Vibe Index
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hicks-law

🎯Skill

from flpbalada/my-opencode-config

VibeIndex|
What it does

Optimizes user decision-making by reducing choice complexity and managing interface options to accelerate cognitive processing.

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flpbalada/my-opencode-config(40 items)

hicks-law

Installation

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Quick InstallInstall with npx
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AddedFeb 4, 2026
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Skill Details

SKILL.md

Overview

# Hick's Law - Less Choice, Faster Decisions

Hick's Law (also Hick-Hyman Law) states that the time it takes to make a

decision increases logarithmically with the number and complexity of choices.

Named after British psychologist William Edmund Hick and American psychologist

Ray Hyman (1952).

When to Use This Skill

  • Designing navigation menus and information architecture
  • Simplifying onboarding and setup flows
  • Reducing form field options
  • Prioritizing feature exposure
  • Optimizing conversion funnels
  • Planning dashboard layouts

Core Concepts

The Formula

```

RT = a + b * log2(n+1)

Where:

RT = Reaction time

a = Time not involved in decision (physical movement, etc.)

b = Empirical constant (~0.155s for choice tasks)

n = Number of equally probable choices

```

Practical Impact

| Choices | Relative Decision Time | User Experience |

| ------- | ---------------------- | --------------------- |

| 2 | Baseline | Quick, confident |

| 4 | +1 unit | Still manageable |

| 8 | +2 units | Starting to slow |

| 16 | +3 units | Noticeable hesitation |

| 32 | +4 units | Overwhelm begins |

| 64+ | +5+ units | Paralysis likely |

The Paradox of Choice

```

User Satisfaction

^

| *

|

|

|

| ____

+-----------------------> Number of Choices

Sweet spot

(4-7 items)

```

Analysis Framework

Step 1: Audit Decision Points

Map all places users must choose:

| Screen/Flow | Decision Type | Options Count | Complexity |

| ----------- | ------------- | ------------- | ---------- |

| [Screen 1] | Navigation | [n] | [H/M/L] |

| [Screen 2] | Selection | [n] | [H/M/L] |

| [Screen 3] | Configuration | [n] | [H/M/L] |

Step 2: Categorize Choices

```

Essential (keep) Nice-to-have (maybe) Remove

| | |

v v v

[_______] [_______] [_______]

[_______] [_______] [_______]

[_______] [_______] [_______]

```

Step 3: Apply Reduction Strategies

  1. Chunking: Group related items (3-4 per group)
  2. Progressive disclosure: Hide advanced options
  3. Smart defaults: Pre-select the common choice
  4. Filtering: Let users narrow options
  5. Recommendations: Highlight "Most Popular"

Output Template

```markdown

Hick's Law Analysis

Interface/Flow: [Name] Analysis Date: [Date]

Decision Point Inventory

| Location | Current Options | Target | Strategy |

| --------- | --------------- | ------ | -------------------- |

| [Point 1] | [n] | [n] | [Chunk/Hide/Default] |

| [Point 2] | [n] | [n] | [Chunk/Hide/Default] |

Reduction Plan

Quick wins (no functionality loss):

  1. [Change 1]
  2. [Change 2]

Strategic reductions (requires tradeoffs):

  1. [Change with impact analysis]

Expected Impact

  • Decision time reduction: ~[X]%
  • Conversion improvement: ~[X]% (estimated)
  • Support ticket reduction: ~[X]% (estimated)

```

Real-World Examples

Example 1: Netflix vs. Cable

Cable TV: 500+ channels = Decision paralysis

  • Users spend more time browsing than watching
  • Satisfaction decreases despite more options

Netflix approach:

  • Curated rows (chunking)
  • "Top 10" highlights (social proof + reduction)
  • "Because you watched..." (personalized filtering)
  • Auto-play (eliminates decision entirely)

Example 2: In-N-Out Burger

Menu has only 4 items vs. competitors' 50+:

  • Order time: 30 seconds vs. 2+ minutes
  • Customer satisfaction: Higher
  • Operation efficiency: Better

The constraint creates confidence in choice quality.

Example 3: Slack's Onboarding

Original: 15 configuration options upfront

  • Completion rate: 62%
  • Time to complete: 8 minutes

Redesigned: 3 essential questions, rest defaulted

  • Completion rate: 89%
  • Time to complete: 2 minutes

Best Practices

Do

  • Aim for 5-7 options maximum in any grouping
  • Use categorization to chunk larger sets
  • Provide clear visual hierarchy
  • Make the "default" choice obvious
  • Offer search/filter for large option sets

Avoid

  • Showing all features at once
  • Flat menus with 10+ items
  • Requiring decisions without clear benefit
  • Equal visual weight for all options
  • Removing options users actively need

When Hick's Law Doesn't Apply

  • Expert users with learned shortcuts
  • Emergency situations (trained responses)
  • When options are not equally weighted
  • Sequential vs. parallel choices

Reduction Techniques

1. Smart Defaults

```

Instead of:

[ ] Option A

[ ] Option B

[ ] Option C

Do:

[x] Option B (Recommended)

[ ] Option A

[ ] Option C

```

2. Progressive Disclosure

```

Basic Options

[Configure]

v Advanced (click to expand)

[_] Setting 1

[_] Setting 2

```

3. Chunking

```

Instead of 12 flat options:

Category A Category B Category C

  • Item 1 - Item 5 - Item 9
  • Item 2 - Item 6 - Item 10
  • Item 3 - Item 7 - Item 11
  • Item 4 - Item 8 - Item 12

```

Integration with Other Methods

| Method | Combined Use |

| -------------------------- | -------------------------------------- |

| Progressive Disclosure | Hide complexity, reveal on demand |

| Cognitive Load | Fewer choices = lower cognitive burden |

| Fogg Behavior Model | Simpler choices increase ability |

| Jobs-to-be-Done | Focus options on user's actual job |

Resources

  • [On the Rate of Gain of Information - Hick (1952)](https://psycnet.apa.org/record/1953-03853-001)
  • [The Paradox of Choice - Barry Schwartz](https://www.amazon.com/Paradox-Choice-Why-More-Less/dp/0060005696)
  • [Don't Make Me Think - Steve Krug](https://sensible.com/dont-make-me-think/)

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