Interactive Model-based Reminiscence
Using a Cognitive Model and Physiological IndicesKazuki Itabashi (itabashi.kazuki.15@shizuoka.ac.jp), Junya Morita (j-morita@inf.shizuoka.ac.jp)
Shizuoka University. 3-5-1 Johoku, Naka-ku, Hamamatsu City, Shizuoka, Japan

Takatsugu Hirayama (takatsugu.hirayama@nagoya-u.jp), Kenji Mase (mase@nagoya-u.ac.jp)
Nagoya University. Furo-cho, Chikusa-ku, Nagoya City, Aichi, Japan

Kazunori Yamada (yamada.kazunori@jp.panasonic.com)
Panasonic Corporation. 1006, Oaza Kadoma, Kadoma City, Osaka, Japan
Abstract
In this study, we developed a photograph slideshow system
to support reminiscence activity. Compared to a conventional
photograph slideshow, the developed system has two features:
incorporating a memory model based on the ACT-R cognitive architecture and modulating the model parameter from theuser’s feedback. We assume that the first feature enables various patterns of photograph presentation by the system, andthe second feature makes the system adaptive to the user’s
response. More importantly, such presentation patterns andfeedback can be theoretically designed using cognitive architecture. In this paper, a preliminary evaluation of the developed system is presented. Through an analysis of the subjective evaluation of the system and changes in mental states, weclarified the effect of model-based reminiscence. In addition,
heart rate variability analysis was conducted to clarify how the
feedback in the model changes the behavior.
Keywords: ACT-R, Autobiographical Memory, Cognitive
model

Introduction
In recent years, the number of patients with mental illnesses,
such as depression and dementia, has increased significantly.
One effective supporting method for such mental illness isreminiscence therapy, which is widely used for adults, including both patients with depression and healthy adults.
Recalling memories has the effect of inducing well-being(Routledge, Wildschut, Sedikides, & Juhl, 2013; Sedikides,
Wildschut, Gaertner, Routledge, & Arndt, 2008). However,controlling the emotions associated with memory is difficult
because of large individual differences. The effective stimulus to intervene in such a process differs among individuals(Qu, Sas, & Doherty, 2019). To solve the problem of individual differences, it is necessary to grasp the autobiographicalmemory of the subject and adjust the presentation of the stimulus according to the user’s psychological state.
To achieve such optimal support for an individual, researchers have developed a system that incorporates a modelof the user’s memory to stimulate memory recall (Yasuda,
Kuwabara, Kuwahara, Abe, & Tetsutani, 2009). Following
such a trend of studies, Morita, Hirayama, Mase, and Yamada(2016) proposed a concept of model-based reminiscence assuming that the user’s mental state can be controlled by presenting the simulation process generated by the personalized
cognitive model. Based on this assumption, they developed aphotograph slideshow system in which a model of the user’s
autobiographical memory sequentially presents photographsof individuals. The model was developed using ACT-R (adaptive control of thought-rational; Anderson, 2007), which isa framework for simulating human cognitive processes. To
model a user’s autobiographical memory, Morita et al. storeda network of photographs of individual users in the declarative memory for ACT-R, extracting semantic attributes, suchas the scene, place, time, and people, from the image data.
Their constructed model sequentially retrieves a user’s memory (photograph) following the network, connecting the current photograph to another photograph via the same attributeas the current photograph.
The benefit of using ACT-R in such a model-based reminiscence is creating a variety of photograph presentation patterns
based on the theory of cognition. We consider that ACT-R
is useful to construct models that both simulate the current
user’s state and represent the optimal (ideal) user’s state. To
represent such a wide variety of mental states, ACT-R provides several parameters controlling the use of knowledge.
For example, the retrieval process of the above model is affected by the ACT-R memory mechanisms, which are controlled by the activation calculation.
The activation value for each memory is determined byseveral factors, but the most basic factors are the learning andforgetting effects, which are called the base level in ACT-R
theory. When applying these effects to free recall tasks, themodel exhibits pathological or ruminative behaviors in which
the same memory is repeatedly retrieved through feedback
looping (Lebiere & Best, 2009; van Vugt, van der Velde, &ESM-MERGE Investigators, 2018). To avoid such defaultmode behavior, the suppression of short-term memory or
adding a high noise parameter to the activation is effective.
Adjusting such parameters, the model-based reminiscencesystem can guide users’ memory recall in both exploratory(divergence) and exploitative (convergence) directions.
Although the previous study exhibited some simulation results revealing a variety of model behaviors, it did not present
how the parameters of activation can be modified and how thepatterns of a photograph presentation affect the user’s mental state. Concerning these problems, this study focuses onmodels of emotion developed in ACT-R. For instance, Juvina,
Larue, and Hough (2018) clarified how emotions can be expressed in the ACT-R and how they can affect memory anddecision-making. Dancy, Ritter, Berry, and Klein (2015) also
constructed an emotion model based on physiological dynam-

ics and pointed out the correlation between noise parametersof the declarative memory of ACT-R and physiological indicators reflecting stress.
Following such studies, this paper presents the proposalof an interactive method of adjusting the parameters of amodel-based reminiscence by monitoring the user’s mental
state. The proposed method employs two interactive parameter modulations: explicit and implicit feedback. The former
is based on the subjective evaluation of the presented photograph, and the latter combines the user’s physiological state(heart rate) to model the noise parameters, as suggested by themodel by Dancy et al. (2015). Adopting these two different
feedback types, we assume the user’s emotional state in termsof the valence (preference) and arousal (stress) axes in thetheory by Russell (1980), can be modeled through the modelbased reminiscence. In this study, we conducted a case study
in which one participant was allowed to view a photographslideshow with three conditions: one random presentationand two model-based presentations (with or without biofeedback). We examined the effect of model-based reminiscenceand its relationship with the mood of the participants. In addition, a heart rate variability (HRV) analysis was performedon the heart rate data recorded during the case study to examine the changes in behavior due to the correlation between
the physiological index and the model.
The following section proposes an interactive system ofmodel-based reminiscence. After the system is presented, acase study that preliminarily evaluates the proposed system
is described. The concluding section summarizes the current
state of the study and provides future perspectives.

Interactive Model-Based ReminiscenceThis section presents our developed system, which extends
the work by Morita et al. (2016) to include feedback from theuser. Following overviewing the system, we will present themodel and two methods of user feedback.

System Overview
Figure 1 shows the overall structure of the system. The left
side presents the model and system, whereas the right side
presents the user. In the system, the content of the declarative memory of the model is constructed from the user’s
photograph database. For each photograph in the database,attributes such as scene, place, time, and people are automatically coded as declarative chunks of ACT-R. Based on thesechunks, the model sequentially retrieves the photograph dataand presents a corresponding photograph image on the display. The user observes such a sequence and simultaneously
evaluates the presented photograph. During this process, aheart rate sensor monitors the user’s autonomic nerve activity
to adjust the noise value of declarative memory (ANS: activation noise s). In the remainder of this section, each component of this system is described.

Figure 1: System overview.

ACT-R Model
The module structure of the ACT-R used in this system is illustrated in Figure 2. The declarative module holds chunks
relating to the user’s photographs. Each photograph is codedwith the following four attributes: the person in the photograph (who), the time the photograph was taken (when),the place where the photograph was taken (where), and thescene in the photograph (what). These attributes were determined by referring to a psychological study (Wagenaar, 1986)
that coded the author’s autobiographical memory with thesefour attributes. In the current implementation, the who andwhat attributes are coded by photograph management software (iPhoto of Macintosh) and CloudVision API (Application Programming Interface)1 provided by Google, respectively. The when and where attributes are extracted from thephotograph metadata such as Exif (exchangeable image fileformat).
The model recognizes these attributes of the current photograph through the visual module and stores the perceived
attributes in the goal module. In the production module, themodel distinctively holds the retrieval rule corresponding to
the four attributes. These rules conflict with each other andare selected each time. When one of the rules fires, the production module sends a request to the declarative module toretrieve a new photograph using one of the stored photographattributes as a cue. The model repeats the recognition and retrieval for a period (5 s), and the last retrieved photograph ispresented on the display as the next photograph.

Figure 2: ACT-R module.

1 https://cloud.google.com/vision/

Utility Calculation and Explicit Feedback
As noted above, the model has four rules to retrieve the next
photograph corresponding to the four attributes. The conflicts
between these rules are resolved by comparing the utility values attached to each rule:Ui (n) = Ui (n − 1) + α[Ri (n) −Ui (n − 1)](1)

Equation 1 represents the update formula of the utility forrule i, where α indicates the learning rate, and Ri (n) represents the reward values. In this study, the reward value isdefined as the evaluation score for the presented photographgiven by the user. During the observation of the slideshow,
the user moves a scroll bar located on the screen to evaluate
the photograph. By attaching this function, the proposed photograph slideshow system dynamically adapts to the user’s
preferences for memory retrieval.

Activation Calculation
In the retrieval of declarative memory, the activation value iscomputed for all chunks that match the retrieval request (sharing attribute) sent from the production module. Among them,the chunk with the highest activation value is retrieved. As
presented in Equation 2, the activation value (Ai ) is calculated
as the sum of the base-level activation value (Bi ), strength ofassociation (Si ), and noise (εi ).
Ai = Bi + Si + εi

(2)

The first term, Bi , is calculated using Equation 3, where nis the number of occurrences of chunk i, t j is the time elapsed
since the jth occurrence, d is the decay factor, and βi is theoffset value:nBi = ln( ∑ t −dj ) + βi(3)

j=1

The second term of Equation 2 is calculated using Equation 4 as the associative strength of chunk i with context C,
which represents the set of attribute values in the goal buffer.
Moreover, W j represents the weight of attention assigned toattribute value j, and S ji represents the associative strength of
the attribute value j and the chunk i of the declarative memory. That is, this term allows the model to retrieve a photograph that shares multiple attributes with the current photograph, even if the retrieval request holds only a single attribute:

Si = ∑ W j S ji

(4)

j∈C

Correspondence Between Model Parameters andPhysiological Indicators
Dancy et al. (2015) proposed a model connecting the parameters of ACT-R with physiological indices to explain the effectof emotion on cognitive processes. In their model, the noise
parameter of the declarative memory (εi in Equation 2) ismapped to the activation of noradrenaline. That is, when themodel is under a stressed situation, the output of the model
converges; whereas in a weak stress situation (relaxed situation), the model outputs a variety of behaviors.
Based on this association, the current system correlates tothe noise parameter εi to the standard deviation of NN intervals (SDNN) computed using the user’s HRV measured during the photograph presentation. The SDNN is assumed to
be an index of stress evaluation, which is calculated from themeasured HRV time-series data (R-R-interval: RRI). A lower
SDNN indicates a stressed state, whereas a higher SDNN indicates a relaxed state. Specifically, the RRIs for the mostrecent 150 samples are divided into three sections (50 samples), and the SDNNs are calculated as the standard deviations for each section. Using the averaged (x) and standard
deviation (s) of the three SDNNs, Equation 5 computes thestandardized score of the latest SDNN (x1 ):x 7−→

x1 − xs(5)

The ACT-R noise parameter εi is updated every 6 s by inputting the value of x computed by Equation 5. The interstimulus interval is the duration of presenting the photograph for
5 s and a blank for 1 s.

Case Study
We conducted a case study to demonstrate how the proposedsystem of model-based reminiscence interacts with a user. In
this case study, we do not aim to verify the universal effect ofthe proposed method but attempt to exhibit an example of theprocess of model-based reminiscence for a single participant.
In this study, we analyzed mood changes, subjective evaluations of the system, and HRV obtained from a participant who
observed the photograph slideshow in several blocks, manipulating different presentation patterns of the photographs.

Photograph Presentation Conditions
In this study, the following three presentation conditions were
set to evaluate the system:
Condition 1: Random condition
Photographs were retrieved and presented randomly fromthe photograph dataset.
Condition 2: Fixed-parameter condition
The retrieval and presentation of the photographs werecarried out using a model of autobiographical memory
presented in the previous section, but the parametersof the model were fixed (BLL: base-level learning 0.2,
BLC: base-level constant 10, MAS: maximum associative strength 10, and ANS 0.5). In these parameter settings, BLC was set to a relatively high value because themodel contained old photographs whose base-level activation tended to be low.

Table 1: Subjective evaluation questionnaire.
No.
1
2
3

Questions
Was the slideshow interesting to you?
Did the viewing of the photographs trigger a memory recall?
Did you feel a connection to the photographs presented?

Results
In this section, we illustrate the results of the case study todemonstrate how the proposed system of model-based reminiscence interacts with a user. The results of the POMS 2
scores, subjective evaluation questionnaire, and HRV analysis are presented.

Changes in Mood
Condition 3: Varied-parameter condition
The retrieval and presentation of photographs were carriedout using the model of autobiographical memory presented
in the previous section. The parameters that defined the
behavior of the model were set to BLL 0.2, BLC 10, andMAS 10. In addition, ANS was modulated by HRV. The
utility values of retrieving the next photograph were alsochanged by the evaluation made with a slide bar.

Participant
One 20-year-old male student from Shizuoka University participated in the case study.

Materials
The study used 299 photographs taken between March 2010
and January 2019, which are owned by the participant. The
attributes (who, what, where, and when) were extracted andconverted to ACT-R chunks before the study. Some of these
attributes were manually coded by the participant.
The timestamp indicating when the photograph was taken
was also used to set chunk parameters (creation time) to compute the initial base levels. In this case study, the simulation
time of the model was set to the day of the study using thebuilt-in function (the mp-process) of ACT-R. By combining
this setting with the time information in the dataset, we simulated the memory retrieval corresponding to the real-worldtime. In other words, in this case study, ACT-R can search forrecently taken photographs more easily. In this case study,
we also used a wearable heart rate sensor, myBeat (WHS-1,
RRD-1), which was manufactured by Union Tool, to measure the heart rate interval. The measurements were made ata sampling frequency of 1000 Hz.

Procedure
The study consisted of six consecutive sessions containing
three blocks where the photographs were presented under different conditions (random, fixed, and varied). Each block
lasted 5 min. Thus, the participant observed photographs in
18 blocks, for a total of 90 min. In each session, the orderof the presentation condition was changed. At the intervalof each block, the participant evaluated the presented photograph slideshow according to the questions presented in Table 1 on a 5-point Likert scale. To assess changes in moodcaused by observing photographs, the participant was asked
to answer the questions in the Profile of Mood States Second Edition (POMS 2) Japanese version (Yokoyama, Araki,
Kawakami, & Tkakeshita, 1990) before and after the study.

Table 2 lists the scores of the seven factors and the total mood disturbance (TMD) score of the POMS 2 before
and after the experiment. From this, we observed that relatively large changes occurred in certain factors, such as
the confusion-bewilderment (CB), vigor-activity (VA), andTMD score. This means that after viewing the photographslideshow, the participant became less confused, more active,
and less disturbed overall. Although we cannot attribute thiseffect to a specific condition of the photograph presentation,
this change is consistent with the reported effect of reminiscence (Sedikides et al., 2008; Routledge et al., 2013).
Table 2: Result of the POMS 2. Numbers in parentheses
indicate the score ranges.

AH (0-20) 1
CB (0-20) 2
DD (0-20) 3
FI (0-20) 4
TA (0-20) 5
VA (0-20) 6
F (0-20) 7
TMD score (-20-100) 8

Pre

Post

1
6
2
5
0
10
10
4

0
2
2
6
1
13
11
-2

Score variation(Post - Pre)
-1
-4
0
+1
+1
+3
+1
-6

1 Anger-Hostility
2 Confusion-Bewilderment
3 Depression-Dejection
4 Fatigue-Inertia
5 Tension-Anxiety
6 Vigor-Activity
7 Friendliness
8 A Total Mood Disturbance (TMD) score is calculated

as (TA + DD + AH + FI + C) - VA.

Subjective Evaluation
Figure 3 presents the results of the subjective evaluationfor each question. We observed the difference in the conditions for each question. To reveal such differences, we
treated the session as a unit of analysis (n = 6) and conducted three one-way analyses of variance (ANOVAs) for
each question, adopting the photograph presentation condition (the random condition vs. fixed-parameter condition vs.
varied-parameter condition) as independent variables. The
results revealed a significant main effect of the photographpresentation condition in Question 3 (Q3; connection to thephotograph presentation) [Q1: F(2, 15) = 3.35, p < .10, Q2:

F(2, 15) = 3.39, p < .10, Q3: F(2, 15) = 11.57, p < .01].
Multiple comparisons using the Bonferroni method revealed
significant differences between the random and other two
conditions (p < .05). Compared to the random condition,in the conditions of the model-based presentation, the participant observed a strong connection between the presented
photographs.

Table 3: Heart rate variability analysis feature.
featuremeanNN
SDNN
RMSSD
pNN50
CVNN

Description
Average value of RRI (ms)Standard deviation of the RRI (ms)Mean square of the difference between adjacent RRI (ms)Ratio of difference between adjacent RRI exceeding 50 ms (%)
The coefficient of variation of RRIn.s.], [RMSSD: F(2,15) = 0.02, n.s.], [pNN50: F(2,15) =0.07, n.s.], and [CVNN: F(2,15) = 0.11, n.s.].
In contrast, we found differences in these indices between sessions [meanNN: F(5, 12) = 9.5 , p < .01], [SDNN:
F(5, 12) = 18.48 , p < .01], [RMSSD: F(5, 12) = 10.19 ,p < .01], [pNN50: F(5, 12) = 4.56 , p < .01], and [CVNN:
F(5, 12) = 4.56 , p < .05]. Figure 5 illustrates the averagevalues (n = 3) of each index for each session. These graphs
indicate that the participants in this experiment increased their
RRI-related indices over time. This result may reflect the habituation process for the experiment.

Time Changes in R-R-Interval in a Block
Figure 3: Result of the subjective evaluation questionnaire.
The error bars indicate the standard error.

Variation in Noise Parameters
In this analysis, we attempt to demonstrate the change in the
behavior of the model by connecting the HRV with the noise
parameter of the memory retrieval. For this purpose, we visualize the variation of RRI and noise parameter values (ANS)
through blocks in the varied-parameter condition. The results
are summarized in Figure 4. The blue line indicates the RRI,
and the red line represents the change in ANS, which is the
SDNN calculated from the RRI.
From these figures, we can observe some artifacts in the
middle of Block 4 and the early part of Block 5. The ANS
suddenly fluctuates owing to the spiky RRI fluctuations. We
must remove such artifacts in future improvements in the system.
Except for such artifacts, it seems to be possible to divide
these blocks into two patterns of ANS: one with an increasing trend of ANS, as presented in Blocks 5 and 6, and theother with a decreasing trend of ANS, as revealed in Block
2. Especially in Block 2, the ANS and RRI appear to be wellcoordinated. In contrast, the changes in ANS in Blocks 5 and6 are not linked to the RRI trend (upward/downward trend).
The variation across the blocks of the RRI is analyzed below.

Analysis of Heart Rate Variability
We calculated several indices of HRV from the obtained RRI,
as presented in Table 3. In this analysis, samples with RRI
less than 500 ms or greater than 1500 ms were excluded.
We first examined the difference between three conditions(random, fixed-parameter, and varied-parameter conditions),
but we could not find any differences between the conditions[meanNN: F(2,15) = 0.01, n.s.], [SDNN: F(2,15) = 0.08,

In the analysis so far, no difference in HRV indices was foundbetween the conditions. However, the above analyses werelimited in that they did not examine the temporal dynamics
of the HRV in a block while averaging the indices over theblock. Therefore, we explored the difference between theconditions by creating an index of the time change of the
HRV. The created index was based on a regression analysis
with time (the horizontal axis in Figure 4) as the independent variable and the change in RRI (the left vertical axis in
Figure 4) as the dependent variable. The estimated regression coefficients were used as indicators representing the upward or downward trends of RRI within the block. The meanvalues of this index are listed in Figure 6. In the fixed andvaried-parameter conditions, the mean of this index became
negative, whereas, in the random condition, it became neutral, suggesting that model-based conditions make the user’s
RRI lower (see Block 2 in Figure 4). However, we find a more
prominent difference between conditions in the variance (error bars) than in the average. In the varied-parameter condition, the error bar is greater than in the other conditions.
In fact, we find a significant difference between the random
and varied-parameter conditions in the size of the variance[F(5, 5) = 12.60, p < .01]. This indicates that a larger temporal change in HRV occurred within the varied-parametercondition.

Conclusion
In this study, we developed an interactive photographslideshow system incorporating a cognitive model and explicit and implicit feedback from the user. Based on thesubjective evaluation questionnaire, we confirmed that theparticipant had different impressions of the model-based
photograph slideshow than with the random photographslideshow. In addition, the HRV analysis revealed large tem-Figure 4: Variation of R-R-interval (RRI) and noise parameter values (ANS). The blue line shows the RRI, and the red line
shows the change in ANS.

Figure 5: Heart rate variability analysis feature. The error bars indicate the standard error.
and the model. As demonstrated in Figure 4, extremely highnoise parameter values were confirmed by a sudden change
in the RRI due to artifacts, such as electrode misalignment
due to body motion. To solve this problem, we developed amechanism to reduce the noise induced by body motion during measurement.

Figure 6: Average value of the coefficient of time for eachcondition. The error bars indicate the standard error.

poral changes in the varied-parameter condition, suggestingsome effect of the interactive model-based reminiscence. Although we did not reveal a specific effect, the interactive parameter modulation in the model-based reminiscence may affect the physiological indicator. Further study is needed tounderstand the mechanisms of such effects and the generalizability of the results.
In the future, we will conduct an evaluation experiment of
this system with additional experimental participants to examine the effectiveness of model-based reminiscence. However, several issues were found in the analysis of the behaviorof the model in connection with the physiological indicators

Despite these limitations, we consider that this paper contributes to the cognitive modeling community by extending
the application field of cognitive architecture. Although many
applications of cognitive modeling exist, such as intelligent
tutoring systems (Anderson, Boyle, & Reiser, 1985), the application of a model incorporating physiological indicators isnovel. The author also considers that, when constructing a
support system for human mental activity, using cognitive architecture has the advantage of designing a system with a theoretical background. The system presented in this paper uses
assumptions based on previous memory and emotion models.
Such a theoretical background is useful in guiding the design
functions and future evaluations of the system.

Acknowledgment
This research was supported by the Center of Innovation Program (Nagoya-COI; Mobility Society leading to an Active
and Joyful Life for Elderly) funded by the Japan Scienceand Technology Agency and JSPS KAKENHI Grant Numbers 20H05560, 17H05859.

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