Clock test

This repository conatins task scripts for obtaining introspective reports based on a fast rotating clock as made popular by Libet et al. (1983).

This version is written in PsychoPy (http://www.psychopy.org/). It was first used for the study:

Vinding, M.C., Pedersen, M.N., & Overgaard, M. (2013). Unravelling intention: Distal intentions increase the subjective sense of agency. Consciousness and Cognition, 22(3), 810–815. https://doi.org/10.1016/j.concog.2013.05.003

The script was written 2012-2014 for PsychoPy2. The current version is re-written 2018-2025 for PsychoPy3.

Download PsychoPy from https://psychopy.org/

Use

Quick Start

1. Open Clock test_2025.py in your PsychoPy environment, perferrable the stand alone version.
2. Configure the experimental parameters (see below)
3. Run the script

Configuration

You can easily customize the adaptive timeout algorithm behavior by editing the config file without touching the main experimental script. The settings are properly documented with comments explaining their purpose and valid ranges.

Important: Adjust these settings before running the experiment:

Display Settings

• monDistance: Distance from subject's eyes to center of monitor (cm).
• monWidth: Physical width of monitor display (cm).
• fullscr: Set to True for fullscreen mode (recommended for experiments).
• framerate: Set the framerate of monitor (in Hz)

Audio Settings

Audio device must be set in PsychoPy preferences. The script will use the default audio device. Aways make sure to test the audio output before running the experiment as the timing might vary from device to device.

In the PsychoPy Coder view, you can set the audio device by going to Files > Preferences > Hardware > Audio and selecting the desired output device from the drop down menu.

Experimental Design

• condition_keys: Choose which conditions to include (see table below).
• blockRepetitions: Number of repetitions of each condition block.
• blockTrials: Number of trials per block.
• letterMode: Display a string of letters one a the time in the middle of the clock (None=use default for the condition, True/False=force option).
• trainingCondition_keys: Conditions for practice trials.
• trainingTrials: Number of practice trials per training condition.
• wildcard_keys: Optional condition modifiers that can be used to denote experimental manipulations that are not covered by the standard conditions (e.g., ['Self', 'Other']). The woildcard keys will be appended to condition names but will not affect the run. The experiment will run with all conditions and repetition for each wildcard. Leave empty list if not used.

Clock appearance

• drawMode: Visual style - 'dot' or 'hand'.
• clockDirection: 'clockwise' or 'counterclockwise'.

Output

• triggerOutput: Send trigger out for EEG during experiment (True/False). Requires parallel port out.

Data Output

Results are automatically saved as CSV files in the raw_data/ folder with the naming convention: subject_[ID]_[condition][wildcard][#].csv

Data columns include: id, condition, trial number, clock angles, response times, letter-mode fields (nFwd, stopCharacter), timeout data, and trigger codes. See config.py dataCategories for complete list.

OPTIONS FOR CONDITIONS:

This script can run several versions of task based on the Libet-clock method first described by Libet et al. (1983). Set which conditions to run in the experiments by specifying relevant keys in the condition_keys variable.

Condition	Description	Original reference
W-press	Press. Report time of intention (W-time)	Libet et al. 1983
M-press	Press. Report time of the movement (M-time)	Libet et al. 1983
IB-press	Press+tone. Indicate when a press is made. Part of the Intentional Binding paradigm	Haggard et al. 2002
IB-tone	Press+tone. Indicate when a tone is heard. Part of the Intentional Binding paradigm	Haggard et al. 2002
IB-singleTone	Tone. Indicate when a single tone is heard. Part of the Intentional Binding paradigm	Haggard et al. 2002
IB-singlePress	Press. Indicate when a single press is made. Part of the Intentional Binding paradigm	Haggard et al. 2002
singleDistPress	Delayed intentions timed with letters, no tone	Vinding et al. 2012
distalPress	Delayed intentions timed with letters. Press followed by a tone. Report press time	Vinding et al. 2012
distalTone	Delayed intentions timed letters. Press followed by a tone. Report tone time	Vinding et al. 2012
interruption	Press with adaptive random timeout and interruptions	Schurger et al. 2012

Permissions

The scripts are free for any use following the BSD 2-Clause License.

If you use the script or modified versions of the script in your research please cite the paper above in your methods section, e.g.:

"We used a modified open-source version of the Libet-clock task (Libet et al., 1983) originally used in Vinding et al. (2013) written in PsychoPy (Peirce et al. 2019)."

If relevant, add a link to this GitHub repository in the relevant section of your paper.

Disclaimer

Use at your own risk. This software is provided "as is" without warranty of any kind, express or implied. It is strongly recommended that you test the timing of the script on the hardware that will be used for data collection.

Known Limitations

• Adaptive timeout: Verify proper algorithm behavior.
• Audio timing: Sound output timing varies significantly across different hardware and operating systems. Always test audio timing on your specific setup before collecting data.
• Platform compatibility: While designed for cross-platform use, timing precision may vary between Windows, macOS, and Linux.

Please report any issues or bugs to the author or the repository's issue tracker.

Important Testing Requirements

1. Validate timing precision on your hardware before data collection.
2. Test audio latency and synchronization with visual events.
3. Verify trigger timing if using EEG/physiological recording and TTL out.
4. Test adaptive timeout algorithm behavior with pilot sessions for interruption condition.
5. Run pilot sessions to ensure stable performance.

Theoretical Note

This task measures subjective timing reports and neural correlates of action preparation. It does not provide evidence for or against free will, regardless of claims in popular media or academic interpretations.

Contact

For questions and more information contact: mvi@psy.ku.dk

References

Papers using versions of this implementation:

Vinding, M.C., Pedersen, M.N., & Overgaard, M. (2013). Unravelling intention: Distal intentions increase the subjective sense of agency. Consciousness and Cognition, 22(3), 810–815. https://doi.org/10.1016/j.concog.2013.05.003

Vinding, M.C., Jensen, M., & Overgaard, M. (2015). The time between intention and action affects the experience of action. Frontiers in Human Neuroscience, 9, 366. https://doi.org/10.3389/fnhum.2015.00366

Hall, S., van den Heever, D., Vinding, M.C., & Morris, L. (2020). Investigating motor preparatory processes and conscious volition using machine learning. bioRxiv. https://doi.org/10.1101/2020.09.07.286351

Original clock method first described by

Libet, B., Gleason, C.A., Wright, E.W., & Pearl, D.K. (1983). Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential). Brain, 106(3), 623–642. https://doi.org/10.1093/brain/106.3.623

Intentional Binding paradigm

Haggard, P., Clark, S., & Kalogeras, J. (2002). Voluntary action and conscious awareness. Nature Neuroscience, 5(4), 382–385. https://doi.org/10.1038/nn827

Libertus interruptus method

Schurger, A., Sitt, J.D., & Dehaene, S. (2012). An accumulator model for spontaneous neural activity prior to self-initiated movement. Proceedings of the National Academy of Sciences, 109(42), E2904–E2913. https://doi.org/10.1073/pnas.1210467109

PsychoPy software

Peirce, J., Gray, J.R., Simpson, S., MacAskill, M., Höchenberger, R., Sogo, H., Kastman, E., & Lindeløv, J.K. (2019). PsychoPy2: Experiments in behavior made easy. Behavior Research Methods, 51(1), 195–203. https://doi.org/10.3758/s13428-018-01193-y