Rapidata/psychology-association-kiki-bouba-etc

--- dataset_info: features: - name: question dtype: string - name: option_1 dtype: image - name: option_2 dtype: image - name: option_1_selections dtype: int64 - name: option_2_selections dtype: int64 - name: detailed_results list: - name: age dtype: string - name: country dtype: string - name: gender dtype: string - name: language dtype: string - name: occupation dtype: string - name: selection dtype: string - name: userScore dtype: float64 splits: - name: train num_bytes: 19085499 num_examples: 20 download_size: 4429683 dataset_size: 19085499 configs: - config_name: default data_files: - split: train path: data/train-* --- # 🧠 Mental Association Dataset (200k Global Responses) ## **Dataset Summary** This dataset explores **mental associations**, the intuitive connections people make between shapes, objects, sounds, or concepts that have no inherent relation. The most famous example is the **Bouba–Kiki effect**, where people reliably associate a *spiky* shape with the sound *“kiki”* and a *rounded* shape with *“bouba.”* (See: [https://en.wikipedia.org/wiki/Bouba/kiki_effect](https://en.wikipedia.org/wiki/Bouba/kiki_effect)) Our dataset expands this experiment to **20 different association questions**, each presenting respondents with **two options** and asking them to choose one based on a specific prompt (e.g., *“Which one is called 'Kiki'?”*). In total, the dataset contains: * **~200,000 responses** * **~10,000 responses per question** * **Respondents from all over the world** * **Demographic metadata** (where available) The data was collected using the [**Rapidata API**](https://www.rapidata.ai/), which enables fast global collection of human-generated judgments. If there is community interest, we may expand this dataset in the future. Please consider leaving a ❤️ if you enjoy this dataset. --- ## **Noteworthy Findings** We encourage the community to explore the data and share insights. See below for details on the dataset structure and how to load it. Below are some observed patterns uncovered so far. ### **1. The Kiki–Bouba effect… reversed?** When asking the question such as: “Which one is ‘Kiki’ and which one is ‘Bouba’?" the following trend emerges: * **Spiky shape → “Kiki”** * **Blob shape → “Bouba”** However, in our dataset we find that globally the **blob-like** shape is selected more often (although marginally) when asking the question: *“Which one is called ‘Kiki’?”*. What is interesting however, is that, as shown below, when aggregating by language, **Arabic speakers** overwhelmingly choose the **blob** as *“Kiki”*, whereas **Japanese speakers** overwhelmingly choose the **spiky** shape.  While the global aggregate does **not** match the classic findings, some possible explanations may be: * Our phrasing (“Which one is called *‘Kiki’*?”) removes the paired *Bouba* context. Typically respondents are informed that one is *Bouba* and the other is *Kiki* adding additional constraints. * We have observed that Arabic speakers may be overrepresented in the data. --- ### **2. “Which one is reckless?” Bowling ball vs rubber duck** In this question, we ask respondents *"Which one is reckless?"*, and present them with an image of * A **rubber duck** * A **bowling ball** Globally, the **bowling ball** is the clear winner, likely due to destructive potential. However, **Polish speakers strongly diverge**, with a much higher proportion selecting the **duck**.  --- ## **Dataset Structure** Each row corresponds to **one of the 20 association questions**, containing: * `question`: The association question presented (e.g., *“Which one is ‘Kiki’?”*) * `option_1` / `option_2`: Image files representing the two choices * `option_1_selections` / `option_2_selections`: Number of respondents choosing each option * `detailed_results`: A list of respondent-level dictionaries Each dictionary includes: * `selection`: which option the respondent chose * `country` * `language` * `age` * `gender` * `occupation` * `userScore`: a respondent-level quality score used by Rapidata ### Example structure of `detailed_results` ```json { "selection": "option_1", "country": "PL", "language": "pl", "age": "25-34", "gender": "Female", "occupation": "Graphic Designer", "userScore": 0.87 } ``` --- ## **Loading the Dataset** Below is an example of how to load all **individual responses** for a given question into a Pandas DataFrame: ```python from datasets import load_dataset import pandas as pd # Login using e.g. `huggingface-cli login` to access this dataset ds = load_dataset("Rapidata/psychology-association-kiki-bouba-etc") df_from_hf = pd.DataFrame(ds['train']) question_idx = 0 q = pd.DataFrame(df_from_hf["detailed_results"][question_idx]) ``` --- ## **Data Collection** All responses were collected using the **Rapidata API**, which enables: * fast global human feedback * targeted or global reach * image, video, audio, and text-based tasks * real-time evaluation at scale 🔗 *More about Rapidata:* **(https://www.rapidata.ai/)** --- ## **Intended Use** * Cognitive science experiments * Cross-cultural analysis * Linguistic influence studies * Human perception & semantic association research * Fun explorations of collective intuition ## **Limitations** * The dataset is primarily for **exploration and entertainment** * No guarantees of scientific rigor * Responses may be skewed by: * demographic imbalances * translation differences * question phrasing * Some demographics are missing or self-reported --- ## **Citation** If you use this dataset, please refer back to this page, and consider leaving a like!数据集信息： 特征： - 字段名：question，数据类型：字符串型 - 字段名：option_1，数据类型：图像型 - 字段名：option_2，数据类型：图像型 - 字段名：option_1_selections，数据类型：64位整型 - 字段名：option_2_selections，数据类型：64位整型 - 字段名：detailed_results，数据类型：列表，列表元素为： - 字段名：age，数据类型：字符串型 - 字段名：country，数据类型：字符串型 - 字段名：gender，数据类型：字符串型 - 字段名：language，数据类型：字符串型 - 字段名：occupation，数据类型：字符串型 - 字段名：selection，数据类型：字符串型 - 字段名：userScore，数据类型：64位浮点型 数据划分： - 划分名称：train，字节数：19085499，样本数：20 下载大小：4429683 数据集总大小：19085499 配置项： - 配置名称：default，数据文件： - 划分：train，路径：data/train-* # 🧠 心理联想数据集（20万条全球反馈） ## 数据集概览 本数据集聚焦**心理联想**——即人类在无固有关联的形状、物体、声音或概念间建立的直觉性联结。其中最广为人知的案例为**Bouba-Kiki效应**：人们会稳定地将带有尖刺特征的形状与“kiki”发音绑定，将圆润形状与“bouba”发音绑定（详见：[https://en.wikipedia.org/wiki/Bouba/kiki_effect](https://en.wikipedia.org/wiki/Bouba/kiki_effect)）。 本数据集将该经典实验拓展至**20组不同的联想问题**，每组均向被试提供**两个选项**，并基于特定提示要求其选择其一（例如：“哪一个是‘Kiki’？”）。 本数据集总计包含： * 约20万条有效反馈 * 每组问题约1万条反馈 * 覆盖全球各地的被试 * （如可获取）附带人口统计元数据 本数据集通过[**Rapidata API**](https://www.rapidata.ai/)采集，该工具可快速完成全球范围内人类判断数据的收集。 若社区有需求，未来我们将对本数据集进行拓展。若您喜爱本数据集，不妨留下一颗❤️以示支持。 ## 值得关注的研究发现 我们鼓励社区成员对数据集展开探索并分享研究见解。下文将详细说明数据集结构与加载方法。以下为目前已发现的部分典型规律。 ### 1. Kiki-Bouba效应……出现反转？ 当提问为“哪一个是‘Kiki’，哪一个是‘Bouba’？”时，会呈现如下经典规律： * 尖刺状形状 → “Kiki” * 团状形状 → “Bouba” 但在本数据集的全球汇总结果中，当提问为“哪一个被称为‘Kiki’？”时，**团状形状**的选择率反而略高（差异幅度较小）。 有趣的是，如下图所示，按语言群体分组统计后：阿拉伯语使用者压倒性地将团状形状选为“Kiki”，而日语使用者则压倒性地选择尖刺状形状。  尽管全球汇总结果与经典实验结论不符，但我们可提出以下潜在解释： * 本次提问的表述（“哪一个被称为‘Kiki’？”）移除了成对出现的Bouba语境。经典实验中通常会告知被试“其一为Bouba，另一为Kiki”，这会增加额外的选择约束。 * 数据中阿拉伯语使用者的占比可能偏高。 ### 2. “哪一个更具轻率性？”——保龄球 vs 橡皮鸭 在该问题中，我们向被试提问“哪一个更具轻率性？”，并展示以下两种物品的图片： * 橡皮鸭 * 保龄球 从全球汇总结果来看，**保龄球**的选择率显著更高，这可能源于其潜在的破坏性。但**波兰语使用者的选择倾向则截然不同**，选择橡皮鸭的比例要高得多。  ## 数据集结构 数据集的每一行对应**20组联想问题中的一组**，包含以下字段： * `question`：展示给被试的联想问题（例如：“哪一个是‘Kiki’？”） * `option_1` / `option_2`：代表两个选项的图像文件 * `option_1_selections` / `option_2_selections`：选择对应选项的被试人数 * `detailed_results`：单被试层面的字典列表 每个字典包含以下字段： * `selection`：被试选择的选项 * `country`：被试所属国家 * `language`：被试使用的语言 * `age`：被试年龄区间 * `gender`：被试性别 * `occupation`：被试职业 * `userScore`：Rapidata平台给出的单被试数据质量评分 ### `detailed_results` 示例结构 json { "selection": "option_1", "country": "PL", "language": "pl", "age": "25-34", "gender": "Female", "occupation": "Graphic Designer", "userScore": 0.87 } ## 数据集加载方法 以下为将指定问题的**全部单被试反馈**加载至Pandas DataFrame的示例代码： python from datasets import load_dataset import pandas as pd # 可通过`huggingface-cli login`命令登录以访问该数据集 ds = load_dataset("Rapidata/psychology-association-kiki-bouba-etc") df_from_hf = pd.DataFrame(ds['train']) question_idx = 0 q = pd.DataFrame(df_from_hf["detailed_results"][question_idx]) ## 数据采集说明 所有反馈均通过**Rapidata API**采集，该工具支持： * 快速完成全球范围的人类反馈采集 * 支持定向或全域覆盖 * 支持图像、视频、音频及文本类任务 * 支持大规模实时评估 🔗 *更多关于Rapidata的信息：* **(https://www.rapidata.ai/)** ## 适用场景 * 认知科学实验 * 跨文化分析 * 语言影响研究 * 人类感知与语义联想研究 * 集体直觉的趣味探索 ## 数据集局限性 * 本数据集主要用于**探索与趣味研究** * 不保证数据的科学严谨性 * 反馈结果可能受以下因素偏移： * 人口统计分布失衡 * 翻译差异 * 提问表述的影响 * 部分人口统计数据缺失或为被试自报 ## 引用说明 若您使用本数据集，请引用本页面，并不妨留下点赞以示支持！