AGE AND EXPERIENCE BASED NEUROCOGNITIVE PERFORMANCE OF SEAFARERS

Year 2017, Volume: 12 Issue: 3, 168 - 177, 07.07.2017

Umut Taç , Leyla Tavacıoğlu , Pelin Bolat

Abstract

The main purpose of this study to assess the age-related cognitive decline rates and
cognitive performances of the seafarers who belongs the different experience
levels and age groups. ANAM4^R computerized cognitive test batteries
were applied to determine the cognitive competencies and performances of the
seafarers. Results were examined separately for each test using descriptive
tables, one-way ANOVA test and The Tukey post-hoc test. Significant differences
were found among groups in terms of cognitive abilities (p<.05). In all tests,
oceangoing masters have the lowest mean accuracy score and represent the
highest risk group for the possibility of human induced errors occurrence due
to cognitive impairment. A direct relationship between cognitive performance
decline and aging was detected in all cognitive tests.

Keywords

Cognition, Seafarers, Neurocognitive Tests, Maritime, Cognitive Abilities

References

• 1. Macrae, C., (2009). Human Factors at Sea: Common Patterns of Error in Groundings and Collisions. Maritime Policy & Management, 36(1), 21-38.
• 2. Chidambaram, P., (2016). Perspectives on Human Factors in a Shifting Operational Environment. Journal of Loss Prevention in the Process Industries, 44, 112-118.
• 3. Koester, T., (2001). Human Factors and Everyday Routine in the Maritime Work Domain. In Human Factors in Transportation, Communication, Health, and the Workplace. Human Factors and Ergonomics Society Europe Chapter Annual Meeting.
• 4. Wiegmann, D. and Shappell, S., (1999). Human Error and Crew Resource Management Failures in Naval Aviation Mishaps: A review of U.S. Naval Safety Center data, 1990-96. Aviation, Space, and Environmental Medicine, 70, pp:1147-51.
• 5. Tac, U., Tavacioglu L., Bolat, P., Kora, K., and Bolat, F., (2014). Monitoring Seafarers Cognitive Performance during a Voyage by Automated Neurological Assessment Metrics, Constanta Maritime University Annals, Vol:2, pp:299-311.
• 6. Burgoyne, A.P., Sala, G., Gobet, F., Macnamara, B.N., Campitelli, G., and Hambrick, D.Z., (2016). The Relationship Between Cognitive Ability and Chess Skill: A comprehensive Meta-analysis. Intelligence, 59, 72-83.
• 7. Salthouse, T.A., (2009). When Does Age-Related Cognitive Decline Begin? Neurobiology of Aging, 30(4), 507-514.
• 8. Plassman, B.L., Welsh, K.A., Helms, M., Brandt, J., Page, W.F., and Breitner, J.C.S., (1995). Intelligence and Education as Predictors of Cognitive State in Late Life: A 50-year Follow-Up. Neurology 45, 1446–1450.
• 9. Deary, I.J., Corley, J., Harris, S.E., Houlihan, L.M., Marioni, R.E., Penke, L., Rafnsson, S.B., and Starr, J.M., (2009). Age-Associated Cognitive Decline, British Medical Bulletin, Vol:92, No:1, pp:135-152, 12.2009,.
• 10. Park, D.C. and Reuter-Lorenz, P., (2009). The Adaptive Brain: Aging and Neurocognitive Scaffolding. Annu Rev Psychol, 60, 173–196.
• 11. Van Horik, J.O., Langley, E.J., Whiteside, M.A., and Madden, J.R., (2017). Differential Participation in Cognitive Tests is Driven by Personality, Sex, Body Condition and Experience. Behavioural processes, 134, 22-30. 12. ANAM4R System Quick Start Guide, (2016). Automated Neuropsychological Assessment Metrics.
• 13. Johnson, D.R., Vincent, A.S., Johnson, A.E., Gilliland, K., and Schlegel, R.E., (2008). Reliability and Construct Validity of the Automated Neuropsychological Assessment Metrics (ANAM) Mood Scale. Archives of Clinical Neuropsychology, 23(1), 73-85.