The exploration-exploitation trade-off in a foraging task is affected by mood-related arousal and valence
Tóm tắt
The exploration-exploitation trade-off shows conceptual, functional, and neural analogies with the persistence-flexibility trade-off. We investigated whether mood, which is known to modulate the persistence-flexibility balance, would similarly affect the exploration-exploitation trade-off in a foraging task. More specifically, we tested whether interindividual differences in foraging behavior can be predicted by mood-related arousal and valence. In 119 participants, we assessed mood-related interindividual differences in exploration-exploitation using a foraging task that included minimal task constraints to reduce paradigm-induced biases of individual control tendencies. We adopted the marginal value theorem as a model-based analysis approach, which approximates optimal foraging behavior by tackling the patch-leaving problem. To assess influences of mood on foraging, participants underwent either a positive or negative mood induction. Throughout the experiment, we assessed arousal and valence levels as predictors for explorative/exploitative behavior. Our mood manipulation affected participants' arousal and valence ratings as expected. Moreover, mood-related arousal was found to predict exploration while valence predicted exploitation, which only partly matched our expectations and thereby the proposed conceptual overlap with flexibility and persistence, respectively. The current study provides a first insight into how processes related to arousal and valence differentially modulate foraging behavior. Our results imply that the relationship between exploration-exploitation and flexibility-persistence is more complicated than the semantic overlap between these terms might suggest, thereby calling for further research on the functional, neural, and neurochemical underpinnings of both trade-offs.
Tài liệu tham khảo
Akbari Chermahini, S., & Hommel, B. (2012). Creative mood swings: divergent and convergent thinking affect mood in opposite ways. Psychological Research, 76(5), 634–640. https://doi.org/10.1007/s00426-011-0358-z
Allen, L., Scott, J., Brand, A., Hlava, M., & Altman, M. (2014). Publishing: Credit where credit is due. Nature News, 508(7496), 312.
Aston-Jones, G., & Cohen, J. D. (2005). An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. Annual Review of Neuroscience, 28(1), 403–450. https://doi.org/10.1146/annurev.neuro.28.061604.135709
Atkinson, J. W., & Birch, D. (1970). The dynamics of action. John Wiley.
Buffalari, D. M., & Grace, A. A. (2007). Noradrenergic modulation of basolateral amygdala neuronal activity: Opposing influences of α-2 and β receptor activation. Journal of Neuroscience, 27(45), 12358–12366. https://doi.org/10.1523/JNEUROSCI.2007-07.2007
Cahilla, L., & Alkireb, M. T. (2003). Epinephrine enhancement of human memory consolidation: Interaction with arousal at encoding. Neurobiology of Learning and Memory, 79, 194–198.
Charnov, E. L. (1976). Optimal foraging, the marginal value theorem. Theoretical Population Biology, 9(2), 129–136. https://doi.org/10.1016/0040-5809(76)90040-X
Cohen, J. D., McClure, S. M., & Yu, A. J. (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. https://doi.org/10.1098/rstb.2007.2098
Cools, R., & D’Esposito, M. (2011). Inverted-U-shaped dopamine actions on human working memory and cognitive control. Biological Psychiatry, 69(12), e113–e125. https://doi.org/10.1016/j.biopsych.2011.03.028
Dreisbach, G., & Goschke, T. (2004). How positive affect modulates cognitive control: reduced perseveration at the cost of increased distractibility. Journal of Experimental Psychology: Learning Memory and Cognition, 30(2), 343–353. https://doi.org/10.1037/0278-7393.30.2.343
Dreisbach, G., Müller, J., Goschke, T., Strobel, A., Schulze, K., Lesch, K.-P., & Brocke, B. (2005). Dopamine and cognitive control: the influence of spontaneous eyeblink rate and dopamine gene polymorphisms on perseveration and distractibility. Behavioral Neuroscience, 119(2), 483–490. https://doi.org/10.1037/0735-7044.119.2.483
Durstewitz, D., & Seamans, J. K. (2008). The dual-state theory of prefrontal cortex dopamine function with relevance to Catechol-O-Methyltransferase genotypes and schizophrenia. Biological Psychiatry, 64(9), 739–749. https://doi.org/10.1016/j.biopsych.2008.05.015
Eich, E., Ng, J. T., Macaulay, D. P. A. D., Percy, A. D., & Grebneva, I. (2007). Combining music with thought to change mood. Handbook of emotion elicitation and assessment, 124-136.
Erdfelder, E., Faul, F., & Buchner, A. (1996). GPOWER: A general power analysis program. Behavior research methods, instruments, & computers, 28(1), 1-11.
Forgas, J. P. (2011). Can negative affect eliminate the power of first impressions? Affective influences on primacy and recency effects in impression formation. Journal of Experimental Social Psychology, 47, 425-429.
Frank, M. J., Doll, B. B., Oas-Terpstra, J., & Moreno, F. (2009). Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation. Nature Neuroscience, 12(8), 1062–1068. https://doi.org/10.1038/nn.2342
Frank, M. J., & Fossella, J. A. (2011). Neurogenetics and pharmacology of learning, motivation, and cognition. Neuropsychopharmacology, 36(1), 133–152. https://doi.org/10.1038/npp.2010.96
Fröber, K., & Dreisbach, G. (2014). The differential influences of positive affect, random reward, and performance-contingent reward on cognitive control. Cognitive, Affective and Behavioral Neuroscience, 14(2), 530–547. https://doi.org/10.3758/s13415-014-0259-x
Goschke, T. (2003). Voluntary action and cognitive control from a cognitive neuroscience perspective. In S. Maasen, W. Prinz, & G. Roth (Eds.), Voluntary action. An issue at the interface of nature and culture (pp. 49–85). Oxford University Press.
Goschke, T., & Bolte, A. (2014). Emotional modulation of control dilemmas: The role of positive affect, reward, and dopamine in cognitive stability and flexibility. Neuropsychologia, 62, 403–423. https://doi.org/10.1016/j.neuropsychologia.2014.07.015
Hills, T. T. (2006). Animal foraging and the evolution of goal-directed cognition. Cognitive Science, 30(1), 3–41. https://doi.org/10.1207/s15516709cog0000_50
Hills, T. T., & Dukas, R. (2012). The Evolution of cognitive search. Cognitive Search: Evolution, Algorithms, and the Brain, 11–24. https://doi.org/10.1111/j.1756-8765.2009.01078.x
Hills, T. T., Todd, P. M., & Goldstone, R. L. (2008). Search in external and internal spaces: Evidence for generalized cognitive search processes. Psychological Science, 19(8), 802–808. https://doi.org/10.1111/j.1467-9280.2008.02160.x
Hills, T. T., Todd, P. M., & Goldstone, R. L. (2010). The central executive as a search process: priming exploration and exploitation across domains. Journal of Experimental Psychology, 139(4), 590–609. https://doi.org/10.1037/a0020666.
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.
Hommel, B. (2015). Between persistence and flexibility: the Yin and Yang of action Control. Advances in Motivation Science, 2, 33–67. https://doi.org/10.1016/bs.adms.2015.04.003
Hommel, B. (2019). Affect and control: a conceptual clarification. International Journal of Psychophysiology, 144, 1–6.
Hommel, B., & Colzato, L. S. (2017). The social transmission of metacontrol policies: Mechanisms underlying the interpersonal transfer of persistence and flexibility. Neuroscience and Biobehavioral Reviews, 81, 43–58. https://doi.org/10.1016/j.neubiorev.2017.01.009
Jefferies, L. N., Smilek, D., Eich, E., & Enns, J. T. (2008). Emotional valence and arousal interact in attentional control. Psychological Science, 19(3), 290–295. https://doi.org/10.1111/j.1467-9280.2008.02082.x
Jepma, M., & Nieuwenhuis, S. (2011). Pupil diameter predicts changes in the exploration-exploitation trade-off: evidence for the adaptive gain theory. Journal of Cognitive Neuroscience, 23(7), 1587–1596. https://doi.org/10.1162/jocn.2010.21548
Kayser, A. S., Mitchell, J. M., Weinstein, D., & Frank, M. J. (2015). Dopamine, locus of control, and the exploration-exploitation tradeoff. Neuropsychopharmacology, 40(2), 454–462. https://doi.org/10.1038/npp.2014.193
Lenow, J. K., Constantino, S. M., Daw, N. D., & Phelps, E. A. (2017). Chronic and acute stress promote overexploitation in serial decision making. Journal of Neuroscience, 37(23), 5681-5689. https://doi.org/10.1523/JNEUROSCI.3618-16.2017
Marković, D., Goschke, T., & Kiebel, S.J. (2019). Meta-control of the exploration-exploitation dilemma emerges from probabilistic inference over a hierarchy of time scales. bioRxiv, 847566. https://doi.org/10.1101/847566
Mather, M., Clewett, D., Sakaki, M., & Harley, C. W. (2016). Norepinephrine ignites local hot spots of neuronal excitation: How arousal amplifies selectivity in perception and memory. Behavioral and Brain Sciences, 39. https://doi.org/10.1016/j.physbeh.2017.03.040
Mekern, V. N., Sjoerds, Z., & Hommel, B. (2019). How metacontrol biases and adaptivity impact performance in cognitive search tasks. Cognition, 182, 251-259. https://doi.org/10.1016/j.cognition.2018.10.001
Morey, R. D., & Rouder, J. N. (2018). BayesFactor: Computation of Bayes factors for common designs. R package version 0.9.12-4.2. URL https://CRAN.R-project.org/package=BayesFactor
R Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Ranjbar-Slamloo, Y., & Fazlali, Z. (2020). Dopamine and noradrenaline in the brain; overlapping or dissociate functions? Frontiers in Molecular Neuroscience, 12, 1–8. https://doi.org/10.3389/fnmol.2019.00334
Riefer, P. S., Prior, R., Blair, N., Pavey, G., & Love, B. C. (2017). Coherency-maximizing exploration in the supermarket. Nature human behaviour, 1(1), 1-4. https://doi.org/10.1038/s41562-016-0017
Russell, J. A., Weiss, A., & Mendelsohn, G. A. (1989). Affect grid: a single-item scale of pleasure and arousal. Journal of Personality and Social Psychology, 57(3), 493–502. https://doi.org/10.1037/0022-3514.57.3.493
Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., ... & Dunbar, G. C. (1998). The Mini-International Neuropsychiatric Interview (MINI): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of Clinical Psychiatry, 59(20), 22-33.
Tabachnick, B. G., & Fidell, L. S. (2012). Chapter 13 principal components and factor analysis. Using multivariate statistics. London: Pearson.
Terbeck, S., Savulescu, J., Chesterman, L. P., & Cowen, P. J. (2016). Noradrenaline effects on social behaviour, intergroup relations, and moral decisions. Neuroscience and Biobehavioral Reviews, 66, 54–60. https://doi.org/10.1016/j.neubiorev.2016.03.031
Todd, P.M., & Hills, T.T. (2020). Foraging in mind. Current Directions in Psychological Science, 29(3), 309-315. https://doi.org/10.1177/0963721420915861
Van Steenbergen, H., Band, G. P. H., & Hommel, B. (2010). In the mood for adaptation: How affect regulates conflict-driven control. Psychological Science, 21(11), 1629–1634. https://doi.org/10.1177/0956797610385951
Vinckier, F., Rigoux, L., Oudiette, D., & Pessiglione, M. (2018). Neuro-computational account of how mood fluctuations arise and affect decision making. Nature Communications, 9(1), 1-12. https://doi.org/10.1038/s41467-018-03774-z
Wolfe, J. M. (2013). When is it time to move to the next raspberry bush? Foraging rules in human visual search. Journal of Vision, 13(3), 1-17. https://doi.org/10.1167/13.3.10