Advances in Cognitive Systems X (20XX) 1-6

Submitted X/20XX; published X/20XX

Representing Motivation in a Simple Perceptual and Motor
Coordination Task based on a Goal Activation Mechanism
Jumpei Nishikawa
Kazuma Nagashima
Ryo Yoneda
Junya Morita
Shizuoka University

NISHIKAWA . JUMPEI .16@ SHGITIZUOKA . AC . JP
NAGASHIMA . KAZUMA .16@ SHIZUOKA . AC . JP
YONEDA . RYO .17@ SHIZUOKA . AC . JP
J - MORITA @ INF. SHIZUOKA . AC . JP

Tetsuya Terada
Mazda Motor Corporation

TERADA . T @ MAZDA . CO . JP

Abstract
An important topic in the field of cognitive systems is modeling a user’s internal state. A computational representation of human internal states (i.e., emotion or feeling) is beneficial for exploring
the conditions of optimally regulating levels of motivation, which is assumed to be a central factor leading to well-being in our life. To construct such models, cognitive architectures, which arecollections of detailed specifications of mental functions, have sometimes been used as tools toachieve an understandable design of information systems. However, previous models of motivational change using cognitive architecture have treated motivation as an independent module andhave not thoroughly examined its relationship with other internal processes. By contrast, this study
represents motivation based on a general knowledge activation mechanism. Throughout the task,
current goal activation was assumed to be fluctuate. When this fluctuation leads to the activationof another activity, mind-wandering or task-switching occurs. Thus, the fluctuation range of activation corresponded to the degree of concentration (motivation) in the current task. In particular,
motivation in our model decreased with time and increased with the occurrence of startling or
novel stimuli. We applied this model to a simple perceptual-motor task and attempted to replicate
the results obtained in the experiments with human participants. Furthermore, simulations exploring intervention methods that maintain optimal motivation levels were presented. Based on thesestudies, we discuss the model’s effectiveness in representing the emotional process of humans andobtain suggestions on interventions that inhibit motivation decline.

1. Introduction
To realize truly intelligent and natural support for daily human activities, machines must monitor
the internal processes of humans to perform timely interventions. Computational models that represent the process of human emotion are essential for developing intelligent interactive systems. This
study focuses on the motivation process as a critical emotional factor that leads to psychologicalwell-being. Maintaining workers’ intrinsic motivation is harder in simple routine tasks than in selfdetermined challenging tasks (Csikszentmihalyi, 1990; Pink, 2009; Ryan & Deci, 2000). Moreover,© 20XX Cognitive Systems Foundation. All rights reserved.

J. N ISHIKAWA , ET. AL .

motivation loss induced by passive routine tasks degrades subjective well-being. Therefore, if intelligent machines can understand the symptoms of such motivation loss and prevent it by presenting
attractive stimuli at the apropriate time, the quality of life of the worker will improve.
We believe that a computational model of human motivation will foster the development ofsuch intelligent systems. To date, many emotion models have been developed. Among them, models developed based on cognitive architecture, a collection of specifications of independent mental
processes (Anderson, 2007; Kotseruba & Tsotsos, 2020; Laird et al., 2017), have the potential toprovide detailed descriptions of the motivational process (Sun et al., 2022; Juvina et al., 2018; Yang
& Stocco, 2022).
However, past models of motivation have not fully described the process of motivation loss in an
integrative form. While following the trend of cognitive architecture, our model focuses on arousal
processes based on the general activation mechanism of knowledge representation (Anderson, 2007;
Anderson & Schooler, 1991; Hebb, 1949). Many psychologists use this mechanism to explain
human memory and learning as a function of brain regions such as the hippocampus (Anderson,
2007; Kelso et al., 1986). Using this mechanism, our model demonstrates that motivation loss iscaused by the fluctuation in activation for the current goal and by the goal being supplanted by othergoals outside the task. More specifically, our model was developed based on the model of mind
wandering, which is defined as task-unrelated thought during the main task (van Vugt et al., 2015).
We will combine the proposed model with one that executes a simple perceptual-motor task (Morita
et al., 2020) and use it to simulate behavioral data obtained from human participants and propose
homeostatic regulation based on external stimulation. Before presenting our model, the next section
presents previous studies related to our model and the simulation.

2. Related Research
2.1 Task Performance and Motivation Mediated by Arousal
There have long been discussions on the relationship between emotional arousal, motivation, andtask performance. In classical literature, the terms arousal and task motivation are used interchangeably. For example, according to Hebb (1955), arousal is a synonym of drive, which refers to thephysiological component of motivation that energizes a target behavior. Following this concept,
this study uses “arousal” as a physiological factor driving human activity, whereas “motivation” isa psychological effort influencing the continuation of the current task.
Psychologists have also discussed the inverted U-shaped theory of the relationship between
arousal and task performance (Yerkes & Dodson, 1908). In this theory, there is an optimal levelof human arousal to accomplish the target task. It has also been posited that the optimal level ofarousal depends on task difficulty (challenge) and human skills (Csikszentmihalyi, 1990). Noveland difficult tasks require high arousal, whereas simple and well-practiced tasks tend to result inboredom. Thus, arousal level serves as a kind of attentional resource (Landers, 1980). Redundant
arousal for the current task tends to be used for a mental activity that is usually engaged outside thetask (default mode thinking) (Buckner et al., 2008). This off-task mental activity associated withdecreased task motivation, is called mind-wandering (Brosowsky et al., 2020). Although mindwandering can foster creative thinking (Baird et al., 2012), it usually diminishes task performance.
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Researchers have explored the external factors affecting arousal levels during monotonous tasks(Obayashi et al., 2019; Yoneda & Morita, 2021). Such studies, which manipulated task environments such as airflow, lighting, and music, confirmed the effects of external stimuli to recoverarousal in the task.
2.2 Cognitive Architecture and Cognitive ModelingAs noted in the Introduction, there are tools known as cognitive architectures for constructing computational models of the human mind. Among several cognitive architectures, adaptive control ofthought-rational (ACT-R) (Anderson, 2007) is most commonly used to simulate various psychological experiments (Kotseruba & Tsotsos, 2020). ACT-R has a modular structure, where basic
cognitive functions such as vision, speech, thinking, and memory are administered with each corresponding module. Such modules in ACT-R are also combined and controlled by a central production
module (functioning as rule engine), which triggers production rules according to the current statesof each module. To control the modules, ACT-R uses a symbolic representation called a chunk. In
each step, the active ACT-R modules hold a chunk to represent a state. Chunks are also stored in a
declarative module (functioning as a database) and retrieved when production rules request them.
This study focuses on ACT-R because it has a well-developed mechanism for the general activation theory presented in Section 1. Recently, Yang & Stocco (2022) claimed that motivation in
ACT-R is considered a scalar value held in the goal module to continue the task. The present study
extends this idea by replacing the scalar value with activation, which is attached to the chunks in
ACT-R. In the ACT-R retrieval process, highly activated chunks are more likely to be selected. Using this mechanism, a model of mind wandering was developed using the ACT-R (van Vugt et al.,2015). In this model, mind-wandering is represented as taking over a goal state (defined as a chunkheld by a goal module) through highly activated off-task chunks. The model continuously refocuses on the state of the goal (retrieving a goal chunk from the declarative module) during a simple,
monotonous task. Chunks representing off-task activities sometimes become mixed in this refocus
process. Thus, this model of mind wandering defines task motivation as the activation of chunks
that represent the task goal.
However, this mind-wandering model does not explain how task motivation (or arousal) fluctuates over time. Several researchers have modeled emotional processes such as fatigue (Atashfeshan& Razavi, 2017; Gunzelmann et al., 2009) and stress (Dancy et al., 2015) within the ACT-R. Thesemodels treat emotion as a physiological substrate that affects cognitive parameters, such as activation (Ritter, 2009). In particular, an extension of ACT-R called ACT-R/Φ (Dancy et al., 2015)
connects the arousal level (corresponding to the activity in the sympathetic nervous system) withrandom fluctuations of the activation, which are represented as probabilistic noise added to the activation. Thus, referring to this model, we can predict that a relaxed state (low arousal) leads to a large
random fluctuation of activation, leading to frequent mind-wandering. However, previous studies
on ACT-R have not developed a model combining motivation loss and mind-wandering. Therefore,
this study constructs a model of motivation loss based on the fluctuation of activation noise (arousal
level) over time.

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Screenshot of the task window

The line

Figure 1: Task interface. Screenshot of the task window (left) and overall view of the line (right)

3. Task and Data
3.1 Line-Following Task
We set up a line-following task (Maehigashi et al., 2013) as a simple monotonous task to examine
the effects of motivational change (habituation and boredom) and external stimuli. Figure 1 shows
the task interface. In the line-following task, a polyline displayed on the screen automatically scrolls
from top to bottom by one pixel every 40 ms (25-fps screen updates). Participants were required tofollow the polyline (stay online) by moving the circular object to the left or right.
We chose this task because there is a publicly available ACT-R model for this task (Morita et al.,2020)1 . In addition, it is relatively easy to modify complexities or challenges (Csikszentmihalyi,1990) in this task by manipulating parameters, such as the ratio of vertical lines included in thepolyline patterns. In this study, to induce boredom over a short period of time, we set this parameterat 90%. An illustration of the constructed pattern is shown in the right panel of Figure 1. In thefollowing experiment/simulation, this line pattern was repeated in a 1-minute cycle for 30-minutes.
1. https://github.com/j-morita-shizuoka/line-following-tak

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Figure 2: Participant performance in the line-following task. The thin lines represent each participant. The thick green line indicates the mean. The dotted red line indicates the result of a polynomial
regression (degree = 2). The vertical axis is a logarithmic scale.
In addition, to examine changes in arousal level during the task, we designed a pop-up window
asking participants to respond to the degree to which they focused on the task (probe). The probe
was presented at an interval of approximately 50 s (randomized noise was added to this interval).
3.2 Average Human Behavioral Data
In this section, we present two types of human data obtained from line-following tasks. The first
concerns changes in human motivation from various individuals. To collect data, we recruitedparticipants from a crowdsourcing website (Lancers.jp). Twenty-seven participants completed theexperimental procedure, in which they first accessed the online system and read the instructions on
the task at their own pace. After completing a test to confirm their understanding of the task, they
engaged in the line-following task for 30 min.
Figure 2 shows the transition of the offline rate (the percentage of time the circle did not follow
the line in the total time of the segments) for each participant. These results are shown for 30

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Figure 3: Results of case study
1-minute segments of a 30-minute task execution. The offline rate decreases in the initial threephases. However, from the midpoint (approximately 15 min), the value gradually increased overtime, suggesting the occurrence of motivation loss in this task. The model presented in this paper
aims to simulate the later part of this graph, focusing on process motivation loss.
3.3 A Case of Physiological Process
Although the online experiment provided behavioral data showing motivation loss in the linefollowing task, the participants’ arousal levels could not be directly observed. Thus, the relationshipbetween task performance and the physiological state remains unclear. For this reason, we collecteda case from a male participant in the laboratory wearing a wireless heart rate monitor (MyBeat,
Union tool).
Figure 3 shows physiological and behavioral data during this task execution. The former data
is presented as the SDNN (Standard Deviation of NN, red line), which is the variance of RRI (R–R
interval) usually corresponding to the dominant parasympathetic nervous activity. In this figure, we
calculated this index with a sliding window of 60 beats width. The latter behavioral data (greenline) is presented as binary values of on/off states of the scrolling line. Whenever the green line isat the bottom, it indicates when the circle is not following the line (Offline). A green vertical line isdrawn when online and offline are switched.
In this case, SDNN increased from the middle to the end of the task, and the frequency of theoffline periods also increased. These results illustrate the physiological processes that are accompanied by a decrease in arousal. As shown in Figure 3, the arousal level gradually decreased withlarge fluctuations.

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4. ModelThis section describes a model that represents the internal processes of the participants related tothe decline in performance during the task owing to loss of motivation.
4.1 Basic Concept
We constructed a model that followed two previous ACT-R models: the perceptual-motor task
(Morita et al., 2020) and mind-wandering (van Vugt et al., 2015) models. Combining these, thecurrent model represents the execution of the task and the deviation from the task caused by thinking outside the task. The model performs a line-following task, as in the human experiment, andresponds to the probes presented during the task.
Figure 4 shows an overview of the model by dividing it into symbolic (Figure 4 (a)) and subsymbolic parts (Figure 4 (b)). These two parts are conventionally divided into ACT-R modeling.
Each part is described in the following subsections.
4.2 Symbolic and Subsymbolic Processes of the Model
This section illustrates the behavior of the models. Section 4.2.1, shows a symbolic process governed by chunks and production rules. Section 4.2.2 describes a subsymbolic process related to
chunk activation.
4.2.1 Symbolic Process
The internal state transit of the model is shown in Figure 4 (a). As seen in the figure, the model
consists of cyclic behaviors of perceptual and motor processing. Because individual cognitive functions support this process, this section explains the model according to the ACT-R modules. Among
several modules implemented in ACT-R, the most important in the line-following model are thevisual, motor, goal, declarative, and production modules. These modules function as follows.
Visual Module: This module simulates interactions with the external environment. It reads the
symbols (e.g., the position of a circle or turn in the line) necessary to perform the task from theexternal environment (in the model, a display on a virtually created window).
Motor Module: This module simulates the operations required for the task. In the line-following
model, the module executes key-presses corresponding to the movements of the circle and responses
to a probe.
Declarative Module: As noted earlier, this module stores symbolic chunks, including episodic
memories, semantic knowledge, and the model’s goals. The last chunk is important for representing
mind wandering in the line-following task. As in the previous mind-wandering model (van Vugt
et al., 2015), two types of goals are available in this model: the goal for current task execution andthe goal for default-mode thinking. In addition to these two goal chunks, the model also has chunks
corresponding to individual memories that are not relevant to the current task.

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Perception①Check the external
environment in thevisual module

Manipulation

Current
State

Objective②Update current
state of goal
module④Manipulations
required for the task inthe motor module③Request chunks
about activity objectives
in declarative modules

Thinking
Outside the Task
Continuous recall of off-task
memories

Probe Response(a) Block diagram showing basic model processing

Objective

Manipulation

Objective

Thinking
Outside the Task

Motivation

Thinking
Outside the Task

Manipulation

Objective

Manipulation

Thinking
Outside the Task

StimuliTime(b) Conceptual diagram of the change in motivation over time as shown in the model. Motivation decreases
over time and increases with stimulation.

Figure 4: Model concept
Goal Module: This module holds one of the two goal chunks retrieved from the declarative module. In addition, the module stores the current state of the task required to control the flow of theline-following task. These states include those obtained from the visual module, such as the circle
position and next turn position.

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Production Module: This module manipulates other modules by selecting and applying production rules using chunks held by the other modules. In the current model, the application of thismodule results in the flow shown in Figure 4 (a). Importantly, each transition (corresponding to asingle application of a production rule) requires a certain time cost (50 ms), which is the default
setting of ACT-R for simulating the human cognitive process in various tasks. By accumulatingthese time costs the model can predict the overall execution time of a human line-following task.
This model integrates each of the modules shown thus far in the following steps.
1. When the model observes the state of the external environment in the visual module (Fig1
ure 4 (a) ⃝),2
2. It updates the current state of the goal module (Figure 4 (a) ⃝),3
3. requests a goal chunk from the declarative module (Figure 4 (a) ⃝),4. and performs the necessary operations (key-presses) for the task through the motor module
4(Figure 4 (a) ⃝).
After the above steps, the visual module checks for a new state in the external environment andreturns to step 1. If the declarative module retrieves the goal chunk that directs attention to defaultmode thinking, it does not perform the operations required for the task (key-presses). Instead, it
enters a state of continued memory recall outside the task (mind-wandering). When the goal chunkof the current task is accidentally recalled in this process, the model returns to the task.
4.2.2 Subsymbolic Process
In the above process, the task execution varies depending on the recalled goal chunk. In ACT-R, theavailability of chunks in the declarative module is controlled by a value called activation assigned
to each chunk. The activation in the current model is expressed as,
Ai = ln(nPt−dj ) + εi(1)

j=1where n is the number of occurrences of memory item i, tj is the time elapsed since the jth occurrence, d is the decay factor (the default value is 0.5), and εi is the transient noise. Applying thisequation and setting, recently obtained and frequently visited memories are highly activated.
As Eq. 1 is constructed to represent concentration on the currently important task (Anderson &Schooler, 1991), the subsymbolic computation in ACT-R enables the model to perform tasks under
the current goal stably. However, if a goal chunk other than the current task is accidentally recalled
during the task (mind-wandering) and this recall is repeated, Ai for that chunk will increase, causing
a task shift (quitting the current task and starting the next task).
The probability of such accidental recall is determined by εi . This parameter (activation noise)
is generated using a Gaussian distribution with a mean of zero, and variance as follows:
σ 2 = (π 2 /3) × s2

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where s is a parameter that determines the size of noise variance. Thus, this parameter represents
the stochastic fluctuation added to the activation value for each retrieval. Under conditions withlarge fluctuations, chunks with a small activation can be retrieved. In other words, the increasein activation noise also increases the probability of mind wandering caused by retrieving the goal
chunk for the default mode activity.
Based on this relationship, in this study, we considered noise (εi ) as a parameter that changes
depending on the autonomic nervous system (Dancy et al., 2015) during the task. As the parasympathetic nervous system becomes more dominant over time, the activation noise increases, and it
decreases when an event directs attention to the task (e.g., falling off the line).
Such a physiological process is supposed to drive a change in motivation, as shown in Figure 4 (b), where the activation of the goal chunk for the task represents motivation. In the initial
phase, the task goal is repeatedly retrieved and the activation of the goal chunk increases. However,as activation noise increases over time, mind-wandering becomes mixed. This repetitive mind wandering resulted in participants quitting the task. It can also be considered that giving external stimuliintervenes in such loss of motivation. The following simulations confirm this process.

5. Simulations
We first tried to replicate the behavioral results obtained in the human experiments (Simulation 1).
Following this, we explored designs of interventions for motivation loss by manipulating the model
parameters (Simulations 2 and 3).
5.1 Simulation 1: Task Motivation as the Fluctuation of Activation Noise
5.1.1 Objective
This simulation was conducted to confirm the occurrence of mind wandering owing to the aforementioned noise changes. Here, the activation noise increased by 0.0004 at each screen update (25fps), which corresponds to increments of one per minute. We also set three conditions for noise
reduction (small: 0.02, medium: 0.04, and large: 0.06), which decrease the activation noise whenthe model perceives that a circular object has fallen off the line. Because the activation noise corresponds to the physiological state (i.e., autonomic nervous activity), the noise reduction range isconsidered to correspond to psychological concentration, which is assumed to vary by environment
or individual differences. Ten independent runs of a 30-minute line-following task were conducted
for each noise-reduction condition.
5.1.2 Result
Figure 5 shows examples of change in the activation noise during the line-following task. The
horizontal axis corresponds to the time of screen updates (25 fps × 60 sec × 30 min). The threegraphs can be described as follows:• Small decrease condition (0.02): The activation noise kept increasing.

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Figure 5: Examples of activation noise fluctuation from each simulation condition. Horizontal axis
represents the screen update (every 40 ms). Note that only the top panel has a different range on thevertical axis.
• Medium decrease condition (0.04): The activation noise gradually increases with repeatedascending and descending fluctuations.
• Large decrease condition (0.06): The activation noise repeatedly moved up and down withina certain range.
From these descriptions, we can see a commonality between the medium decrease condition andthe human heart rate fluctuation presented in Figure 3. This suggests the validity of our assumptions
regarding the correspondence between activation noise and the autonomous nervous system. In ad11

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a Small decreaseb Medium decreasec Large decrease

Figure 6: Simulation results. Each column corresponds to a condition (a: small decrease (0.02), b:
medium decrease (0.06), c: large decrease (0.08)).From the top, the offline rate (logarithmic verticalaxis), the activation noise (logarithmic vertical axis) and the mind-wandering rate are shown. The
red line is the mean of 10 runs; error bars are standard errors.
dition, the description for the large decrease condition suggests that the noise reduction range mustbe larger than a certain value (the state in which it is easier to concentrate on the task) to maintainappropriate activation noise (arousal level) during the task. With regard to the small decrease condition, the behavior was significantly different from the other conditions. We discuss this issue atthe end of this paper.
To confirm such trends and to explore how the activation noise relates to the performance ofthe task, Figure 6 summarizes the results obtained from all cases. The horizontal axes in Figure 6
correspond to the 30-minute simulation time divided into 30 segments of one minute each. Eachcolumn in the figure ((a)–(c)) corresponds to the conditions for the noise reduction range. The top,
middle, and bottom panels show the ratio in which the circle was offline (offline rate), the activationnoise, and the ratio in which the model had the goal outside of the task (mind-wandering rate),
respectively. In particular, Figure 6 (b) shows that the mind-wandering rate (bottom panel) increased
as the activation noise (middle panel) increased. This eventually degrades the task performance (toppanel). These qualitative characteristics of the model are also observed in the human experiments(increase in offline rate from the middle point in Figure 2).
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Figure 7: Examples of activation noise fluctuation from explicit event (modify noise reduction)
conditions. Horizontal axis represents the screen update (every 40 ms).The gray dotted verticallines indicate the timing of stimulus presentation (change in the noise reduction range).
5.2 Simulation 2: Stimulation for Motivation
5.2.1 Objective
The previous simulation showed the process of motivation loss due to the occurrence of mind wandering. Simulation 2 explored the conditions for preventing loss of motivation. To achieve this, we
assume that the optimal level of arousal is maintained by giving participants explicit events (stimuli)
at a certain time point in the task. To represent such stimuli, the range of decrease in activation noise
was altered during the task. Specifically, the noise reduction was set to 0.04 at the beginning of thetask, and was modified to 0.08 after half of the task (15 min). We assumed that the participants
were presented with a stimulus, such as a startle sound, at this point (Dancy et al., 2015). The othersettings were the same as those in Simulation 1.
5.2.2 Result
The results are shown in the Figure 7 and Figure 8, respectively. The gray dotted vertical lines
indicate the timing of stimulus presentation (change in the noise reduction range). The activationnoise gradually increases in the first half (0.04 noise reduction), whereas it tends to decrease after
the noise reduction range is increased. The mind-wandering rate (bottom panel) also fluctuates withactivation noise, showing a similar trend. Thus, the noise reduction setting worked as expected (i.e.,
to represent external stimuli for recovering mind-wandering).
5.3 Simulation 3: Adaptive Stimulation for Motivation
5.3.1 Objective

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The final simulation explored the theoreticalplausibility of the adaptive stimulus presentation to maintain motivation. In this simulation,
we assumed that participants’ mind-wandering
can be recognized by utilizing sensing devices
such as eye trackers or heart rate monitors.
Based on this assumption, we explored the effectiveness of stimulus presentation with proper
timing for suppressing mind-wandering (motivation loss). This mechanism is considered to
be a type of homeostasis, which is a physiological process, maintaining an organism’s internal state under steady conditions (Cannon,
1929; Billman, 2020). However, unlike natural
homeostasis, the method described herein canartificially set a steady condition.
To build such an artificial homeostasis
mechanism, the mind-wandering rate, an internal variable of the model, was monitored, andthe model was presented with a stimulus to focus on the task when the mind-wandering rate
exceeded a certain level. Similar to the previous simulation, the stimulus to the model was
represented by changing the range of decreasein noise.
To determine the timing of the stimulus presentation, the baseline of the mind-wandering
Figure 8: Results of explicit event (modify noise rate was obtained by an independent run of the
reduction) condition. The gray dotted vertical model (the setting was the same as the mediumlines indicate the timing of stimulus presentation decrease condition in Section 5.1). From this(change in the noise reduction range). From the procedure, the baseline is 0.441, which is a+3ς
left, the offline rate (logarithmic vertical axis), the calculated from the mean a and standard deviaactivation noise (logarithmic vertical axis) and the tion ς of the mind-wandering rate.
mind-wandering rate are shown. The red line is
Simulations were performed using the obthe mean of 10 runs; error bars are standard er- tained baseline to verify the artificial homeostarors.
sis mechanism. When the mind-wandering rate
exceeded the baseline (0.441), the noise reduction range changed from a normal value (0.04) to a concentration value (0.08). The other settings
were the same as those described in Section 5.2.

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Noise Value
Mind wandering rate

R EPRESENTING M OTIVATION

Mind wandering rate
Noise Value

1.0
0.5
0.0

0

5

10

15
Time (min)

20

25

30

Figure 9: Example of activation noise change (red) and mind-wandering rate (yellow)
5.3.2 Result
Figure 9 shows an example of the evolution of activation noise in the simulation of a homeostasismaintenance model. In Figure 9, the red, yellow, and gray lines indicate the activation noise, mindwandering rate computed for the last minute, and baseline for triggering a stimulus, respectively.
This figure shows that the activation noise and mind-wandering rate gradually increased until approximately 20,000 frames. Around this point, the mind-wandering rate exceeds the baseline andcauses a stimulus presentation, leading to a rapid decrease in the activation noise.
The graphs presented in Figure 10 indicate that all indices maintained a low stable value, suggesting that the increase in the activation noise was suppressed and that mind-wandering was reduced by the stimuli (increase in noise reduction). Such effects of the adaptive stimuli can becharacterized by comparing them with the results of Simulation 2 (Figure 8). In Simulation 2, although the overall trend shows a reduction in mind-wandering with the stimuli, some cases (seethin lines in the figure) show large fluctuations in the indices just before the stimulus presentation.
In contrast, the adaptive stimulus presentation in Simulation 3 suppressed such large fluctuations ineach run, suggesting that the method is effective in controlling the differences that emerged in eachcase (such as individual differences).

6. Conclusion
This study constructed a motivation model using ACT-R to implement adaptive interventions for
simple routine tasks. The model was developed based on previous ACT-R studies on mind-wanderingand perceptual-motor tasks. In addition, the model in this study stochastically switched the activatedgoal (task-focused or off-task thinking) during the task. By mapping the activation noise of knowledge to changes in the autonomic nervous system, we represented the mind-wandering that occurred
over time. The model was used to simulate the basic behaviors of human participants and to explore
methods of stimulating concentration on the task.

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This study contributes to the advancementof the field of cognitive systems by presenting a computational representation of physiological (arousal) and psychological (motivation) changes over time. Referring to such concrete representations, we can design future interactive systems that promote well-being. As
the next step in this study, we will develop asystem for suppressing mind-wandering during
the task, as presented in Simulation 3. Although there are several technical problems tobe solved, such as sensing accuracy or choice ofstimulation, the computational model presented
here will guide exploration to solve such issues.
The proposed model has several limitations
that need to be refined or validated. For example, in the small decrease condition of Simulation 1, the mind-wandering rate was reduced
despite the high offline rate and the failure of
the task (Figure 6 (a), top and bottom panels). This is caused by the increase in memory retrieval time accompanied by a low activation level (in ACT-R, retrieval time is calculated from chunk activation). Thus, during thelong retrieval of the goal chunk, the activity offollowing the line was stopped. Such behavior is not included in the assumptions of the
Figure 10: Results of proper timing stimulus con- model and needs to be validated. We will reditions. From the left, the offline rate (logarith- vise the setting and definitions of the indices in
mic vertical axis), the activation noise (logarith- the model in the future and conduct an analysis
mic vertical axis) and the mind-wandering rate are that will allow us to fully examine the optimal
shown. The red line is the mean of 10 runs; error level of motivation.
bars are standard errors.
Moreover, future models need to include
factors of initial learning, which are observedin the initial phase of the human data (Figure 2). We emphasize this factor because motivation isconsidered to interact with task learning (Csikszentmihalyi, 1990). Regarding this factor, we havealready constructed a pilot model that includes motor learning in this task (Nagashima et al., 2022).
Compared to the pilot model, we assume that the model presented in this paper is a simplified
version that focuses on the factors of motivation loss caused by mind-wandering and recoveringby homeostatic regulation. In the future, we will integrate these two models to fully explain thephenomenon related to motivation.

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The final limitation of the study is the generality of the proposed mechanism. We selected ACTR as the platform to implement the proposed mechanism. However, we believe that the proposedmechanism is not specific to the ACT-R architecture because the core of the mechanism is generalmemory activation, which has long been discussed since the classical literature by Hebb (1955). In
the future, we hope to connect the proposed mechanism with a general cognitive modeling framework such as the common cognitive model (Laird et al., 2017).

References
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