application (e.g.,therapeutic assistance). Several types of agent dialogues havebeen developed, such as text-based dialogues in the styleof messaging apps [3]–[6] and CG animated virtual agents[7]–[9]. CG animated virtual agents have the advantage ofproviding face-to-face multimodal interaction and are expected
to promote engagement with participants with face-to-faceinteraction [10]. Besides, some studies on virtual agents basedon psychiatric care reflect participant states’ estimation fromverbal and non-verbal information in the training contents[11]. A previous work [9] confirmed its effectiveness of CBT
dialogue with a virtual agent for better mood improvement.
Although evaluations of the use of virtual agents for CBT havebeen conducted, it is still not clear which parts of the dialogue
content are effective. Automatic thoughts are thoughts or interpretations that automatically occur as a response to a situation.
One of the important points of CBT is identifying automatic
thoughts and evaluating them. Therapists ask various factfinding questions to prompt patients to evaluate and modify
their automatic thoughts.
We hypothesized that evaluating and modifying automatic
thoughts would affect the effectiveness of the virtual agent’s
dialogue in questioning. Our aim is to investigate the effect ofquestions in the dialogue of the CBT. We focus on cognitive
restructuring, one of the central techniques in CBT, which
examines biased unhelpful thoughts and seeks solutions tocurrent problems [1]. In cognitive restructuring, patients perform what is called evaluating automatic thoughts. Cognitive
restructuring often uses a worksheet called thought recordand has been widely used in cognitive restructuring withtherapists and personal use. A thought record was originallyproposed by Beck et al. [1], and an updated version by
Greenberger’s thought record [12] was also widely used. The
main change between Beck’s and Greenberger’s thought record
is the inclusion of questions, called Socratic questioning, that
evaluate for and against automatic thought. The questions aretaken from [1] and can be used broadly for general automatic
thoughts. This paper compares mood improvement effects
with and without evaluating automatic thoughts in cognitive
restructuring with a virtual agent.
II. M ETHOD
A. Virtual Agent Architecture
We used a CG animated virtual agent, named “Shibata”(Fig. 1). We used a virtual agent platform, Greta [13], andmodified it by Tanaka et al [14] for Japanese text-to-speech,
lip-synching, and Japanese-style animation. We used default

TABLE I
S CENARIO OF UTTERANCES SPOKEN BY THE VIRTUAL AGENT.
No.

Item

Q1

Situation

Q2

Mood

Q3

Mood score (0 to 100%)

Q4

Question to identify
automatic thoughts

Q5

Q6

Q7
Q8
Q9
Q10
Q11
Q12
Q13

Check if there areother automatic thoughts
Question to evaluate and modify
an automatic thought:
Set up automatic
thought for the agenda
Question to evaluate and modify
an automatic thought 1
Question to evaluate and modify
an automatic thought 2
Question to evaluate and modify
an automatic thought 3
Question to evaluate and modify
an automatic thought 4
Question to evaluate and modify
an automatic thought 5
Question to evaluate and modify
an automatic thought 6
Question to evaluate and modify
an automatic thought 7

Q14

Question to evaluate and modify
an automatic thought:
Balanced thought

Q15

Mood score after change(0 to 100%)[End]

Concluding a session

Sentence (V means a virtual agent, P means a fictional participant.)
V: Hello, my name is Shibata, and I am a therapist. Come join me in training to face your worries.
Is there anything that’s been bothering you lately that’s been difficult to deal with or to face? Pleasetell me something that you think is painful or burdensome.
P: Although I sent my friend an email, I haven’t gotten a reply.
V: How did your mood change at that time?
P: I’m so sad.
V: How would you describe the intensity of your mood from 0 to 100?
P: 75.
V: What thoughts came to your mind at that time?
For example, “I am hated from my coworkers.”, “My father is an ignoramus.”, or “I’ll be fired from my job”.
P: He hates me.
V: I see. Is there anything else? If you say everything you can think of, please say, ”this is all.”.
P: This is all.
V: What is the strongest thought that comes to your mind?
P: I’m hated.
V: If your automatic thought is true, what evidence do you have to support this?
P: In fact, I hadn’t received a reply even after one night.
V: If your automatic thought is not true, what evidence do you have to support this?
P: My friends haven’t told me they don’t like me to my face yet.
V: Besides your automatic thoughts, is there an alternative explanation?
P: The email was not urgent, and no particular damage has been done.
V: What’s the effect of your believing automatic thought?
P: I’ll avoid getting involved with her.
V: I see. So, what’s the most realistic outcome?
P: She may not be interested in me, but that doesn’t mean she doesn’t like me.
V: I see. If your friend was in the situation and had this thought, what would you tell him or her?
P: Why don’t you wait for a response without taking it too seriously?
V: What action would you do about your automatic thoughts?
P: I’m going to take the reply easy and wait.
V: In addition to the automatic thoughts you mentioned, maybe you can find another idea.
If you think of a new thought, try to connect them to your automatic thoughts with ”but...”.
P: I think the fact that he hasn’t responded means that she doesn’t like me.
But I’m not sure yet, so I’m going to take it easy and talk about the e-mail directly when I see her again.
V: How has the intensity of your original mood changed? Express it on a scale from 0 to 100.
P: 40.
V: If it’s different from the beginning, that suggests that you organized my thoughts well.
That’s it for today. Thank you for your hard work. Please feel free to call again.

parameters for its speech, such as speaking rate and voice
pitch. It has a neutral facial expression setting. Shibata outputs
spoken language and the participant inputs spoken natural
language through a headset microphone. Our virtual agent
automatically recognizes the participant’s response by voiceand takes turns.
B. Dialogue Scenario
Table I shows our dialogue scenario.
1) Questioning to Identify Automatic Thoughts: It is known
that, in cognitive restructuring, patients sometimes fail to
identify automatic thoughts. This is because automatic thought
is a concept that is not usually recognized. We began ourinvestigation of this guiding function by investigating thepercentage of failures to identify automatic thoughts in the
cognitive reconstruction method with virtual agents. In thisinvestigation, we used a dataset collected in a previous work[9]. From the dialogue between the virtual agent and 36
participants, we extracted participant response sentences about
automatic thoughts and labeled whether a medical doctor
considered these sentences as automatic thoughts or not. The

results showed that 23 out of 36 participants were unsuccessful
in identifying automatic thoughts. Therefore, it became clearthat virtual agents must guide users in the identification of
automatic thoughts.
Dialogue control with a classifier is performed in Q6. We
automated the guide to identify an automatic thought by thefollowing procedure.
1) Determine if a machine learning model identified an
automatic thought.
Success: Move to the next item.
Unsuccess: Ask a question to guide identifying an
automatic thought and have a participant answer again.
2) Unsuccess: Make a judgement with the automatic
thought classifier. If it is unsuccessful again, provide
another hint.
If the participant does not identify the automatic thought, thevirtual agent gives one hint and asks the participant to answer
again (e.g., So, what did you think about yourself when you
were in that situation?). There are six hints in total, and up
to six unsuccessful attempts to identify the automatic thoughtFig. 1. Shibata, our virtual agent for dialogue based on CBTI. In this study, we prepared two dialogue scenarios: one
which asked all the questions and one which left out
questions Q7 to Q13. Our hypothesis of the experiment isthat evaluating automatic thoughts improves mood. In theexperiment, we prepared these seven questions to guide theevaluating automatic thoughts and asked them in round-robin,assuming that one would be effective for the participant. To
clarify the hypothesis, we compared two groups (Fig. 2):
Group A: a scenario with questions to evaluate an automatic
thought; Group B: a scenario without questions to evaluate
an automatic thought.
C. Experimental Evaluation

Fig. 2. Dialogue flow of Group A and Group B.

will be handled. Six hints will be given to all participants inthe same order. If the participant fails seven times, they areforced to proceed to the next item. The hints are taken from
Beck et al. [1].
The classification model was newly implemented in thisstudy. The labeled data‘set [9] was used for test data (n=36).
We used 33 sentences on automatic thought from a home
medicine book by Greenberger et al. [12] for training data. In
order to practice the distinction between automatic thoughts,
situations, and moods, Greenberger et al. [12] provides aclassed list of the three ways: automatic thoughts (e.g., I
am always going to feel this way., I will never be cured.),situations (e.g., Talking to a friend on the phone., Driving
in my car.), and moods (e.g., Nervous., Sad.). We labeled
only automatic thoughts as positive among the three classes.
The feature extraction methods used for the classifier in thispaper are term frequency-inverse document frequency (TFIDF). TF-IDF calculates importance by considering words thatare common in the document library and words that are not.
In the calculation of TF-IDF, we used MeCab morphologicalanalyzer1 for tokenize. For vectorization, we counted only the
vocabulary in the training data. We used TF-IDF as a feature
set. The classifier algorithm was a support vector machine(linear kernel). As a result, precision was 0.800 and, the f1score was 0.833. We used this model in our study to guide theidentification of automatic thoughts.
2) Questioning to Evaluate and Modify AutomaticThoughts: We prepared questions to evaluate and modify
automatic thoughts using 9 questions Q6 to Q14 in Table
1 https://taku910.github.io/mecab/

1) Participants: We recruited 20 healthy graduate students
as participants, with 10 in Group A and 10 in Group B. The
research ethics committee of Nara Institute of Science andTechnology reviewed and approved this experiment (reference
number: 2019-I-24-2). Written informed consent was obtained
from all participants before this experiment. We also confirmedthat the participants had no severe depressive tendencies basedon the Kessler Psychological Distress Scale (K6) [15]. K6 isconverted to a scale of 0 to 24 (six questions each coded 0 to
4 and summed), with a score of 13 or higher being the cutoff
point for assessing the prevalence of severe mental illness. K6
score for Group A was mean (M)=3.90, standard deviation(SD)=4.04 and Group B was M=4.30, SD=2.83. There was no
significant difference in K6 scores between the two groups.
2) Experiment Procedure: The same procedure was applied
for the two groups. To begin, the participants read a leaflet
explaining CBT 2 . This leaflet is available to the public andis designed for both clinical and general public use. We thenexplained how to use our virtual agent. The experiment was
conducted using a desktop personal computer.
3) Mood Score Rating: Participants were asked twice
about their negative mood intensities for their mood score.
The first was Q3, and the second was Q15 of the questionsin Table I. The moods that the participants described in this
dialogue are controlled to be negative moods only, such as
anxious, depressed, sad, inferior, anger. We uniformly lumped
all such feelings under the rubric of negative moods andonly evaluated them by mood scores to focus on the changes
themselves. Based on Persons et al. [16], we calculated themood change using the following formula:

M ood change =(M ood score at beginning) − (M ood score at end)
(M ood score at beginning)(1)

III. RESULTS
A. Mood Change through Interaction
In Group A, the results were as follows: mood score at
the start was M=54.5, SD=20.6; mood score at the end was
M=32.0, SD=17.5; mood change was M=0.41, SD=0.23. In
2 https://www.cbtjp.net/downloads/skillup/pdf/

Fig. 3. Boxplot of mood change in mood score between Group A and GroupB (*: p <0.05 ).
Fig. 5. Scatter plot between helpful questions and Mood change.

C. Correlation between Mood Score and Number of Helpful
QuestionsFig. 4. Distribution of helpful questions (n=10, multiple answers possible).

Group B, the results were as follows: mood score at the start
was M=42.8, SD=26.5; mood score at the end was M=32.2,
SD=18.0; mood change was M=0.13, SD=0.32. Fig. 3 shows
the improvement in negative mood in the two groups. This result shows that the questioning to evaluate automatic thoughts
significantly affected the improvement in the negative moodscore (p=0.037, Hedge’s g=0.97). There was no significant
difference in mood intensity between the two groups at the
beginning and end of the study.
B. Distribution of Questions that Participants Felt Helpful
Group A evaluated the questions from Q6 to Q14 in Table
I that they felt helped them discover new ideas. At the endof each participant’s dialogue, a questionnaire containing thevirtual agent’s questions was presented. The participants rated
each question in two classes: helpful or unhelpful. Fig. 4
shows the distribution of the questions that were answered
as helpful. As a result, Q12 and Q13 were rated as helpful
by the most participants, while Q9 and Q10 were rated as
the least useful. One of the reasons for this result is thedifference in the intention of the questions: Q9 and Q10 asked
the participants to dig deeper into automatic thought. On theother hand, Q12 and Q13 were intended to examine automatic
thought from a new perspective. This suggests that questions
that change the participant’s perspective can contribute moreto the modification of the automatic thought.

We investigated the correlation between the mood score andthe number of helpful questions for Group A in the evaluating
automatic thoughts. Each participant answered one questionnaire after the dialogue. The questionnaire is an evaluationof the questions from Q6 to Q14 conducted by the virtual
agent. Participants asked yes or no if they felt helpful foreach question. The results showed that there was a strong
correlation between the number of helpful questions and themood change (ρ=0.81, p=0.005) (Fig. 5). There also was acorrelation between the number of helpful questions and themood score at the end (ρ=-0.68, p=0.032) (Fig. 6).
IV. DISCUSSION
This study showed that a virtual agent’s questions toevaluate automatic thoughts improved mood. Furthermore,
the more questions that were helpful to the participant, themore the mood improved. Asking questions may provide new
information to users. Because gathering information worked
well, users improved their mood. We found that questioning
to evaluating automatic thought was an important factor in
improving participants’ negative moods.
As with the cognitive reconstruction methods that humans
have used, we have found that better questions are important inchanging emotions. Therefore, our virtual agent suggested theeffectiveness of the ability of guided modification of automatic
thoughts. On the other hand, the survey in this study did
not investigate the interaction between the questions given
or the effect of timing. The questions’ order may affect the
helpfulness of each question, and further investigation such as
randomization should be considered.
ACKNOWLEDGEMENT
This work was supported by JST CREST Grant Number
JPMJCR19A5, Japan.

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