@affectively/behavioral-taxonomy

A comprehensive, structured taxonomy of 1,140+ human behavioral loops, emotions, cognitive biases, and personality traits for affective computing applications.

Overview

This package provides the foundational behavioral science data structures used by the AFFECTIVELY platform. It categorizes human behavior into structured, machine-readable formats suitable for:

• Emotion AI / Affective Computing
• Natural Language Processing
• Behavioral Analysis
• Mental Health Applications
• Human-Computer Interaction Research
• Game Design & Narrative Systems

Installation

npm install @affectively/behavioral-taxonomy
# or
yarn add @affectively/behavioral-taxonomy
# or
bun add @affectively/behavioral-taxonomy

Quick Start

import { 
 getBehavioralLoops, 
 getEmotions, 
 getBiases, 
 getTraits 
} from '@affectively/behavioral-taxonomy';

// Get all behavioral loops
const loops = getBehavioralLoops();
console.log(`Loaded ${loops.metadata.totalLoops} behavioral loops`);

// Find loops by category
const digitalLoops = loops.categories.find(c => c.id === 'digital-cognitive');

// Get emotion taxonomy
const emotions = getEmotions();

// Get cognitive biases
const biases = getBiases();

Data Structures

Behavioral Loops

Each loop follows a structured format:

interface BehavioralLoop {
 id: number;
 name: string;
 logic: {
 given: string; // Initial condition
 when: string; // Trigger event
 then: string; // Resulting behavior
 result: string; // Long-term outcome
 };
 taxonomy: {
 origin: 'GENETIC' | 'BEHAVIORAL' | 'NARRATIVE' | 'DIGITAL' | 'ENVIRONMENTAL';
 modality: 'VISUAL_STATIC' | 'VISUAL_DYNAMIC' | 'AUDITORY' | 'HAPTIC' | 'OLFACTORY' | 'META' | 'PROXEMIC';
 mutability: 'IMMUTABLE' | 'STUBBORN' | 'FLUID' | 'VOLATILE';
 valences: ('ATTRACTION' | 'REPULSION' | 'DOMINANCE' | 'SUBMISSION' | 'TRUST' | 'DECEPTION' | 'DISRUPTION')[];
 };
 operator: {
 name: string; // Named psychological mechanism
 mechanism: string; // Description of how it works
 };
 veracity: {
 objectiveGrounding: number; // 0-1: Evidence basis
 socialConsensus: number; // 0-1: Social agreement
 recursiveAmplification: number; // 0-1: Self-reinforcing strength
 frictionIndex: number; // 0-1: Resistance to change
 };
 intervention: {
 difficulty: number; // 1-10: How hard to change
 interdict: string; // How to block the loop
 minimize: string; // How to reduce intensity
 recognize: string; // How to detect it
 leverage?: string; // How to use positively
 };
 meta: {
 tags: string[];
 relatedArchetypes?: string[];
 academicFields?: string[];
 };
}

Categories

Loops are organized into 279 categories including:

• Digital & Cognitive Loops
• Dental & Oral Signals
• Dermatological & Tactile Signals
• Aging & Temporal Biomarkers
• Ocular & Gaze Signals
• Olfactory & Chemical Signals
• Micro-movement & Gait
• And many more...

Direct JSON Access

For maximum flexibility, you can import the raw JSON data:

// ESM
import behavioralLoops from '@affectively/behavioral-taxonomy/data/behavioral-loops';
import emotions from '@affectively/behavioral-taxonomy/data/emotions';
import biases from '@affectively/behavioral-taxonomy/data/biases';
import traits from '@affectively/behavioral-taxonomy/data/traits';

// CommonJS
const behavioralLoops = require('@affectively/behavioral-taxonomy/data/behavioral-loops');

Academic Citation

If you use this dataset in academic work, please cite:

@dataset{affectively_behavioral_taxonomy_2026,
 author = {AFFECTIVELY},
 title = {Human Behavioral Loop Taxonomy},
 year = {2026},
 publisher = {npm},
 version = {1.0.0},
 url = {https://github.com/affectively-ai/behavioral-taxonomy}
}

License

This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

You are free to:

• Share — copy and redistribute the material in any medium or format
• Adapt — remix, transform, and build upon the material for any purpose, even commercially

Under the following terms:

• Attribution — You must give appropriate credit to AFFECTIVELY, provide a link to the license, and indicate if changes were made.

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Related Packages

• @affectively/utils - Utility functions
• @affectively/mcp-server-framework - MCP server scaffolding

Support

• GitHub Issues
• Documentation
• Discord Community

Made with ️ by AFFECTIVELY