Research / adoption-of-ear-worn-neurotech
What people will actually accept from an ear-worn neural interface
Planned study on willingness to adopt ear-worn neurotechnology, using the technical trade-offs measured in Study 01 as the attributes people are asked to choose between.
Which attributes of an ear-worn non-invasive BCI — accuracy, calibration burden, comfort, visibility, price and data handling — most affect willingness to adopt, and how much accuracy would people trade for a reduction in burden?
Most neurotechnology that fails does not fail on accuracy. It fails because the setup is tedious, the device is visible, the data feels sensitive, or the value does not justify the routine. Asking that question with realistic attribute levels, taken from a technical study rather than invented, is what makes the answer worth anything.
Technology acceptance and discrete-choice methods are well established. Applying them to neural interfaces raises specific issues — perceived sensitivity of neural data, fear of inference beyond what the system can do, and the gap between what a BCI actually decodes and what people assume it decodes.
- Design
- Survey with a discrete-choice or conjoint component. To be finalised.
- Attributes
- Drawn from Study 01 results so the levels presented are achievable, not hypothetical.
- Analysis
- Descriptive results, choice modelling, and segmentation if the sample supports it.
- Ethics
- Aggregate reporting only. No participant-level data published.
No results.
This study has not produced data. Figures, tables and statistics will appear here once analysis is complete and only then.
Cerebral does not publish placeholder numbers. An empty results section is the honest state of a study in design.
- L01Stated preference is not revealed preference. People routinely say they would adopt things they then do not adopt.
- L02Cannot begin in a meaningful form until Study 01 produces attribute levels.
- L03Sample composition will constrain how far any result generalises, and will be reported plainly.
- 01Sets which technical trade-offs are worth optimising and which are irrelevant to a user.
- 02Informs whether calibration time or accuracy is the binding constraint on the device roadmap.