In life, no matter what age you are, you will always make social decisions as people are social beings. The way you make social decisions and what aspects you value more can however change and your age may influence the social choices you make. Moreover,
attention has shown to influence decision making in general and has shown to be affected by age. It could consequently also influence social decisions. The question in this research therefore was “How do social decisions regarding who to spend time with change over the lifespan?” and “Does attention explain these age differences or individual differences generally? Do age and/or attention have an effect on how social decisions are made?”. A significant effect for age on social choice was found in the additional information open-ended scenario. This significance suggests that older adults were more driven towards the novel options compared to younger adults that went more for the familiar option. Moreover, a significant result was found for attention on choice in the person open-ended and additional information time limited. This implied that in these scenarios attention to specific boxes led to that choice. No significant results for the effect of age on attention were found. Even with some of the results being significant for the effect of age on choice and attention on choice, there is insufficient evidence to conclude an overall effect.
Research on social decision making, age and attention is promising and should be further investigated. Especially in a time where social contact has been restricted this could have implications for the future and change the theories regarding social decision making.
This research was a starting point to see whether these theories had already been affected by the social climate we are in and whether attention played a role in this. Therefore, many applications for future research remain. Firstly, the overall design can be improved by randomizing the scenarios, getting more detailed and accurate demographics and having more trials and different people to choose between. Secondly, the way attention is recorded can be experimented with. It can either be improved to be more accurate with less loss of data or other methods of recording attention can be used such as eye-tracking in a lab environment instead of remote, online and at home. Lastly, the different brain areas related to social
decision making, aging and attention are discussed in this paper however the exact neuroscience, brain areas and brain activity can be further investigated through brain imaging. It would also be a suggestion to conduct a longitudinal study instead of cross-sectional to see a clear change in brain structure and activity and its effects on social choice and how it changed over the lifespan of that person.
References
Awh, E., Belopolsky, A. V., & Theeuwes, J. (2012). Top-down versus bottom-up attentional control: A failed theoretical dichotomy. Trends in cognitive sciences, 16(8), 437-443.
Bardach, S. H., Rhodus, E. K., Parsons, K., & Gibson, A. K. (2021). Older Adults’ Adaptations to the Call for Social Distancing and Use of Technology: Insights From Socioemotional Selectivity Theory and Lived Experiences. Journal of Applied Gerontology, 0733464821996864.
Bhatti, Z. A., Arain, G. A., Akram, M. S., Fang, Y. H., & Yasin, H. M. (2020). Constructive voice behavior for social change on social networking sites: A reflection of moral identity. Technological Forecasting and Social Change, 157, 120101.
Blascheck, T., Kurzhals, K., Raschke, M., Burch, M., Weiskopf, D., & Ertl, T. (2014). State-of-the-Art of Visualization for Eye Tracking Data. In EuroVis (STARs).
Burke, S. N., & Barnes, C. A. (2006). Neural plasticity in the ageing brain. Nature reviews neuroscience, 7(1), 30-40.
Burgess, N., Maguire, E. A., & O'Keefe, J. (2002). The human hippocampus and spatial and episodic memory. Neuron, 35(4), 625-641.
Cacioppo, J. T., Berntson, G. G., Bechara, A., Tranel, D., & Hawkley, L. C. (2011). Could an aging brain contribute to subjective well-being? The value added by a social neuroscience perspective.
Carstensen, L. L. (2006). The influence of a sense of time on human development. Science, 312(5782), 1913-1915.
Carstensen, L. L., & Fredrickson, B. L. (1998). Influence of HIV status and age on cognitive representations of others. Health Psychology, 17(6), 494.
Carstensen, L. L., Isaacowitz, D. M., & Charles, S. T. (1999). Taking time seriously: a theory of socioemotional selectivity. American psychologist, 54(3), 165.
Carstensen, L. L., & Mikels, J. A. (2005). At the intersection of emotion and cognition: Aging and the positivity effect. Current directions in psychological science, 14(3), 117-121.
Charles, S. T., & Carstensen, L. L. (2010). Social and emotional aging. Annual review of psychology, 61, 383-409.
Charles, S. T., Mather, M., & Carstensen, L. L. (2003). Aging and emotional memory: the forgettable nature of negative images for older adults. Journal of Experimental Psychology: General, 132(2), 310.
Chiarelli, T. M., & Batistoni, S. S. T. (2021). An analysis of socioemotional selectivity theory in the context of older adults’ use of Facebook. Educational Gerontology, 47(1), 13-24.
Cumming, E. & Henry, W.H. (1961), Growing old: the process of disengagement. New York: Basic Books Daniels, D. P., & Zlatev, J. J. (2019). Choice architects reveal a bias toward positivity and
certainty. Organizational Behavior and Human Decision Processes, 151, 132-149.
Depping, M. K., & Freund, A. M. (2011). Normal aging and decision making: The role of motivation. Human Development, 54(6), 349-367.
Djamasbi, S. (2014). Eye tracking and web experience. AIS Transactions on Human-Computer Interaction, 6(2), 37-54.
Driscoll, I., Hamilton, D. A., Petropoulos, H., Yeo, R. A., Brooks, W. M., Baumgartner, R. N., & Sutherland, R.
J. (2003). The aging hippocampus: cognitive, biochemical and structural findings. Cerebral cortex, 13(12), 1344-1351.
Duchowski, A. (2007). Eye tracking methodology: Theory and practice. Springer, Berlin Heidelberg.
Fjell, A. M., & Walhovd, K. B. (2010). Structural brain changes in aging: courses, causes and cognitive consequences. Reviews in the Neurosciences, 21(3), 187-222.
Fredrickson, B. L., & Carstensen, L. L. (1990). Choosing social partners: how old age and anticipated endings make people more selective. Psychology and aging, 5(3), 335.
Freeman, J. B., & Dale, R. (2013). Assessing bimodality to detect the presence of a dual cognitive process. Behavior research methods, 45(1), 83-97.
Fung, H. H., Carstensen, L. L., & Lutz, A. M. (1999). Influence of time on social preferences: Implications for life-span development. Psychology and aging, 14(4), 595.
Gächter, S., Starmer, C., & Tufano, F. (2015). Measuring the closeness of relationships: a comprehensive evaluation of the'Inclusion of the Other in the Self'scale. PloS one, 10(6), e0129478.
Ge, Y., Grossman, R. I., Babb, J. S., Rabin, M. L., Mannon, L. J., & Kolson, D. L. (2002). Age-related total gray matter and white matter changes in normal adult brain. Part I: volumetric MR imaging analysis. American journal of neuroradiology, 23(8), 1327-1333.
Gilbert, C. D., & Sigman, M. (2007). Brain states: top-down influences in sensory processing. Neuron, 54(5), 677-696.
Giorgio, A., Santelli, L., Tomassini, V., Bosnell, R., Smith, S., De Stefano, N., & Johansen-Berg, H. (2010).
Age-related changes in grey and white matter structure throughout adulthood. Neuroimage, 51(3), 943-951.
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J., & Frackowiak, R. S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage, 14(1), 21-36.
Gosling, S. D., Rentfrow, P. J., & Swann Jr, W. B. (2003). A very brief measure of the Big-Five personality domains. Journal of Research in personality, 37(6), 504-528.
Gray, D. T., & Barnes, C. A. (2015). Distinguishing adaptive plasticity from vulnerability in the aging hippocampus. Neuroscience, 309, 17-28.
Hehman, E., Stolier, R. M., & Freeman, J. B. (2015). Advanced mouse-tracking analytic techniques for enhancing psychological science. Group Processes & Intergroup Relations, 18(3), 384-401.
Hess, T. M. (2005). Memory and aging in context. Psychological bulletin, 131(3), 383.
Huang, J., White, R., & Buscher, G. (2012, May). User see, user point: gaze and cursor alignment in web search. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1341-1350).
Janiszewski, C., Kuo, A., & Tavassoli, N. T. (2013). The influence of selective attention and inattention to products on subsequent choice. Journal of Consumer Research, 39(6), 1258-1274.
Kieslich, P. J., Schoemann, M., Grage, T., Hepp, J., & Scherbaum, S. (2020). Design factors in mouse-tracking:
What makes a difference?. Behavior Research Methods, 52(1), 317-341.
Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature neuroscience, 13(10), 1292-1298.
Krajbich, I., Lu, D., Camerer, C., & Rangel, A. (2012). The attentional drift-diffusion model extends to simple purchasing decisions. Frontiers in psychology, 3, 193.
Labouvie‐Vief, G., Grühn, D., & Studer, J. (2010). Dynamic integration of emotion and cognition: Equilibrium regulation in development and aging. The handbook of life‐span development.
Levin, F., Fiedler, S., & Weber, B. (2021). Positivity effect and decision making in ageing. Cognition and Emotion, 1-15.
Lighthall, N. R. (2020). Neural mechanisms of decision‐making in aging. Wiley Interdisciplinary Reviews:
Cognitive Science, 11(1), e1519.
Lim, S. L., O'Doherty, J. P., & Rangel, A. (2011). The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention. Journal of Neuroscience, 31(37), 13214-13223.
Löckenhoff, C. E., & Carstensen, L. L. (2004). Socioemotional selectivity theory, aging, and health: The increasingly delicate balance between regulating emotions and making tough choices. Journal of personality, 72(6), 1395-1424.
Maddox, G. L. (1963) Activity and morale: a longitudinal study of selected elderly subjects. Social Forces, 42,195-204
Mather, M., & Carstensen, L. L. (2003). Aging and attentional biases for emotional faces. Psychological science, 14(5), 409-415.
Mather, M., & Carstensen, L. L. (2005). Aging and motivated cognition: The positivity effect in attention and memory. Trends in cognitive sciences, 9(10), 496-502.
Mayford, M., Siegelbaum, S. A., & Kandel, E. R. (2012). Synapses and memory storage. Cold Spring Harbor perspectives in biology, 4(6), a005751.
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual review of neuroscience, 24(1), 167-202.
Miller, D. B., & O’callaghan, J. P. (2005). Aging, stress and the hippocampus. Ageing research reviews, 4(2), 123-140.
Mormann, M., Griffiths, T., Janiszewski, C., Russo, J. E., Aribarg, A., Ashby, N. J., ... & Mrkva, K. J. (2020).
Time to pay attention to attention: using attention-based process traces to better understand consumer decision-making. Marketing Letters, 31, 381-392.
Mormann, M., Towal, B., & Koch, C. (2013). The Role of Visual Attention in Decision-Making: an Eye-Tracking Experiment. Advances in Consumer Research, 41, 1–.
Nummenmaa, L., Glerean, E., Viinikainen, M., Jääskeläinen, I. P., Hari, R., & Sams, M. (2012). Emotions promote social interaction by synchronizing brain activity across individuals. Proceedings of the National Academy of Sciences, 109(24), 9599-9604.
Orquin, J. L., & Mueller Loose, S. (2013). Attention and choice: A review on eye movements in decision making. Acta Psychologica, 144(1), 190–206. https://doi.org/10.1016/j.actpsy.2013.06.003
Pruzan, K., & Isaacowitz, D. (2006). An Attentional Application of Socioemotional Selectivity Theory in College Students. Social Development (Oxford, England), 15(2), 326–338.
Reed, A. E., & Carstensen, L. L. (2012). The theory behind the age-related positivity effect. Frontiers in psychology, 3, 339.
Reed, A. E., Chan, L., & Mikels, J. A. (2014). Meta-analysis of the age-related positivity effect: age differences in preferences for positive over negative information. Psychology and aging, 29(1), 1.
Reynolds, J. H., & Chelazzi, L. (2004). Attentional modulation of visual processing. Annual review of neuroscience, 27, 611–647. https://doi.org/10.1146/annurev.neuro.26.041002.131039
Richardson, D. C., & Spivey, M. J. (2004). Eye tracking: Characteristics and methods. Encyclopedia of biomaterials and biomedical engineering, 3, 1028-1042.
Salat, D. H., Buckner, R. L., Snyder, A. Z., Greve, D. N., Desikan, R. S., Busa, E., ... & Fischl, B. (2004).
Thinning of the cerebral cortex in aging. Cerebral cortex, 14(7), 721-730.
Schlagman, S., Schulz, J., & Kvavilashvili, L. (2006). A content analysis of involuntary autobiographical memories: Examining the positivity effect in old age. Memory, 14(2), 161-175.
Sharot, T., & Sunstein, C. R. (2020). How people decide what they want to know. Nature Human Behaviour, 4(1), 14-19.
Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003).
Mapping cortical change across the human life span. Nature neuroscience, 6(3), 309-315.
Stillman, P. E., Shen, X., & Ferguson, M. J. (2018). How mouse-tracking can advance social cognitive theory. Trends in cognitive sciences, 22(6), 531-543.
Swickert, R. J., Rosentreter, C. J., Hittner, J. B., & Mushrush, J. E. (2002). Extraversion, social support processes, and stress. Personality and Individual Differences, 32(5), 877-891.
Tisserand, D. J., & Jolles, J. (2003). On the involvement of prefrontal networks in cognitive ageing. Cortex, 39(4-5), 1107-1128.
West, M. J. (1993). Regionally specific loss of neurons in the aging human hippocampus. Neurobiology of aging, 14(4), 287-293.
Willemsen, M. C., & Johnson, E. J. (2019). (Re)visiting the decision Factory: Observing Cognition with MouselabWEB. In A Handbook of Process Tracing Methods (Vol. 6, pp. 76–95).
Willemsen, M.C, (2021) GitHub Mouselabweb. Retrieved from https://github.com/MCWillemsen/mouselabWEB20
Yankner, B. A., Lu, T., & Loerch, P. (2008). The aging brain. Annual review of pathology, 3, 41–66.
https://doi.org/10.1146/annurev.pathmechdis.2.010506.092044
Yıldırım, Z., & Taşabat, S. E. (2019). Dynamic MouselabWEB: Individualized information display matrixes. Journal of Dynamic Decision Making, 4-1.
Appendix.
Appendix A. Closeness and Learning Value scores
Appendix B. Attentional data time: Age effect on Attention
Appendix C. Attentional data time: Attention effect on Choice scenario 1 (person open-ended).
Appendix D. Attentional data time: Attention effect on Choice scenario 2 (person time-limited).
Appendix E. Attentional data time: Attention effect on Choice scenario 3 (additional info time-limited).
Appendix F. Attentional data time: Attention effect on Choice scenario 4 (additional info open-ended).