OpenSensation™
An open knowledge infrastructure for biological and artificial sensing. Connecting stimuli, sensors, signals, and context to candidate interpretations.
- Stimuli & sensors
- Signals & thresholds
- Context & interpretation
AN OPEN RESEARCH INITIATIVE
Open research into
the building blocks
of artificial minds.
Studying the functional attributes of artificial cognitive systems, one layer at a time.
Precise questions. Open infrastructure. Evidence before assumption.
FUNCTIONAL ATTRIBUTE MAPFIG. 01
Memory + Attention
Self-model + Embodiment
Representing signals from biological and artificial sensors.
CONCEPTUAL RELATIONSHIPS NOT A VALIDATED SEQUENCE
ONE ATTRIBUTE AT A TIME
Sensation · Perception · Emotion · Memory · Agency · Embodiment
01 / THE PREMISEBig questions.
Big questions.
Smaller, testable parts.
Why AI Attributes?
AI research often studies large systems end-to-end. The functions within them can be harder to isolate, describe, and compare.
AI Attributes™ explores how sensation, emotion, perception, memory, motivation, agency, and embodiment can be represented, studied, and combined in artificial cognitive systems.
We start with individual capabilities and transparent models, building toward infrastructure for more precise questions.
Discover the initiative02 / THE PROJECT FAMILY
All projects Current open projects.
Distinct attributes. A shared commitment to open, evidence-aware knowledge.
OpenEmotions™
An open, evidence-aware knowledge infrastructure for emotional states. Mapping triggers, appraisals, transitions, and response tendencies with traceable provenance.
- States & appraisals
- Intensity & transitions
- Evidence & provenance
03 / RESEARCH FRAMEWORKUnderstand the parts.
Understand the parts.
Explore the connections.
What happens when we add specific functional attributes one by one?
01
Input & Sensing
- Sensation
- Perception
- Attention
Internal State
- Emotion
- Memory
- Motivation
- Drives
Self & Cognition
- Self-model
- Identity
- Reasoning
- Learning
Agency
- Decision
- Action
- Planning
- Goal pursuit
Embodiment
- Physical sensing
- Proprioception
- Artificial interoception
- Environment interaction
An evolving research framework, not a finalized scientific taxonomy.
Explore the framework04 / EVIDENCE BEFORE ASSUMPTION“Unknown is better
“Unknown is better
than fabricated
certainty.”
Useful research makes its uncertainty visible.
Community proposals, editorial seed data, expert review, and research-supported claims should remain distinguishable. Disagreement and unknowns belong in the record, too.
Our approach to evidence05 / OPEN RESEARCH INFRASTRUCTUREKnowledge you can inspect.
Explore the open model Knowledge you can inspect.
Connections you can question.
Projects aim to make knowledge citeable, versioned, machine-readable, reusable, challengeable, and improvable.
- 01Open dataset
- 02Structured schemas
- 03Evidence
- 04Expert review
- 05Public API
- 06Knowledge graph
- 07Experiments
LONG-TERM ARCHITECTURE
Toward an AI Attributes Knowledge Graph
Connect OpenSensation™, OpenEmotions™, and potential future modules through interoperable relationships carrying provenance, evidence status, source references, context, uncertainty, and version history.
Explore the graph visionStimulus→Sensation→Perception→Appraisal→Emotion→Motivation→Action
06 / QUESTIONS, NOT CONCLUSIONSBetter questions.
Better questions.
Shared exploration.
Questions we want to make easier to study, with clear definitions and testable hypotheses.
All research questionsHow should artificial systems represent internal sensory states?
A starting point is to specify the signal, its context, and the uncertainty in its interpretation. The representation itself is a research question.
How does context change interpretation of identical inputs?
Compare the same input across explicitly recorded environmental and internal conditions, while keeping other variables controlled.
How should affective states be represented computationally?
Candidate representations may use categories, dimensions, or dynamic variables. Their usefulness needs to be evaluated against a stated task.
What happens when persistent memory meets emotion-like state?
Comparative experiments could isolate memory and affect-like variables, then measure how their combination changes observable behavior.
07 / WHAT COMES NEXT
Future research areas.
Potential future modules, not committed products. Each direction begins with a question worth making precise.
FROM KNOWLEDGE TO EXPERIMENTSOne variable.
One variable.
A clearer comparison.
Future controlled sandbox research could compare persistent memory, emotional state, sensory input, self-model, motivation, and environmental constraints.
Comparative agent experiments would examine bounded behavior, including human-speed and accelerated settings, with explicit baselines and human oversight.
Read the research roadmap08 / BUILD UNDERSTANDING, TOGETHERExperts welcome.
Experts welcome.
Curiosity required.
Researchers, developers, students, and careful questioners: help review claims, submit evidence, improve ontologies, or contribute code and documentation.
Find your way to contribute