What Is the Effect of Age (15–20) on the ′Reaction Time′ in the Form of Responding to a Color Changing?

Abstract

This research investigates the age change reaction, particularly for the 15-20 age group of adolescents and young adults. The experiment involves participants using a smartphone application called “Reaction Speed” to respond to the alteration of color from red to green by pressing the button as fast as possible when it changes. Haptic feedback response is measured in milliseconds to look at this population’s cognitive processing and motor skills. The findings are based on prior works that have already produced detailed insights into reactivity factors in many other situations, such as social communication, food security, and environmental precautions. This research aims to see how an individual’s reaction time differs depending on age and conclude about developmental stages, cognitive abilities, and adolescents’ and young adults positions of them. Previous studies discovered that perception speed has significant importance during social communication. The researchers Alhaddad, Cabibihan, & Bonarini investigated the validity of the emotional response shown by an agent robot when faced with aggressive action from a child. Research revealed that rapid responses are needed in social situations where one’s fast reaction can significantly affect what will come after the interaction.

Moreover, Haghani, Sarvi, & Shahhoseini (2020) in their research, looked at the evacuation behavior choice of crowds considering different degrees of urgency field, giving special attention to response time, exit choice, and adaptation of exit choice. According to the investigation, no one’s reaction time is zero, so a rapid decision and act can be life-saving. On the other hand, we must remember that a tremendous reaction time is a primary factor in applying environmental issues. Shokrani et al. (2020) emphasized sonophotocatalytic wastewater treatment, considering the time variables as a modeling input. That study proved that the reaction time determines how fast the treatment process can be completed, thus highlighting the criticality of reaction time in delivering environmental sustainability. In food safety, Gao et al. reported that time-temperature indicators can be used to observe the quality of fresh food. The project stressed the importance of prompt measures aimed at preventing food spoilage and, as a result, guaranteeing food safety. For instance, the study of reaction time does not limit itself to sight but covers multisensory contexts. Shaw et al. (2020) upheld multisensory reaction time facilitation alongside inter-sensory task-switching effects by embarking on the complexities of reactions to different sensory environments. In short, the research highlights how the speed of reaction changes depending on the age experienced among the subjects in the adolescent and young adult decades. The fact that we make these findings hints at the things that trainers can do in educational settings, sports, and the kind of activities that are meant for cognitive-motor responses in this age group.

Introduction

The reaction time or the interval that goes from a stimulus and the response it triggers is a critical issue in the study of processes of the cognitive skills of motors. The discovery of reaction time fluctuations depending on a particular age stage, particularly that of 15-20 years with adolescents and young adults, can provide a clear picture of developmental stages and one’s cognitive functions. In this study, we will look into the persuasive nature of the aging process on reaction time, specifically in an app called “Reaction Speed,” which consists of changing the color of the background from red to green. Participants must click the screen on their phone as fast as possible after detecting the color change. The time will be measured in milliseconds as the reaction time.

Research conducted a while back has enumerated the criticality of reaction time in different scenarios. Arodin, Cabibihan, & Bonarini (2020) performed experiments about the extent to which the reaction time of a partner robot influences the emotional response of a child to their aggressive interaction, making accent the fact that fast reactions are significant in social intercourse. As Asgari et al. (2020) showed, there is an application breakthrough of response surface methodology and artificial neural networks in modeling sonophotocatalytic treatment of wastewater, manifesting the importance of treatment time in environmental uses. For example, Gao et al. (2020) have researched the likelihood of keeping fresh foods safe using time-temperature indicators to gauge their safety. The significance of timely reaction has also been emphasized in the process. Furthermore, in their work, Shaw et al. (2020) showed a link between multisensory reaction time facilitation and changes in attention from one sense to another, indicating the vast exploring potential for reaction time cognitive tasks in science.

The diversity of effects of age on reaction time is quite intriguing as they can shed light on changes in cognition and motor skills development within adolescence and early adulthood. National studies on drivers’ reaction times during distracted driving have implicated that awareness of such changes affects different learning aspects in education, sports training, and various occupations. By limiting the age range to the 15-20 sphere, the present study will capture the critical period of cognitive development and will further figure out how reaction time moves during this significant interval. The involvement of a smartphone app for the estimated reaction time enhances the research with a contemporary aspect since smartphones are omnipresent among youngsters and adults, making the setup avenue very practicable and within the grasp of the participants’ understanding. Presenting research on the effect of aging on a teenager’s reaction time can provide a wealth of knowledge on the development of cognitive and motor skills during adolescence and early adulthood. Smartphone-based applications were chosen for data collection in the study to facilitate the participation and engagement of the participants in the research while building the bigger narrative of how individuals influence an individual reaction. The examples given us are a piecemeal picture of how reaction time research is used in many areas. It shows its relevance based on the branches it embraces and its possible applications in understanding human behavior.

Research question

What influence does being between the ages of 15 and 20 have on “reaction time” when responding to a color shifting from red to green in a smartphone app called “Reaction Speed”? This program measures “reaction speed” as the speed at which the smartphone screen clicks, measured in milliseconds.

Independent Variables

The study looks at whether age is the main factor (independent variable) leading to different academic achievement levels between students aged 15 and 20. Of all the things to be measured, the reactive time of participants, which is assumed to depend on population variables, is among the most critical aspects. By selecting participants during this age range, the study intends to investigate how reaction time varies among adolescents and young adults, which can help highlight cognitive developmental aspects of processing and motor skills. The period might be the critical time of transition from adolescence to adulthood. The decision to use this particular age bracket ensures a specific study to unearth the age reactions on the rate, hoping that we could discover any patterns or gaps if they could be found.

Dependent Variables

In this study, the dependent variable is the participants’ reaction time measured in mill-seconds as they go through the red to-green color change in the smartphone “Reaction Speed” app. Response Time is a critical marker of cognitive performance due to several different determinants, among them age, which was indicated in the Shaw et al. work (2020). The emphasis of this research on the reaction time (the reaction time of the participants aged 15-20 years will be the dependent variable) will offer an insight into how fast teenagers can process and act to visual stimuli. The measure of reaction time looks into the quality of something at the cognitive process level, giving the shorter reaction time. This is representative of the more efficient neural processing that, unfortunately, executes the appropriate response. The application of a mobile app to determine the reaction time prolongs a modern touch and practical direction to the study, the execution of computerized and accurate measurements of data. The second variable is reliant on determining if members of varying age groups within the defined range show any difference in their reaction among themselves. The knowledge of the difference in the time response of the age can result in the diversification of the issues, which in turn can affect multiple areas such as education and sports, wherein instant decision-making and physical reaction are crucial. By judging the reaction times as the dependent variable, this study intends to help develop and improve a general field of cognitive development and performance among students and the young generation.

Controlled Variables

At first, the environment in the experiment was controlled to reduce distractions and allow the participants to express themselves freely without any external influence on their vision time (Elkelawy et al., 2020). The researcher had to ensure that this consisted of no notifications/other stimuli that would distract the participants during the experiments. Furthermore, activating the “do not disturb” smartphone mode disconnected the phones and prevented other communication networks, like text messages, from disrupting the study. Second, the instructions given to the participants were the same and were uniform for everyone who took part to ensure that they understood the task and performed it in the same manner. This also came to work because it diminished variance in how participants took the test, thus making the differences in the reaction times to be attributed to the age and not to people’s differences in understanding or performance. Among the many connotations associated with the effect of smartphone use on reaction speed tests, the performance of a well-functioning smartphone with a fast internet connection is one of the most critical. Its main goal was to ensure that the app “Reaction Speed” worked well and that the stimulus was consistently presented to study subjects. Besides, the subjects were prompted to click the smartphone screen as they proceeded using their dominant hand, which helped unify the motor response of each one of them. Conclusively, these variables helped make clear that any variations in reaction times were caused by the participants irrespective of the external factors.

Uncontrolled Variables

Because numerous types of manipulations were made to manage the experiment, there may have been some uncontrolled variables that were responsible for affecting the results (Bibi Sadeer et al.- 2020). However, there are several factors that are considered as variables in such cases of the experiment. These factors include the participants’ individual differences in cognitive abilities and motor skills. Although the age of 15-20 was solely considered for selection purposes, the participants, including the youngest and the oldest, may still possess varying physical abilities, which is expected to affect the reaction times. Factors of attention control, response speed, and making coordinated movements could be dissimilar in participants and lead to inconclusive findings.

Furthermore, the subjects’ knowledge of smartphones and their technology experience overall influenced their performance. Those who were used to using more smartphones definitely had an advantage in the sense of reaction time than those who were less in touch with the idea of using smartphones. In addition, the fatigue degree of participants could have affected their reaction times. Varied states of mind and tiredness throughout the process involved different speeds of reaction time for participants in comparison to those who were more alert and focused. The remaining factor that needs to be in control is whether the participants are motivated or participate in the task. The participants who were more motivated or interested in the accuracy of the responses tended to expend more effort, thus yielding faster reaction times, as opposed to some who were less motivated or interested in these tasks being more lax. This highlights the fact that these unregulated variables are overtly how hard it is to pinpoint the actual causal factors in a particular reaction time as there could be several influencing factors, such as contextual or individual differences.

Apparatus

Materials and equipment needed:

1. Downloaded app “Reaction Speed”
2. Plain Paper for writing the results
3. Pen for reporting the results
4. 22 participants aged between 15 and 20 years
5. A smartphone that is well-functioning and has a fast internet connection

Methodology

1. The “Reaction in Research” participants were carefully selected, with particular attention paid to their age range of 15-20 years, which serves as the investigation’s independent variable.
2. A consent form and a paper attesting to the experiment’s “controlled” variables were signed by each participant.
3. Before participating in the experiment, each participant received a detailed explanation of how the “Reaction Speed” app operates. When the smartphone’s color changed from red to green, the participants were told to use their dominant hand to click the screen. They had five trials to complete.
4. Every effort was made to ensure that the participant in the experiment was not distracted by any notice. To verify that no outside elements would interrupt the course of the research and ultimately influence the result, the “do not disturb” mode was turned on, and the battery % status and internet connection were checked before conducting the research.
5. To become acquainted with how the app functions, the participant began the control trial with the “Reaction Speed” app without interruptions.
6. The individual launched the “Reaction Speed” application on their mobile device.
7. The smartphone displayed the standard blue screen with the message “Tap to start.”
8. A red screen with the words “wait for green” emerged after the participant tapped the screen.
9. The red screen on the smartphone became green. The participant tapped the screen of their smartphone as fast as they could as soon as they detected the color shift from red to green.
10. Following the participant’s click on the smartphone’s screen, a blue screen displaying the score—which is the millisecond-based reaction time to the color change—appears.
11. The participants wrote the outcomes down on a sheet of paper.
12. The participant used the “Reaction Speed” software for five legitimate trials, ensuring all the variables were under control. For the five trials, the same procedures as previously outlined were followed.
13. Every trial’s reaction time was meticulously noted and entered into a table.
14. The Microsoft Excel software was used to process and compute the recorded data.

Figure: showing the experiment steps

Table 1: Each participant’s reaction time as a response to a visual stimulus in color changed from red to green.

Graph 1- The distribution of the average reaction times of specific participants measured in milliseconds.

Table: The mean reaction time to the change of color from red to green in the app “Reaction Speed” for all participants and the standard deviation of their results measured in milliseconds.

The results are less scattered when the minimum, first quartile, median, third quartile, and maximum values are included. This indicates that there was less variation in the reaction time results. Their interquartile range (Q3 – Q1) is 41.75 ms.

Based on the data, it is evident that when participants had to wait longer for the color to change, they reacted more quickly and got better results. This suggests that the participants replied more quickly when the app changed the color from red to green later in the trial, exposing them to a longer wait and giving them more time to concentrate on the job.

There was a tendency for faster reaction times among those who said the task was simple. They acknowledged using their smartphone regularly as well. This could be caused by the fact that the myelin sheath around the nerves engaged in a given movement thickens with each repetition. Better isolation and a more vital impulse between fibers result from this.

Discussion

The experiment revealed that age was the determinant factor of reaction time, and younger participants (aged 15 to 20) got better reactions than older participants (Alizadeh-Sani et al., 2021). This result is in line with the previous studies, which established age-related changes in reaction time that improve with age (from early childhood years to post-puberty) and are delayed during adulthood. The mean reaction time of the 15-20 age groupers was 298.0 milliseconds; the SD is 39.54, and the relatively minor standard deviation indicates a narrow range of reaction times within this age group. This difference in reaction speeds between young and older participants probably rests on intricate factors. One likely reason, though, is the developmental process that starts in adolescence and ends in young adulthood. During that time, the cognitive and motor skills are improved. With age, the brain’s structure and functioning undergo modifications, which may cause the individual’s decline in response and processing speed. Furthermore, younger participants may have been more familiar with the formation of hand-held smartphones, which contributed to their better reaction time. Nevertheless, we still need to know how to see the exacforraging-relying-relying on the brain response time modulation effectively.

The results of this study are significant for establishing a link between cognition and performance. Still, they also make a difference by adding a new or different perspective to cognitive development theory. A study finally uncovers that reaction time is a time-dependent component of older adults, the face of their developing cognitive processing ability and motor skills (Haghani et al., 2020). This study shows the importance of age and emphasizes the need for researchers to consider age as a dimension when examining reaction time and other cognitive abilities. Another line of future research is to focus on other variables affecting aging people’s reaction times. Factors like fitness, quantity, quality of sleep, and environment could be explored to understand reaction times across ages further. Also, different sorts of data analyses, like ANOVA or regression analysis, could be used to clarify the connection between age and reaction time and spot any potential mediators or moderators of the relationship. Finally, in conclusion, a review of results shifts to level up the knowledge about how older adults’ reaction time changes and contributes to the mental and physical processes at the end of maturity and the beginning of adulthood.

Conclusion

In conclusion, the article was set to identify the impact of adult age on the reaction time of the participants aged 15-20 using the “Reaction Speed” smartphone app. Reaction time depended on age; this finding was significant and was discovered across the specified time frame. The task which examined whether younger participants tended to be faster than older was most promising. This indicated that reaction speeds for younger individuals were higher than those of older ones during youth and early adulthood. These outcomes are also close to those provided by the precedent science about age-related changes in brain and body functioning body. The application of the smartphone app for measuring reaction time offered the study practicality and a feasible means of data collecting. Thus making the data collection process more engaging and relevant for the participants.

On the other hand, it should be acknowledged that the study had several drawbacks, including the small sample size and the possibility of the influence of uncontrolled variables like individual differences in cognitive abilities and level of motivation, as described by Instructor Wolfe (2021). While the research has its share of boundaries, it successfully contributes to comprehending diversity in reaction time as an age factor. It draws our attention to the significance of the uniqueness of age-associated cognitive and motor disorders. The potential of other factors leading to varying reaction times of senior and younger individuals should be investigated in the next round of research. Among them are physical fitness, restfulness, and conditions of the surroundings. After all, the study is very informative as it addresses many developmental issues in motor skills and points out that age is one of the most crucial factors in cognition and motor performance.

Strengths of the method

Practical worth is one of the essential strengths of this study’s method. This is accomplished by utilizing the smartphone app for measuring reaction time to involve participants in a familiar and convenient setting. This inadvertently improves the participants’ experience and reduces dropout risks (Haghani et al., 2020). Using a smartphone app, we stripped out the imprecision and allowed for data to be collected automatically, thus excluding human error and increasing the accuracy of the results. Moreover, the application used the same prompt (a color change has happened from red to green) and reaction (a tap on the screen) in every trial and gestures for all the participants, hence ensuring consistency between all the trials and participants. There are two strengths: its easy and straightforward implementation and its application ability. The directions to the experiment were simple and undemanding; thus, the participants got an idea of the task clearly and did it conveniently. Simplicity and the reduced probability of confounding variables served due to the visual integrity of the presented stimulus were maintained. To sum up, this research approach offered a chance to find out reaction time in a way that was standardized and precise, hence allowing a lot of information on older age effects on cognitive skills and motor functions to be found out.

Improvements in the method

The procedure applied to this study has limitations, unlike how it could be implemented to transform the authenticity and reliability of the outcomes effectively. To enhance this, the participants should be of mixed ethnic backgrounds, and the sample should also represent that background. The study confined its scope to participants aged 15 to 20; nonetheless, future research investigating subjects from a broader age range is warranted to gauge the gradual variation of psychomotor speed across the lifespan. Moreover, enlarging the sample size by including individuals of different aspects, such as culture and socio-economic status, can make the results applicable to a larger populace. A further adjustment might be experimenting in an ordinary environment with sources of error controlled. Although the experiment was carefully arranged to minimize the implications of the distractions, there may still have been some external factors till the time we account, which could have influenced the results (Bibi Sadeer et al., 2020). To tackle this, tomorrow’s researchers must experiment in an enclosed environment, experiencing tight control over any background light and sound. Also, managing the task using a uniform protocol for giving instructions and recording the results can decrease the variability and make the findings more reliable.

Furthermore, a more comprehensive measure of cognitive function will suffice to power the improvement. Although speed reaction is an essential characteristic of cognitive processing, it gives only a glimpse into global cognitive functioning. Future studies choosing task batteries, which allow for a more detailed assessment of multiple cognitive domains like memory, attention, and executive function, could be promising. This would let us see whether everyone deteriorates in the same way, and we could see which cognitive processes are the first to be impacted. Consequently, employing a better scale, like a computer-assisted task, provides exact reaction time data, and would add new knowledge to cognitive aging research. The method applied in the present study is indeed helpful in comprehending the impact of age on reaction time; however, it also opens the possible room for improvement to gain more accurate and legitimate results in the future. It can be achieved through examining the current obstacles and making suggestions to overcome them. Hence, future studies would give a complete picture of how reaction time changes in the human aging process and the factors that influence it, like age, cognitive functions, and environmental variables.

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