Resources

Recordings of all meetings

Slack group for connecting with other participants

workshop reports

1. Dubova, M., Galesic, M., & Goldstone, R. L. (2022). Cognitive Science of Augmented Intelligence. Cognitive Science, 46(12), e13229.

2. Wu, C., Dale, R., & Hawkins, R. Group coordination catalyzes individual and cultural intelligence (provisional draft). 

3. Seth Frey's piece on social mechanics (provisional draft).

4. Dale, R. & Rosen, Z. Augmented Bibliometric Community Analysis (provisional draft).

5. Rajaram, S. False Augmented Intelligence: Cognitive Science of Misinformation Spread. 

unstructured & incomplete AugInt literature

1. Chine, D. R., Brentley, C., Thomas-Browne, C., Richey, J. E., Gul, A., Carvalho, P. F., ... & Koedinger, K. R. (2022). Educational Equity Through Combined Human-AI Personalization: A Propensity Matching Evaluation. In International Conference on Artificial Intelligence in Education (pp. 366-377). Springer, Cham.

2. Nathan, M. J. (2021). Foundations of embodied learning: A paradigm for education. Routledge.

3. Painter, D. T., Daniels, B. C., & Laubichler, M. D. (2021). Innovations are disproportionately likely in the periphery of a scientific network. Theory in Biosciences, 140(4), 391-399.

4. Fogliato, Riccardo and De-Arteaga, Maria and Chouldechova, Alexandra, A Case for Humans-in-the-Loop: Decisions in the Presence of Misestimated Algorithmic Scores.

5. Sukumar, S., Ward, A. F., Elliott-Williams, C., Hakimi, S., & Mozer, M. C. (2022). Overcoming Temptation: Incentive Design For Intertemporal Choice. arXiv preprint arXiv:2203.05782.

6. Heyes, C. (2020). Psychological mechanisms forged by cultural evolution. Current Directions in Psychological Science, 29(4), 399-404

7. Heyes, C. (2019). Précis of cognitive gadgets: The cultural evolution of thinking. Behavioral and Brain Sciences, 42.

8. Bailenson, J. (2018). Experience on demand: What virtual reality is, how it works, and what it can do. WW Norton & Company.

9. Zhang, J., Conway, J., & Hidalgo, C. A. (2022). Why do people judge humans differently from machines? The role of agency and experience. arXiv preprint arXiv:2210.10081.

10. Silver, I., Mellers, B. A., & Tetlock, P. E. (2021). Wise teamwork: Collective confidence calibration predicts the effectiveness of group discussion. Journal of Experimental Social Psychology, 96, 104157.

11. Goldstone, R. L., Andrade-Lotero, E., Hawkins, R. D., & Roberts, M. E. (in press). The emergence of specialized roles Within groups. Topics in Cognitive Science.

12. Andrade-Lotero, E., & Goldstone, R. L. (2021).  Self-organized division of cognitive labor.  PLoS ONE, 16(7): e0254532. 

13. Hawkins, R. X. D., & Goldstone, R. L. (2016). The formation of social conventions in real-time environments.  PLoS One, 11(3): e0151670.

14. Roberts, M. E., & Goldstone, R. L. (2011).  Adaptive Group Coordination and Role Differentiation.  PLoS One, 6, 1-8.

15. Pellert, M., Metzler, H., Matzenberger, M., & Garcia, D. (2021). Validating daily social media macroscopes of emotions. arXiv preprint arXiv:2108.07646.

16. Garcia, D., Pellert, M., Lasser, J., & Metzler, H. (2021). Social media emotion macroscopes reflect emotional experiences in society at large. arXiv preprint arXiv:2107.13236.

17. Frey, S., Zhong, Q., Bulat, B., Weisman, W. D., Liu, C., Fujimoto, S., ... & Schweik, C. M. (2022). Governing online goods: Maturity and formalization in Minecraft, Reddit, and World of Warcraft communities. arXiv preprint arXiv:2202.01317.

18. Frey, S., & Sumner, R. W. (2019). Emergence of integrated institutions in a large population of self-governing communities. PloS one, 14(7), e0216335.

19. Steyvers, M., Tejeda, H., Kerrigan, G., & Smyth, P. (2022). Bayesian modeling of human–AI complementarity. Proceedings of the National Academy of Sciences, 119(11), e2111547119.

20. Lewandowsky, S., Cook, J., Ecker, U., Albarracin, D., Amazeen, M., Kendou, P., ... & Zaragoza, M. (2020). The debunking handbook 2020.

21. Lewandowsky, S., Cook, J., Ecker, U. K. H., & Newman, E. J. (2021). Under the Hood of The Debunking Handbook 2020: A consensus-based handbook of recommendations for correcting or preventing misinformation.

22. Holford, D., Fasce, A., Tapper, K., Demko, M., Lewandowsky, S., Hahn, U., ... & Wulf, M. (2022). A manifesto for science communication as collective intelligence.

23. Lewandowsky, S., Cook, J., Schmid, P., Holford, D. L., Finn, A., Leask, J., ... & Vraga, E. K. (2021). The COVID-19 vaccine communication handbook. A practical guide for improving vaccine communication and fighting misinformation.

24. Galesic, M., Barkoczi, D., Berdahl, A., Biro, D., Carbone, G., Giannoccaro, I., ... & Stein, D. L. (2022). Beyond collective intelligence: Collective adaptation.

25. Gordon, D. M. (2014). The ecology of collective behavior. PLoS biology, 12(3), e1001805.

26. Pagliara, R., Gordon, D. M., & Leonard, N. E. (2018). Regulation of harvester ant foraging as a closed-loop excitable system. PLoS computational biology, 14(12), e1006200.

27. Davidson, J. D., Arauco-Aliaga, R. P., Crow, S., Gordon, D. M., & Goldman, M. S. (2016). Effect of interactions between harvester ants on forager decisions. Frontiers in ecology and evolution, 4, 115.

28. Chandrasekhar, A., Gordon, D. M., & Navlakha, S. (2018). A distributed algorithm to maintain and repair the trail networks of arboreal ants. Scientific reports, 8(1), 1-19.

29. Chandrasekhar, A., Marshall, J. A., Austin, C., Navlakha, S., & Gordon, D. M. (2021). Better tired than lost: Turtle ant trail networks favor coherence over short edges. PLoS computational biology, 17(10), e1009523.

30. Sloman, S., & Fernbach, P. (2018). The knowledge illusion: Why we never think alone. Penguin.

31. Geana, M. V., Rabb, N., & Sloman, S. (2021). Walking the party line: The growing role of political ideology in shaping health behavior in the United States. SSM-population health, 16, 100950.

32. Fullerton, M. K., Rabb, N., Mamidipaka, S., Ungar, L., & Sloman, S. A. (2021). Evidence against risk as a motivating driver of COVID-19 preventive behaviors in the United States. Journal of Health Psychology.

33. Sloman, S., Kupor, D., & Yokum, D. (2021). Are voters influenced by the results of a consensus conference?. Behavioural Public Policy, 1-22.

34. Rabb, N., Han, J. J., & Sloman, S. A. (2021). How others drive our sense of understanding of policies. Behavioural Public Policy, 5(4), 454-479.

35. Pacheco, D., Hui, P. M., Torres-Lugo, C., Truong, B. T., Flammini, A., & Menczer, F. (2021). Uncovering Coordinated Networks on Social Media: Methods and Case Studies. ICWSM, 21, 455-466.

36. Torres-Lugo, C., Yang, K. C., & Menczer, F. (2022, May). The Manufacture of Partisan Echo Chambers by Follow Train Abuse on Twitter. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 16, pp. 1017-1028).

37. Sasahara, K., Chen, W., Peng, H., Ciampaglia, G. L., Flammini, A., & Menczer, F. (2021). Social influence and unfollowing accelerate the emergence of echo chambers. Journal of Computational Social Science, 4(1), 381-402.

38. Hahn, U. (2022). Collectives and epistemic rationality. Topics in Cognitive Science.

39. Rajaram, S. (2011). Collaboration both hurts and helps memory: A cognitive perspective. Current Directions in Psychological Science, 20(2), 76-81.

40. Choi, H. Y., Blumen, H. M., Congleton, A. R., & Rajaram, S. (2014). The role of group configuration in the social transmission of memory: Evidence from identical and reconfigured groups. Journal of Cognitive Psychology, 26(1), 65-80.

41. Rajaram, S. (in press). Collaborative Remembering and Collective Memory. Chapter to appear in M. J. Kahana &. A. D. Wagner (Eds.), Handbook on Human Memory. Oxford University Press.

42. Wang, X., Varol, O. & Eliassi-Rad, T. Information Access Equality on Generative Models of Complex Networks. Applied Network Science 7, 54 (2022). https://doi.org/10.1007/s41109-022-00494-8

43. Breazeal C. 2002. Designing Sociable Robots. Cambridge, MA: MIT Press

44. Hasan, E., Eichbaum, Q., Seegmiller, A. C., Stratton, C., & Trueblood, J. (2021). Harnessing the wisdom of the confident crowd in medical image decision-making.

45. Shusterman, Einat, et al. "Seeding Course Forums using the Teacher-in-the-Loop." LAK21: 11th International Learning Analytics and Knowledge Conference. 2021.

46. Yogev, Eran, et al. "Classifying and visualizing students' cognitive engagement in course readings." Proceedings of the Fifth Annual ACM Conference on Learning at Scale. 2018.

47. Segal, Avi, et al. "Keeping the teacher in the loop: Technologies for monitoring group learning in real-time." International Conference on Artificial Intelligence in Education. Springer, Cham, 2017.

48. Fazelpour, S., & De-Arteaga, M. (2022). Diversity in sociotechnical machine learning systems. Big Data & Society, 9(1), 20539517221082027.

49. Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people. Cambridge, UK, 10, 236605.

50. Yannier, N., Hudson, S. E., Koedinger, K. R., Hirsh-Pasek, K., Golinkoff, R. M., Munakata, Y., ... & Brownell, S. E. (2021). Active learning:“Hands-on” meets “minds-on”. Science, 374(6563), 26-30.

51. Mehlhorn, K., Newell, B. R., Todd, P. M., Lee, M. D., Morgan, K., Braithwaite, V. A., ... & Gonzalez, C. (2015). Unpacking the exploration–exploitation tradeoff: A synthesis of human and animal literatures. Decision, 2(3), 191.

52. Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. F., ... & Volfovsky, A. (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216-9221.

53. Eskreis-Winkler, L., Milkman, K. L., Gromet, D. M., & Duckworth, A. L. (2019). A large-scale field experiment shows giving advice improves academic outcomes for the advisor. Proceedings of the national academy of sciences, 116(30), 14808-14810.

54. Eskreis-Winkler, L., Fishbach, A., & Duckworth, A. L. (2018). Dear Abby: Should I give advice or receive it?. Psychological science, 29(11), 1797-1806.

55. Juvina, I. (2022). Knowledge Spillover, Trust, Effort, and Error Exposure in Peer-Assisted Learning.

56. Kalish, M. L., Lewandowsky, S., & Kruschke, J. K. (2004). Population of linear experts: knowledge partitioning and function learning. Psychological review, 111(4), 1072.

57. Centola, D., Becker, J., Brackbill, D., & Baronchelli, A. (2018). Experimental evidence for tipping points in social convention. Science, 360(6393), 1116-1119.

58. Voelkel, J. G., Stagnaro, M. N., Chu, J., Pink, S., Mernyk, J. S., Redekopp, C., ... & Willer, R. (2022). Megastudy identifying successful interventions to strengthen Americans’ democratic attitudes. Preprint.

59. Sheen, M., Kemp, S., & Rubin, D. (2001). Twins dispute memory ownership: A new false memory phenomenon. Memory & Cognition, 29(6), 779-788.

60. Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37(9), 1539–1553. https://doi.org/10.1037/0022-3514.37.9.1539

61. Schwartz, D. L. (1995). The emergence of abstract representations in dyad problem solving. The journal of the learning sciences, 4(3), 321-354.

62. Garrod, S., & Doherty, G. (1994). Conversation, co-ordination and convention: An empirical investigation of how groups establish linguistic conventions. Cognition, 53(3), 181-215.

63. Voiklis, J., & Corter, J. E. (2012). Conventional wisdom: Negotiating conventions of reference enhances category learning. Cognitive Science, 36(4), 607-634.

64. Fazelpour, S., & Danks, D. (2021). Algorithmic bias: Senses, sources, solutions. Philosophy Compass, 16(8), e12760.

65. Stevenson, M. T., & Doleac, J. L. (2022). Algorithmic risk assessment in the hands of humans. Available at SSRN 3489440.

66. Lorenz-Spreen, P., Lewandowsky, S., Sunstein, C. R., & Hertwig, R. (2020). How behavioural sciences can promote truth, autonomy and democratic discourse online. Nature human behaviour, 4(11), 1102-1109.

67. He, L., Richie, R., & Bhatia, S. (2022). Limitations to Optimal Search in Naturalistic Active Learning. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 44, No. 44).

68. Lewandowsky, S., Facer, K., & Ecker, U. K. (2021). Losses, hopes, and expectations for sustainable futures after COVID. Humanities and Social Sciences Communications, 8(1), 1-17.

69. Rollwage, M., Dolan, R. J., & Fleming, S. M. (2018). Metacognitive failure as a feature of those holding radical beliefs. Current Biology, 28(24), 4014-4021.

70. Gonzalez, C., Lerch, J. F., & Lebiere, C. (2003). Instance‐based learning in dynamic decision making. Cognitive Science, 27(4), 591-635.

71. Jhaver, S., Birman, I., Gilbert, E., & Bruckman, A. (2019). Human-machine collaboration for content regulation: The case of reddit automoderator. ACM Transactions on Computer-Human Interaction (TOCHI), 26(5), 1-35.

72. Peppler, K., Schindler, E., & Huang, J. AI in Classrooms: Impacts of Merlyn Mind on Teacher Technostress and Time Savings.