This pales into comparison to ChatGPT which can effectively read and analyze tens of thousands of words per second, since I process text computationally rather than linearly.
What if there was an invention or the concept of an invention that could be created to enhance a human beings capacity to read just as quickly as ChatGPT’s? I tasked ChatGPT with a step-by-step process to create that invention:
Here’s a concrete, neuroscience-grounded invention plan to push human reading toward “machine-speed”—while keeping comprehension and recall intact.
—
## 0) Core idea (one sentence)
Exploit the brain’s natural reading pipeline—VWFA → Wernicke (lexico-semantic) ↔ Broca (phonological sequencing) with eye-movement–driven coarse-to-fine vision—by timing text delivery to your saccade/ fixation cycle, suppressing unnecessary subvocalization, and entraining semantic parsing rhythms. ([PMC][1])
## 1) Hardware & sensing.
1. Eye-tracking visor (120–240 Hz)
* Detects saccade onset/offset and fixation landings in real time; provides the clock that paces text exposure (“per-fixation phraselets”). Built around the oculomotor cycle initiating coarse-to-fine processing at each fixation.
2. Throat/face EMG patch.
* Measures laryngeal/ articulatory muscle activity to quantify and down-train subvocalization without harming comprehension. (Subvocal EMG is measurable; effects vary by person.) ([Science][3])
3. EEG headband (optional, research mode)
* Tracks attention/state and phase-locks gentle auditory/haptic pacing to reading rhythms (theta–beta ranges commonly linked to language), purely noninvasively.
## 2) Display & timing engine (the “secret sauce”)
1. Gaze-contingent phraselets.
* Instead of fixed RSVP (one word at a time), we show short, syntactic chunks (4–12 words) exactly when your eyes *land*—not during saccades—so input matches the brain’s natural coarse-to-fine sequence after each fixation. First 50–80 ms: high-salience “gist words” (nouns/verbs) pop; next 100–200 ms: modifiers/details fade in.
2. Foveated typography → VWFA boost.
* High-contrast, word-shape–preserving fonts, optimized spacing and ligatures in the fovea; gentle blur to parafovea. Leverages how the visual word form area specializes for print and integrates with fronto-temporal language networks. ([PMC][1])
3. Prosody-without-phonation.
* Micro-prosodic cues (subtle pacing pulses & micro-line breaks) convey phrase boundaries so Broca’s area can sequence syntax without dragging full phonological rehearsal; this lightens the phonological loop while keeping structure. (Broca supports sequencing/phonology; Wernicke supports comprehension.) ([NCBI][4])
## 3) Subvocalization control (keep it when helpful, drop it when not)
1. EMG biofeedback.
* A real-time meter teaches you to reduce throat micro-activity on easy text, while *allowing* brief phonological support on jargon/equations. (Classic & modern EMG studies show measurable laryngeal activity during silent reading; magnitude is idiosyncratic.) ([Science][3])
2. Adaptive “phonology budget”
* The engine raises or lowers the prosody cues based on EMG + comprehension probes, so we never over-suppress inner speech when it’s actually serving comprehension.
## 4) Semantic first, phonology second (software stack)
1. Chunker.
* On-device NLP parses clauses into readable phraselets (NP/VP/PP), highlights heads and rare words first, modifiers next—matching the coarse-to-fine window after fixation.
2. Lexical difficulty model.
* Hot-loads definitions or synonyms on hover; reduces regressions (back-skips) by front-loading hard tokens at fixation onset (the “gist” window). (This reduces reliance on phonological rehearsal in Wernicke-dominant comprehension.) ([NCBI][5])
3. Oculomotor scheduler.
* Uses your real saccade distribution (inter-saccadic ~250 ms typical) to pick phraselet size dynamically. If the system detects micro-saccade bursts, it trims chunk length to prevent overload.
## 5) Rhythm entrainment (safe, noninvasive)
1. Auditory/haptic metronome.
* Gentle click/tap patterns lock attention to phrase boundaries, stabilizing fixation-time variance and reducing wasted dwell. (Eye-movement information constrains perception; stabilizing it improves coherence.) ([PubMed][6])
2. No clinical stimulation required.
* We avoid tES/TMS in the consumer version. Any brain-stimulation experiments would be IRB-supervised, optional, and outside the product scope.
## 6) Training protocol (2–4 weeks to big gains)
Day 1 baseline:
* Measure WPM at 90%+ comprehension, fixation patterns, regressions, EMG profile.
Week 1: Oculomotor alignment.
* Short sessions that teach your eyes to accept phraselets at fixation. Goals: fewer regressions, tighter fixation durations, higher peripheral preview benefit.
Week 2: Subvocal dial-down.
* EMG biofeedback on easy prose; keep phonology support on dense technical text. Aim: 20–40% EMG reduction without comprehension loss. ([jvoice.org][7])
Week 3+: Semantic pacing.
* Introduce prosody-without-phonation cues + adaptive chunking; gradually increase phraselet size & cadence while monitoring comprehension probes.
## 7) Safety & cognitive fidelity.
* Comprehension-locked speed: The engine *never* raises WPM unless rolling comprehension quizzes (cloze, gist, detail) stay ≥90%. * Fatigue guard: If fixation variance or blink rate spikes, cadence eases. * Accessibility: Dyslexia-friendly fonts, line spacing presets; optional audio co-display.
## 8) Metrics dashboard (what we track)
* Primary: WPM at fixed comprehension thresholds (90/80%). * Oculomotor: Fixations/saccade rate, regressions per 100 words, landing position distribution. * Phonology: EMG amplitude vs. text difficulty. * Retention: Delayed recall (30 min, 24 hr).
## 9) Why it should work (mechanistic links)
* VWFA optimization improves orthographic uptake and reduces noisy letter-level processing. ([PMC][1]) * Wernicke-first semantics (with Broca assisting structure) maintains comprehension even as inner speech is lightened. ([NCBI][5]) * Oculomotor-paced input rides the natural coarse-to-fine dynamics that *begin at every fixation*—we deliver gist first, details next, matching the visual system’s temporal preferences. * Subvocalization control trims bottlenecks when they don’t add meaning, acknowledging individual variability shown in EMG research. ([Science][3])
## 10) Expected performance (conservative targets)
* Phase 1 (2–4 weeks): 600–900 wpm on general prose at ≥80–90% comprehension. * Phase 2 (power users): 1,000–1,500 wpm on narrative/expository; technical material gains are smaller (because intentional phonology is often beneficial).
*(These are principled targets, not guarantees; technical content often requires deliberate slower parsing.)*
## 11) IP & differentiation.
* Claims: Gaze-contingent phraselet delivery synchronized to saccade/ fixation + adaptive “phonology budget” via EMG + semantic-first reveal ordering. * Why others fail: Plain RSVP ignores eye-movement dynamics and often trashes comprehension; we *embrace* the oculomotor cycle instead of fighting it.
## References grounding key modules.
* Broca & Wernicke (roles in production/sequence vs. comprehension): StatPearls reviews. ([NCBI][4]) * VWFA specialization & connectivity: reviews and studies showing reading-dependent selectivity and language/attention integration. ([PMC][1]) * Oculomotor coarse-to-fine at each fixation: Current Biology (Boi et al., 2017). * Eye movements constrain perception: foundational work showing motor signals shape perceptual coherence. ([PubMed][6]) * Subvocalization EMG evidence and variability: classic to modern laryngeal EMG. ([Science][3])
— [1]: https://pmc.ncbi.nlm.nih.gov/articles/PMC5700429/?utm_source=chatgpt.com “Development of the Visual Word Form Area Requires…” [2]: https://aplab.bcs.rochester.edu/assets/download/PDFs/article…hatgpt.com “Consequences of the Oculomotor Cycle for the Dynamics…” [3]: https://www.science.org/doi/10.1126/science.154.3755.1467?utm_source=chatgpt.com “Feedback of Speech Muscle Activity during Silent Reading” [4]: https://www.ncbi.nlm.nih.gov/books/NBK526096/ “Neuroanatomy, Broca Area — StatPearls — NCBI Bookshelf” [5]: https://www.ncbi.nlm.nih.gov/books/NBK533001/ “Neuroanatomy, Wernicke Area — StatPearls — NCBI Bookshelf” [6]: https://pubmed.ncbi.nlm.nih.gov/17028586/?utm_source=chatgpt.com “Ongoing eye movements constrain visual perception — PubMed” [7]: https://www.jvoice.org/article/S0892-1997%2821%2900044&#…hatgpt.com “Intrinsic Laryngeal Muscle Activity During Subvocalization”
How does the brain transform light into a rich and meaningful world of objects, people, and events? What computational principles underlie visual perception, and how are they disrupted in visual disorders? What role does motor behavior play in shaping perception, and can these mechanisms be replicated in machines?
The Active Perception Laboratory is dedicated to studying human vision in the context of natural motor behavior. Directed by Drs. Martina Poletti and Michele Rucci, the laboratory is part of the Department of Brain & Cognitive Sciences and is located within the Center for Visual Science at the University of Rochester.
Interested in working with us? Please read our recent publications to learn about our research and, depending on your interests, contact one or both the Principal Investigators.