jake/Lumotia
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat(docs): add brief and brand reference docs to phase-2 branch
Some checks failed
check / cargo check (macos-latest) (push) Has been cancelled
Details
check / cargo check (ubuntu-22.04) (push) Has been cancelled
Details
check / cargo check (windows-latest) (push) Has been cancelled
Details
check / svelte build + lint (push) Has been cancelled
Details

Browse Source 
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
jake
2026-03-21 12:01:50 +00:00
committed by Jake
parent 8ba5641451
commit e75f676fc1
34 changed files with 2115 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

28
docs/brief/appendix-latency-memory.md Normal file
View File

@@ -0,0 +1,28 @@
<!-- Source: Kon Master Brief — Appendix A4: Latency, Working Memory Decay, and Software Architecture -->
## A4. Latency, Working Memory Decay, and Software Architecture
**Core finding:** 75–81% of ADHD cases show measurable working memory deficits (d = 1.63–2.03). Every millisecond of interface latency disproportionately taxes ADHD working memory. Local-first architecture is a cognitive accessibility requirement, not a technical preference.
**Working memory deficits in ADHD:**
- **Kofler et al. 2020** (*Neuropsychology*): 172 children, bifactor modelling. **Very large magnitude central executive WM deficits: d = 1.63–2.03**, affecting **75–81% of ADHD cases**. These deficits "determined consistent difficulties in anticipating, planning, enacting, and maintaining goal-directed actions."
- **Weigard & Huang-Pollock 2017** (*Clinical Psychological Science*): Applied the Time-Based Resource-Sharing (TBRS) model to ADHD. Children with ADHD experienced **higher cognitive load than controls in identical task conditions** because slower processing speed leaves less time for WM refreshing. Every millisecond of additional processing demand disproportionately taxes ADHD working memory.
- **Barrouillet, Bernardin & Camos 2004** (*Journal of Experimental Psychology: General*): The TBRS model — WM recall is a **negative linear function of cognitive load**, where cognitive load equals the proportion of time the attentional bottleneck is occupied by processing rather than refreshing memory traces.
**HCI response time thresholds:**
- **Miller 1968** (*AFIPS Conference*) and **Nielsen 1993** (*Usability Engineering*): Delays beyond **100ms** break direct manipulation feel. Beyond **1 second**: flow of thought disrupted. Beyond **10 seconds**: complete attentional disengagement. These are neurotypical baselines — effective thresholds for ADHD users are almost certainly shorter given reduced WM capacity.
- **Card, Moran & Newell 1983** (*The Psychology of HCI*): Expert users completed tasks **30–40% faster** with sub-second response systems vs. 2-second systems — a penalty amplified in ADHD populations with elevated switch costs.
**ADHD-specific latency vulnerability:**
- **Barack et al. 2024** (*Proceedings of the Royal Society B*): Pre-registered foraging study, **457 participants**. Those screening positive for ADHD **departed resource patches significantly sooner** — their exploration/exploitation trade-off is biased toward exploration. Every loading delay creates an artificial "depleting patch" that triggers the ADHD exploration impulse, manifesting as tab-switching, app-switching, and task abandonment.
- **Ardalani et al. 2020** (*Psychological Research*): Inattentive traits predict higher switch costs under working memory load — each navigation step imposes a disproportionate cognitive tax.
- **Madore et al. 2020** (*Nature*): Pre-encoding attentional lapses directly predict memory failure. Software that minimises attention-capturing events (loading screens, error states) directly supports better memory encoding.
**Applied studies (from earlier research):**
- **127 ADHD knowledge workers study (KLM + EEG):** 4.7 seconds cognitive overhead per app switch. 11.3 seconds context-reconstruction latency. Tools with >90-second setup increase cognitive load by 2.3x.
- **NIH study of 247 ADHD adults (8-week baseline):** Zero-friction AI tools achieved 31–47% reduction in task-switching latency, 58% reduction in off-task interruptions, 42% increase in on-time completion.
**Local-first as cognitive ergonomics:**
- **Kleppmann et al. 2019** (*ACM Onward! '19*): Seven ideals of local-first software. Ideal #1 — "No spinners: your work at your fingertips." Primary copy of data on the user's device means read/write operations at local disk speed (sub-millisecond), not network speed (50–500+ ms). Synchronisation happens asynchronously in background.
**Implication for Kon:** Local-first architecture keeps all interactions within Miller's 100ms direct-manipulation threshold, preventing the WM decay → exploration bias → task abandonment cascade. The 90-second setup threshold is a hard design constraint. Voice capture must work in under 3 seconds from app open.