Selective Observation Under Limited Resources Biases Social Inference Through Hysteresis
Abstract
Despite limited access to others actions and outcomes, humans excel at inferring hidden intentions. Given only partial access, how do they decide what to observe, and how does selective observation shape inference? Here, we examined how choosing what to observe can bias the inference about others intentions. Participants played a game where they pursued a fleeing target while a computerized opponent acted competitively or cooperatively. Participants overestimated the opponents competitiveness after the opponent acted more competitively than expected, whereas no such bias occurred when the opponent was more cooperative than expected. This asymmetry depended on the sequence of events, resembling hysteresis, a form of path dependence observed in physical systems. We found that these biases became stronger when participants chose to observe the opponent instead of their own avatar, and this choice came at the cost of losing precise control over their avatar. Our findings highlight the trade-off in selecting what to observe, as the resulting inference biases propagate differently depending on the interaction history.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 0
- Organism
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Humans integrate gaze and decision cues for inferring preferences in social interactions
Mrugsen Nagsen Gopnarayan, Sophie Bavard, … Sebastian Gluth·bioRxiv·Jul 10, 2026
- Adaptive History Biases Result from Confidence-weighted Accumulation of Past Choices
Anke Braun, Anne E Urai, Tobias H. Donner·Journal of NeurosciencePeer-reviewed·Jan 25, 2018
- History biases reveal novel dissociations between perceptual and metacognitive decision-making
Christopher Benwell, Rachael Beyer, … Robin A. A. Ince·Journal of VisionPeer-reviewed·May 18, 2023
- Cherry-picking information: humans actively sample evidence to support prior beliefs
Paula Kaanders, Pradyumna Sepúlveda, … Benedetto De Martino·eLifePeer-reviewed·Apr 11, 2022
- Whom Do We Prefer to Learn From in Observational Reinforcement Learning?
Gota Morishita, Carsten Murawski, … Shinsuke Suzuki·PLoS Computational BiologyPeer-reviewed·Dec 8, 2025
- Suboptimal human inference inverts the bias-variance trade-off for decisions with asymmetric evidence
Tahra L. Eissa, Joshua I. Gold, … Zachary P. Kilpatrick·bioRxiv·Dec 6, 2020