Experience Resonance Screening
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What is Experience Resonance?
Kazimierz Dąbrowski observed that some people feel, sense and think with more intensity and depth. These patterns, known as overexcitabilities, were once linked to giftedness, which often left out people who also faced barriers. The idea of resonance offers a kinder and more accurate way to describe these qualities without placing them in hierarchies. This neuroaffirmative approach draws on Dąbrowski’s theories while reframing the language used to describe experience.
We are still expanding our datasets and refining the model. By taking part in the early development phase, you will receive free access to your report and a follow up session.
- Request Early Access: Share your first name and email to join the list.
- Complete the screener: You will receive an invite when it is your turn, along with a link to the questionnaire.
- Receive your report: Your results will be shared after a short wait period.
How it Works:
The Screening
You answer 15 open questions in your own words. Two independent AI models review your responses and look for patterns across five areas of experience, adapted from Dąbrowski’s model, including feeling, sensing, creating, thinking, and moving. Each response is scored across all five dimensions of how you tend to respond to experience.
The Scoring Model
Each response is given a score from 0 to 5 in every dimension. The numbers describe how much resonance is present in your answer. This differs from Dąbrowski’s original model because AI makes it possible to work with a level of detail and complexity that was not practical before.
The History
Dąbrowski’s original approach relied on written reflections. Two trained assessors read each response and looked for patterns across different areas of experience. Later attempts to turn this into a psychometric scale used fixed statements, which were easier to score but often felt detached or unclear for the people completing them.
More Consistency, Less Bias
With current technology, we can return to the original method. Instead of rating statements, you answer open questions in your own words. Two independent AI models look for patterns in the same way human raters once did, only with greater consistency and far less bias.