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J Camosun Psyc Res. (2021). Vol. 3(1), pp. 99-110.
Submitted Fall 2020; accepted July 2021.

What Determines an Individual’s Interests and
Motivation?
Authors: Noah Juniper*, Jonah Talbot, and Gavinjit Bassi
Supervising Instructor & Assistant: Michael Pollock and Kristina Andrew, Psyc 110
(“Experimental Psychology”)
Department of Psychology, Camosun College, 3100 Foul Bay Road, Victoria, BC, Canada V8P 5J2
*Corresponding author email: noahjuniper@gmail.com

ABSTRACT
In this paper, we sought to understand where individual motivation and interests come from
and how we can better understand them so that we could better understand how to accomplish
our goals. Previous research has found that how interested someone is in a subject can be
predicted by their understanding of that subject, positive feedback received, and time spent doing
a task on that subject. In our first (correlational) study, we tested the strength of these
relationships by examining naturalistic daily changes in their variables longitudinally over a twoweek period. We recorded time spent on tasks throughout the day and after each task measured
on subjective scales how interested we were in the tasks, positive feedback received on task
performance, and level of interest in the tasks. Data pooled across participants showed a
significant correlation of interest level with personal understanding, but not with positive
feedback nor with time spent on tasks. Based on the strength of correlation found between
personal understanding and interest level in our correlational study, we then conducted a second
(experimental) study to test for specifically a causal relationship between these two variables.
Over a two-week period, we randomly assigned participants each day to either a higher personal
understanding condition or a neutral condition and measured the effect this had upon interest
level each day. The results of our experimental study showed significant differences found in
interest level between conditions. Possible practical applications of our current findings show
that having a high personal understanding of a subject increases the likelihood you will be
interested in that subject. This was found to be true during our correlational study, and during our
experimental study it was found that trying to manipulate one's personal understanding does
increase interest levels. In other words it was found that forcing yourself to understand a subject
will make you more interested in it. All of this shows that personal understanding (that is how
well you understand a subject) is (at least in part) what determines interest and motivation.
1. Introduction
1.1 Research Problem
Motivation and interest are what drive us
to accomplish goals. Our motivation for

change is what sparks our interest in
progress. As well as the fact the most
motivated people are often the most
successful. By understanding our
motivations and interests we can better
understand how to accomplish our goals.
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Through understanding individual
motivation and interest we can gain a better
understanding of the world around us and
how we can fully realize our life goals to
thus becoming a happier healthier society.
1.2 Literature Review
One factor that contributes to interest is
an appraised ability to understand a
particular subject. In a study by Sylvia
(2005) designed to determine if
understanding influences interest, sixteen
people were designated into two groups:
high ability and low ability. Each group was
given a questionnaire to fill out, with the
first page comprised of seven personality
appraisal scale items (e.g., “I am a very
insightful person”). The purpose of this first
page was to divert the participants from
knowing the real aim of this study, which
was tested on the second page of the
questionnaire. The second page was a task to
read a poem and give their thoughts,
feelings, and reactions. However, the
researchers manipulated the participant’s
ability to understand by having different
introductions for the reading . The low
ability group had an introduction that
explained the basics of the poem (author,
name of the poem, the book it came from),
whereas the high ability group was given
additional information (the origin and
meaning of the words in the title -and an
explanation of the subjects in the poem).
Their findings indicate that manipulating the
participants understanding (how they viewed
and understood the poem going into it)
significantly affected feelings of interest.
Based on these findings one could surmise
that an individual’s personal feelings of
understanding (how well they feel they
understand a subject) can predict how
interested they are in a particular subject.

Another possible factor that benefits
interest is positive feedback. In a study by
Tanaka (2001), 448 junior college students
were assigned to one of four stories.
Students rated the characters in the story on
a 0-10 scale based on their levels of interest
and motivation to do a task: score of 0 being
they have no interest at all and score of 10
being they are very interested and motivated
to complete the task. The main characters in
these stories were given tasks that varied in
the level of interest the characters expressed
in them and in the expectation of an
extrinsic reward. The results showed that
subjects believed positive feedback
motivated the character to complete a task
more than a situation where one would not
expect positive feedback. Based on these
findings, it is predicted that increased
positive feedback will lead to a higher level
of interest and success.
Another possible factor involved with
motivation is the time spent completing a
task. In an experiment by Thoman et al.
(2019), 886 undergraduate students with the
median age of 18 were divided into two
separate groups. One group was given a
boring task of copying letters to another
paper and the other group was given the
interesting task of finding hidden words in a
matrix (a chart with numbers expressions
and symbols arranged in columns). This was
done to see whether a person can make a
boring task interesting by simply doing it for
a certain period of time. In both groups it
was found that approximately half of the
participants who completed the task used
strategies to keep themselves engaged and
found it interesting by the end.
1.3 Hypotheses
Based on the above literature review, we
predicted the following hypotheses:
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● Hypothesis #1: If one feels they
understand a subject then they will be more
interested in said subject.
● Hypothesis #2: If positive feedback
is increased then interest will also increase.
● Hypothesis #3: If the time spent on a
task increases then interest in that task will
increase.
2. Methods
2.1 Participants
The three authors of this paper served as
the participants in its studies. The
participants ranged in age from 18 to 19
years old, with an average age of 18.5 years,
and included three men. The participants
were all undergraduate students at Camosun
College who completed the current studies
as an assignment for Psyc 110
(“Experimental Psychology”) and were
grouped together due to their mutual interest
in what determines an individual’s
motivation and interest.
2.2 Materials and Procedure
2.2.1 Correlational Study Methods
We first performed a correlational study
to test concurrently all of our hypotheses by
examining naturalistic daily changes in their
variables longitudinally. Each participant
kept a study journal with them at all times
over this study’s two-week period in order to
record self-observations of the following
four variables: (1) personal understanding,
(2) positive feedback, (3) time spent on
tasks, and (4) interest.
To measure the variable of personal
understanding, each participant recorded
their level of personal understanding of
individual tasks throughout the day using the
1-5 scale shown in Appendix C. The data
was recorded in a journal throughout the day

and was compiled at the end of the day with
average understanding and average interest
level calculated using the results.
To measure the level of positive
feedback, each participant recorded
themselves based on the level of feedback
they received using the 1-5 scale shown in
Appendix B, participants record this right
after receiving feedback from tasks. This
data was recorded daily at the end of the day
and was compiled into an average level of
positive feedback for the day, if no feedback
was recorded during the day no value was
applied.
To measure the time spent on an
individual task, each of the participants
measured, using a stopwatch on their mobile
devices, the amount of time (in minutes)
spent on individual tasks, and wrote the time
down in a personal journal. The total time
spent on tasks was calculated at the end of
the day, and recorded with the rest of the
data.
To measure their level of interest, the
participants used the 1-5 scale found in
Appendix A. The scale ranged from very
interested to very disinterested and was
measured after any given task. The
participant’s level of interest was recorded
for each individual task during the day and
the average level of interest was calculated
using the recorded results at the end of the
day. Participants also recorded in their study
journals what tasks they were working on.
To assess the strength and statistical
significance of associations between
variables predicted by our three hypotheses,
we performed Pearson product-moment
correlations of their predictor variables
(personal understanding, positive feedback,
time spent on tasks) with their outcome
variable (interest). For testing hypothesis #1,
we correlated the level of participant
understanding of any given task with that
participant’s level of interest in the same
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task. For testing hypothesis #2, we
correlated the level of feedback participants
received after completing tasks and with
their level of interest in the same tasks. For
testing hypothesis #3, we correlated the
amount of time participants took doing tasks
with their level of interest in the same tasks.
We performed all of the above correlations
separately for each participant as well as
using data pooled across all of the
participants. For the correlations using
pooled data, in addition to using the raw
data, we also performed correlations after
we had first transformed the data from each
participant into z-scores in order to
standardize differences in averages and
variability seen between the participants in
their data and thus make them more
comparable. A correlation coefficient was
considered statistically significant if the
probability of its random occurrence (p) was
< .05 (i.e., less than 5% of the time expected
by chance alone).
2.2.2 Experimental Study Methods
Based on the strength of the correlation
between personal understanding and interest
level found in our correlational study, we
then chose to conduct an experimental study
to test for a causal relationship between
these two variables from Hypothesis #1.
We manipulated the independent
variable, personal understanding, over a
two-week period by randomly assigning
participants each day to either a higher
understanding condition or a neutral
condition. On high personal understanding
(experimental) days, before each task
participants thought for at least one minute
about how the concepts in the task related to
well-understood subjects and ideas. On
neutral (control) days, participants
approached tasks as normal and did not try
to relate concepts to better-understood
subjects. For both conditions, interest level

was recorded using a 1-5 scale with the
average across each task calculated at the
end of the day (no value was imputed on
days with no completed tasks). Average
interest levels were calculated and recorded
at the end of the day based on all tasks
completed (no value was imputed on days
with no completed tasks).
In order for participants to remain
unbiased as to what condition they were
assigned to, participants used a random
assignment system. Participants flipped a
coin each day to determine whether they
were in the experimental group (heads) or
control group (tails). Heads meant
participants were assigned to the high
personal understanding experimental group
and tails meant participants were assigned to
the neutral control group. Personal
understanding was manipulated before each
task and interest level was recorded after
each individual task in order to limit
confounding results. Unfortunately, given
the nature of the manipulation, participants
were unable to accomplish a double-blind
procedure for their experiment.
To assess the statistical significance of
differences seen in interest level on higher
personal understanding experimental days
vs. neutral control days, Student’s t-tests
were performed. We performed t-tests
separately for each participant as well as
using data pooled across all of the
participants. For the t-tests using pooled
data, in addition to using the raw data, we
also performed t-tests after we had first
transformed the data from each participant
into z-scores in order to standardize
differences in averages and variability seen
between the participants in their data and
thus make them more comparable. An
average difference between conditions was
considered statistically significant if, using a
two-tailed distribution (i.e., allowing this
difference to be positive or negative), the
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probability of its random occurrence (p) was
< .05 (i.e., less than 5% of the time expected
by chance alone).

4. Discussion

3. Results

Based on previous research, we
hypothesized that increases in three
variables would be followed by an increased
interest level: the level of personal
understanding (Hypothesis #1), the level of
positive feedback (Hypothesis #2), and the
time spent on tasks (Hypothesis #3). Data
pooled across all participants supported the
predicted relationship with personal
understanding and with positive feedback,
but not with time spent on tasks. Personal
understanding proved to be the strongest
correlation and our experimental findings
have shown that a high personal
understanding of a subject will increase your
interest on that subject.

3.1 Correlational Study Results
As shown in Table 1, interest showed a
statistically significant correlation with
personal understanding and with positive
feedback, but not with time spent on tasks.
Time spent on tasks was not found to be
significantly correlated with interest using
any single participant’s data, pooled raw
data (r = 0.18, p = 0.293403; see Figure 3),
or pooled standardized data. Positive
feedback was significantly correlated with
interest using Participant #1’s data and using
pooled standardized data, but not using
pooled raw data (r = 0.30, p= 0.0744808;
see Figure 2). Personal understanding was
significantly correlated with interest using
Participant #3’s data, using both pooled raw
data (r = 0.58, p =0.00013, see Figure 1),
and using pooled standardized data. Personal
understanding showed the strongest
correlation with interest level using both
pooled raw and pooled standardized data.
3.2 Experimental Study Results
As shown in Table 2, the high-personal
understanding (experimental) condition
showed significantly increased interest
levels compared to the neutral-personal
understanding (control) condition.
Statistically significant differences between
these conditions were seen using Participant
#3’s data (p = 0.0065161), pooled raw data
(p = 0.0465434; see Figure 4), and pooled
standardized data (p = 0.0043896).

4.1 Summary of Results

4.2 Relation of Results to Past Research
Our correlational study was able to
predict interest level based on personal
understanding, which falls in line with
previous research (Sylvia, 2005).
Furthermore, our experimental study found
that manipulating one's personal
understanding leads to an increased interest
level. Any limitations of our experimental
results were likely due to the somewhat
subjective nature of our recordings along
with our inability to perform a double blind
procedure. Sylvia (2005) found that an
individual’s personal feelings of
understanding (how well they feel they
understand a subject) can predict how
interested they are in a particular subject.
While Sylvia (2005) sampled 16 people for
their experiment, we longitudinally assessed
these variables in three college students. The
similarity of both our conclusions from our
correlational study despite using different
research designs suggests a relationship
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exists between personal understanding and
interest level. It was found in our
experimental study that forcing yourself to
understand a subject/task will increase
interest levels in the subject/task.
Considering all this, it is likely that personal
understanding (that is how well you
understand a subject) is a major determinant
of interest level.
A strong relationship was found between
positive feedback and interest level in our
correlational study that is consistent with
past research. Tanaka (2001) found that
large amounts of positive feedback lead to a
higher level of interest. Tanaka (2001) had
participants do different tasks such as sports,
school, and work. However, our
correlational study longitudinally recorded
participants doing school work as the task
(academic tasks). The fact that we found the
same relationship between positive feedback
and level of interest despite these differences
in methodology speaks to the universality of
its relationship.
The inability of our correlational study to
predict interest level based on the amount of
time spent on a task is not in line with
previous research. While Thoman et al.
(2019) used a comparison of two tasks, we
longitudinally recorded the time spent on
academic tasks from three college students
and found that time spent on a task did not
significantly correlate with increased interest
level.

4.3 Implications of Results
We originally conducted the current
studies because through understanding
individual motivation and interest we sought
to better understand the world around us and
how we can fully realize our life goals, thus
becoming a happier healthier society. Based
on our experimental study results we
recommend attempting to increase your
interest level through manipulating your
personal understanding of the subject you
are trying to gain interest in.

References
Silvia, P. J. (2005). What Is Interesting?
Exploring the Appraisal Structure of
Interest. Emotion, 5(1), 89–102.
https://doiorg.libsecure.camosun.bc.ca:2443/10.103
7/1528-3542.5.1.89
Tanaka, S. (2001). The assumed effects of
positive feedback paired with success on
motivation to do a task: The cases of
characters with high and low initial levels
of interest. Japanese Psychological
Research, 43(1), 37–42. https://doiorg.libsecure.camosun.bc.ca:2443/10.111
1/1468-5884.00157
Thoman, D. B., Sansone, C., Robinson, J.
A., & Helm, J. L. (2020). Implicit
theories of interest regulation. Motivation
Science, 6(4), 321. https://doiorg.libsecure.camosun.bc.ca:2443/10.103
7/mot0000160

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Table 1
Correlation coefficient (r) values, with number of daily trials (n) per correlation in brackets.
Participant Participant Participant
#1
#2
#3

Pooled
raw data

Pooled
standardized
data

Personal understanding &
interest level

0.53(12)

0.35(13)

0.68(11)*

0.58(36)*

0.51(36)*

Positive feedback &
interest level

0.59(12)*

0.45(13)

0.50(11)

0.30(36)

0.51(36)*

Time spent on tasks &
interest level

0.31(12)

0.23(13)

-0.10(11)

0.18(36)

0.16(36)

Variables correlated

* p < .05.

Table 2
Descriptive statistics on interest level for high-personal understanding and neutral
understanding conditions.
Participant Participant Participant
#1
#2
#3

Pooled
raw
data

Pooled
standardized
data

Condition

Statistic

High personal
understanding

Mean

3.98

3.25

3.50

3.49

0.45

S.D.

0.24

0.46

0.58

0.52

0.61

n

4

8

4

16

16

Neutral personal
understanding

Mean

3.44

2.58

2.00

2.92

-0.60

(Control)

S.D.

0.66

0.79

0.00

0.85

1.04

n

6

4

2

12

12

(experimental)

Interest level was measured on a 1-5 scale of unpleasant feelings, where 1 = no understanding
and 5 = fully understand.
* p < .05 for comparison of high-personal understanding condition with its respective neutral
understanding condition.
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Figure 1
Scatterplot of personal understanding and interest level using pooled raw data across
participants.

Marker color indicates which participant data is from: red = participant #1, orange = participant
#2, and yellow = participant #3. Some data might not be visible in the figure due to overlapping
markers.

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Figure 2
Scatterplot of positive feedback and interest level using pooled raw data across participants.

Marker color indicates which participant data is from: red = participant #1, orange = participant
#2, and yellow = participant #3. Some data might not be visible in the figure due to overlapping
markers.

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Figure 3
Scatterplot of time spent on tasks and interest level using pooled raw data across participants.

Marker color indicates which participant data is from: red = participant #1, orange = participant
#2, and yellow = participant #3. Some data might not be visible in the figure due to overlapping
markers.

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Figure 4
Bar graph of average personal understanding across high-congener and neutral-congener
conditions using pooled raw data from participants, with error bars showing ± 95% confidence
levels, and with an overlapping scatterplot of data from each participant.

Marker color indicates which participant data is from: red = participant #1, orange = participant
#2, and yellow = participant #3.

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Appendix A
Scale for measuring the level of interest felt.
5: very interested 4: somewhat
interested

3: neutral

2: somewhat
disinterested

1: very
disinterested

Appendix B
Scale for measuring feedback
5: very positive
feedback

4: somewhat
positive
feedback

3: neutral
feedback

2: somewhat
negative
feedback

1: very negative
feedback

Appendix C
Scale for measuring personal understanding
5: fully
understand

4: partly
understand

3: neutral
understanding

2: partly
confused

1: no
understanding

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