Adaptive Human Behavior and Physiology

, Volume 5, Issue 2, pp 148–163 | Cite as

Experimentally-Induced Inflammation Predicts Present Focus

  • Jeffrey GassenEmail author
  • Anastasia Makhanova
  • Jon K. Maner
  • E. Ashby Plant
  • Lisa A. Eckel
  • Larissa Nikonova
  • Marjorie L. Prokosch
  • Gary W. Boehm
  • Sarah E. Hill



Here, we provide an experimental test of the relationship between levels of proinflammatory cytokines and present-focused decision-making.


We examined whether increases in salivary levels of proinflammatory cytokines (interleukin-1β and interleukin-6) engendered by visually priming immunologically-relevant threats (pathogen threat, physical harm) and opportunities (mating) predicted temporal discounting, a key component of present-focused decision-making.


As hypothesized, results revealed that each experimental manipulation led to a significant rise in both salivary interleukin-1β and interleukin-6. Moreover, post-manipulation levels of each cytokine independently predicted temporal discounting across conditions. These results were not moderated by pre-manipulation levels of either cytokine, nor were they found using the difference between pre- and post-manipulation levels of cytokines as a predictor.


Together, these results suggest that levels of proinflammatory cytokines may play a mechanistic role in the desire for immediately available rewards.


Inflammation Life history theory Temporal focus Cytokines Impulsivity 



This project was supported by two National Science Foundation awards: BCS #1551201 awarded to S. E. Hill and BCS #1227089 awarded to J. K. Maner and L. A. Eckel.

Compliance with Ethical Standards

Conflicts of Interest

The authors declare that they have no conflict of interest.

Supplementary material

40750_2019_110_MOESM1_ESM.sav (13 kb)
ESM 1 (SAV 13 kb)


  1. Addicott, M. A., Pearson, J. M., Sweitzer, M. M., Barack, D. L., & Platt, M. L. (2017). A primer on foraging and the explore/exploit trade-off for psychiatry research. Neuropsychopharmacology, 42(10), 1931–1939.Google Scholar
  2. Ainsworth, S. E., & Maner, J. K. (2014). Assailing the competition: Sexual selection, proximate mating motives, and aggressive behavior in men. Personality and Social Psychology Bulletin, 40(12), 1648–1658.Google Scholar
  3. Alba, E., & Dorronsoro, B. (2005). The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Transactions on Evolutionary Computation, 9(2), 126–142.Google Scholar
  4. Aubert, A., Vega, C., Dantzer, R., & Goodall, G. (1995). Pyrogens specifically disrupt the acquisition of a task involving cognitive processing in the rat. Brain, Behavior, and Immunity, 9(2), 129–148.Google Scholar
  5. Banks, W. A. (2005). Blood-brain barrier transport of cytokines: A mechanism for neuropathology. Current Pharmaceutical Design, 11(8), 973–984.Google Scholar
  6. Benveniste, E. N. (1992). Inflammatory cytokines within the central nervous system: Sources, function, and mechanism of action. American Journal of Physiology-Cell Physiology, 263(1), C1–C16.Google Scholar
  7. Berk, M., Williams, L. J., Jacka, F. N., O’Neil, A., Pasco, J. A., Moylan, S., et al. (2013). So depression is an inflammatory disease, but where does the inflammation come from? BMC Medicine, 11(1), 200.Google Scholar
  8. Brumbach, B. H., Figueredo, A. J., & Ellis, B. J. (2009). Effects of harsh and unpredictable environments in adolescence on development of life history strategies. Human Nature, 20(1), 25–51.Google Scholar
  9. Bulley, A., Henry, J., & Suddendorf, T. (2016). Prospection and the present moment: The role of episodic foresight in intertemporal choices between immediate and delayed rewards. Review of General Psychology, 20(1), 29–47.Google Scholar
  10. Caraco, T., Martindale, S., & Whittam, T. S. (1980). An empirical demonstration of risk-sensitive foraging preferences. Animal Behaviour, 28(3), 820–830.Google Scholar
  11. Chovatiya, R., & Medzhitov, R. (2014). Stress, inflammation, and defense of homeostasis. Molecular Cell, 54(2), 281–288.Google Scholar
  12. Clark, M. A., Hentzen, B. T., Plank, L. D., & Hill, G. L. (1996). Sequential changes in insulin-like growth factor 1, plasma proteins, and total body protein in severe sepsis and multiple injury. Journal of Parenteral and Enteral Nutrition, 20(5), 363–370.Google Scholar
  13. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  14. Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24(4), 385–396.Google Scholar
  15. Cohen, J. D., McClure, S. M., & Angela, J. Y. (2007). Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1481), 933–942.Google Scholar
  16. Dantzer, R. (2001). Cytokine-induced sickness behavior: Mechanisms and implications. Annals of the New York Academy of Sciences, 933(1), 222–234.Google Scholar
  17. Dantzer, R. (2009). Cytokine, sickness behavior, and depression. Immunology and Allergy Clinics, 29(2), 247–264.Google Scholar
  18. Dantzer, R., & Kelley, K. W. (2007). Twenty years of research on cytokine-induced sickness behavior. Brain, Behavior, and Immunity, 21(2), 153–160.Google Scholar
  19. Dantzer, R., O'Connor, J. C., Freund, G. G., Johnson, R. W., & Kelley, K. W. (2008). From inflammation to sickness and depression: When the immune system subjugates the brain. Nature Reviews Neuroscience, 9(1), 46–56.Google Scholar
  20. Del Giudice, M., & Gangestad, S. W. (2018). Rethinking IL-6 and CRP: Why they are more than inflammatory biomarkers, and why it matters. Brain, Behavior, and Immunity, 70, 61–75.Google Scholar
  21. Dinarello, C. A. (1991). Interleukin-1 and interleukin-1 antagonism. Blood, 77(8), 1627–1652.Google Scholar
  22. Dinarello, C. A. (1996). Biologic basis for interleukin-1 in disease. Blood, 87(6), 2095–2147.Google Scholar
  23. Dong, Y., & Peng, C. Y. J. (2013). Principled missing data methods for researchers. SpringerPlus, 2(1), 222.Google Scholar
  24. Evenden, J. L. (1999). Varieties of impulsivity. Psychopharmacology, 146(4), 348–361.Google Scholar
  25. Fawcett, T. W., McNamara, J. M., & Houston, A. I. (2012). When is it adaptive to be patient? A general framework for evaluating delayed rewards. Behavioural Processes, 89(2), 128–136.Google Scholar
  26. Frankenhuis, W. E., & de Weerth, C. (2013). Does early-life exposure to stress shape or impair cognition? Current Directions in Psychological Science, 22(5), 407–412.Google Scholar
  27. Griskevicius, V., Tybur, J. M., Delton, A. W., & Robertson, T. E. (2011). The influence of mortality and socioeconomic status on risk and delayed rewards: A life history theory approach. Journal of Personality and Social Psychology, 100(6), 1015–1026.Google Scholar
  28. Griskevicius, V., Ackerman, J. M., Cantú, S. M., Delton, A. W., Robertson, T. E., Simpson, J. A., et al. (2013). When the economy falters, do people spend or save? Responses to resource scarcity depend on childhood environments. Psychological Science, 24(2), 197–205.Google Scholar
  29. Hey, J. D. (1982). Search for rules for search. Journal of Economic Behavior & Organization, 3(1), 65–81.Google Scholar
  30. Higginson, A. D., Fawcett, T. W., Houston, A. I., & McNamara, J. M. (2018). Trust your gut: Using physiological states as a source of information is almost as effective as optimal Bayesian learning. Proceedings of the Royal Society of London B: Biological Sciences, 285(1871), 2017–2411.Google Scholar
  31. Hill, S. E., Boehm, G. W., & Prokosch, M. L. (2016). Vulnerability to disease as a predictor of faster life history strategies. Adaptive Human Behavior and Physiology, 2(2), 116–133.Google Scholar
  32. Hills, T. T., Todd, P. M., Lazer, D., Redish, A. D., Couzin, I. D., & Cognitive Search Research Group. (2015). Exploration versus exploitation in space, mind, and society. Trends in Cognitive Sciences, 19(1), 46–54.Google Scholar
  33. Hopkins, S. J., & Rothwell, N. J. (1995). Cytokines and the nervous system I: Expression and recognition. Trends in Neurosciences, 18(2), 83–88.Google Scholar
  34. Humphries, M. D., Khamassi, M., & Gurney, K. (2012). Dopaminergic control of the exploration-exploitation trade-off via the basal ganglia. Frontiers in Neuroscience, 6, 9.Google Scholar
  35. Katz, K., & Naug, D. (2015). Energetic state regulates the exploration–exploitation trade-off in honeybees. Behavioral Ecology, 26(4), 1045–1050.Google Scholar
  36. Kidd, C., Palmeri, H., & Aslin, R. N. (2013). Rational snacking: Young children’s decision-making on the marshmallow task is moderated by beliefs about environmental reliability. Cognition, 126(1), 109–114.Google Scholar
  37. Kiecolt-Glaser, J. K., Preacher, K. J., MacCallum, R. C., Atkinson, C., Malarkey, W. B., & Glaser, R. (2003). Chronic stress and age-related increases in the proinflammatory cytokine IL-6. Proceedings of the National Academy of Sciences, 100(15), 9090–9095.Google Scholar
  38. Korf, R. E. (1985). Depth-first iterative-deepening: An optimal admissible tree search. Artificial Intelligence, 27(1), 97–109.Google Scholar
  39. La Fratta, I., Tatangelo, R., Campagna, G., Rizzuto, A., Franceschelli, S., Ferrone, A., et al. (2018). The plasmatic and salivary levels of IL-1β, IL-18 and IL-6 are associated to emotional difference during stress in young male. Scientific Reports, 8(1), 3031.Google Scholar
  40. Lazer, D., & Friedman, A. (2007). The network structure of exploration and exploitation. Administrative Science Quarterly, 52(4), 667–694.Google Scholar
  41. Liesenjohann, T., Liesenjohann, M., Trebaticka, L., Sundell, J., Haapakoski, M., Ylönen, H., & Eccard, J. A. (2015). State-dependent foraging: Lactating voles adjust their foraging behavior according to the presence of a potential nest predator and season. Behavioral Ecology and Sociobiology, 69(5), 747–754.Google Scholar
  42. Lochmiller, R. L., & Deerenberg, C. (2000). Trade-offs in evolutionary immunology: Just what is the cost of immunity? Oikos, 88(1), 87–98.Google Scholar
  43. March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.Google Scholar
  44. Mason, W. A., Jones, A., & Goldstone, R. L. (2008). Propagation of innovations in networked groups. Journal of Experimental Psychology: General, 137(3), 422–433.Google Scholar
  45. Matzinger, P. (2002). The danger model: A renewed sense of self. Science, 296(5566), 301–305.Google Scholar
  46. McGuire, J. T., & Kable, J. W. (2013). Rational temporal predictions can underlie apparent failures to delay gratification. Psychological Review, 120(2), 395–410.Google Scholar
  47. McNamara, J. M., & Houston, A. I. (1992). Risk-sensitive foraging: A review of the theory. Bulletin of Mathematical Biology, 54(2–3), 355–378.Google Scholar
  48. McNamara, J. M., Merad, S., & Houston, A. I. (1991). A model of risk-sensitive foraging for a reproducing animal. Animal Behaviour, 41(5), 787–792.Google Scholar
  49. Medzhitov, R. (2008). Origin and physiological roles of inflammation. Nature, 454(7203), 428–435.Google Scholar
  50. Medzhitov, R., Schneider, D. S., & Soares, M. P. (2012). Disease tolerance as a defense strategy. Science, 335(6071), 936–941.Google Scholar
  51. Metcalfe, N. B., Fraser, N. H., & Burns, M. D. (1998). State–dependent shifts between nocturnal and diurnal activity in salmon. Proceedings of the Royal Society of London B: Biological Sciences, 265(1405), 1503–1507.Google Scholar
  52. Nonacs, P. (2001). State dependent behavior and the marginal value theorem. Behavioral Ecology, 12(1), 71–83.Google Scholar
  53. Olsson, O., Brown, J. S., & Smith, H. G. (2002). Long-and short-term state-dependent foraging under predation risk: An indication of habitat quality. Animal Behaviour, 63(5), 981–989.Google Scholar
  54. Pyke, G. H., Pulliam, H. R., & Charnov, E. L. (1977). Optimal foraging: A selective review of theory and tests. The Quarterly Review of Biology, 52(2), 137–154.Google Scholar
  55. Radner, R., & Rothschild, M. (1975). On the allocation of effort. Journal of Economic Theory, 10(3), 358–376.Google Scholar
  56. Real, L. (1990). Search theory and mate choice. I. Models of single-sex discrimination. The American Naturalist, 136(3), 376–405.Google Scholar
  57. Real, L., & Caraco, T. (1986). Risk and foraging in stochastic environments. Annual Review of Ecology and Systematics, 17(1), 371–390.Google Scholar
  58. Rickard, I. J., Frankenhuis, W. E., & Nettle, D. (2014). Why are childhood family factors associated with timing of maturation? A role for internal prediction. Perspectives on Psychological Science, 9(1), 3–15.Google Scholar
  59. Salimetrics (2019). Salivary IL-6 (Interleukin-6) analysis for scientific research – Salimetrics. (2019). 15 January 2019.
  60. Schaller, M., Miller, G. E., Gervais, W. M., Yager, S., & Chen, E. (2010). Mere visual perception of other people’s disease symptoms facilitates a more aggressive immune response. Psychological Science, 21(5), 649–652.Google Scholar
  61. Sidhu, J. S., Commandeur, H. R., & Volberda, H. W. (2007). The multifaceted nature of exploration and exploitation: Value of supply, demand, and spatial search for innovation. Organization Science, 18(1), 20–38.Google Scholar
  62. Slavish, D. C., Graham-Engeland, J. E., Smyth, J. M., & Engeland, C. G. (2015). Salivary markers of inflammation in response to acute stress. Brain, Behavior, and Immunity, 44, 253–269.Google Scholar
  63. Steptoe, A., Hamer, M., & Chida, Y. (2007). The effects of acute psychological stress on circulating inflammatory factors in humans: A review and meta-analysis. Brain, Behavior, and Immunity, 21(7), 901–912.Google Scholar
  64. Teles, R. P., Likhari, V., Socransky, S. S., & Haffajee, A. D. (2009). Salivary cytokine levels in subjects with chronic periodontitis and in periodontally healthy individuals: A cross-sectional study. Journal of Periodontal Research, 44(3), 411–417.Google Scholar
  65. Thomson, A. W., & Lotze, M. T. (Eds.). (2003). The cytokine handbook, two-volume set. London: Elsevier.Google Scholar
  66. Voss, G. B., Sirdeshmukh, D., & Voss, Z. G. (2008). The effects of slack resources and environmentalthreat on product exploration and exploitation. Academy of Management Journal, 51(1), 147–164.Google Scholar
  67. Wang, X. T., & Dvorak, R. D. (2010). Sweet future: Fluctuating blood glucose levels affect future discounting. Psychological Science, 21(2), 183–188.Google Scholar
  68. Watkinson, S. C., Boddy, L., Burton, K., Darrah, P. R., Eastwood, D., Fricker, M. D., & Tlalka, M. (2005). New approaches to investigating the function of mycelial networks. Mycologist, 19(1), 11–17.Google Scholar
  69. Waynforth, D. (2012). Life-history theory, chronic childhood illness and the timing of first reproduction in a British birth cohort. Proceedings of the Royal Society B: Biological Sciences, 279(1740), 2998–3002.Google Scholar
  70. Williamson, S., Munro, C., Pickler, R., Grap, M. J., & Elswick, R. K. (2012). Comparison of biomarkers in blood and saliva in healthy adults. Nursing Research and Practice, Article ID: 246178, 1–4
  71. Winterhalder, B., Lu, F., & Tucker, B. (1999). Risk-senstive adaptive tactics: Models and evidence from subsistence studies in biology and anthropology. Journal of Archaeological Research, 7(4), 301–348.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jeffrey Gassen
    • 1
    Email author
  • Anastasia Makhanova
    • 2
  • Jon K. Maner
    • 2
  • E. Ashby Plant
    • 2
  • Lisa A. Eckel
    • 2
  • Larissa Nikonova
    • 2
  • Marjorie L. Prokosch
    • 3
  • Gary W. Boehm
    • 1
  • Sarah E. Hill
    • 1
  1. 1.Department of PsychologyTexas Christian UniversityFort WorthUSA
  2. 2.Department of PsychologyFlorida State UniversityTallahasseeUSA
  3. 3.Department of PsychologyTulane UniversityNew OrleansUSA

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