pith. sign in

arxiv: 1907.07759 · v1 · pith:LWIAUFYLnew · submitted 2019-07-09 · 💻 cs.CY · cs.HC

The Mass, Fake News, and Cognition Security

Bin Guo , Yasan Ding , Yueheng Sun , Shuai Ma , Ke Li This is my paper

Pith reviewed 2026-05-24 23:45 UTC · model grok-4.3

classification 💻 cs.CY cs.HC
keywords fake newscognition securitymisinformationsocial networksdebunkingopinion diffusioncognitive sciencebot detection
0
0 comments X

The pith

This paper proposes Cognition Security (CogSec) as a new multidisciplinary field to study how fake news affects human cognition from misperception to biased decisions and to develop debunking methods.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The authors define Cognition Security as the study of fake news effects on cognition and the search for countermeasures, drawing on social science, psychology, cognitive science, neuroscience, AI, and computer science. They review prior work on fake news propagation and cognition mechanisms while identifying research challenges such as human-content cognition, social influence, opinion diffusion, and detection of fake news and bots. The paper outlines open issues including early detection, explainable debunking, and social contagion models. A sympathetic reader would care because the proposal frames cognitive science as a route to reduce harms from misinformation in social networks. The central premise rests on the idea that advances in understanding human cognition can translate into practical prevention.

Core claim

Cognition Security (CogSec) studies the potential impacts of fake news to human cognition, ranging from misperception, untrusted knowledge acquisition, targeted opinion/attitude formation, to biased decision making, and investigates the effective ways for fake news debunking, as a multidisciplinary field that leverages knowledge from social science, psychology, cognition science, neuroscience, AI and computer science.

What carries the argument

Cognition Security (CogSec), the proposed multidisciplinary research field that examines fake news impacts on cognition mechanisms and develops debunking approaches.

If this is right

  • Fake news can produce misperception, untrusted knowledge, targeted opinion formation, and biased decisions.
  • Human-content cognition mechanism, social influence, opinion diffusion, fake news detection, and malicious bot detection form core research challenges.
  • Early detection of fake news, explainable debunking, and social contagion models remain open research directions.
  • Multidisciplinary methods combining psychology, AI, and computer science can address propagation and cognition mechanisms.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Platform features informed by CogSec could prioritize cognitive-resilient information flows over raw engagement metrics.
  • The framework suggests testable links between bot activity and measurable shifts in user decision patterns.
  • Extending CogSec to offline media environments would require adapting online detection techniques to slower information cycles.

Load-bearing premise

Advances in cognitive science provide effective tools for preventing fake news impacts on human thinking.

What would settle it

A controlled study in which cognitive-science-based interventions produce no measurable reduction in susceptibility to fake news on perception, knowledge trust, or decision bias.

read the original abstract

The wide spread of fake news in social networks is posing threats to social stability, economic development and political democracy etc. Numerous studies have explored the effective detection approaches of online fake news, while few works study the intrinsic propagation and cognition mechanisms of fake news. Since the development of cognitive science paves a promising way for the prevention of fake news, we present a new research area called Cognition Security (CogSec), which studies the potential impacts of fake news to human cognition, ranging from misperception, untrusted knowledge acquisition, targeted opinion/attitude formation, to biased decision making, and investigates the effective ways for fake news debunking. CogSec is a multidisciplinary research field that leverages knowledge from social science, psychology, cognition science, neuroscience, AI and computer science. We first propose related definitions to characterize CogSec and review the literature history. We further investigate the key research challenges and techniques of CogSec, including human-content cognition mechanism, social influence and opinion diffusion, fake news detection and malicious bot detection. Finally, we summarize the open issues and future research directions, such as early detection of fake news, explainable fake news debunking, social contagion and diffusion models of fake news, and so on.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript proposes a new interdisciplinary research area called Cognition Security (CogSec) focused on the impacts of fake news on human cognition (misperception, untrusted knowledge acquisition, targeted opinion/attitude formation, biased decision making) and on effective debunking strategies. It defines CogSec as drawing from social science, psychology, cognitive science, neuroscience, AI, and computer science; reviews relevant literature history; identifies key challenges and techniques including human-content cognition mechanisms, social influence and opinion diffusion, fake news detection, and malicious bot detection; and outlines open issues and future directions such as early detection, explainable debunking, and social contagion models.

Significance. If adopted, the proposed framing could help consolidate research on the cognitive dimensions of misinformation beyond detection algorithms alone, by synthesizing existing work across disciplines to identify gaps in propagation and cognition mechanisms. The paper's contribution is primarily organizational and definitional rather than empirical or derivational; its value would lie in whether the community finds the delineated challenges and directions useful for guiding subsequent studies.

minor comments (3)
  1. [Abstract] Abstract: The opening sentence contains a grammatical issue ('The wide spread of fake news' should read 'The widespread spread of fake news' or 'The wide dissemination of fake news').
  2. [Abstract] Abstract: The statement that cognitive science 'paves a promising way for the prevention of fake news' is asserted without accompanying citations or brief justification; this background claim would benefit from one or two supporting references in the main text to strengthen readability.
  3. The manuscript would benefit from explicit section headings or a table that maps the proposed CogSec challenges (human-content cognition, social influence, detection) to specific cited works, to improve traceability for readers.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the detailed summary of our manuscript and for the positive assessment of its potential to consolidate research on the cognitive dimensions of misinformation. The recommendation for minor revision is noted. No specific major comments were provided in the report, so we have no points to address point-by-point at this stage. We will make any minor revisions as appropriate in the next version.

Circularity Check

0 steps flagged

No significant circularity: position paper proposes CogSec framing without derivations or self-referential reductions

full rationale

The manuscript is a position paper that defines Cognition Security (CogSec) as a multidisciplinary area motivated by existing cognitive science literature on fake news impacts. It reviews literature, lists challenges (human-content cognition, opinion diffusion, detection), and suggests future directions, but contains no equations, models, fitted parameters, or derivations. The central claim is definitional and organizational rather than predictive or deductive; the motivation from cognitive science is presented as background, not a load-bearing inference that reduces to self-citation or tautology. No self-citation chains, uniqueness theorems, or ansatzes are invoked to justify core results. The proposal stands independently of any prior results by the same authors.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The paper rests on the assumption that cognitive science offers effective prevention tools and introduces CogSec as a new organizing concept without independent evidence or free parameters.

axioms (1)
  • domain assumption The development of cognitive science paves a promising way for the prevention of fake news
    Invoked in the abstract to justify the new research area.
invented entities (1)
  • Cognition Security (CogSec) no independent evidence
    purpose: A new multidisciplinary research field to study fake news impacts on cognition and debunking methods
    Newly proposed without external validation or falsifiable predictions in the abstract.

pith-pipeline@v0.9.0 · 5748 in / 1285 out tokens · 19966 ms · 2026-05-24T23:45:49.047627+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

150 extracted references · 150 canonical work pages · 11 internal anchors

  1. [1]

    Scientific communication in a post -truth society,

    S. Iyengar, and D. S. Massey, “Scientific communication in a post -truth society,” Proceedings of the National Academy of Sciences , vol.116, no.16, pp.7656-7661, 2019

    work page 2019

  2. [2]

    The science of fake news ,

    D. M. J. Lazer, et al ., “The science of fake news ,” Science, vol.359, no.6380, pp.1094-1096, 2018

    work page 2018

  3. [3]

    Online misinformation: Challenges and future directions,

    M. Fernandez, and H. Alani, “Online misinformation: Challenges and future directions,” in Companion Proceedings of the The Web Conference

    work page

  4. [4]

    International World Wide Web Conferences Steering Committee, 2018, pp. 595-602

    work page 2018

  5. [5]

    Selective exposure to misinformation: Evidence from the consumption of fake news during the 2016 US presidential campaign,

    A. Guess, B. Nyhan, and J. Reifler, “Selective exposure to misinformation: Evidence from the consumption of fake news during the 2016 US presidential campaign,” European Research Council, vol.9, 2018

    work page 2016

  6. [6]

    Fake news: Fundamental theories, detection strategies and challenges ,

    X. Zhou, et al., “Fake news: Fundamental theories, detection strategies and challenges ,” in Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. ACM, 2019, pp.836-837

    work page 2019

  7. [7]

    The spread of true and false news online,

    S. Vosoughi, D. Roy, and S. Aral, “The spread of true and false news online,” Science, vol.359, no.6380, pp.1146-1151, 2018

    work page 2018

  8. [8]

    The misinformation machine ,

    D. Ruths, “The misinformation machine ,” Science, vol.363, no.6425, pp.348-348, 2019

    work page 2019

  9. [9]

    Limited individual attention and online virality of low - quality information ,

    X. Qiu, et al., “Limited individual attention and online virality of low - quality information ,” Nature Human Behaviour , vol.1, no.7, pp. 0132, 2017

    work page 2017

  10. [10]

    The Future of Deception: Machine -Generated and Manipulated Images, Video, and Audio? ,

    J. Bakdash, et al. , “The Future of Deception: Machine -Generated and Manipulated Images, Video, and Audio? ,” in 2018 International Workshop on Social Sensing (SocialSens). IEEE, 2018, pp.2-2

    work page 2018

  11. [11]

    Artificial intelligence, deepfakes and a future of ectypes ,

    L. Floridi, “Artificial intelligence, deepfakes and a future of ectypes ,” Philosophy & Technology, vol.31, no.3, pp.317-321, 2018

    work page 2018

  12. [12]

    DeepFakes: a New Threat to Face Recognition? Assessment and Detection

    P. Korshunov, and S. Marcel, “Deepfakes: a new threat to face recognition? assessment and detection,” arXiv preprint arXiv:1812.08685, 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  13. [13]

    Protecting World Leaders Against Deep Fakes ,

    S. Agarwal, et al., “Protecting World Leaders Against Deep Fakes ,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. IEEE, 2019, pp.38-45

    work page 2019

  14. [14]

    Cognitive attraction and online misinformation ,

    A. Acerbi, “Cognitive attraction and online misinformation ,” Palgrave Communications, vol.5, no.1, pp.15-21, 2019

    work page 2019

  15. [15]

    Detection and resolution of rumours in social media: A survey ,

    A. Zubiaga, et al., “Detection and resolution of rumours in social media: A survey ,” ACM Computing Surveys (CSUR) , vol.51, no.2, pp. 32-67, 2018

    work page 2018

  16. [16]

    Disinformation on the web: Impact, characteristics, and detection of wikipedia hoaxes,

    S. Kumar, R. West, and J. Leskovec, “Disinformation on the web: Impact, characteristics, and detection of wikipedia hoaxes,” in Proceedings of the 25th international conference on World Wide Web . International World Wide Web Conferences Steering Committee, 2016, pp.591-602

    work page 2016

  17. [17]

    Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter ,

    S. Volkova, et al., “Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter ,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2017, pp.647-653

    work page 2017

  18. [18]

    Mining misinformation in social media ,

    L. Wu, et al., “Mining misinformation in social media ,” in Big Data in Complex and Social Networks , 1 st ed., London, UK: Chapman and Hall/CRC, 2016, pp.135-162

    work page 2016

  19. [19]

    Fake news detection on social media: A data mining perspective,

    K. Shu, et al. , “Fake news detection on social media: A data mining perspective,” ACM SIGKDD Explorations Newsletter, vo.19, no.1, pp.22- 36, 2017

    work page 2017

  20. [20]

    Fake news: A survey of research, detection methods, and opportunities,

    X. Zhou, and R. Zafarani, “Fake news: A survey of research, detection methods, and opportunities,” arXiv preprint arXiv:1812.00315, 2018

    work page arXiv 2018

  21. [21]

    Detection of human, legitimate bot, and malicious bot in online social networks based on wavelets ,

    G. F. C. Campos, et al. , “Detection of human, legitimate bot, and malicious bot in online social networks based on wavelets ,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol.14, no.1s, pp.26-42, 2018

    work page 2018

  22. [22]

    On the study of social interactions in twitter,

    S. A. Macskassy, “On the study of social interactions in twitter,” in Sixth International AAAI Conference on Weblogs and Social Media. 2012

    work page 2012

  23. [23]

    B. A. Forouzan, Cryptography & network security, New York, NY, USA: McGraw-Hill, Inc., 2007

    work page 2007

  24. [24]

    Filter bubbles and fake news,

    D. DiFranzo, and M. J. K. Gloria, “Filter bubbles and fake news,” ACM Crossroads, vol.23, no.3, pp.32-35, 2017

    work page 2017

  25. [25]

    From echo chamber to persuasive device? Rethinking the role of the Internet in campaigns ,

    C. Vaccari, “From echo chamber to persuasive device? Rethinking the role of the Internet in campaigns ,” New Media & Society , vol.15, no.1, pp.109-127, 2013

    work page 2013

  26. [26]

    The echo chamber: Strategic voting and homophily in social networks,

    A. Tsang, and K. Larson, “The echo chamber: Strategic voting and homophily in social networks,” in Proceedings of the 2016 international conference on autonomous agents & multiagent systems . International Foundation for Autonomous Agents and Multiagent Systems, 2016 , pp.368-375

    work page 2016

  27. [27]

    Filter bubbles, echo chambers, and online news consumption ,

    S. Flaxman, S. Goel, and J. M. Rao, “Filter bubbles, echo chambers, and online news consumption ,” Public opinion quarterly , vol.80, no.S1, pp.298-320, 2016

    work page 2016

  28. [28]

    Falling for fake news: investigating the consumption of news via social media,

    M. Flintham, et al., “Falling for fake news: investigating the consumption of news via social media,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 2018, pp.376-385

    work page 2018

  29. [29]

    Tweeting from left to right: Is online political communication more than an echo chamber?,

    P. Barberá , et al. , “Tweeting from left to right: Is online political communication more than an echo chamber?,” Psychological science, vol. 26, no.10, pp.1531-1542, 2015

    work page 2015

  30. [30]

    Echo chambers on Facebook,

    W. Quattrociocchi, A. Scala, and C. R. Sunstein, “Echo chambers on Facebook,” Available at SSRN 2795110, 2016

    work page 2016

  31. [31]

    Attitudinal effects of mere exposure ,

    R. B. Zajonc, “Attitudinal effects of mere exposure ,” Journal of personality and social psychology, vol.9, no.2p2, pp.1-27, 1968

    work page 1968

  32. [32]

    The spreading of misinformation online ,

    M. Del Vicario, et al. , “The spreading of misinformation online ,” Proceedings of the National Academy of Sciences, vol.113, no.3, pp.554- 559, 2016

    work page 2016

  33. [33]

    Online journalists: Foundations for research into their changing roles ,

    J. B. Singer, “Online journalists: Foundations for research into their changing roles ,” Journal of computer -mediated communication , vol.4, no.1, pp.JCMC412, 1998

    work page 1998

  34. [34]

    News media, search engines and social networking sites as varieties of online gatekeepers ,

    R. K. Nielsen, “News media, search engines and social networking sites as varieties of online gatekeepers ,” in Rethinking journalism again , London, UK: Routledge, 2016, pp.93-108

    work page 2016

  35. [35]

    HOW ONLINE GATEKEEPERS GUARD OUR VIEW - NEWS PORTALS'INCLUSION AND RANKING OF MEDIA AND EVENTS,

    C. L. Bui, “HOW ONLINE GATEKEEPERS GUARD OUR VIEW - NEWS PORTALS'INCLUSION AND RANKING OF MEDIA AND EVENTS,” Global Media Journal, vol.9, no.16, 2010

    work page 2010

  36. [36]

    Talking to the broadcasters on Twitter: Networked gatekeeping in Twitter conversations with journalists ,

    W. W. Xu, and M. Feng, “Talking to the broadcasters on Twitter: Networked gatekeeping in Twitter conversations with journalists ,” Journal of Broadcasting & Electronic Media , vol.58, no.3, pp.420-437, 2014

    work page 2014

  37. [37]

    Political discourse on social media: Echo chambers, gatekeepers, and the price of bipartisanship,

    K. Garimella, et al., “Political discourse on social media: Echo chambers, gatekeepers, and the price of bipartisanship,” in Proceedings of the 2018 World Wide Web Conference . International World Wide Web Conferences Steering Committee, 2018, pp.913-922

    work page 2018

  38. [38]

    Ferreting facts or fashioning fallacies? Factors in rumor accuracy,

    N. DiFonzo, “Ferreting facts or fashioning fallacies? Factors in rumor accuracy,” Social and Personality Psychology Compass , vol.4, no.11, pp.1124-1137, 2010

    work page 2010

  39. [39]

    Framing bias: Media in the distribution of power ,

    R. M. Entman, “Framing bias: Media in the distribution of power ,” Journal of communication, vol.57, no.1, pp.163-173, 2007

    work page 2007

  40. [40]

    Media bias and influence: Evidence from newspaper endorsements,

    C. F. Chiang, and B. Knight, “Media bias and influence: Evidence from newspaper endorsements,” The Review of Economic Studies, vol.78, no.3, pp.795-820, 2011

    work page 2011

  41. [41]

    Iyengar, and D

    S. Iyengar, and D. R. Kinder, News that matters: Television and American opinion. Palo Alto, CA, USA: University of Chicago Press, 2010 , pp.6- 34. 8

    work page 2010

  42. [42]

    K. H. Jamieson, and K. K. Campbell, Interplay of Influence: News, Advertising, Politics and the Internet Age (with InfoTrac). Belmont, CA, USA: Wadsworth Publishing, 2005, pp.22-46

    work page 2005

  43. [43]

    Being the New York Times: the political behaviour of a newspaper,

    R. Puglisi, “Being the New York Times: the political behaviour of a newspaper,” The BE Journal of Economic Analysis & Policy, vol.11, no.1, 2011

    work page 2011

  44. [44]

    Does the media matter? A field experiment measuring the effect of newspapers on voting behavior and political opinions ,

    A. S. Gerber, D. Karlan, and D. Bergan, “Does the media matter? A field experiment measuring the effect of newspapers on voting behavior and political opinions ,” American Economic Journal: Applied Economics , vol.1, no.2, pp.35-52, 2009

    work page 2009

  45. [45]

    Media bias monitor: Quantifying biases of social media news outlets at large -scale,

    F. N. Ribeiro, et al., “Media bias monitor: Quantifying biases of social media news outlets at large -scale,” in Twelfth International AAAI Conference on Web and Social Media. 2018

    work page 2018

  46. [46]

    Fair and balanced? quantifying media bias through crowdsourced content analysis,

    C. Budak, S. Goel, and J. M. Rao, “Fair and balanced? quantifying media bias through crowdsourced content analysis,” Public Opinion Quarterly, vol.80, no.S1, pp.250-271, 2016

    work page 2016

  47. [47]

    Influence of fake news in Twitter during the 2016 US presidential election ,

    A. Bovet, and H. A. Makse, “Influence of fake news in Twitter during the 2016 US presidential election ,” Nature communications, vol.10, no.1, pp.7-20, 2019

    work page 2016

  48. [48]

    Post-truth: Study epidemiology of fake news ,

    A. Kucharski, “Post-truth: Study epidemiology of fake news ,” Nature, vol.540, no.7634, pp.525-525, 2016

    work page 2016

  49. [49]

    Validity judgments of rumors heard multiple times: the shape of the truth effect ,

    N. DiFonzo, et al., “Validity judgments of rumors heard multiple times: the shape of the truth effect ,” Social Influence, vol.11, no.1, pp.22 -39, 2016

    work page 2016

  50. [50]

    Social media research: Theories, constructs, and conceptual frameworks,

    E. W. T. Ngai, S. S. C. Tao, and K. K. L. Moon, “Social media research: Theories, constructs, and conceptual frameworks,” International Journal of Information Management, vol.35, no.1, pp.33-44, 2015

    work page 2015

  51. [51]

    Social media and fake news in the 2016 election,

    H. Allcott, and M. Gentzkow, “Social media and fake news in the 2016 election,” Journal of economic perspectives , vol.31, no.2, pp.211 -236, 2017

    work page 2016

  52. [52]

    Rumor clustering, consensus, and polarization: Dynamic social impact and self -organization of hearsay ,

    N. DiFonzo, et al. , “Rumor clustering, consensus, and polarization: Dynamic social impact and self -organization of hearsay ,” Journal of Experimental Social Psychology, vol.49, no.3, pp.378-399, 2013

    work page 2013

  53. [53]

    Less than you think: Prevalence and predictors of fake news dissemination on Facebook ,

    A. Guess, J. Nagler, and J. Tucker, “Less than you think: Prevalence and predictors of fake news dissemination on Facebook ,” Science advances, vol.5, no.1, pp.eaau4586, 2019

    work page 2019

  54. [54]

    What happened? The Spread of Fake News Publisher Content During the 2016 US Presidential Election ,

    C. Budak, “What happened? The Spread of Fake News Publisher Content During the 2016 US Presidential Election ,” in The World Wide Web Conference. ACM, 2019, pp:139-150

    work page 2016

  55. [56]

    Natural pedagogy as evolutionary adaptation,

    G. Csibra, and G. Gergely, “Natural pedagogy as evolutionary adaptation,” Philosophical Transactions of the Royal Society B: Biological Sciences , vol.366, no.1567, pp.1149-1157, 2011

    work page 2011

  56. [57]

    Selection and transmission processes for information in the emerging media environment: Psychological motives and message characteristics ,

    J. N. Cappella, H. S. Kim, and D. Albarrací n, “Selection and transmission processes for information in the emerging media environment: Psychological motives and message characteristics ,” Media psychology, vol.18, no.3, pp.396-424, 2015

    work page 2015

  57. [58]

    A neural model of valuation and information virality,

    C. Scholz, et al., “A neural model of valuation and information virality,” Proceedings of the National Academy of Sciences , vol.114, no.11, pp.2881-2886, 2017

    work page 2017

  58. [59]

    How a user’s personality influences content engagement in social media ,

    N. O. Hodas, and R. Butner, “How a user’s personality influences content engagement in social media ,” in International Conference on Social Informatics. Springer, Cham, 2016, pp.481-493

    work page 2016

  59. [60]

    Creating buzz: the neural correlates of effective message propagation ,

    E. B. Falk, et al. , “Creating buzz: the neural correlates of effective message propagation ,” Psychological Science , vol.24, no.7, pp.1234 - 1242, 2013

    work page 2013

  60. [61]

    Who will share my image?: Predicting the content diffusion path in online social networks,

    W. Hu, et al. , “Who will share my image?: Predicting the content diffusion path in online social networks,” in Proceedings of the eleventh ACM international conference on web search and data mining . ACM, 2018, pp.252-260

    work page 2018

  61. [62]

    Retweet prediction with attention -based deep neural network,

    Q. Zhang, et al., “Retweet prediction with attention -based deep neural network,” in Proceedings of the 25th ACM international on conference on information and knowledge management. ACM, 2016, pp.75-84

    work page 2016

  62. [63]

    Misinformation and its correction: Continued influence and successful debiasing,

    S. Lewandowsky, et al., “Misinformation and its correction: Continued influence and successful debiasing,” Psychological Science in the Public Interest, vol.13, no.3, pp.106-131, 2012

    work page 2012

  63. [64]

    Depression: perspectives from affective neuroscience,

    R. J. Davidson, et al. , “Depression: perspectives from affective neuroscience,” Annual review of psychology , vol.53, no.1, pp.545-574, 2002

    work page 2002

  64. [65]

    Cognitive neuroscience of emotional memory,

    K. S. LaBar, and R. Cabeza, “Cognitive neuroscience of emotional memory,” Nature Reviews Neuroscience, vol.7, no.1, pp.54-64, 2006

    work page 2006

  65. [66]

    Neuroscience and education: myths and messages,

    P. A. Howard-Jones, “Neuroscience and education: myths and messages,” Nature Reviews Neuroscience, vol.15, no.12, pp.817-824, 2014

    work page 2014

  66. [67]

    Neuroscience-inspired artificial intelligence ,

    D. Hassabis, et al. , “Neuroscience-inspired artificial intelligence ,” Neuron, vol.95, no.2, pp.245-258, 2017

    work page 2017

  67. [68]

    Neuroeconomics: How neuroscience can inform economics,

    C. Camerer, G. Loewenstein, and D. Prelec, “Neuroeconomics: How neuroscience can inform economics,” Journal of economic Literature , vol.43, no.1, pp.9-64, 2005

    work page 2005

  68. [69]

    Progress and challenges in probing the human brain,

    R. A. Poldrack, and M. J. Farah, “Progress and challenges in probing the human brain,” Nature, vol.526, no.7573, pp.371-382, 2015

    work page 2015

  69. [70]

    Audience preferences are predicted by temporal reliability of neural processing,

    J. P. Dmochowski, et al. , “Audience preferences are predicted by temporal reliability of neural processing,” Nature communications, vol.5, pp.4567-4575, 2014

    work page 2014

  70. [71]

    From neural responses to population behavior: Neural focus group predicts population-level media effects,

    E. B. Falk, E. T. Berkman, and M. D. Lieberman, “From neural responses to population behavior: Neural focus group predicts population-level media effects,” Psychological science, vol.23, no.5, pp.439-445, 2012

    work page 2012

  71. [72]

    Intersubject synchronization of cortical activity during natural vision,

    U. Hasson, et al., “Intersubject synchronization of cortical activity during natural vision,” science, vol.303, no.5664, pp.1634-1640, 2004

    work page 2004

  72. [73]

    Cognitive neuroscience of human social behavior,

    R. Adlolphs, “Cognitive neuroscience of human social behavior,” Nature Reviews Neuroscience, vol.4, pp.165-178, 2003

    work page 2003

  73. [74]

    Reaching a consensus ,

    M. H. DeGroot, “Reaching a consensus ,” Journal of the American Statistical Association, vol.69, no.345, pp.118-121, 1974

    work page 1974

  74. [75]

    Attitude and attitude change,

    R. B. Cialdini, R. E. Petty, and J. T. Cacioppo, “Attitude and attitude change,” Annual review of psychology, vol.32, no.1, pp.357-404, 1981

    work page 1981

  75. [76]

    Maximizing the spread of influence through a social network ,

    D. Kempe, J. Kleinberg, and É. Tardos, “Maximizing the spread of influence through a social network ,” in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2003, pp.137-146

    work page 2003

  76. [77]

    Negativity bias, negativity dominance, and contagion,

    P. Rozin, and E. B. Royzman, “Negativity bias, negativity dominance, and contagion,” Personality and social psychology review , vol.5, no.4, pp.296-320, 2001

    work page 2001

  77. [78]

    Emotional contagion,

    E. Hatfield, J. T. Cacioppo, and R. L. Rapson, “Emotional contagion,” Current directions in psychological science, vol.2, no.3, pp.96-100, 1993

    work page 1993

  78. [79]

    Positive consumer contagion: Responses to attractive others in a retail context ,

    J. J. Argo, D. W. Dahl, and A. C. Morales, “Positive consumer contagion: Responses to attractive others in a retail context ,” Journal of Marketing Research, vol.45, no.6, pp.690-701, 2008

    work page 2008

  79. [80]

    Financial contagion ,

    F. Allen, and D. Gale, “Financial contagion ,” Journal of political economy, vol.108, no.1, pp.1-33, 2000

    work page 2000

  80. [81]

    Influence maximization in complex networks through optimal percolation,

    F. Morone, and H. A. Makse, “Influence maximization in complex networks through optimal percolation,” Nature, vol.524, no.7563, pp.65- 147, 2015

    work page 2015

Showing first 80 references.