The neurocomputational basis of self-benefitting vs pro-environmental behaviour
Poster No:
707
Submission Type:
Abstract Submission
Authors:
Boryana Todorova1, Ronald Sladky2, Kimberly Doell3, Claus Lamm1
Institutions:
1Faculty of Psychology, University of Vienna, Vienna, Vienna, 2Faculty of Psychology, University of Vienna, Vienna, Austria, 3University of Konstanz, Konstanz, Germany
First Author:
Co-Author(s):
Introduction:
The fight against climate change hinges on people's decisions. Everyday choices-such as those related to mobility, energy use, and consumption-play a crucial role in shaping emission trajectories. These decisions are influenced by people's motivation, a process often framed as a cost-benefit evaluation, where individuals weigh the effort required against the potential rewards gained. Decision neuroscientists have extensively studied these trade-offs through experimental paradigms (e.g., Contreras-Huerta et al., 2022; Lockwood et al., 2022). Drawing on this body of research, we addressed recent calls to integrate neuroscientific methods into the study of pro-environmental motivation (Doell et al., 2023). We conducted an fMRI experiment involving real costs and tangible rewards, and assessed if and how individuals devalue rewards based on effort for self-serving versus pro-environmental decisions.
Methods:
We tested 72 healthy, neurotypical participants (36 f, mean age = 24.4 yrs). The participants engaged in a decision-making paradigm where on 50% of the trials they earned money for themselves and on the other 50% for reducing CO₂ emissions (via donation to an organization). On each trial they were offered two options: a small reward for no effort or a bigger reward for investing effort measured via grip-force device; see Figure 1). Participants engaged in this task while undergoing functional magnetic resonance imaging (fMRI) in a 3 Tesla Skyra MRI scanner and a 32-channel-head coil. Functional scans were acquired with a multiband accelerated EPI sequence with the following parameters: TR = 1200 ms; TE = 34 ms; flip angle = 66°; slices = 52; multiband acceleration factor = 4; FOV = 210×210 mm, voxel size = 2×2×2 mm. Neuroimaging data was preprocessed and analyzed using SPM12 and nipype. We used rigorous model building and testing under the hierarchical Bayesian computational modeling framework to quantify the subjective value (SV) of the offers. We compared models with different combinations of parameters for reward sensitivity, effort discounting, and choice stochasticity and accounting for different discounting shapes. We then used SV as a parametric modulator of brain activity during the decision phase.
Results:
In line with prior research on effort-based decision-making, computational modeling revealed the participants devalued rewards based on effort for themselves and the environment parabolically (Lockwood et al., 2017, 2022). Whole-brain analysis of the decision phase revealed that, consistent with the literature (Lopez-Gamundi et al., 2021), ventromedial prefrontal cortex (vmPFC) activation scaled positively with SV while anterior insula activation scaled negatively. We also observed a large cluster of activation involving the dorsal anterior cingulate cortex, pre-supplementary motor area and the dorsomedial PFC scaling negatively with SV (Figure 2A). Interestingly, we found behaviourally identical choices (i.e. participants were equally motivated to invest effort for themselves and for reducing emissions), but a difference in the neural processing during the decision phase: There was significantly lower activation in several clusters, in areas related to valuation and reward processing, including the vmPFC and the right caudate nucleus during pro-environmental decisions vs decisions for themselves (p < 0.001, cluster-level corrected; Figure 2B). There were no areas that responded more strongly during pro-environmental decisions than self decisions.
Conclusions:
With this work, we illustrate how an approach from decision neuroscience can be used to gain a better understanding of pro-environmental motivation and its neural underpinnings. Our results indicate lower valuation of pro-environmental actions does not necessarily prevent people from engaging in them. This highlights the added value of neuroimaging in the climate change domain, showing that behaviourally identical choices can be explained by different valuation processes.
Emotion, Motivation and Social Neuroscience:
Social Neuroscience Other
Emotion and Motivation Other
Higher Cognitive Functions:
Decision Making 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Bayesian Modeling
Keywords:
Computational Neuroscience
FUNCTIONAL MRI
Modeling
MRI
Other - climate change
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
2. Doell, K. C., Berman, M. G., Bratman, G. N., Knutson, B., Kühn, S., Lamm, C., Pahl, S., Sawe, N., Van Bavel, J. J., White, M. P., & Brosch, T. (2023). Leveraging neuroscience for climate change research. Nature Climate Change, 13(12), 1288–1297. https://doi.org/10.1038/s41558-023-01857-4
3. Lockwood, P. L., Hamonet, M., Zhang, S. H., Ratnavel, A., Salmony, F. U., Husain, M., & Apps, M. A. J. (2017). Prosocial apathy for helping others when effort is required. Nature Human Behaviour, 1(7), 1–10. https://doi.org/10.1038/s41562-017-0131
4. Lockwood, P. L., Wittmann, M. K., Nili, H., Matsumoto-Ryan, M., Abdurahman, A., Cutler, J., Husain, M., & Apps, M. A. J. (2022). Distinct neural representations for prosocial and self-benefiting effort. Current Biology, 32(19), 4172-4185.e7. https://doi.org/10.1016/j.cub.2022.08.010
5. Lopez-Gamundi, P., Yao, Y.-W., Chong, T. T.-J., Heekeren, H. R., Mas-Herrero, E., & Marco-Pallarés, J. (2021). The neural basis of effort valuation: A meta-analysis of functional magnetic resonance imaging studies. Neuroscience & Biobehavioral Reviews, 131, 1275–1287. https://doi.org/10.1016/j.neubiorev.2021.10.024