Many thanks to the editor and reviewers! 🌷

If you want to read more about the nuts and bolts of our reward learning account of social learning—the Social Feature Learning (SFL) model—check out our recent @nathumbehav.nature.com paper: www.nature.com/articles/s41...

Many thanks to @bjornlindstrom.bsky.social & @lucasmolleman.bsky.social

Altogether, we present a mechanistic account for the substantial flexibility and individual variability observed in social learning. A domain-general reward learning model shows that personal experience can shape social learning to render it adaptive.

Thanks to the reviewers and editorial team! 💐

Going beyond these experiments, we explore the implications of the SFL model for social learning under a variety of environmental contexts (such as spatial & environmental variability or dangerous environments, check out the full paper for these agent-based simulations and much much more).

In a final experiment (Exp. 6), we find evidence suggesting that learning about social- and non-social features follows the same principles. We do so by establishing feature competition (i.e., shared associative strength between social- and non-social features, see full text for details).

This suggests that SLS can be shaped by rewards. We show that among multiple available features, people pick out reward-predictive ones (Exp. 3), that this learning pattern also holds with 4 (instead of 2) choice options (Exp. 4), and that learning generalizes to dissimilar contexts (Exp. 5)

Exps. 1 & 2: Consistent with our model, social learning was shaped by rewards: Having learnt that the majority (or minority) choice is reward predictive, pps copied the majority (or minority) when encountering novel targets. Exp. 2 replicates this with others' payoffs instead of choice behaviour.

☀️ We advance a domain-general reinforcement learning model—the Social Feature Learning (SFL) model—explaining SLS as the result of associating social features (e.g., others choices, their payoffs, or their age) with rewards. We test core assumptions and predictions across 6 experiments (n = 1941).

Selective social learning is essential to navigate the vast and often contradictory forest of social information. At least 26 social learning strategies (SLS; such as 'follow the majority'; 'copy the prestigious') have been documented, but which mechanisms underlie the emergence of SLS? 🤔

with @philipparnamets.bsky.social

Thanks to my great team!

w/ Philip Pärnamets, Ekatarina Yarmolenko & @bjornlindstrom.bsky.social

Together, these findings show that fairness concerns & conformity jointly but independently shape moral norms. Understanding their interplay can help explain the successes & failures of third-party judgments regulating prosociality in social systems

Having established these two separable factors, how may their interplay affect larger social systems? Using agent-based simulations, we find:

🔹 Inequality aversion helped societies move toward prosocial states (blue)
🔹 Commonness bias reinforced selfish defaults (red) instead of promoting fairness

How do both factors interact in a shared setting? In Study 2, we combined both Study 1 games into a novel game, manipulating inequality & commonness independently. Results closely track those of Study 1: Both motivations shaped moral judgments, but were only weakly related.

In Study 1, participants judged others’ behavior in economic games designed to isolate each motivation. Results show distinct effects of inequality aversion & commonness, however: only weak associations between both factors emerged, pointing to separate cognitive contributions

Third-party judgments help regulate social life—as in cooperation & coordination problems—but which principles guide them?

Past research has employed monocausal approaches, focused on 1) inequality aversion or 2) the common-is-moral heuristic, neglecting the complex nature of many moral judgments

Thanks to my great team!

w/ Philip Pärnamets, Ekatarina Yarmolenko & @bjornlindstrom.bsky.social