Learn How to Be Effective: Neural and Computational Insights into Charity Efficiency Learning

Learn How to Be Effective: Neural and Computational Insights into Charity Efficiency Learning | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 21 October 2025 V1 Latest version Share on Learn How to Be Effective: Neural and Computational Insights into Charity Efficiency Learning Authors : Yue He , Lijun Xu , Xianmeng Li , and Peng Li 0009-0005-9627-3568 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176105018.84695662/v1 Published Social Cognitive and Affective Neuroscience Version of record Peer review timeline 133 views 68 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Effective altruism (EA) advocates maximizing the social benefits of charitable resources. However, real-world altruistic behaviors such as charitable giving often appear inefficient, possibly because individuals fail to learn about the effectiveness of charitable organizations. To investigate this issue, we employed reinforcement learning (RL) computational modeling and electroencephalography (EEG), adopting a domain-specific comparative view to examine whether donors can learn efficiency and monetary information across multiple trials in charitable and reward tasks to guide subsequent decisions. Results from Experiment 1 showed that in charitable tasks, efficiency information was assigned a higher value weight and updated with a lower learning rate, with donation preferences primarily driven by efficiency differences. In contrast, monetary information dominated reward-based decisions. Experiment 2 replicated this ”efficiency-first” behavioral pattern and key computational findings under a revised feedback framework. EEG results further revealed that monetary information elicited a significant negative waveform over the right temporoparietal junction (rTPJ), with weaker neural responses in charitable tasks compared to reward tasks. Together, these findings demonstrate that individuals can learn and utilize efficiency information to guide charitable decisions, supporting the practical feasibility of EA. Supplementary Material File (artical.docx) Download 7.75 MB Information & Authors Information Version history V1 Version 1 21 October 2025 Peer review timeline Published Social Cognitive and Affective Neuroscience Version of Record 26 Mar 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Yue He Shenzhen University School of Psychology View all articles by this author Lijun Xu Guangdong University of Finance & Economics View all articles by this author Xianmeng Li Shenzhen University School of Psychology View all articles by this author Peng Li 0009-0005-9627-3568 [email protected] Shenzhen University School of Psychology View all articles by this author Metrics & Citations Metrics Article Usage 133 views 68 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yue He, Lijun Xu, Xianmeng Li, et al. Learn How to Be Effective: Neural and Computational Insights into Charity Efficiency Learning. Authorea . 21 October 2025. DOI: https://doi.org/10.22541/au.176105018.84695662/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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