Miss Kanza Jamil
Activities
2026
- Institut Jean Nicod
Research visit to the Collective Intelligence Group at Institut Jean Nicod (CNRS/ENS/EHESS), Paris. International research visit to Institut Jean Nicod, Paris, hosted by Dr Hugo Mercier focuses on collaborative research in collective intelligence, social cognition, group reasoning, and team decision-making. Activities include research meetings, seminar attendance, presentation of doctoral research findings, development of future collaborative projects, and exploration of cross-cultural research on social perception, insight, and collective decision-making.
Sep 2026 – Oct 2026
Activity: Visiting an external academic institution (Visiting researcher) - Team dynamics, dominance, and decision making process
Our research investigates how insight, influence, competence, and leadership dynamics shape team decision-making. Across two studies (711 participants, 221 teams), we found that while individual competence is vital, optimal performance requires distinct metacognitive insights into both self and team capabilities. During group conflict, individual influence is driven by competence, confidence, and perceived leadership. However, at the team level, universally high leadership perceptions correlate with poorer outcomes, whereas mutual perceptions of warmth predict superior performance. Crucially, a universal desire for leadership within teams was associated with lower competence, lower insight, and reduced performance. We suggest the potential for integrating these behavioural findings with social neuroscience by exploring how the cognitive tension between collaborative and competitive group dynamic maps onto underlying neural mechanisms regulating metacognition, theory of mind, and dominance circuitry.
24 Jun 2026 – 26 Jun 2026
Activity: Oral presentation (Speaker)
2025
- Confidently Wrong, Persuasively Right? Exploring Individual Influence in Team Decision-Making Dynamics
Social perception plays a central role in group dynamics, where status, assertiveness, and perceived credibility can significantly shape group outcomes. In particular, confidence is often used by others as a heuristic for correctness, despite being only modestly correlated with actual accuracy. Our study examined individual influence within team-based decision-making and its associations with social perceptions, competence, confidence, and metaperceptive insight. A total of 356 participants were assigned to 111 teams and completed a two-phase fluid reasoning task based on Raven-like matrices. Participants first responded individually and then collaborated to provide team answers. Influence was defined as the extent to which a participant’s individual response matched the final team decision. Participants also provided self-ratings and were rated by teammates across four social attributes: warmth, competence, leadership, and team contribution. Metaperceptive insight was calculated as the accuracy of participants’ perceptions of how others viewed them. Influence was further categorized by correctness and the presence or absence of group consensus. We hypothesized that participants with greater metaperceptive insight would exert more influence, particularly when their initial answers were incorrect but ultimately accepted by the group. More broadly, the study aimed to assess whether perceived credibility and social alignment play a more substantial role in influence than actual task performance.
25 Jun 2025
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Activity: Oral presentation (Speaker)
2024
- Collective knowledge, Crowdsourcing and the suprising effectiveness of Small Teams
Objectives. We assessed the efficiency of small teams relative to their team members by comparing a team's performance to crowdsourcing algorithms run on the team's members. Crowdsourcing algorithms attempt to find optimal methods for aggregating individual decisions to produce an accurate "crowd" response. They are demonstrably highly effective and vary from a simple consensus to weighted combinations of individual participants' accuracy and confidence.
Design. Small teams were tested on problems presented in two phases: first, the Individual Phase, where each team member privately recorded their own answer, and then the Team Phase, where team members came together and agreed on an answer.
Methods. We tested 635 people organised into 211 teams of 2–4 people. Either general knowledge or novel matrices problems were presented to different teams.
Results. Team performance was highly correlated (r > .7) with the output of crowdsourcing models run on individual team members. In every case, 75% or more of real teams outperformed the crowdsourcing algorithms. We also calculated each team's "collective knowledge" as the union of all correct responses made in the Individual Phase. Separate analyses showed that collective knowledge was a hard ceiling on team performance and that teams varied greatly in their ability to tap into this knowledge.
Conclusions. Real teams outperformed even the best crowdsourcing algorithms. The best teams were not necessarily discovering new knowledge but were better at surfacing knowledge they already had.
27 Aug 2024
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Activity: Oral presentation (Speaker) - The "i" in team insight impacting collective performance
We investigated the efficiency of small teams compared to their best-performing members to explore team synergy or collective intelligence that surpasses individual competence. Building on Woolley and colleagues' pioneering research highlighting the collective intelligence factor, or "c factor," which exceeds individual capabilities, we examined various team member attributes to identify key factors contributing to high-performing teams. Our methodology involved a two-phase testing process: the Individual Phase, where members privately recorded their answers, and the Team Phase, where they collaborated to agree on an answer. Participants rated their confidence in both their own and their team's answers and assessed each other on leadership, followership, warmth, and competence across self, meta, and social ratings. We tested 345 participants organized into 111 teams of 3-4 people, presenting them with 20 novel matrix problems. Using multiple linear regression, we analyzed the relationship between perception, insight, and performance to determine the effects of these attributes on individual and team performance.
19 Jun 2024
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Activity: Oral presentation (Speaker)