Project
We built the measurement before the claim.
The goal is reading. That is also the claim we are refusing to make. Reading is a motor act as much as a cognitive one — skilled readers execute several precise eye movements per second, and in people who struggle to read those movements look different. Whether that difference contributes to the difficulty, or simply reflects it, is an open question. This is an instrument built to help answer it honestly.
Status: instrument built and tested. No data has been collected.
Why the question is still worth asking
The observation that eye movements differ in struggling readers has been over-interpreted for decades. The dominant scientific account of dyslexia is phonological — a difficulty with the sound structure of language, not with the eyes. The professional bodies in paediatrics and ophthalmology have specifically cautioned against selling eye exercises as a dyslexia treatment, and they have been right to: the evidence for transfer is weak, and the field is full of confident claims resting on nothing.
We think the underlying question survives that criticism, for one reason. Establishing whether oculomotor control matters for reading requires measuring it well, and it has rarely been measured well outside a laboratory. So we started there.
The measurement
A small, cluttered symbol is unreadable in peripheral vision. That failure is the instrument. If a person correctly names which way the gap in a ring points, their eye was pointed at it — no inference required.
The symbol changes state every fifth of a second, which turns identity into a clock: the state someone reports says when their eye arrived, with reaction time algebraically removed. It measures where the eye was and when it got there, using only a keyboard.
What we track
Peak scores are the least informative thing about a skill. We measure how performance decays across a sustained block, and how far it falls when a second task competes for attention. If control genuinely improves, it should become cheaper, not merely higher.
How we avoid fooling ourselves
The program that presents sessions cannot import the analysis code — a separation enforced by an automated test that fails the build if it is ever breached.
Phases advance when measured performance plateaus, not on a schedule, and only when the effort measures have flattened rather than the scores alone.
An internal check requires that a person holding still and guessing scores no better than chance; if they do not, the stimulus is wrong and every result downstream of it is void.
The reading-outcome field in our data model is deliberately empty, unwritable by the current software, so that no outcome can be quietly invented to match a hope.
What this can and cannot show
This is a single-subject study under tightly controlled conditions. At best it can demonstrate that something changed for one person, in a way that survives the checks above. It cannot show that a training method works in general, and it will not be described as if it could. It may well show nothing at all — the design is built to make that outcome visible rather than deniable.