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Behavioral Research Lab

What is JADS Behavioral Lab?

The JADS Behavioral Lab is a collaborative research facility dedicated to conducting behavioral research at JADS. It serves as a hub for generating new datasets and insights in response to research questions that require controlled and experimental environments as well as human subjects. The lab takes an interdisciplinary approach, bringing together researchers from social sciences (e.g., psychology, economics, marketing, cognitive science) and data science disciplines (e.g., machine learning, artificial intelligence).

As an innovation center for experimental behavioral research, the JADS Behavioral Lab aims to be the first point of contact for the aforementioned types of research. By offering shared resources, the lab addresses the need of JADS researchers and affiliates across various research groups to benefit from its services and facilities.

In pursuit of these goals, JADS Behavioral Lab gives its researchers and affiliates the possibility to use an array of resources, including access to JADS facilities that can be used for data collection, an overview of best research practices, and cutting-edge hardware and software. In addition, JADS Behavioral Lab offers technical support for managing the hardware.

The lab’s resources support a wide range of research activities, such as

  • process tracing (e.g., mouse-movement tracking, eye-tracking, EEG)
  • behavior recording in context (e.g., audio and video recording of social interactions, smart meeting rooms)
  • questionnaire-based studies (e.g., online experiment and survey)
  • interaction with AI technologies (e.g., developing generative AI applications).

What Research Resources does the Lab have?

  • Equipment for audio and video recording (set up with the JADS IT team)
  • Equipment for (remote/online) audio and video recording (self-supervised setup; web cameras and JABRA speakers)
  • Equipment for eye-tracking
  • Smart meeting room setup (space that integrates hardware and software in the meeting room itself to create an incredibly productive meeting experience for participants, whether they’re joining the meeting from the office or remotely)

Software for setting up experiments and experiments interface:

oTree
• Use case: Online behavioral experiments, often used in economic or psychological studies.
PsychoPy
• Use case: An open-source software written in Python, PsychoPy is ideal for running experiments in behavioral sciences. It can be used to design experiments that involve stimulus presentation, reaction time measurements, and data collection.
OpenSesame
• Use case: Similar to PsychoPy, OpenSesame is used for creating psychological and behavioral experiments. It features an intuitive interface and allows for the design of custom tasks and cognitive experiments.
Limesurvey
• Use case: This is an open-source survey tool that can be used as an alternative to Qualtrics. It provides robust options for questionnaire design, data collection, and survey distribution, perfect for behavioral experiments requiring structured feedback from participants.
Psychtoolbox
• Use case: A free set of Matlab and GNU Octave functions that allow you to control experiments in cognitive neuroscience and psychology. It’s particularly helpful for experiments that require precision in timing and stimuli presentation.
Lab.js
• Use case: A platform for building and running web-based experiments. It’s an open-source solution that supports a wide range of behavioral tasks, making it suitable for online or in-person experiments.

Software for collecting process tracing data:

GazeRecorder
• Use case: Open-source eye-tracking software that allows researchers to analyze where and for how long participants focus on certain elements. It can be used to study visual attention and behavior.
MouselabWEB
• Use case: Open-source mouse action (e.g., clicking) software that allows researchers to record where and how long participants move their mouse in a certain environment.
Opengazer
• Use case: An open-source software that can perform gaze-tracking through a standard webcam, without the need for specialized eye-tracking hardware. This could be useful for experiments where understanding visual attention is key.

Software for annotating audio-video and image data:

OpenSmile
• Use case: Audio analysis and emotion detection through voice, ideal for social interaction studies.
OpenFace
• Use case: Facial expression analysis for emotion detection, useful in behavior recording experiments.

Software for data collection:

Qualtrics
• Use case: Collecting questionnaire-based data, useful for pre- and post-experiment surveys.

Software for data analysis:

Matlab
• Use case: Data analysis, machine learning, and experiment control for complex algorithms and data from behavioral experiments.
NVivo
• Use case: Qualitative data analysis, useful for analyzing interviews and open-ended responses.
SPSS
• Use case: Statistical analysis, especially for hypothesis testing, regression, and other quantitative data interpretation.
GraphPad Prism
• Use case: Data plotting and graphing for experimental results.

Software for annotating audio-video and image data:

FaceReader
• Use case: This software specializes in automatic facial expression analysis, detecting emotions such as happiness, sadness, anger, and surprise.

Best Research Practices Involving Human Participants

Conducting ethical and responsible research involving human participants is essential to ensure integrity, transparency, and respect for participants’ rights. Below are key best practices that all researchers should follow.

1. Informed Consent and Respect for Participants’ Rights

Informed Consent (IC) is the first and most important step in any research involving human participants. It ensures that participants fully understand the nature, purpose, and procedures of the study before agreeing to take part. The consent form should clearly outline:

  • The purpose and procedures of the study
  • Potential risks and benefits
  • Confidentiality measures
  • Participants’ rights, including the right to withdraw at any time

Researchers at Tilburg University and Eindhoven University of Technology (TUE) can refer to the following templates and guidelines when creating their informed consent forms:

Debriefing should be conducted after participation, providing all participants with a clear explanation of the study’s purpose, any use of deception, and expected outcomes.

2. Institutional Oversight and Ethical Approval

Before starting data collection, every research project involving human participants must receive ethical approval from the relevant Institutional Review Board (IRB). This ensures that all research activities comply with ethical and legal standards.

3. Study Design

A sound research design starts with a clear and specific research question (RQ) and well-defined objectives. Researchers are encouraged to consider preregistering their research questions and analysis plans before beginning data collection or analysis.

Preregistration increases transparency, reduces bias, and strengthens the credibility of research findings. While it is mandatory for many psychology journals, it remains optional in other fields.
Learn more here: What is a Preregistration? – Nature Human Behaviour (2022)

4. Data Collection and Management

A Data Management Plan (DMP) should be created before any data collection begins. This formal document outlines how research data will be:

  • Collected, organized, and documented
  • Stored securely and preserved for long-term access
  • Shared and archived responsibly after project completion

Resources and templates are available from:

5. Legal Compliance

Researchers must ensure that their study design and data management practices comply with relevant data protection laws, such as the General Data Protection Regulation (GDPR) in Europe or HIPAA in the United States. These laws govern how personal and sensitive data should be collected, processed, and stored.

For guidance and local policies, see:

6. Transparent Dataset Documentation for Responsible AI (Optional)

For studies involving the creation of datasets used in Artificial Intelligence (AI) training, researchers are encouraged to adopt transparent dataset documentation practices.

Data Cards (Pushkarna et al., 2022) provide structured summaries of essential facts about a dataset, such as its composition, collection methods, intended use, and limitations, supporting responsible AI development throughout the dataset’s lifecycle.

Read more: Data Cards: Documenting ML Datasets for Responsible AI

Ethical research is a continuous process that extends beyond obtaining consent or IRB approval; it encompasses the entire research lifecycle. By following these best practices, researchers can ensure their studies uphold the highest standards of respect, transparency, and integrity.

If you are in need of a physical laboratory space with special equipment, both the Tilburg School of Economics and Management at Tilburg University and the Human Technology Interaction Group at the Eindhoven University of Technology have available labs where this research can be conducted. Additionally, at the Tilburg School of Economics and Management at Tilburg University there is a subject recruitment system available. Please note that a fee is typically required if you are not affiliated with one of these research groups. For further information, please refer to the link below.
TiSEM lab: https://www.tilburguniversity.edu/research/economics-and-management/lab
HTI labs: https://www.tue.nl/en/research/research-groups/innovation-sciences/human-technology-interaction
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