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Introduction
Note. N = 353, 257 (72.80%) male, 96 (27.20%) female. Participants who made unusually high number of mistakes were excluded. Average age of the participants is 31.51 (SD= 13.56). The ‘younger’ and ‘older’ groups were formed by splitting participants into those who are under 30 (55%) and those who are older (45%). Lat.: Latency, Congru: Congruent, Incongru: Incongruent. Latency: Milliseconds taken from when the word or square flashes on the screen to when a response is provided. Correct: The percentage of right responses. Congruent condition: Word colour matches with the meaning of this word, Incongruent condition: word colour does not match with the meaning of the word, Control condition: a coloured square without a word.
Findings
It is possible to note that the findings of the present study are quite consistent with other similar studies. Thus, Van der Elst, Van Boxtel, Van Breukelen & Jolles (2006) note that age and gender have an impact on Stroop activity. Likewise, Moering, Schinka, Mortimer & Graves (2004) stress that there is a strong correlation between performance in Stroop activity and age as well as gender and education. Numerous researchers focus on the impact education and training has on performance in Stroop activity. Meijer et al. (2009) stress that education is strongly correlated with Stroop activity performance. It is found that higher education is “an age-independent predictor” of higher performance in the Stroop activity (Noble, Korgaonkar, Grieve & Brickman, 2013, p. 653). Findings of Dos Santos, Tudesco, Caboclo and Yacubian (2011) support this viewpoint and the researchers stress that lower level of education often leads to faster brain decline.
At the same time, the present study shows that gender does not significantly affect brain function though the influence is still apparent. Some researchers are reluctant to admit the strong tie between such demographic variables as age, gender and education. Campanholo et al. (2014) state that age; gender or education does not affect Stroop activity performance. Zarghi et al. (2012) support this viewpoint and note that these variables do not influence on attention.
Therefore, though there are studies suggesting that the correlation between the variables mentioned above are insignificant, most researchers agree that age, gender and education affect Stroop activity performance.
Limitations
The major focus of the present research is the impact of age and gender on the Stroop Activity performance. This can be regarded as one of the strengths of the research as it concentrates on the most debatable aspect of the issue (since education has proved to affect people’s performance). Nonetheless, there are a number of limitations. First, the average age of the participants was 31.51 years and they note that there were two groups (those who at the age between 18 and 30 and those who are older). However, the researchers do not reveal the age of older participants. It may seem they are around thirty or forty which is quite insufficient for the research concentrating on the way age affects brain functioning.
Moreover, the percentage of male participants is far too low to ensure relevance of the research, as gender is one of major variables. The number of men and women participating in such research should be approximately equal. The present research cannot be regarded as a profound and comprehensive or relevant enough as there can be certain bias.
In future studies, it is crucial to make sure that the age of participants is highlighted. It can be effective to follow the example of Van der Elst et al. (2006) as the researchers revealed performance of participants according to their age. Admittedly, it is essential to make sure the number of male and female participants is (at least approximately) equal. The number of participants can also be increased.
Real World Implication
The present research can have a number of implications. Clearly, it can be the first step in further and deeper research of the exact effects (e.g. at what age impairments appear, to what extent gender affects brain functioning and so on). One of the real world implications can be development of specific training programs which can slow down the process of brain aging. Zomanczuk et al. (2006) claim age correlates with Stroop activity performance and report about positive effects of training among elderly people. Researchers agree that education often slows down brain aging (Moering et al, 2004; Santos et al., 2011). It is necessary to develop a program including training and raising awareness activities. Stroop Activity test (as well as other similar tests) can be used to train young and older people’s attention.
At the same time, it is crucial to make people aware of the necessity to train their brain at different periods of their life. In schools, students have to do Stroop Activity tests (and similar tests) monthly. They should also be exposed to results of similar researches which show that training and education enables people live fuller lives when they age. However, adults should also take part in this process and it can be effective to work with employers or HR professionals who could involve adults in this incentive. Adults should do tests and understand the importance of self-development. Elderly people should also be involved and their caregivers should encourage them to do tests and training courses.
Reference
Campanholo, K.R., Romão, M.A., Machado, M.A.D., Serrao, V.T., Coutinho, D.G.C., Benute, G.R.G., Miotto, E.C., & de Lucia, M.C.S. (2014). Performance of an adult Brazilian sample on the Trail Making Test and Stroop Test. Dement Neuropsychol, 8(1), 26‐31. Web.
Dos Santos, E.B., Tudesco, E.S.S., Caboclo, L.O.S.F., & Yacubian, E.M.T. (2011). Low educational level effects on the performance of healthy adults on a Neuropsychological Protocol suggested by the Commission on Neuropsychology of the Liga Brasiliaera de Epilepsia.Arq Neuropsiquiatr, 69(5), 778‐784. Web.
Meijer, W.A., van Boxtel, M.P.J., Van Gerven, P.W.M., van Hooren, S.A.H., & Jolles, J. (2009) Interaction effects of education and health status on cognitive change: A 6-year follow-up of the Maastricht Aging Study, Aging & Mental Health, 13(4), 521-529. Web.
Moering, R.G., Schinka, J.A., Mortimer, J.A., & Graves, A.B. (2004). Normative data for elderly African Americans for the Stroop Color and Word Test. Archives of Clinical Neuropsychology, 19(1), 61‐71. Web.
Noble, K.G., Korgaonkar, M.S., Grieve, S.M., & Brickman, A.M. (2013). Higher education is an age-independent predictor of white matter integrity and cognitive control in late adolescence.Developmental Science, 16(5), 653-654. Web.
Van der Elst, W., Van Boxtel, M.P.J., Van Breukelen, G.J.P., & Jolles, J. (2006). The Stroop Color‐Word Test: Influence of age, sex, and education; and normative data for a large sample across the adult age range. Assessment, 13(1), 62‐79. Web.
Zarghi, A., Tehranidost, M., Ashrafi, F., Zarindast, M., Moazzezi, M., & Khodadadi, S. (2012). Assessment of selective attention with CSCWT (Computerized Stroop Color-Word Test) among children and adults.US-China Education Review, 1(1), 121‐127. Web.
Zomanczuk, P., Milczarek, B., Dmitruk, K., Sikorski, W., Adamczyk, W., Zegarski, T., Tafil-Klawe, M., Chêsy, G., Klawe, J.J., & Rakowski, A. (2006). Improvement in the face/name association performance after three months of physical training in elderly women.Journal of Psychology and Pharmacology, 57(4), 417‐424. Web.
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