Tool Cognitive Load Estimate
Category: Cognitive Transport · Tier: All
Estimate cognitive load for a persona on page metrics. Returns per-dimension breakdown: information, visual, attention, decision, motor, text, memory, patience. Identifies the bottleneck dimension.
When to Use
Cognitive transport tools treat persona traits as probability measures in Wasserstein space. Distance between personas predicts how differently they experience the same interface. Based on 40+ neuroscience papers where transport cost = cognitive processing cost.
Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
| persona | string | Yes | Persona name |
| informationDensity | number | Yes | Content density (0=sparse, 1=dense) |
| visualComplexity | number | Yes | Visual complexity (0=simple, 1=complex) |
| interactiveElements | number | Yes | Number of interactive elements |
| textDensity | number | Yes | Text heaviness (0=minimal, 1=wall of text) |
| animationLevel | number | Yes | Animation/motion amount |
| choiceCount | number | Yes | Number of decision points/options |
| navigationDepth | number | Yes | Clicks needed to reach content |
Example
Use cognitive_load_estimate on your target page
Tips
- All operations are sub-millisecond for 26-dimensional trait spaces
- Trait distributions sum to 1 (probability simplex)
- The ground metric weights traits by cognitive domain proximity
Research
- Dabney et al. (Nature 2020) — Distributional code for value in dopamine neurons
- Thual et al. (NeurIPS 2022) — Fused unbalanced Gromov-Wasserstein brain alignment
- Esfahani & Kuhn (Math Programming 2018) — Distributionally robust optimization
- Nadjahi et al. (NeurIPS 2020) — Sliced Wasserstein properties
Related
- Tools Overview — All 120 tools by category
- Cognitive Optimal Transport — Research foundation