@watarutoyokawa.bsky.social @thecharleywu.bsky.social @bjornlindstrom.bsky.social @ketikagarg.bsky.social @rdhawkins.bsky.social @shoalgroup.bsky.social @psmaldino.bsky.social

Thanks so much to @ralfkurvers.bsky.social and @promanczuk.bsky.social, but especially to Valerii: www.scienceofintelligence.de/people/valer... 🙏

@arc-mpib.bsky.social @mpib-berlin.bsky.social @scioi.bsky.social @unimarburg.bsky.social @humboldtuni.bsky.social

Finally, to prove our model insights could explain the observed dynamics, we developed an ABM reproducing key aspects of the behavioral data.

Simulations also provided generalizable insights beyond our experiment, revealing why collective intelligence can only emerge in certain environments.

We then used a comp model predicting movement decisions to test how private and social features guided participants' behavior.

Results confirmed that payoff information let participants selectively tune behavior to the position and direction of successful peers, unlocking collective intelligence!

Using high-resolution time-series data of participants' visual information and movement trajectories, we found that payoff information boosted performance by allowing collectives to flexibly reorganize visibility networks over time and adaptively guide information flow between group members.

Results show that sociality was a double-edged sword:

Groups could outperform solitary individuals through both superior tracking and search performance, but only when full payoff information was available.

In the absence of payoff information, they sometimes even performed worse!

We challenged individuals with a spatially-explicit search-and-tracking task in an immersive 3D environment.

By manipulating task complexity and the availability of social cues, we study how individuals adapt their visual attention and social learning strategies to different dynamic contexts.