Visual-spatial dynamics drive adaptive social learning in immersive environments

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: June 29, 2023

Human cognition is distinguished by our ability to adapt different environments and circumstances. Yet the mechanisms driving adaptive behavior have predominantly been studied in separate asocial social contexts, with an integrated framework remaining elusive. Here, we use a collective foraging task virtual Minecraft environment integrate these two fields, leveraging automated transcriptions of visual field data combined high-resolution spatial trajectories. Our behavioral analyses capture both structure temporal dynamics interactions, which are then directly tested using computational models sequentially predicting each decision. These results reveal that adaptation selective learning driven individual success (rather than factors). Furthermore, it degree adaptivity---of learning---that best predicts performance. findings not only theories across domains, but also provide key insights into adaptability human decision-making complex dynamic landscapes.

Language: Английский

Ill-informed Consensus or Truthful Disagreement? How Argumentation Styles and Preference Perceptions Affect Deliberation Outcomes in Groups with Conflicting Stakes

Erkenntnis, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 27, 2025

Language: Английский

Adaptive mechanisms of social and asocial learning in immersive collective foraging

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 25, 2025

Abstract Human cognition is distinguished by our ability to adapt different environments and circumstances. Yet the mechanisms driving adaptive behavior have predominantly been studied in separate asocial social contexts, with an integrated framework remaining elusive. Here, we use a collective foraging task virtual Minecraft environment integrate these two fields, leveraging automated transcriptions of visual field data combined high-resolution spatial trajectories. Our behavioral analyses capture both structure temporal dynamics interactions, which are then directly tested using computational models sequentially predicting each decision. These results reveal that adaptation selective learning driven individual success (rather than factors). Furthermore, it degree adaptivity—of learning—that best predicts performance. findings not only theories across domains, but also provide key insights into adaptability human decision-making complex dynamic landscapes.

Language: Английский