Behavioural dataset for "Perceptual decision-making: extending a standardized rodent task to humans"
Authors/Creators
-
1.
Leiden University
-
2.
Vrije Universiteit Amsterdam
Description
This dataset contains trial-level behavioural data and participant information from a human visual decision-making task. It is inspired by the data format used by the International Brain Laboratory (e.g. here). The release includes 80 participants. For more information, please see our preprint and GitHub repo.
|
File |
Unit of observation |
Contents |
|---|---|---|
|
processed_data_2026.csv |
One row per trial |
Task events, responses, stimulus information etc. |
|
participants_info.csv |
One row per participant |
Session start time, gender, age, handedness, and questionnaire responses. |
The files can be linked using subject (read this identifier as text to retain leading zeros).
Dataset overview
This dataset includes 80 participants (65 female, 13 male, and 2 nonbinary participants; mean age = 19.99 years, SD = 3.14, range = 18–35 years) and 48000 trials in total. The instruction condition is stored as instructions in both files. Its code-to-condition mapping and group sample sizes:
|
instructions |
Condition |
Participants |
|---|---|---|
|
0 |
Not Instructed |
41 |
|
1 |
Instructed |
39 |
1. Trial file: processed_data_2026.csv
Each row corresponds to one trial.
|
Column |
Description and values |
|---|---|
|
subject |
Participant identifier, stored as a three-character string (e.g., 006). |
|
session |
Session identifier (e.g., 22121620). Each participant has one session in this dataset. |
|
trial |
Trial number within the session; between 0 and 599. |
|
instructions |
Instruction condition: 1 = instructed, 0 = not instructed. |
|
probabilityLeft |
Probability of the target assigned to the left side in the current block: 0.2, 0.5, or 0.8. These sequences were pre-generated, see the Methods section of the preprint. |
|
contrastLeft, contrastRight |
Rescaled contrast on each side: 0, 0.1, 0.25, 0.5, or 1, corresponding to displayed contrasts of 50%, 52%, 55%, 60%, and 70%, respectively. |
|
choice |
Response: 1 = left, -1 = right, 0 = no recorded response. |
|
firstMovement_times_from_stim |
Time (s) from stimulus onset to the first recorded movement. |
|
response_times_from_stim |
Time (s) between stimulus onset and the completion of the response, when the stimulus crossed the midline of the screen; missing for no-response trials. |
|
feedbackType |
Recorded feedback code: 1 = correct, -1 = incorrect, missing = no response. |
|
stimContrast |
Sum of the rescaled left and right contrasts (contrastLeft + contrastRight); observed values: 0, 0.1, 0.25, 0.5, 1. |
|
sideContrast |
Signed contrast, calculated as contrastLeft - contrastRight. Positive values indicate greater contrast on the left; negative values indicate greater contrast on the right. Observed values: -1, -0.5, -0.25, -0.1, 0, 0.1, 0.25, 0.5, 1. |
|
stimSide |
Side on which the target stimulus was presented: 1 = left, -1 = right. |
|
cursorPosition |
Recorded cursor positions over the trial, stored as an array-like string in the CSV. Each value corresponds to a sample in cursorTime. |
|
cursorTime |
Times at which cursor positions were sampled, stored as an array-like string in the CSV. |
|
dot.started |
Recorded onset time of the visual stimuli, in seconds from the start of the session. |
2. Participant file: participants_info.csv
Each row corresponds to one participant. An unanswered question may appear as a missing value when the CSV is read.
|
Column |
Description |
|---|---|
|
subject |
Participant identifier, matching the trial file. |
|
instructions |
Instruction condition: 1 = instructed, 0 = not instructed. |
|
session_start |
Recorded session start date and time. |
|
gender |
Selected gender response, if provided. |
|
age |
Age response (years). |
|
handedness |
Selected handedness response, if provided. |
|
textbox1_text |
Free-text response. Please describe how you think the game worked and what the rules were. |
|
textbox2_text |
Free-text response. How well do you think you did in the game? |
|
textbox3_text |
Free-text response. What strategies did you use during the game? |
|
textbox4_text |
Free-text response. Did you notice any patterns in the game? |
|
note |
Additional information about the participant’s session, where applicable. |
Loading the data
For example, use python pd.read_csv(path, dtype={'subject': str}) with pandas. In the CSV, cursorPosition and cursorTime are stored as strings representing arrays and must be parsed before numerical analysis, for example with:
import ast
import pandas as pd
data = pd.read_csv(
'processed_data_2026.csv',
dtype={'subject': str, 'session': str},
converters={
'cursorTime': ast.literal_eval,
'cursorPosition': ast.literal_eval,
},
)
Pupil and facial video data
Eye-tracking data (gaze and pupil measurements) and facial video recordings were also collected during the task. Pupil data are described in Johnson et al. (2025) and are planned for inclusion in a future update of this release. For privacy reasons, facial video recordings are not included in the public release and can be obtained from the corresponding author on request.
Files
README.md
Additional details
Related works
- Is supplemented by
- Software: https://github.com/cocosys-lab/2026-human-ibl-analyses (URL)