Does perception of time change with age?

The hypothesis

Children (12-18) and young adults (19-25) have a slower perception of time compared to adults (26-90).

Explanation of the hypothesis

Perception of time can change based on two main factors: how much information is processed during a given period and how time critical a situation is.

According to Ornstein (1975), when looking back on experiences, the more information1 processed in a given period the longer it was perceived to take. This is because subconsciously our brains think that the more ‘new information’ we store, the longer the duration must have been. He also found that this may correlate to the complexity of the task because the more difficult a problem, the more information a person is taking in when trying to solve it. This is important for my experiment because, as we grow older, on average, we begin to follow more of a routine, resulting in less ‘new information’ being stored, which causes older people to have a faster perception of time. In contrast, younger people tend to have less of a routine, thus they take in more ‘new information’, resulting in a slower perception of time.

An experiment conducted in 2011 by Dan Zakay (professor of Cognitive psychology at Tel-Aviv University in Israel), also found that perception of time can be influenced according to how important time is in a situation. If time is an important factor it will seem to move slower because we are concentrating on that single aspect, therefore the more focused the task is on time, the slower time will seem to move. Although his findings were not related to age, it can still influence my experiment. This is because, if I have told someone to try and guess a time interval, time will be an important factor thus, their perception of time may become slower than it originally would have been.

Both theories above will affect the perception of time of the participant, and thus, will affect the results of my experiment.

Several studies have already been completed to support this theory (and my hypothesis), for example, the experiment completed by psychologists Marc Wittmann and Sandra Lenhoff in 2005. In their experiment they asked 499 participants, ranging from the ages of 14 to 94, about the pace in which they felt the time was moving. The answers were simple, ranging from ‘very slow’ to ‘very fast’, however, a clear pattern emerged: older people tended to perceive time as moving faster. In fact, they found that older people recalled that their lives passed slowly during childhood and teenage years and then speed up throughout adulthood.

The information used for this reasoning comes from a variety of sources; however, this explanation covers the general thoughts of philosophers, psychologists, and professors from around the world.

1: in this context information applies to new experiences, education and the memories that were created.

Independent variable

In this experiment, the independent variable is age, in this case, the age of the participants. The age range of participants for this experiment is 10-90 years old. I will record the ages of the participants by subtracting their year of birth with the current year, 2020; the year of birth is one of the questions the participants can answer before they complete the test. The independent variable will change based on the year the participants answer the question with. I am not changing the independent variable myself as it is based on information from the participants, thus I cannot ensure that the changes are all valid throughout the experiment. However, I can try to convince people in the age groups where I am lacking numbers to do the test to ensure an even spread of data.

To get accurate results, I need a minimum of 50 participants to take part in my test. I have chosen 50 participants as my minimum sufficient amount because for each participant there are 3 sections to the test, which means that if 50 people take part I would get 150 results, which should be sufficient data to accurately extrapolate from. This is enough data to draw sufficient and relevant conclusions because it will show me what the overall trend would be. However, the test will be open, meaning that more than 50 participants can do the test. This is because while I have a minimum number of participants I need before I can conclude, I have no maximum. This means that the more people who do my test, the better. Additionally, I think that if I had fewer than 50 participants, I would not be able to get a general view of the overall trend, as I would not have enough data to conclude from.

Dependent variable

The dependent variable will be the results of the test, which can be found in my database. These results measure how long it takes for participants to press the stop button once the start button had been clicked. The results will be in milliseconds; if the numbers are negative it means the participant was too fast, and if the numbers are positive the participant was too slow. Per participant, I expect to get 3 different results, one from each of the tests. If my minimum number of participants is reached, I should have at least 150 results.

See figure 1: diagram of the test

Figure 1: Diagram of the test:

Control variables

In this experiment, I must keep the following variables the same: the intervals between consecutive number, the target numbers and the numbers that appear on the test (1, 7 and 15).

I need to keep the intervals between the numbers constant as that is what the participant will be trying to infer: how long the intervals are. If the intervals are not the same, it would mean that I would not be able to compare the different results, as they would be based on different time intervals and would not be compatible. I avoided using a  1 second interval as participants could easily count when the target number would appear, rather than estimating based on the previous timings. I will keep the intervals the same by coding them to be the same. Because I am coding the test myself, I have full control over how long each interval can be; they will be 750 milliseconds per sequential number change.

The target numbers are a control variable so need to be kept the same for each of their designated tests (test 1 should always have a target number of 32, test 2 should always have a target number of 45 and test 3 should always have a target number of 26). This is because these are the numbers participants are aiming to reach, so if participants have different target numbers then they will be aiming for a longer or shorter duration. This means that I will not be able to compare the results, as it will not be the age that is affecting the dependent variables but the target numbers. I will keep these numbers the same by ensuring that my coding is the same for each of the participants. Because I am coding the test by myself, I can make ensure that the target numbers stay the same for each of the participants. 

The final control variable is the guide numbers that appear on the screen which are intended to help guage how much time is passing.  The guide numbers are fixed at 1, 7 and 15 and are kept the same for all the tests. I decided to include guide numbers as they help prompt the participant as to which number they should be at by a certain point. By fixing them, they will not interfere with the dependent variable. They can be controlled by ensuring that they are all the same in my coding. This is easy to do as I will be coding the test and will, therefore, ensure that they are kept the same.

Method

Making the test

The easiest way for me to reach a large audience and to accurately record the results is to make a website for the test. To do this, I need to first buy a domain using domain.com. With this domain I need to create the homepage, using a visual editor, with the following sections included: a short introduction, instructions on how the test works, a link to the test, a disclaimer, an explanation on how I will use the data and a short biography. After that, I need to start coding the test on a new page within the domain. I will code the test using WordPress and JavaScript, which I will need to learn as I have not used them before.

When designing the experiment, I need to first choose the target numbers and incrementing numbers that will appear on the test. Then, I will code the start button, ensuring that once it is clicked a timer starts. After that, I will code the stop button so that it replaces the start button, and when it is clicked the timer stops. The stop button also needs to lead to the next test when clicked. Once the start and stop buttons have been coded, I can move onto the numbers. To do this, I will need to create an array of numbers (in image form) which will show up on the screen. The period between numbers will increase as the test goes on. After that, I will set the time interval in which the numbers will increase by one (the time it takes from 7 to go to 8, for example)

Beta testing

To begin with, I will send the website link to my beta testing group, which will be my family and supervisor. Once I have collected, assessed and implemented their responses, I will then make the site live and distribute the domain address to as many people as possible to obtain as many results as possible. I will send the domain address via social media, word of mouth and by email. I will wait 3 weeks before I start to analyse the data, to ensure that I get as many responses as possible. I chose 3 weeks because that was the maximum amount of time I could allow in my schedule. 

Collecting the results

The results will be stored directly within my database, under the headings: reference number, gender, age, time 1, time 2, time 3 and time stamp. The information will be stored in a table format, which will allow me to analyse the data and place it into a variety of different graphs to visualise it. I chose to use a database because it will allow me to easily display and sort the raw data and display it in a graph. The drawback of using the database is that as I do not know how to link the database to the website, someone else had to do it for me. This means that I will not be able to change the categories once they are set.

Repeating the test

I would prefer it if participants would not complete the test more than once, as they would have gotten used to the intervals from the last time they did the test and their results would be biased. However, I cannot control this as participants can click on the link as many times as they want and the test is anonymous, in line with the IB ethical guidelines.

Safety

In this experiment, although no personal information is taken, the website is susceptible to hacking as it is not secure. To rectify this, I purchased a SSL certificate which would make the connection to my website secure.

Furthermore, to ensure that all participants are happy and safe I will keep to the IB ethical guidelines. The main guidelines that I will be following are: children under the age of 12 cannot complete the test, children from the ages of 12 and 16 must ask a parent or guardian for consent, participants are not obliged to finish the test and all the data must be kept anonymous. Additionally, all data must be deleted on completion of my project (November 2020). To follow these guidelines, I will place a disclaimer below the test to inform people of what they can expect from the test.

Results

The results of the experiment are shown in figures 2 and 3 below. As shown in figure 3, the fitted linear trendline shows a steady decline in time scores (a negative gradient) when compared to age. Furthermore, the trendline in figure 3 follows the equation y= -16.772x + 885.26 and is linear as it follows a straight line. Figure 3 also shows that participants in the age range of 12 to 55 were slower on average, whilst participants in the age range of 56 to 82 tended to be faster. From figure 2 I can see that most results are positive. All the data from figures 2 and 3 are quantitative.

Figure 2: Table of average results

Age range (years)Mid Age (years)Average prediction error in ms (tests 1,2,3)Frequency of participants  
10-14121526.331
15-19171758.008
20-2422-1349.003
25-2927-1260.338
30-34321179.676
35-3937651.3317
40-4442331.678
45-49471568.6717
50-5452-341.0015
55-5957-2189.001
60-6462269.333
65-69670.000
70-7472-105.005
75-7977321.675
80-8482-908.006

Figure 3: Graph of the results (positive numbers indicate participants were too fast, whilst negative numbers indicate they were too slow)

Discussion

In this experiment, we are measuring the perception of time for people aged 11 to 82. To do this, I am measuring when people predicted a target number to appear in a sequence. Overall, I got 220 participants to do my test, meaning I got 660 results. However, I only analysed 103 participants as the rest were invalid because they were outliers. Nevertheless, this was enough data to make a valid trendline, as it was over my minimum target number of participants.

The results of this experiment were predicted to show that children and young adults (12-25) would have a slower perception of time compared to adults (26-90). In a graph this would be presented as a steady and linear trendline, declining in time to age. This was predicted to happen because the perception of time can be dictated based on the amount of information taken in, and it has been shown that adults take in less information in on average compared to children and young adults. Based on this, we can predict that children and young adults will have a slower perception of time because they are taking in more information compared to adults.

The main reason why older people are the faster their perception of time is linked to the amount of ‘new information’ they take in. In this case, information refers to the instructions of how to do the test, the timing of the sequence and anything that was happening the background. The amount of ‘new information’ being taken in will affect the perception of time because the brain subconsciously thinks that more ‘new information’ must equal a longer interval. In the case of this experiment, the complexity does not play a role, as even though the test had 3 different sections, each of the sections had the same complexity.

This theory is supported by my data as the results from figures 1 and 2 show a split between people aged 12 to 55 and people aged 56 to 82. This indicates that participants from the ages of 12 to 55 demonstrated a slower perception of time compared to participants aged 56 to 82. Overall, this shows that younger participants had a slower perception of time (they pressed stop slower than they should have) compared to older participants.

All the data I received from this experiment is shown in figures 1 and 2 and is quantitative. On figure 2, the x-axis represents the independent variable, which was the age of the participants. The y-axis represents the results of the participants. These results are how far off they were from the correct time; 0 would mean the participant pressed stop at the correct time, -1000 means they were 1 second too slow etc. The results on the y-axis are all in milliseconds. I was not able to collect any qualitative data for this experiment, as I was not able to do the test in person, due to COIVD regulations. However, if I were to collect qualitative data, I would have collected data on how concentred they were on the test. This would have been useful because it would have given evidence to support the theory that the more concentrated on the test participants were, the slower their perception of time would be. Despite this, I think that the quantitative data is enough to draw conclusions from my experiment. This is because all the data showing the changes in perception of time compared to age (the aim of my experiment) is quantitative as it is shown through the results of the test.

In this experiment, there were some outliers that I ignored in my results. These results were any 0’s in the age category and any 9999’s in the results. If an age was given as 0, this meant that the participant had not filled out the age category. Because age was the independent variable in my experiment the results were not valid if I did not have this information. This was why I removed any results where the age was 0 before generating figures 2 and 3. If  the predicted error equalled 9999ms then I also removed these values as this is the default time I set the interval to be when coding the experiment. That is, if the results were at the default time, I assume that the participant did not finish the section (they did not press the stop button) and as such is invalid. By removing these outliers, I will stop them from being included in my final analysis, and thus stop them from unfairly affecting my trendline.