socius/Psych-201-RT

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From 39 experiments containing RT measurements, we excluded two unpublished experiments and removed 18 experiments in which more than five RT entries were invalid (zero, negative, or NaN), yielding 19 candidate experiments.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}
}