B7
Lifespan age differences in the arbitration of learning strategies
Projektziele
Many everyday tasks (such as commuting to work) can be pursued efficiently by relying on past experiences. However, such habits may be mal-adaptive if the environment changes unexpectedly (e.g. train got cancelled). Under these conditions we have to use knowledge about the structure of the environment for forward planning (e.g. find an alternative means of transportation). However, often the scenario is not that straightforward and the preferred train might be late rather than cancelled. In such a situation, we have to arbitrate between our habitual strategy (wait for the late train) and alternative courses of action (take the bus), that may involve forward planning (Balleine & O'Doherty, 2010; Dayan & Niv, 2008). One of the core hypotheses of the collaborative research center (CRC) is that adaptive behavior in dynamic environments depends on the ability to balance complementary control functions in a context-sensitive manner (Goschke, 2003; Gruber & Goschke, 2004). In this proposal we directly address this hypothesis in the domain of learning and decision-making by asking the question how the ability to arbitrate between learning strategies changes across the human lifespan. In line with recent theories (Daw, Gershman, Seymour, Dayan, & Dolan, 2011; O'Doherty, Lee, & McNamee, 2015), we assume that habitual decisions rely on model-free (experience-based) reinforcement learning; In contrast, we define goal-directed decisions as model-based mechanisms that involve learning the structure of the environment, which is used for forward planning. The central premise of the proposed project is that adaptive decision-making in dynamic environments depends on an arbitration mechanism that prioritizes these different learning strategies. Specifically, it is postulated that the arbitration between model-based and model-free learning strategies depends on the relative reliability with which these strategies lead to the desired goal (Daw, Niv, & Dayan, 2005b; Lee, Shimojo, & O'Doherty, 2014; Yoshida & Seymour, 2014). In the framework of the CRC reliability estimates of model-based and model-free learning strategies are conceptualized as meta-control parameters that are used by the arbitration mechanism to balance complementary control demands. Sub-optimality in the adjustments of these meta-control parameters either due to immaturity or aging (Eppinger, Haemmerer, & Li, 2011; Hämmerer & Eppinger, 2012) may compromise arbitration mechanisms and may underlie age-related deficits in adaptive behavior. These deficits are relevant for daily life when navigating in dynamic environments (e.g. in school or during grocery shopping) in which we have to rely on multiple learning strategies.
Projektmitarbeiter
Projektleiter
Dr. phil. Ben Eppinger
Professor
Phone: +1-514-848-2424
E-Mail:
Prof. Dr. rer. nat. Andrea Reiter W1-Professur für Lernprozesse in der Entwicklungspsychiatrie, Psychotherapie und Prävention
Phone: +49 (0)931-20178050
E-Mail:
Mitarbeiter
Dr. rer. nat. Theresa McKim https://scholar.google.com/citations?user=fudhLzUAAAAJ&hl=en
Ausgeschiedene Mitarbeiter
M.Sc. Florian Bolenz
Doctoral researcher
Phone: +49 (0)351 463-36274
E-Mail:
M.Sc. Susanne Neupert
Doctoral researcher
Phone: +49 (0)351 463-32065
E-Mail:
Publications
Reiter, A. M. F., Suzuki, S., O'Doherty, J. P., Li, S.-C., & Eppinger, B. (in press). Risk contagion by peers affects learning and decision-making in adolescents. Journal of Experimental Psychology: General. doi:10.1037/xge0000512
Kroemer, N. B., Lee, Y., Pooseh, S., Schad, D. J., Eppinger, B., Goschke, T., & Smolka, M. N. (2019). L-DOPA reduces model-free control of behavior by attenuating the transfer of value to action. Neuroimage, 186, 113-125. doi:10.1016/j.neuroimage.2018.10.075
Eppinger, B., Heekeren, H. R., & Li, S.-C. (2018). Age differences in the neural mechanisms of intertemporal choice under subjective decision conflict. Cerebral Cortex, 28(11), 3764-3774. doi:10.1093/cercor/bhx239
Rodriguez-Buritica, J. M., Heekeren, H. R., Li, S.-C., & Eppinger, B. (2018). Developmental differences in the neural dynamics of observational learning. Neuropsychologia, 119, 12-23. doi:10.1016/j.neuropsychologia.2018.07.022
van den Bos, W., Bruckner, R., Nassar, M. R., Mata, R., & Eppinger, B. (2018). Computational neuroscience across the lifespan: Promises and pitfals. Developmental Cognitive Neuroscience, 33, 42-53. doi:10.1016/j.dcn.2017.09.008
Wittkuhn, L., Eppinger, B., Bartsch, L., Thurm, F., Korb, F. M., & Li, S.-C. (2018). Repetitive transcranial magnetic stimulation over dorsolateral prefrontal cortex modulates value-based learning during sequential decision-making. Neuroimage, 167, 384-395. doi:10.1016/j.neuroimage.2017.11.05
Bolenz, F., Reiter, A. M. F., & Eppinger, B. (2017). Developmental changes in learning: Computational mechansims and social influences. Frontiers in Psychology, 8, 2048. doi:10.3389/fpsyg.2017.02048
Eppinger, B., Walter, M., & Li, S.-C. (2017). Electrophysiological correlates reflect the integration of model-based and model-free decision information. Cognitive, Affective, & Behavioral Neuroscience, 17(2), 406-421. doi:10.3758/s13415-016-0487-3
Nassar, M. R., Bruckner, R., Gold, J. I., Li, S.-C., Heekeren, H. R., & Eppinger, B. (2016). Age differences in learning emerge from an insufficient representation of uncertainty in older adults. Nature Communications, 7, 11609. doi:10.1038/ncomms11609
Nassar, M. R., Bruckner, R., & Eppinger, B. (2016). What do we GANE with age? (commentary). Behavioral and Brain Sciences, 39, e218. doi:10.1017/S0140525X15001892
van den Bos, W., & Eppinger, B. (2016). Developing developmental cognitive neuroscience: From agenda setting to hypothesis testing. Developmental Cognitive Neuroscience, 17, 138-144. doi:10.1016/j.dcn.2015.12.011