Joint commitment in human cooperative hunting through an “Imagined We”

Cooperation involves the challenge of jointly selecting one from multiple goals while maintaining the team’s joint commitment to it. We test joint commitment in a multi-player hunting game, combining psychophysics and computational modeling. Joint commitment is modeled through an \"Imagined We\" (IW) approach, where each agent uses Bayesian inference to infer the intention of “We”, an imagined supraindividual agent controlling all agents as its body parts. This is compared against a Reward Sharing (RS) model, which frames cooperation through reward sharing via multi-agent reinforcement learning (MARL). Both humans and IW, but not RS, maintained high performance by jointly committing to a single prey, regardless of prey quantity or speed. Human observers rated all hunters in both human and IW teams as making high contributions to the catch, regardless of their proximity to the prey, suggesting that high-quality hunting stemmed from sophisticated cooperation rather than individual strategie..., Data Collection:The dataset was collected through offline laboratory experiments involving human subjects, machine simulations, and human-machine collaboration. The machine simulations and collaborative tasks were based on models implemented in Python, including reinforcement learning, neural networks, and Bayesian inference. Participants engaged in tasks designed to assess cognitive processes, with data recorded in controlled laboratory conditions.
Data Processing:The collected data was processed using Python’s pandas library. This involved data cleaning, transformation, and preparation for further analysis. For statistical analysis, we used the Jeffreys’s Amazing Statistics Program (JASP) software to perform various statistical tests., , # Joint Commitment in Human Cooperative Hunting through an Imagined We

[https://doi.org/10.5061/dryad.brv15dvjn](https://doi.org/10.5061/dryad.brv15dvjn)

## Location of the Data and Code

*The data and code are compressed in the file ‘imaginedWeCodeRelease.zip’, which can be downloaded from the ‘Files’ tab.*

## Description of the data and file structure

**Data Collection:**

The dataset was collected through offline laboratory experiments involving human subjects, machine simulations, and human-machine collaboration . The machine simulations and collaborative were based on models implemented in Python, including reinforcement learning, neural networks, and Bayesian inference. Participants engaged in tasks designed to assess cognitive processes, with data recorded in controlled laboratory conditions.

**Data Processing:**

The collected data was processed using Pythons pandas library. This involved data cleaning, transformation, and preparation for further analysis. For statistical ..., All human subjects data included in this dataset were collected with the explicit informed consent of participants, including their consent to publish the de-identified data in the public domain.

To ensure anonymity, participants are identified only by the time of their participation (e.g., \"20221221-1550\"). The data content and file names do not contain any personally identifiable information or demographic details such as names, gender, age, or other identifying characteristics.

合作的核心挑战在于，需从多个目标中共同选定其一，并始终维持团队对该目标的联合承诺（joint commitment）。本研究结合心理物理学（psychophysics）与计算建模（computational modeling），在多人狩猎游戏中对联合承诺展开测试。我们采用「想象性集体（Imagined We, IW）」方法对联合承诺进行建模：每个智能体通过贝叶斯推断（Bayesian inference），推断「我们」——即一个将所有智能体视作自身肢体的想象性超个体智能体（supraindividual agent）——的意图。本研究将该模型与奖励共享（Reward Sharing, RS）模型进行对比，后者通过多智能体强化学习（multi-agent reinforcement learning, MARL）框架下的奖励共享机制阐释合作行为。实验结果显示，人类参与者与IW模型均能通过对单一猎物达成联合承诺来维持高性能表现，且不受猎物数量与移动速度的影响，而RS模型则未展现出该特性。人类观察者对人类团队与IW团队中的所有猎手均给出了较高的捕获贡献评分，无论猎手与猎物的距离远近，这表明高质量的狩猎表现源自成熟的合作行为而非个体策略……

数据采集：本数据集通过线下实验室实验采集，实验涵盖人类被试、机器模拟以及人机协作三类范式。机器模拟与协作任务基于Python实现的模型搭建，模型类型包括强化学习、神经网络与贝叶斯推断。被试参与旨在评估认知过程的实验任务，所有数据均在可控的实验室环境中记录。
数据处理：采集到的数据通过Python的pandas库进行处理，流程涵盖数据清洗、格式转换与预处理，以支撑后续分析。统计分析环节，我们使用Jeffreys神奇统计程序（Jeffreys’s Amazing Statistics Program, JASP）完成各类统计检验。

# 基于想象性集体的人类合作狩猎中的联合承诺
[https://doi.org/10.5061/dryad.brv15dvjn](https://doi.org/10.5061/dryad.brv15dvjn)

## 数据与代码存放位置
数据与代码压缩于文件"imaginedWeCodeRelease.zip"中，可通过"Files"标签页下载。

## 数据与文件结构说明
**数据采集：** 本数据集通过线下实验室实验采集，实验涵盖人类被试、机器模拟以及人机协作三类范式。机器模拟与协作任务基于Python实现的模型搭建，模型类型包括强化学习、神经网络与贝叶斯推断。被试参与旨在评估认知过程的实验任务，所有数据均在可控的实验室环境中记录。

**数据处理：** 采集到的数据通过Python的pandas库进行处理，流程涵盖数据清洗、格式转换与预处理，以支撑后续分析。统计分析环节……本数据集收录的所有人类被试数据，均在被试明确签署知情同意书后采集，包括同意将去标识化后的数据公开发布。

为保障匿名性，被试仅以参与时间作为标识（例如"20221221-1550"）。数据内容与文件名均未包含任何个人可识别信息或人口统计细节，如姓名、性别、年龄或其他身份特征。