Biology Internal Assessment
Purpose:
My interest in this topic derived from an observation that the majority of elderly have some sort of optical prescription. In addition, both of my parents wear glasses on a daily basis. Because of this, I’ve developed an interest in learning about how our eyes change throughout our lifetime, and since aging is inevitable, I think it’s a worthy topic to investigate.
Research Question:
To what extent does age affect one’s ability to differentiate color?
Hypothesis:
H1 Alternative hypothesis: It is expected that the youngest age group of my trials (10-20 years-old) will score in the superior to average range of competence for color discrimination. In other words, they will be the most able to differentiate between the different hues of color. In addition, the oldest age group (60-75 years-old) are expected to score in the lower percentiles, indicating poor color discrimination and a possible color differentiation deficiency.
H0 Null hypothesis: There is no significant difference in one’s ability to differentiate color based on their age.
Introduction:
The Farnsworth-Munsell (F-M) 100 hue test is widely used for measuring chromatic discrimination by clinicians and vision scientists. The test consists of 100 colored tiles separated into four separate rows. Each row is made up of 25 tiles, each having a slight variation in hue, with an anchor square on either side in a fixed position. The participant is then able to adjust the remaining 23 tiles to form a gradient across the row. After completing the test, participants receive a total error score (TES). The TES measures the participant’s accuracy in arranging the colored tiles so as to form a gradual transition from the two anchor color tiles on either side of each of the rows. The TES is measured by two factors: the amount of times a tile is misplaced, and the severity of a tile misplacement (ie. the distance between where a tile was placed compared to where it should’ve been placed). Therefore, the higher the TES score, the more severe of a color vision deficiency.
The colors in the first row of the test range from red to yellowish-green, the second box from yellowish-green to turquoise, the third box from turquoise green to a bluish-purple, and the fourth from bluish-purple to red. This experiment aims to examine the visual perception of four varying age groups. Specifically, the experiment aims to investigate the participating individuals’ ability to differentiate hues of color.
Background Knowledge:
The ability to see different colors is made possible by the photoreceptors, called rods and cones, located in the retina of the eye. These photoreceptors are able to recognize color through their light-sensitive pigments. Each photoreceptor is sensitive to either red, blue, or green light. The photoreceptors distinguish these colors based upon their wavelength, then send that information to the brain via the optic nerve, enabling the ability to distinguish numerous shades of color. However, if a photoreceptor does not have one or more light-sensitive pigments, the individual will not be able to see that color.
Variables:
Table 1: Variables selected for this experiment
Independent Variable
Age (in years)
Dependent Variable
Ability to differentiate hues of color
Control Variable:
How to Control Variable
Age (years)
Participants were selected to fit the needs of the age group being tested (ie. a forty year old was not selected to participate in the 10-20 years old group)
Brightness of Computer
The same computer was used to test participants, upon testing, the brightness was set to the highest setting.
Possible Optical Disability
Every participant was either an emmerotroph or wore their prescribed optical correction.
Lighting
Every examination was given between 10am-5pm in either natural or artificial lighting.
Safety, ethical or environmental issues:
Safety: All human participants signed consent forms before participating. Between trials, participants were highly encouraged to take breaks in order to give their eyes a break from the brightness of the laptop. The laptop used for testing had no cords attached, therefore, did not pose as a tripping hazard.
Ethical: There are no ethical issues to examine.
Environmental: There are no environmental issues to examine.
Method:
Materials
Electronic Device
Farnsworth-Munsell 100 Hue Color Vision Test
Human Participants (Homo sapiens), 5 from each of the following age groups: 10-20, 30-40, 40-50, 60-75
Participants were divided into four main age groups, 10-20, 30-40, 40-50, and 60-75. The Farnsworth-Munsell 100 Hue Color Vision Test was then administered on a laptop with the brightness on the maximum setting. Participants were given the same instructions before starting: “Organize the caps in each row to form a gradient between the two end caps”. There was no time constraint set. This ensured results were based on accuracy rather than speed. All participants who had an optical impairment were wearing their correct prescription.
Each participant was given the examination three times, with a short break in between each trial.
Justification:
The Farnsworth-Munsell 100 Hue Color Vision Test was used due to its popularity with visual scientists and clinicians as a measure of chromatic discrimination ability. Each participant repeated the test three times in order to improve the accuracy of the data being collected, and lower the possibility of error. The independent variable groups (age in years) were chosen in order to collect data from a variety of ages/phases of optical development. The dependent variable (ability to differentiate color) was chosen because abnormal color vision has been shown to increase significantly as an individual ages.
Raw Data:
Table 2: A table showing the TES scores of five participants between the ages of 10-20 years old after three trials.
Table 3: A table showing the TES scores of five participants between the ages of 30-40 years old after three trials.
Table 4: A table showing the TES scores of five participants between the ages of 50-60 years old after three trials.
Table 5: A table showing the TES scores of five participants between the ages of 60-75 years old after three trials.
Calculation for average TES score: TES score for each repeat Number of repeats
Table 6: Average TES scores
Notes and Observations:
- Participants with an optical prescription tended to receive a higher TES score.
- A huge increase in average TES scores between the 50-60 age group and 60-75 group.
- Many participants in the 60-75 years group expressed uncertainty multiple times throughout the test.
- On average, participants in the 10-20 group spent the least amount of time on the test.
- The most commonly misplaced colors were the bluish-purple ones.
Table 7: Average TES score from Table 8: Difference in average each age group. TES score from each age group.
Average difference in TES Score between age groups: (-4.6+17.6+52.6=65.6) 65.6/3=21.9
Figure 1: A graph showing the average TES score from each age group
The error bars were set to 0.05 to reflect the uncertainty of the F-M 100 Test.
Chi-Squared (X2)Test:
In order to establish whether there is a statistical significance between TES scores and the different age groups, the Chi-Squared (X2) test was conducted using Google Sheets.
Equation for calculating X2:
Note: observed value (o), expected value (e)
Table 9: Chi-Squared calculations based on difference in TES score
Age Groups (in years)
Expected Difference in TES Score
Observed Difference in TES Score
Number of Degrees of Freedom: (rows-1)(columns-1) = (3-1)(2-1)= 2
X2crit=5.991 for p= 0.05
Due to the Chi-Squared value of 74.8 exceeding the low probability level of 5.99, the H0 Null hypothesis is rejected and hence, H1is accepted. Therefore, there is a statistically significant relationship between age and ability to differentiate color.
Conclusion:
From the results of this experiment, I can conclude that as and individuals age increases, their ability to differentiate colors decreases. Therefore, the null hypothesis (H0) stated on page 1 is rejected, and the alternative hypothesis (H1) is accepted. This is shown on Table 9 with the Chi-squared test. The results from the Chi-squared test exceeded the low probability value, indicating a statistically significant relationship between one’s age and their ability to differentiate colors. One possible explanation for this result could be that, overtime, the pupil can reduce in size. As an individual ages, the muscles that control the pupil and it’s reaction to light can lose some of its strength, leading to the pupil admitting less light to the eye and therefore, making it harder to differentiate different shades of color. Overtime, this can cause the pupil to shrink in size. It has been suggested that the reasoning for the pupil decreasing in size is due to the senile iris degeneration, leading to increased rigidity. Another possible explanation could be the yellowing of the crystalline lens. When this happens the lens begins to scatter the light, reducing the saturation of the retinal image, and resulting in a decrease in the colors saturation. This is evident from the data collected, since average TES score of the participants (Table 7) in the 60-75 age group (91 points) was 65.6 points higher than the average TES score of the 10-20 group (25.4 points). Furthermore, the large increase in TES score between the 50-60 group and the 60-75 years old group can be linked to a study done by the Mashhad University of Medical Sciences in 2017. The study concluded that individuals above the age of 60 had the highest percentage of obtaining a color vision deficiency, approximately 20.93%.
Strengths:
A strength of this investigation can be seen in the amount of trials conducted for each age group. Within each age group, five participants were selected and completed the test three separate times, improving the accuracy of the data. In addition, the small error bars indicated on figure 1 suggests a low chance of error, increasing the reliability and certainty of the results obtained through this investigation.
Weaknesses and Suggested Improvements:
The lighting used in each examination was not consistently measured. Examinations took place in both natural and artificial lighting during different times between 10am-5pm. This lack of control over the lighting could have affected the visibility of the computer screen to the participants taking the test. To improve this, I could have administered the examinations in a completely darkened room. In addition, a strategic computer brightness would be set for each examination to ensure accuracy, and protect the eyes of the participants.
Another weakness of this investigation can be seen in the fact that it does not examine individuals younger than ten years old, between twenty to thirty, between forty to fifty, or older than seventy-five years old. This would mean that the results attained throughout this investigation could only be applied to individuals who are between the ages examined, therefore hindering the extent of which the results show a positive association between age and ability to differentiate color. A potential improvement to this would be to expand the age range of the participants being tested, therefore yielding a larger set of results that reflected a wider range of ages. In addition, expanding the age range would also decrease the uncertainty of the results and conclusion obtained in this experiment.
Furthermore, this investigation was aimed at examining participants who did not already have a color vision deficiency due to causes other than age. However, the participants were never examined for a non age-related color vision deficiency by the test administrator prior to starting the test. This limits the certainty of a control variable to the dependability of the participants. This can be rectified by having the participants complete an optical exam by a registered optometrist to confirm they are suited to participate in this investigation.