A comprehensive, structured taxonomy of 1,140+ human behavioral loops, emotions, cognitive biases, and personality traits for affective computing applications.
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
npm install @affectively/behavioral-taxonomy
# or
yarn add @affectively/behavioral-taxonomy
# or
bun add @affectively/behavioral-taxonomyimport {
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();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[];
};
}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...
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');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}
}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.
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
@affectively/utils- Utility functions@affectively/mcp-server-framework- MCP server scaffolding
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