ArticleBiology letters2020
Behavioural variability contributes to over-staying in patchy foraging.
Article in Biology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Stochastic choice drives variability in patch foraging decisions in humans and rats.Communications psychology · 2026Article
- Human foraging strategies flexibly adapt to resource distribution and time constraints.Cognitive, affective & behavioral neuroscience · 2026Article
- Rats pursue food and leisure following the same rational principles.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Foraging animals use dynamic Bayesian updating to model meta-uncertainty in environment representations.PLoS computational biology · 2025Article
- Risk preferences depend on environmental richness in rats performing a patch-foraging task.bioRxiv : the preprint server for biology · 2025Article
- Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment Representations.bioRxiv : the preprint server for biology · 2024Article
- Statistical information about reward timing is insufficient for promoting optimal persistence decisions.Cognition · 2023Article
- Sex differences in patch-leaving foraging decisions in rats.Oxford open neuroscience · 2023Article
- Choices favoring cognitive effort in a foraging environment decrease when multiple forms of effort and delay are interleaved.Cognitive, affective & behavioral neuroscience · 2022Article
- The size dependency of foraging behaviour: an empirical test performed on aquatic amphipods.Oecologia · 2022Article
- Neuromodulation of Foraging Decisions: The Role of Dopamine.Frontiers in behavioral neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Foragers often systematically deviate from rate-maximizing choices in two ways: accuracy and precision. That is, they use suboptimal threshold values and also show variability in their application of those thresholds. We hypothesized that these biases are related and, more specifically, that foragers' widely known accuracy bias--over-staying--could be explained, at least in part, by their imprecision. To test this hypothesis, we analysed choices made by three rhesus macaques in a computerized patch foraging task. Confirming previously observed findings, we found high levels of variability. We then showed, through simulations, that this variability changed optimal thresholds, meaning that a forager aware of its own variability should increase its leaving threshold (i.e. over-stay) to increase performance. All subjects showed thresholds that were biased in the predicted direction. These results indicate that over-staying in patches may reflect, in part, an adaptation to behavioural variability.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.