Datasets:
dataset_info:
features:
- name: experiment
dtype: string
- name: text
dtype: string
- name: RTs
list: float64
- name: log_RTs
list: float64
splits:
- name: train
num_bytes: 159575797
num_examples: 3818
- name: test
num_bytes: 39849490
num_examples: 945
download_size: 30076965
dataset_size: 199425287
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- text-generation
language:
- en
tags:
- cognitive-science
- psychology
- human-behaviour
- psych-201
size_categories:
- 1K<n<10K
pretty_name: Psych-201 Reaction Time Dataset
Psych-201 Reaction Time Dataset
A curated dataset of human reaction time (RT) measurements extracted from Psych-201, a large-scale collection of naturalistic language transcriptions of human psychology experiments. The dataset is designed for modeling individual-level response latency distributions across diverse experimental paradigms. Introduced in Small Foundation Models of Human Cognition and Behaviour (COLM'26).
Dataset Summary
| Train | Test | Total | |
|---|---|---|---|
| Samples (participants) | 3,810 | 953 | 4,763 |
| Choice–RT pairs | 1,328,717 | 332,848 | 1,661,565 |
| Experiments | 18 | 18 | 18 |
Each sample corresponds to a single experimental participant — specifically, the complete sequence of choice–RT pairs recorded from that participant within a given experiment.
Preprocessing Pipeline
We curated RT data from Psych-201 via a three-stage filtering pipeline. For full details, see create_rt_dataset.html.
1. Experiment-level filtering
From 39 experiments containing RT measurements, we excluded two unpublished experiments and removed 18 experiments in which more than five RT entries were invalid (zero, negative, or NaN), yielding 19 candidate experiments.
2. Sample-level filtering
Among the 19 remaining experiments, we excluded one experiment (tessler2018notunreasonable) in which 86.25% of samples exhibited mismatches between the number of recorded responses and corresponding RT measurements. For the remaining 18 experiments, we removed individual samples containing any invalid RT value. This affected 7 samples across 4 experiments: anllo2024weird (1/564), guenther2020ts (2/145), rutledge2023happiness (3/48,925), and zika2023traitanxiety (1/89). After filtering, 53,196 valid samples remained.
3. Class rebalancing
The rutledge2023happiness experiment comprised over 91% of all remaining samples (48,922 of 53,557). To mitigate this severe class imbalance, we applied stratified subsampling, randomly retaining 1% of its samples (489 participants).
Log-transformed RTs were computed for all samples to accommodate the characteristically right-skewed nature of RT distributions.
Usage
from datasets import load_dataset
dataset = load_dataset("socius/Psych-201-RT")
# Access splits
train = dataset["train"]
test = dataset["test"]
# Example: inspect one participant's data
sample = train[0]
print(f"Experiment: {sample['experiment']}")
print(f"Number of trials: {len(sample['RTs'])}")
print(f"Mean RT: {sum(sample['RTs']) / len(sample['RTs']):.3f}s")
Source
This dataset is derived from:
Binz et al. (2025). Psych-201. GitHub.
Citation
If you use this dataset, please cite:
@inproceedings{oh2026smallcogfm,
title = {Small Foundation Models of Human Cognition and Behaviour},
author = {Oh, Nick and Gobet, Fernand},
booktitle = {Third Conference on Language Modeling (COLM)},
year = {2026},
note = {arXiv:2608.05224}
}