BitterBuster: A virtual world for understanding naturalistic patch foraging
4 affiliations
- 1Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
- 2Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), Faculté de Psychologie, Université de Strasbourg, Strasbourg, France.
- 3Entertainment Technology Center, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
- 4Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
Abstract
The past decade has seen a resurgence of interest in the naturalistic behavior of patch foraging in both the neural and cognitive sciences. Patch foraging is observed throughout the animal kingdom and requires the integration of multiple cognitive processes, including value and perceptual learning, spatial estimation, the evaluation of instantaneous return rates, and anticipating future effort. Here, we present a novel virtual environment, BitterBuster, for studying patch foraging in humans. In the BitterBuster game, players navigate a virtual world with four neighborhoods (patches) populated with houses (harvest points) containing inhabitants known as “Oomplets”. The goal is to search for “bitter” Oomplets, characterized by the union of specific body colors and textures. Each neighborhood has a different overall probability of containing bitter Oomplets and, within each, specific house locations predict the relative rate of bitter Oomplets. BitterBuster can be used for solo or team based foraging, where the tasks of exploring and perceptual learning are implemented by two separate players working together. Our results show how both the solo and team version of BitterBuster can accurately measure multiple aspects of naturalistic patch foraging, with participants learning to identify optimal patches, classify harvestable resources, and make rational decisions about when to leave a patch before overharvesting. BitterBuster is well-suited for researchers interested in the contextual, neural, and cooperative dynamics of foraging.
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BibTeX
@article{kasarda2026bitterbuster,
title = {{BitterBuster: A virtual world for understanding naturalistic patch foraging}},
author = {John P Kasarda and Jyotika Bahuguna and Angela Zhang and Hua Tong and Yuan Tan and Ruizi Wang and Michael J Tarr and Timothy Verstynen},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0361502},
url = {https://doi.org/10.1371/journal.pone.0361502},
note = {CC BY},
}
RIS
TY - JOUR
TI - BitterBuster: A virtual world for understanding naturalistic patch foraging
AU - John P Kasarda
AU - Jyotika Bahuguna
AU - Angela Zhang
AU - Hua Tong
AU - Yuan Tan
AU - Ruizi Wang
AU - Michael J Tarr
AU - Timothy Verstynen
PY - 2026
DA - 2026/10/30
JO - PLOS One
DO - 10.1371/journal.pone.0361502
UR - https://doi.org/10.1371/journal.pone.0361502
AB - The past decade has seen a resurgence of interest in the naturalistic behavior of patch foraging in both the neural and cognitive sciences. Patch foraging is observed throughout the animal kingdom and requires the integration of multiple cognitive processes, including value and perceptual learning, spatial estimation, the evaluation of instantaneous return rates, and anticipating future effort. Here, we present a novel virtual environment, BitterBuster, for studying patch foraging in humans. In the BitterBuster game, players navigate a virtual world with four neighborhoods (patches) populated with houses (harvest points) containing inhabitants known as “Oomplets”. The goal is to search for “bitter” Oomplets, characterized by the union of specific body colors and textures. Each neighborhood has a different overall probability of containing bitter Oomplets and, within each, specific house locations predict the relative rate of bitter Oomplets. BitterBuster can be used for solo or team based foraging, where the tasks of exploring and perceptual learning are implemented by two separate players working together. Our results show how both the solo and team version of BitterBuster can accurately measure multiple aspects of naturalistic patch foraging, with participants learning to identify optimal patches, classify harvestable resources, and make rational decisions about when to leave a patch before overharvesting. BitterBuster is well-suited for researchers interested in the contextual, neural, and cooperative dynamics of foraging.
SN - 1932-6203
ER -