Two LEVYNA Team Members Receive Prestigious Awards
We are delighted to share that two members of the LEVYNA team have recently received major awards recognizing their outstanding research.
Religious practices can be costly in terms of time and effort, and these costs can make them useful signals of commitment to a religious community. But this creates a puzzle: if costly religious signals bring social benefits, why don’t less committed people simply imitate them and enjoy the benefits of cooperation?
In a new paper, Martin Lang, Eva Kundtová Klocová, Alexandra Ružičková, Katarína Čellarová, and Radim Chvaja examine this question across three preregistered studies with 1,341 participants. The studies included both general cooperation settings and religious contexts, including Christian participants who produced religious signals by transcribing religious texts.
The authors propose that the answer may lie in how people estimate the costs and benefits of signaling. The same signal can have different subjective value for committed and uncommitted individuals. For committed religious participants, producing a costly signal may be associated with additional social and cooperative benefits that are less valuable to people who are not similarly committed.
The findings provide evidence that these differences in cost-benefit estimation can help explain why costly signals remain reliable indicators of commitment. More broadly, they suggest that the stability of costly signaling may depend not only on what a signal objectively costs, but also on how its costs and benefits are represented by the people producing it.
Read the open-access paper in Collabra: Psychology here
We are delighted to share that two members of the LEVYNA team have recently received major awards recognizing their outstanding research.
In a new paper published in Trends in Cognitive Sciences, Martin Lang, Khatereh Borhani, Alexandra Ružičková, Eva Kundtová Klocová, and Radim Chvaja propose that ritual performance and persistence can be understood through reinforcement learning.