Updated Article:
Neuroligence applies cognitive modeling and signal-processing techniques to study how users perceive and process information.
1. Cognitive Response Modeling
Signals (neural, physiological, behavioral) can reveal attention, cognitive load, and engagement levels.
2. Emotional State Modeling
ML-based time-series models classify emotional responses, mapping how users react to stimuli.
3. Implicit Bias Detection
Our systems use structured models to identify unconscious decision drivers.
4. Human–AI Interaction Insights
We analyze how users interact with systems, identify friction points, and improve decision workflows.
5. Predictive Decision Models
Signals can be used to forecast how users respond to new features or strategies.
Outcome:
A deeper understanding of human decision-making grounded in signal processing and cognitive science.
Neuroligence 