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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Psychol.</journal-id>
<journal-title>Frontiers in Psychology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Psychol.</abbrev-journal-title>
<issn pub-type="epub">1664-1078</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fpsyg.2025.1682163</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Psychology</subject>
<subj-group>
<subject>Editorial</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Editorial: Exploration of decision neuroscience research in the digital era</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Shan</surname> <given-names>Wei</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x0002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/2149711/overview"/>
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<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Luan</surname> <given-names>Jing</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1517093/overview"/>
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<contrib contrib-type="author">
<name><surname>Evans</surname> <given-names>Richard</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<aff id="aff1"><sup>1</sup><institution>School of Economics and Management, Beihang University</institution>, <addr-line>Beijing</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>School of Economics and Management, Beijing Jiaotong University</institution>, <addr-line>Beijing</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>College of Digital Transformation, Faculty of Computer Science, Dalhousie University</institution>, <addr-line>Halifax, NS</addr-line>, <country>Canada</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited and reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/17443/overview">Carolyn Yoon</ext-link>, University of Michigan, United States</p>
</fn>
<corresp id="c001">&#x0002A;Correspondence: Wei Shan <email>shanwei&#x00040;buaa.edu.cn</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>30</day>
<month>10</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1682163</elocation-id>
<history>
<date date-type="received">
<day>08</day>
<month>08</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>05</day>
<month>09</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2025 Shan, Luan and Evans.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Shan, Luan and Evans</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<related-article id="RA1" related-article-type="commentary-article" xlink:href="https://www.frontiersin.org/research-topics/59507/exploration-of-decision-neuroscience-research-in-the-digital-era" ext-link-type="uri">Editorial on the Research Topic <article-title>Exploration of decision neuroscience research in the digital era</article-title></related-article>
<kwd-group>
<kwd>digital era</kwd>
<kwd>decision neuroscience</kwd>
<kwd>decision-making scenarios</kwd>
<kwd>behavior</kwd>
<kwd>multimodal</kwd>
</kwd-group>
<counts>
<fig-count count="0"/>
<table-count count="0"/>
<equation-count count="0"/>
<ref-count count="0"/>
<page-count count="2"/>
<word-count count="578"/>
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<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Decision Neuroscience</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1">
<title>1 Introduction</title>
<p>The digital era has revolutionized the way we study human decision-making. Advances in neuroimaging, computational modeling, and machine learning have provided insights into these complex processes. This Research Topic, &#x0201C;<italic>Exploration of Decision Neuroscience Research in the Digital Era</italic>&#x0201D;, brings together cutting-edge studies that leverage modern technologies&#x02014;such as eye-tracking, neuroimaging, digital dynamic assessment, and generative narrative survey&#x02014;to examine the neural and behavioral underpinnings of decision-making.</p>
</sec>
<sec id="s2">
<title>2 Contributions to the Research Topic</title>
<p>The articles featured in this Research Topic illustrate the breadth of current approaches: <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fnins.2024.1495975">Huang et al.</ext-link> introduced a maze-based digital assessment paradigm to detect early cognitive decline in Parkinson&#x00027;s disease; <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fpsyg.2024.1412131">Wong et al.</ext-link> applied generative narrative surveys to capture real-world decision-making in varied social contexts; <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fpsyg.2024.1451674">Zhou et al.</ext-link> employed eye-tracking to study intertemporal loss decisions; and <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fnins.2023.1191213">Horr et al.</ext-link> demonstrated how machine learning applied to EEG signals can accurately predict online purchasing behavior.</p>
</sec>
</body>
<back>
<sec sec-type="author-contributions" id="s3">
<title>Author contributions</title>
<p>WS: Writing &#x02013; review &#x00026; editing, Writing &#x02013; original draft. JL: Writing &#x02013; review &#x00026; editing, Writing &#x02013; original draft. RE: Writing &#x02013; review &#x00026; editing, Writing &#x02013; original draft.</p>
</sec>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="s4">
<title>Generative AI statement</title>
<p>The author(s) declare that no Gen AI was used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="s5">
<title>Publisher&#x00027;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
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</back>
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