Outline

  • Pupil physiology and th main sources of noise in measurement and interpretation of the pupil signal.

  • Recommendations on experimental design and setting characteristics in cognitive pupillometry.

  • Degrees of freedom encountered in the data processing and visualization pipeline and the multiverse approach to pupillometry pre-processing.

  • The functional and cognitive interpretation of pupillometric measures in light of foundational studies in the field.

Hands-on Materials ↗

My Academic Journey

Increasing interest in pupillometry

Epistemological fundations: physiology of pupil changes in cognitive sciences

Grujic, N., Polania, R., & Burdakov, D. (2024). Neurobehavioral meaning of pupil size. Neuron

Grujic, N., Polania, R., & Burdakov, D. (2024). Neurobehavioral meaning of pupil size. Neuron

The main sources of noise in measurement and interpretation of the pupil signal

Eye Calibration and Eye movements

Pupil light response

Eye movement and pupil measurement errors and artifacts

Psychosensory response

Working Memory Maintenance

Pupil Phasic State investigation

A Solid Replication: Pupil dilation, Working memory and Cognitive effort

Sirois and Brisson, 2014 materials ↗

From Design to Data Analysis

Setting characteristics

  • Light Control: Pupil size is primarily influenced by light. Therefore, maintaining a constant and controlled light level is essential, especially when using visual stimuli. Researchers often use standard luminance images or control the brightness of stimuli to avoid confounding effects of light changes on pupil size.

  • Minimizing External Stimuli: Background noise, visual distractions, and other external stimuli can influence pupil responses. Conducting experiments in a quiet, dimly lit room with minimal distractions helps to isolate the specific effects of the stimuli being investigated.

  • Participant Comfort and Positioning: Participants should be seated comfortably, often with a chin or headrest, to maintain a consistent distance between their eyes and the recording device. This helps ensure that changes in pupil size are not due to changes in head position or distance from the stimulus.

  • Head and Eye Tracking: Modern pupillometry often utilizes head-mounted eye trackers or head-tracking devices to allow for more natural head movements while still maintaining accurate tracking of eye position. However, even with these devices, chin rests can be useful to minimize local luminance and distance effects.

Classical paradigms in cognitive sciences

Open tools: OpenSesame R studio

Degrees of freedom in data processing and data visualization

  • data parsing
  • sanity check to “weight” noise sources
    e.g. extreme values, AOI
  • missing data — interpolation ?
  • baseline correction
  • model selection

Hands-on

Excercise 1

  1. create data dictionary

  2. explore a portion of the multiversewith R

design: familiarisation, visual vs audiovisual

Hands-on

Excercise 2

  1. modify the OpenSesame Digit Span task

  2. explore a portion of the multiverse with R

Hands-on

Excercise 3

  1. Explore the whole multiverse with R

Hands-on

Multilab Multiverse

Interested in learning more?

Access the full ManyBabies Workshop:

Pupillometry functional and cognitive interpretation in light of the studies across fields

Grujic, N., Polania, R., & Burdakov, D. (2024). Neurobehavioral meaning of pupil size. Neuron

Grujic, N., Polania, R., & Burdakov, D. (2024). Neurobehavioral meaning of pupil size. Neuron

Hands-on

Excercise 4
documentation

  1. Develop a research question and hypothesis

  2. Develop the design and implement it e.g. OpenSesame, PsychoPy etc.

  3. Develop your pre-processing multiverse path

  4. Develop the pipeline to visualize and test the hypothesis

Tips for a safe trip in cognitive pupillometry

  1. Define the research question
  2. Choose a solid design (complementary measures)
  3. Adapt the design for pupil recording
  4. Pre-select degrees of freedom in pre-processing and analysis
  1. When interpreting, ask:
    • physiological artifacts?
    • latency from stimulus to peak?
    • when does the effect emerge?
    • extra cognitive processes being tapped?

Across all the steps always look for epistemology soundness

Thank you

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