Relationship between Dwell-Time and Model Human Processor for
Dwell-based Image Selection
TOSHIYA ISOMOTO, University of Tsukuba, JAPANSHOTA YAMANAKA, Yahoo Japan Corporation, JAPANBUNTAROU SHIZUKI, University of Tsukuba, JAPAN1983]. MHP is a well-known model that demonstrates a human’s
perceptual behavior in response to a visual (and auditory) stimulus.
To examine the relationship between dwell-time and human behavior, we focus on MHP to explore the design of dwell selection insteadof using the speed-accuracy trade-off. First, we devise an equationthat can represent the time neccesary for recognizing a designatedimage based on MHP. Then, we evaluate whether the equation can
represent the time and whether the time estimated by the equationfits a user’s preferred dwell-time. The contribution of our paper
will allow researchers and developers to use this user’s preferreddwell-time to improve the performance of the dwell selection fromthe aspect of humans’ perceptual behavior.

We investigated the relationship between dwell-time and the model humanprocessor (MHP). First, we devised an equation that can represent the time
taken for recognizing an image based on MHP. Then, we evaluated whether
the equation can represent the time and wheter the time estimated by theequation matches the user’s preferred dwell-time. The experiment consistedof two tasks: image selection with a button (button-task) and image selection
with a dwell (dwell-task). From the results of the button-task, we found thatthe equation derived by MHP can estimate the time; the time taken for button
selection was 662 ms on average, and the time estimated by the equation
was 660 ms on average. Also, we showed that the estimated time represented
the user’s preferred dwell-time; all participants in the experiment answeredthat 500 ms and 600 ms were their preferred dwell-times.
CCS Concepts: • Human-centered computing → Human computer interaction (HCI); HCI theory, concepts and models; User studies.

2

Additional Key Words and Phrases: gaze-based interaction, cognitive system,
user preference
ACM Reference Format:
Toshiya Isomoto, Shota Yamanaka, and Buntarou Shizuki. 2021. Relationshipbetween Dwell-Time and Model Human Processor for Dwell-based Image
Selection. In ACM Symposium on Applied Perception 2021 (SAP ’21), September
16–17, 2021, Virtual Event, France. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3474451.3476240

1

DWELL-TIME AND MODEL HUMAN PROCESSORFirst, we describe research that investigates the user’s preferreddwell-time. Then, we describe the principle of MHP and how MHP
demonstrates a human’s perceptual behavior during dwell selection.

2.1

User Preferred Dwell-time

Researchers who desire to prevent the Midas-touch problem use
180–600 ms as the user’s preferred dwell-time for dwell-typing [Majaranta et al. 2009; Mott et al. 2017; Pi and Shi 2017; Räihä andOvaska 2012; Špakov and Miniotas 2004]. In a pilot study, 400 ms
was chosen as a preferred dwell-time [Nayyar et al. 2017; Ware andMikaelian 1987] for dwell selection of circles or images1 .
These dwell-times are determined from the speed-accuracy tradeoff to achieve a faster selection and fewer Midas-touch problems.
However, a user of a fast dwell selection with under 100 ms ofdwell-time feels that the target is selected before the user looks at
it [Isomoto et al. 2018]. Moreover, the visual search time varies withthe size, arrangement, and color of a target or its content [Penkar
et al. 2012; Sears et al. 2001]. As a dwell-time that can maintainthe speed-accuracy trade-off of dwell selection, 650 ms is adequate
for a low-cognitive task (search and select a two-digit); however,
1,100 ms is short for a high-cognitive task (search and select a twoword) [Zhang et al. 2011].

INTRODUCTION

In gaze-based interaction, looking has two significant purposes. One
is for a cognitive process: a user recognizes the type and locationof an object by looking at it. The other is for manipulation: a user
selects an object (e.g., icon) or activates a command (e.g., copy) suchas by left-clickikng with a mouse or pressing a hotkey.
The most-established gaze-based interaction is dwell selection,
where the user selects an object by looking at it over a time-threshold(dwell-time) [Jacob 1990, 1991, 1993]. This time-threshold-based
schema may cause the Midas-touch problem, which is a user’s unwanted selection [Jacob 1990, 1991, 1993] because of the difficulty of
distinguishing the purpose of the looking within the dwell-time. Researchers have attempted to solve the Midas-touch problem with ashort dwell-time following the manner used in the human-computerinteraction field, in which a faster and more accurate manipulation
is superior. However, the dwell-time should not be too short because
it should include a time for the cognitive process.
In this paper, we investigate the relationship between the dwelltime and the model human processor (hereinafter, MHP) [Card et al.

2.2

Model Human Processor

The MHP demonstrates a human’s perceptual behavior in responseto the visual (and auditory) stimulus by dividing the informationprocessing system into three subsystems: 1) the perception system,2) the cognitive system, and 3) the motor system. Here, we describe
the processes in these three subsystems responding to a visual
stimulus. The perception system encodes a visual stimulus into
visual code that the human can recognize and transmits it to working

SAP ’21, September 16–17, 2021, Virtual Event, France© 2021 Association for Computing Machinery.
This is the author’s version of the work. It is posted here for your personal use. Not
for redistribution. The definitive Version of Record was published in ACM Symposium
on Applied Perception 2021 (SAP ’21), September 16–17, 2021, Virtual Event, France,
https://doi.org/10.1145/3474451.3476240.

1 Unfortunately, a detailed experimental design and discussion were not provided.

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isomoto, et al.

2-3) match the instruction and the visual code; if they match,
the next step is 2-4); if they do not match, the process repeatsfrom 2-1).
2-4) decide whther to select the image. The process ends.

memory (WM); the time for the perception process (𝜏𝑝 ) is 100 [50–200] ms [Card et al. 1983]. With the visual code, the cognitive system
recognizes, matches, classifies, and/or decides an action to the motor
system to achieve the purpose; the time for the cognition process 𝜏𝑐
is 70 [25–170] ms [Card et al. 1983]. According to the decision fromthe cognitive system, the motor system requests an action such aspressing a button; the time for the motor process (𝜏𝑚 ) is 70 [30–100] ms [Card et al. 1983]. The ranges for each time indicate thata slowman (e.g., a novice) takes the maximum time and a fastman(e.g., an expert) takes a minimum time.
Using MHP, we can estimate the time necessary for a task. Fora typing task of pressing the ‘a’ key, when the user looks at thestimulus, 1) the perception system transmits the visual code to theWM, requiring 𝜏𝑝 , the cognitive system 2-1) recognizes the visualcode as a character (i.e., the visual code is ‘a’), 2-2) matches thecharacter and ‘a’ and 2-3) requests to press the key if they match,requiring 3𝜏𝑐 , and 3) the motor system acts to press the button,
requiring 𝜏𝑚 . The total time, according to MHP is

If the system selects a target before 2-4), the user may feel thatthe target is selected before recognizing the image. In our work,
we assume a human usually looks at points within an image (orinformation in the real world) more than once to recognize theimage and performs steps 2-1)–2-3) in one fixation. Note that theuser’s view specifies a certain point (i.e., the display coordinates) for
gaze-based interaction. We use the MHP that describes the decision
process, assuming that a user only considers one stimulus at a time.
With the times 𝜏𝑝 , 𝜏𝑐 , and the number of fixations (Nfixation ) required
for recognizing the image, the time that a user feels confortable withis estimated as𝜏p + (3Nfixation + 1)𝜏c .
(1)
Using this equation, if we predict Nfixation , we can estimate theuser’s preferred dwell-time.

380 [155−810] = 𝜏p + 3𝜏c + 𝜏m = 100 + 3 × 70 + 70.

4Another example of a simple stimulus-response task where a userpresses a specific key in reaction to a stimulus, the required processes for this task are 1) encoding the stimulus, 2) deciding, and 3)
requesting to press the button. Therefore, the total time is240 [105−470] = 𝜏p + 𝜏c + 𝜏m = 100 + 70 + 70.
The processes for dwell-typing are similar to those of the typing
task except for the process in the motor system. When a user looks
at the ‘a’ key, 1) the perception system transmits the visual code,
the cognitive system 2-1) recognizing the visual code as a character,2-2) matching the recognized visual code and ‘a’, and 2-3) deciding
to select the key (i.e., deciding to dwell at it) if they match. No motor
action is required. Therefore, the total time is

4.1

Participants and Apparatus

Sixteen volunteers (all Japanese males, our university students)
aged 21 to 25 years (mean = 22.2) participated. Four had previously
participated in an experiment using an eye tracker. None of theparticipants used an eye tracker daily.
We used a Tobii Eye Tracker 4C with a pro-license for research.
A 24-inch (525×285 mm) display with a non-glare property for preventing reflections with a resolution of 2,560×1,440 pixels was used.
Each participant placed the head approximately 60 cm from the display, and we positioned a keyboard used for controlling the task near
the participant’s hand. Before the task, the participants completed a
9-point calibration for the eye tracker with the supplied application.
We used the same display for the button-task and dwell-task.

310 [125−710] = 𝜏p + 3𝜏c = 100 + 3 × 70.
Compared with previous research (e.g., [Majaranta et al. 2009; Räihä
and Ovaska 2012], the range of the user’s preferred dwell-time (180–600 ms) is similar to one calculated from the MHP (125–710 ms).
Regarding the dwell selection with a shorter dwell time, if the dwelltime is under 100 ms, the target is selected before the informationprocessing system finishes the whole process, and thus, the user
feels that the target is selected before the user looks at it. By using
MHP, we can understand a dwell-time we should use for each task.

3

EXPERIMENT

We investigated the relationship between dwell-time and MHP byevaluating whether Equation 1 is appropriately derived from MHP
and the equation can estimate the user’s preferred dwell-time. This
experiment consisted of two tasks: a button-task where users selecta target by looking at it and then pressing a button, and a dwell-task
where users select a target by performing dwell selection.

4.2

Task

Fig. 1 shows the display, which consists of instructions written in
Japanese such as “Select (image)” where (image) represents a nameamong 28 kinds of image (e.g., “bird” or “horse”) and the images2 .
We designed the arrangement and the size of the images allowing
the robust selection against the performance of the eye-tracker.
Regarding the arrangement, we made it a 5×4 matrix excluding
the outermost area of the display, on the basis of findings that theeye-tracking performance decreases in the outermost area [Feit et al.
2017; Schuetz et al. 2020]. Each image was 40.0 mm of a square.
Moreover, to exclude noize in the gaze data, we applied a low-pass

MODEL HUMAN PROCESSOR FOR DWELL-BASED
IMAGE SELECTION

A dwell selection schema to select a designated image can be described as follows (in this case, the image is “bird’). When the user
looks at an image, the information-processing system performs thefollowing:
1) transmit the visual code,2-1) recognize the visual code (the stimulus is ‘wing’),2-2) classify the recognized code (this image which has wing is‘bird’),

2 we use the images from https://visualgenome.org/ which is licensed under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)

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Relationship between Dwell-Time and Model Human Processor for Dwell-based Image Selection

SAP ’21, September 16–17, 2021, Virtual Event, France

Fig. 3. Histogram of preferred dwell-times for all participants.
Fig. 1. Display used in the experiment.

4.3

Results

We report the results of the button-task and dwell-task.
4.3.1 Button-task. First, we calculated the button press time fromwhen the gaze entered the target until the participant pressed the
button. We used the button press time as the ground-truth time thatthe participant requires to finish the cognitive process. The average
button press time was 662 ms (SD=251). Histograms of the buttonpress times in 100 ms are shown in Fig. 2. The distribution indicatesthat there was a peak at 700 ms.
4.3.2 Dwell-task. Regarding the comment for each dwell-time, when
the dwell-time was shorter than 500 ms, the meanings of all of the
responses were the same: “There was not enough time to understandthe image, so I could not understand if the target had been selected
correctly or not”. Fifteen participants for 100 ms, ten participantsfor 200 ms, five participants for 300 ms, and three participants for
400 ms commented so. Similarly, when the dwell-time was longer
than 500 ms, the meanings of all of the responses were the same: “I
felt that the dwell-time was too long to select”. Fig. 3 shows the user’s
preferred dwell-time. All participants (16/16) preferred 500 ms and
600 ms as a dwell-time and half of the participants (8/16) preferred
300–800 ms Fig. 3.

Fig. 2. Histogram of button press times for all participants.

filter expressed by 𝑃𝑖 = 0.25𝑝𝑖 + 0.75𝑃𝑖−1 , where 𝑃𝑖 is the i-th lowpass filtered gaze point and 𝑝𝑖 is the i-th raw gaze point sampledfrom the eye tracker [Isomoto et al. 2020].
In each trial, we first displayed only the instruction and asked theparticipant to press the Space key to proceed. Then, we displayed
20 images (one designated image and 19 distractors). When theparticipant selected a designated image, we hid the 20 images anddisplayed the next instruction. We randomized the order of the
instruction and randomly selected 19 distractors. During the tasks,
to eliminate the effect of the Midas-touch problem on the user’s
preference, only the designated image was selectable, i.e., the other19 distractors were not selectable. The participants could find thedesignated image freely by moving their gaze over the entire view.
We displayed the current dwell-time during the dwell-task.
In the button-task, we asked participants to select 28 images
in total. In the dwell-task, we investigated the dwell-time thatparticipants wanted to use by examining 13 different dwell-times(0 ms, 100 ms, 200 ms, 300 ms, 400 ms, 500 ms, 600 ms, 700 ms, 800 ms,
900 ms, 1,000 ms, 1,500 ms, and 2,000 ms). We first asked participants
to attempt the selection with each dwell-time and then answer the
preference and comment about each dwell-time. After answering foreach dwell-time, we asked the participants to answer the preferreddwell-time(s) that they want to use for dwell selection. Since all participants usually manipulate a computer with a mouse and do not
manipulate them with gaze, we asked participants to start with the
button-task that the participants are familiar with and then move onto the dwell-task. The average time required for the button-task wasfour minutes, and the dwell-task was 20 minutes per participant.

5

RELATIONSHIP BETWEEN RESULTS AND MODELHUMAN PROCESSOR

We describe the relationship between the results and MHP.

5.1

Button Press TimeMHP explains the process that a participant recognizes a designatedimage and presses a button as follows: 1) encoding the stimulus into
the visual code, 2-1) recognizing the visual code, 2-2) classifying therecognized code, 2-3) matching the classified code and instruction;
if they match, the next step is 2-4); if they do not match, the next
step is 2-1), 2-4) requesting to press a button, and 3) pushing the
button. Given this process, the button press time estimated by MHP
is expressed by Equation 2, which is the same as Equation 1 withthe additional time in moter system.
𝜏p + (3Nfixation + 1)𝜏c + 𝜏m

(2)

The total time depends on how many times the process 2-1)–2-3) was
repeated; if a participant can recognize that the designated image
by one fixation (i.e., Nfixation = 1), the time is 450 [180–980] ms; by
two fixations (i.e., Nfixation = 2), the time is 660 [255–1,490] ms. Note
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that because the size of the image used in this experiment was small,the I-VT (Velocity-Threshold Identification) algorithm [Salvucci andGoldberg 2000] with a velocity threshold of 30◦ /sec did not detect
the saccade for 37.1% (166/448) of the attempts and detected one
saccade for 59.1% (265/448) of the attempts that occur after the gaze
enters the target; another 3.8% (17/448) of attempts include two or
more saccades. We treated these saccades as outliers that did notaffect our results, and thus did not add the saccade duration (i.e.,
the time taken for gaze movement) in this equation.
We counted the number of fixations representing points wherea participant looks for recognizing the image. We used the I-DT
(Dispersion-Threshold Identification) algorithm [Salvucci and Goldberg 2000] with a time window of 100 ms and dispersion thresholdof 1.0◦ . As a result, the attempts containing one, two, three, four,and five or more fixations were 51 (11.4%), 253 (56.4%), 111 (24.8%),
26 (5.8%), and 7 (1.5%), respectively; the average number of fixations across whole attempts was 2.30 (SD=0.82). These numbers
show that the participants could recognize the image used in thisexperiment as the designated one with one–three fixations. Using
Equation 2, the estimated time and range with two fixations (660
[255–1,490] ms) cover 95.5% (428/448) of the button press times; andthose with three fixations (870 [330–2,000] ms) cover 92.9% (416/448)of the button press times. This result shows that if we could predict
the number of fixations required for recognizing the image, we canestimate the button press time with Equation 2.

5.2

affect the usability [Majaranta et al. 2004, 2006]. In dwell-typing,visual feedback notifies that the user’s gaze is inside a target or
the duration regarding the dwell-time. Such visual feedback may
affect the results of our experiment. For example, one participant responded, “because the dwell-time was so long, I was worried about
whether my gaze was recognized correctly.” for 2,000 ms of dwelltime. Thus, visual feedback emphasizing that the gaze is inside atarget may positively affect the user’s preferred dwell-time.
We used an image as the target in the experiment. However, in
actual cases of manipulating a computer, some targets are a text
such as a URL, in which case the information-processing would
differ. According to a previous study [Rayner 1995], while reading asentence, the distance of a saccade is 8 [1–15] characters, and thetime until the next saccade (i.e., fixation time) is on average 250 (100–500) ms. This average time and range are similar to one estimated by
the three cognitive processes (recognition, classification, match), i.e.,
210 (75–510) ms. Moreover, the keystroke time range is 80–280 ms
for the best typist and nonsecretary (not the slowman) [Card et al.
1980; Devoe 1967]. This knowledge may also be helpful to expand
our findings to the text (i.e., words, phrases) selection.
Regarding Nfixation , predicting the number of fixations for a task
is challenging. For the image selection, further investigation will
help to determine the appropriate number. For text selection, as
mentioned above, we can predict the number of saccades from thecharacters of the text; if the fixation and saccade occur alternatively,
we can also predict the number of fixations.

Dwell-time

For the image selection with one to three fixations, the user’s preferred dwell-times estimated from Equation 1 were
590 ms [225−1, 390] = 100 + (3 × 2 + 1) × 70 (Nfixation = 2),7

800 ms [300−1, 900] = 100 + (3 × 3 + 1) × 70 (Nfixation = 3).

We investigated the relationship between the dwell-time and themodel human processor (MHP) [Card et al. 1983], unlike previous
research which focuses on the relationship between dwell-timeand the speed-accuracy trade-off. MHP demonstrates a human’s
perceptual behavior in response to a visual (and auditory) stimulusby dividing the information-processing system into three interacting
subsystems: the perception system, cognitive system, and motors
system. In addition, MHP defines the time requires for completing
each system. We assumed that one fixation represents the process ofrecognition, matching, and decision in the cognitive system. Throughthis assumption and using MHP, we devised an equation that canestimate the time taken for recognizing a designated image with
MHP. Using this equation, we then investigated the relationshipbetween the dwell-time and MHP and whether the equation canestimate the user’s preferred dwell-time. We conducted an image
selection task with a button (button-task) and an image selection
task with a dwell (dwell-task). From the results of the button-task,
we found that the equation derived by MHP can estimate the dwelltime; the time taken for button selection was 662 ms on average,
and the time estimated by the equation was 660 ms on average. In
addition, we showed that this estimated time represented the user’s
preferred dwell-time; all participants in the experiment answeredthat 500 ms and 600 ms were their preferred dwell-time. From theseresults, we conclude that we can estimate a user’s preferred dwelltime for image selection from MHP.

These times are consistent with the fact that the participants feltthat a dwell-time shorter than 500 ms was too short to understandthe image because the system selects the target during or before thecognitive process is completed. Since the cognitive process requires
225 ms even for the fastman, a period shorter than 200 ms is too
short as a dwell-time for the image selection in our experiment.
Moreover, all participants preferred 500 ms and 600 ms as dwelltime (Fig. 3) and the estimated user’s preferred dwell-time from
Equation 1 was 590 ms with two fixations that are caused 56.4% for
the button-task. These results show that Equation 1 represents thetime required for recognizing the designated image, and that we
can estimate the user’s preferred dwell-time using MHP.

6

CONCLUSION

LIMITATIONS

Since gaze movement heavily varies with the diversity of the participants, the diversity affects our finding concluded from all young
male participants. If the preferred dwell-time is different among
participants, we should determine a dwell-time for different user
types, e.g., dwell-times for the young, for the elder, for males, andfor females. Moreover, familiarity with the manipulation may also
affect the findings. Therefore, further investigation is needed forrevealing how diversity affects the user’s preferred dwell-time.
The effect of visual feedback will affect the results, because previous research has reported that giving visual feedback to users may
4Relationship between Dwell-Time and Model Human Processor for Dwell-based Image Selection

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