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Trait Index

CBrowser's cognitive simulation uses 25 research-backed psychological traits in 6 tiers (7 core + 4 emotional + 5 decision-making + 3 planning + 2 perception + 4 social). Each trait is a measurable dimension of human cognition that shapes web interaction.

Trait Tiers Overview

Tier Category Traits Description
1 Core Traits 8 Fundamental cognitive capacities
2 Emotional Traits 4 Affective and motivational factors
3 Decision-Making Traits 5 Choice and judgment processes
4 Planning Traits 3 Strategic and procedural cognition
5 Perception Traits 2 Attention and awareness limitations
6 Social Traits 4 Social influence and comparison

Tier 1: Core Traits

Fundamental cognitive capacities that form the foundation of user behavior.

Trait Scale Primary Research
Patience 0.0-1.0 Nah (2004) - 8-10 second tolerance threshold
Risk Tolerance 0.0-1.0 Kahneman & Tversky (1979) - Prospect Theory
Comprehension 0.0-1.0 Card, Moran & Newell (1983) - GOMS Model
Persistence 0.0-1.0 Duckworth et al. (2007) - Grit Scale
Curiosity 0.0-1.0 Berlyne (1960) - Epistemic Curiosity
Working Memory 0.0-1.0 Miller (1956) - 7±2 Chunks
Reading Tendency 0.0-1.0 Nielsen (2006) - F-Pattern

Tier 2: Emotional Traits

Affective factors that influence persistence, confidence, and recovery from setbacks.

Trait Scale Primary Research
Resilience 0.0-1.0 Smith et al. (2008) - Brief Resilience Scale
Self-Efficacy 0.0-1.0 Bandura (1977) - Self-Efficacy Theory
Trust Calibration 0.0-1.0 Fogg (2003) - Stanford Credibility
Interrupt Recovery 0.0-1.0 Mark et al. (2005) - Cost of Interruption

Tier 3: Decision-Making Traits

How users evaluate options, make choices, and allocate cognitive resources.

Trait Scale Primary Research
Satisficing 0.0-1.0 Simon (1956) - Bounded Rationality
Information Foraging 0.0-1.0 Pirolli & Card (1999) - Info Foraging
Anchoring Bias 0.0-1.0 Tversky & Kahneman (1974) - Anchoring
Time Horizon 0.0-1.0 Laibson (1997) - Hyperbolic Discounting
Attribution Style 0.0-1.0 Weiner (1985) - Attribution Theory

Tier 4: Planning Traits

Strategic thinking, procedural knowledge, and learning transfer capabilities.

Trait Scale Primary Research
Metacognitive Planning 0.0-1.0 Flavell (1979) - Metacognition
Procedural Fluency 0.0-1.0 Sweller (1988) - Cognitive Load
Transfer Learning 0.0-1.0 Thorndike (1901) - Transfer of Practice

Tier 5: Perception Traits

Limitations in visual attention and mental model updating.

Trait Scale Primary Research
Change Blindness 0.0-1.0 Simons & Chabris (1999) - Gorilla Study
Mental Model Rigidity 0.0-1.0 Johnson-Laird (1983) - Mental Models

Tier 6: Social Traits

How social context and comparison affect user behavior.

Trait Scale Primary Research
Authority Sensitivity 0.0-1.0 Milgram (1963) - Obedience
Emotional Contagion 0.0-1.0 Hatfield et al. (1993) - Contagion
FOMO 0.0-1.0 Przybylski et al. (2013) - FoMO Scale
Social Proof Sensitivity 0.0-1.0 Goldstein, Cialdini et al. (2008)

Estimated Trait Correlations

Correlation estimates are derived from related research findings and theoretical models. Empirical calibration is planned (GitHub #95).

Traits interact. Research-backed correlations:

Trait Pair Correlation Research Basis
Patience ↔ Persistence r = 0.45 Both load on conscientiousness
Working Memory ↔ Comprehension r = 0.52 Cognitive capacity overlap
Self-Efficacy ↔ Persistence r = 0.48 Bandura (1977)
FOMO ↔ Impatience r = -0.41 Przybylski et al. (2013)
Resilience ↔ Self-Efficacy r = 0.56 Protective factors research

Using Traits in CBrowser

Via MCP Tool

await cognitive_journey_init({
  persona: "custom",
  goal: "complete checkout",
  startUrl: "https://example.com",
  customTraits: {
    patience: 0.3,
    workingMemory: 0.5,
    riskTolerance: 0.2
  }
});

Via CLI

cognitive-journey has no per-trait flag; since 20.0 it rejects --trait, which it never read. Build a persona with the traits you want, then pass its name:

npx cbrowser \
  persona-questionnaire start \
  --comprehensive \
  --name checkout-tester

npx cbrowser cognitive-journey \
  --persona checkout-tester \
  --start https://example.com \
  --goal "complete checkout"

See Also

Bibliography

See Bibliography for all academic sources.

Copyright: (c) 2026 Alexa Eden.

License: MIT License

Contact: [email protected]

Originally created by Alexa Eden 2026. Learn more at https://cbrowser.ai

Runtime variables (not traits)

These look like traits and live alongside them for backwards compatibility, but they describe a persona-site pair rather than the person, and are supplied per run:

Variable Range Supplied by
Site Familiarity 0.0-1.0 the siteFamiliarity parameter, or site-knowledge data gathered by page_understand

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