89c63891fa
Replace all instances of the legacy product names "Kon" and "Corbie" with "Magnotia" across user-facing copy, code identifiers, package names, bundle ids, file paths, and documentation. Preserves the unrelated "konsole" (KDE terminal) reference and the parent CORBEL company name. - Renames 10 Rust crates (kon-* → magnotia-*) and the tauri binary - Updates package.json, tauri.conf.json (productName + identifier) - Renames CSS classes (kon-rh-* → magnotia-rh-*) and animations - Renames brand and roadmap docs - Regenerates Cargo.lock and package-lock.json Verified: svelte-check passes; pure-rust crates compile under new names.
29 lines
4.2 KiB
Markdown
29 lines
4.2 KiB
Markdown
<!-- Source: Magnotia 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 Magnotia:** 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.
|