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Does Hemispheric asymmetry decrease with age?

Table of Contents

Abstract 2 Hemispheric asymmetry

Introduction. 3

Aims and Objectives. 3

Hypothesis: 4

Method. 4

Results. 4

Discussion. 5

Conclusion. 6

References. 7


Hemispheric asymmetry has a significant relationship with age in such that it decreases with the increase in age. The present studied conducted continuous cognition experiment using visual memory. Pearson correlation r=-0.52, p<0.05 showed that there was a significant relationship between age and hemispheric asymmetry such that asymmetry decreases with age.


The human brain consists of two hemispheres, left and right. These two hemispheres are different in terms of performing various functions, size, shape, and volume a phenomenon known as hemispheric asymmetry (Ocklenburg & Güntürkün, 2012). Similarly, humans’ left and right hemispheres are not equally capable of handling all types of visual cognitive processing. There are four possible statistical values whose measurement can give us the idea of hemispheric asymmetry for visual systems. These are phase lag index (PLI), mean phase coherence (MPC), conventional coherence function (CCF), cross wavelet correlation (CWC) (Aydın et al., 2018). The PLI is a measure of the asymmetry of the distribution of phase differences between two signals (Stam et al., 2007). MPC is the measurement of synchronization of the left and right hemispheres while processing visual information (Raeisi et al., 2020). CCF is the measurement of the general relationship between the two signals originating from the left and right hemispheres in the presence of some other signal (He & Fu, 2001). Similarly, CWC is capable of measuring phase covariance between two distinct brain oscillations with high time-frequency resolution.

Apart from the above mentioned method, the divided-visual-field task. This this methods, the participants are exposed to a visual stimuli in the form of images to the right visual field (RVF) as well as left visual field (LVF). The result is measured using the percentage of correctly guessed images.

Previously, A study by De Bellis et al., (2000) reports that the pattern of left-sided dominance in the representation of language common in children and young adults is not seen among aging people. The result of the findings is in line with the general assumption by the Hemispheric Asymmetry Reduction in Older Adults (HAROLD) model that hemispheric asymmetry is biologically related to aging in that, aging decreases hemispheric asymmetry (Cabeza, 2002). The model suggests that age reduces functional hemispheric lateralization in the left region of the brain by initiating neurofunctional changes in the prefrontal cortex.

The present study will analyze whether hemispheric asymmetry decreases with age using a
visual memory experiment for each visual field. On the contrary, the current study will differ from the initial tests because it will use a visual recognition experiment with specialized fixations area for the participants.

Aims and Objectives

  • To carry out continuous recognition experiment in order estimate whether there is a significant relationship between hemispheric asymmetry and age or not.
  • To know whether hemispheric asymmetry decreases or increases with age.

Null Hypothesis:

  1. The hemisphere asymmetry does not decrease with age.
  2.  Older participants will guess less images correctly than the younger ones.

Alternate Hypothesis:

  1. The hemisphere symmetry decreases with age.
  2. Younger participants will guess more images correctly than the older.


The study included 124 participants, 72 men and 52 women. Everyone who took part was right-handed. The ages varied from 18 to 70 years, with a mean of 35 years and a standard deviation of 15.2. Continuous recognition was utilized in this experiment. On each attempt, the participants were shown a word and asked to spell it. The first eight trials all use ‘new’ terms that have never been seen before. Following that, the computer decides whether to display an old or new word at random. The word will be presented momentarily to the left of fixation on half of the trials, so if you concentrated on the fixation point, the word should be in your left visual field, which connects to your right hemisphere. The word was presented to the right of fixation on the subsequent trials, indicating the right visual field that relates to the left hemisphere.

Table 1: Participant information – Gender and Age

Participants Male Female Age
124 72 52 18-70 years


Table 2: Result of continuous cognition experiment. RVF-LVF in a percentage and their standard deviation

  Percentage Standard Deviation
Right Visual Field 85% 14.1
Left Visual Field 81% 13.5

Figure 1: The difference between Right Visual Field and Left Visual field indicates that asymmetry decreases with the increase in age


The main outcome of the experiment is the percentage (%) of correctly identified words as old or new for each visual field. For the right visual field the mean was 85%, with a standard deviation of 14.1 for the left visual field the mean was 81% with a standard deviation of 13.5 as can be observed in Table 2.

Hemispheric asymmetry has been measured by the estimating the difference between the percentages correct for the RVF and LVF for each individual. The smaller the value was, the less hemispheric asymmetry each participant showed. As indicated by various researches previously (Aydın et al., 2018; Cabeza, 2002; De Bellis et al., 2000; Ocklenburg & Güntürkün, 2012), this study also confirms that the hemispheric asymmetry does decrease with the age.

A Pearson correlation showed a significant negative correlation between age and hemispheric
asymmetry, r=-0.52, p<0.05. This means there was a significant relationship between age and
hemispheric asymmetry such that older participants were likely to have lower asymmetry.

Hence, the null hypothesis that asymmetry does not decrease with age and older participants will guess more images correctly than the young ones can be rejected. On the other hand, the alternate hypothesis can be accepted that there exists a significant relationship between age and hemispheric asymmetry as indicated by Pearson correlation.


As per the study’s findings, hemispheric asymmetry decreases with age. Therefore, age has a
negative impact on hemispheric asymmetry. Older adult has reduced hemispheric asymmetry in
comparison with young adults. The results from the current study concur with other findings from different scholars who also proved a negative correlation between age and hemispheric asymmetry.



Aydın, S., Demirtaş, S., Tunga, M. A., & Ateş, K. (2018). Comparison of hemispheric asymmetry measurements for emotional recordings from controls. Neural Computing and Applications, 30(4), 1341–1351. https://doi.org/10.1007/s00521-017-3006-8

Cabeza, R. (2002). Hemispheric asymmetry reduction in older adults: The HAROLD model. Psychology and Aging, 17(1), 85–100. https://doi.org/10.1037//0882-7974.17.1.85

De Bellis, M. D., Clark, D. B., Beers, S. R., Soloff, P. H., Boring, A. M., Hall, J., Kersh, A., & Keshavan, M. S. (2000). Hippocampal volume in adolescent-onset alcohol use disorders. American Journal of Psychiatry, 157(5), 737–744. https://doi.org/10.1176/appi.ajp.157.5.737

He, J., & Fu, Z.-F. (2001). Multi-input multi-output modal analysis methods. In Modal Analysis (pp. 198–223). Elsevier. https://doi.org/10.1016/b978-075065079-3/50010-3

Ocklenburg, S., & Güntürkün, O. (2012). Hemispheric asymmetries: The comparative view. In Frontiers in Psychology (Vol. 3, Issue JAN). Frontiers Media SA. https://doi.org/10.3389/fpsyg.2012.00005

Raeisi, K., Mohebbi, M., Khazaei, M., Seraji, M., & Yoonessi, A. (2020). Phase-synchrony evaluation of EEG signals for Multiple Sclerosis diagnosis based on bivariate empirical mode decomposition during a visual task. Computers in Biology and Medicine, 117, 103596. https://doi.org/10.1016/j.compbiomed.2019.103596

Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human Brain Mapping, 28(11), 1178–1193. https://doi.org/10.1002/hbm.20346

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