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COVID-Dynamic Longitudinal Study Probing the dynamics of emotional and social attitudes in the time of COVID-19
COVID-Dynamic  /  Study Aims  /  background

Background

The COVID-19 pandemic is ravaging the world, with over 1 million people currently infected. Currently roughly 1 in 4 cases around the world is in the US, which has more confirmed COVID-19 cases than any other country [1]. Current projections predict millions of infected Americans and several hundred thousand deaths from the pandemic in the US alone. These alarming numbers are sadly on the low end— they apply only if the population strictly adheres to the government's social-distancing precautions and stay-at-home messages [2]; due to a number of factors, actual numbers may be much higher [3].

It is thus imperative that people in America understand and follow the public health guidelines of their local, state, and federal government. For example, there is clear evidence that social distancing can and will mitigate the pandemic [4], yet not all individuals are adhering to it [5, 6]. If we could better understand the psychological factors that account for this heterogeneity in behavior, we could inform better policy strategies.

It is well established that human decision-making is influenced by emotional factors [7-9], especially in times of crisis [10]. Some of the factors that affect decision making will be explicit—in the sense that people can readily report them consciously. Others are known to be implicit—i.e., affecting people's behavior outside of their awareness. Both explicit and implicit emotions are hypothesized to be strongly modulated as the pandemic sweeps through the US. A main translational goal of our project is to help to improve messaging and compliance to public health regulations through a better understanding of the emotional factors that influence people's decision-making.

Background: A large body of work in social science has demonstrated that people have reliable and often strong emotional biases that influence how they behave [1,2]. For instance, political party affiliation, race, gender, and social status are well-known to induce biases that are typically negative towards groups other than one's own. People also acquire emotional biases for other objects (flowers are more liked than insects; people differ over cats and dogs as good pets). Moreover, much of these biases are inaccessible to the subject, and often denied by the subject [12]: white people generally show a reliable bias against blacks, and men against women, even though they deny these biases on explicit questionnaires. However the biases are revealed with implicit tasks, and in the decisions that people make [3].

A fundamental question about emotional biases is how malleable they are. For instance, young children already acquire race biases against black people [4] — but such race bias can be shifted with interracial dating. Both mere exposure to examples, and the influence of social opinions and norms likely contribute to changes in bias [5,6], and both are prominently present during the COVID-19 pandemic. It remains unclear how easily new biases can be learned, and how easily already established biases can be changed. These questions cannot generally be investigated in the laboratory, because it is both infeasible and unethical to induce biases experimentally; when they have been attempted, effects are typically fleeting [7]. The COVID-19 pandemic presents social scientists with a unique experiment of Nature, in which people will be exposed to strong emotional associations with a range of stimuli (masks, respirators, Chinese people, Republicans, etc.). How will this shift emotional biases over time, and how will it impact decision-making?

These questions are not merely of interest to basic research in social science, but have very important practical implications. Shifting people's emotional attitudes towards other groups (Chinese, Republicans, health-care workers, rich people) has consequences both for public policy in preventing the spread of COVID-19 (targeted interventions depend on which groups of people are trusted, for instance), and for social and economic unrest. Shifting people's social decisions (whom to approach, whom to trust) and other health-relevant decisions has obvious consequences on personal and public health. So while the questions we are investigating build on strong background primarily in social psychology, the impact of the work extends to informing public health, governance, economics, and a broad range of local and federal policy.

References

  1. COVID-19 Coronavirus Pandemic
  2. NY Times COVID-19 data
  3. Ferguson, N., and the Imperial College COVID-19 Response Team, Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. 16 March 2020.
  4. Tian, H., et al., An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science, March 31, 2020.
  5. Coronavirus Live Updates: White House Debates How Far to Go on Face Mask Guidelines. [cited 2020 April 2]
  6. Mason, J. Do social distancing better, White House doctor tells Americans. Trump objects.
  7. Tversky, A. and D. Kahneman, Judgment under uncertainty: heuristics and biases. Science, 1974. 185: 1124-1131.
  8. Kugler, T., T. Connolly, and L.D. Ordóñez, Emotion, decision, and risk: Betting on gambles versus betting on people. Journal of Behavioral Decision Making, 2012. 25: 123-134.
  9. Anderson, D.J. and R. Adolphs, A framework for studying emotions across species. Cell, 2014. 157: 187-200.
  10. Lu, Y. and Y.-H.C. Huang, Getting emotional: An emotion-cognition dual-factor model of crisis communication. Public Relations Review, 2018. 44: 98-107.
  11. Greenwald, A.G., Banaji, M.R., Rudman, L.A., Farnham, S.D., Nosek, B.A., and Mellott, D.S. (2002). A unified theory of implicit attitudes, stereotypes, self-esteem, and self-concept. Psychol. Rev. 109, 3–25.
  12. Banaji, M. R., & Greenwald, A. G. (2013). Blindspot: Hidden biases of good people. New York: Delacorte Press.
  13. Stanley, D. A., Sokol-Hessner, P., Banaji, M. R., & Phelps, E. A. (2011). Implicit race attitudes predict trust- worthiness judgments and economic trust decisions. Proceedings of the National Academy of Sciences, USA, 108, 7710–7715.
  14. Baron, A. S., & Banaji, M. R. (2006). The development of implicit attitudes: Evidence of race evaluations from ages 6 and 10 and adulthood. Psychological Science, 17, 53–58.
  15. Abelson, R.P., Aronson, E., McGuire, W.J., Newcomb, T.M., Rosenberg, M.J., and Tannenbaum, P.H. (1968). Theories of Cognitive Consistency: A Sourcebook (Chicago: Rand McNally).
  16. Cialdini, R.B., and Goldstein, N.J. (2004). Social influence: compliance and conformity. Annu. Rev. Psychol. 55, 591–621.
  17. Lai, C.K. et al. (2016). Reducing implicit racial preferences: II. intervention effectiveness across time. Journal of Experimental Psychology: General. 145: 1001- 1016.