The Core Question
Human decision-making degrades predictably under stress, fatigue, and emotional load. This degradation follows observable patterns that manifest in behavior, physiology, and interaction dynamics before the actual mistake occurs.
Our central hypothesis: cognitive drift toward error can be detected 10-30 seconds before the error manifests, creating a window for intervention.
We're building the infrastructure to detect these pre-error signatures and deliver appropriately-timed interventions that redirect the decision-maker without disrupting their cognitive flow.
Scientific Foundations
Our approach draws on established research across multiple disciplines.
Cognitive Load Theory
Decision quality degrades when cognitive resources are depleted or overwhelmed. We model cognitive load in real-time to predict capacity constraints.
Behavioral Economics
Systematic biases—loss aversion, anchoring, recency effects— become more pronounced under pressure. We detect behavioral signatures of bias activation.
Psychophysiology
Emotional and cognitive states have physiological correlates. Heart rate variability, facial micro-expressions, and voice patterns provide real-time state information.
Human Factors Engineering
Error patterns in high-stakes domains (aviation, medicine, nuclear operations) follow predictable trajectories. We apply these frameworks to trading.
Research Phases
Our development follows a structured research program.
Pattern Discovery
Collect multimodal data from trading sessions to identify pre-error behavioral and physiological signatures. Establish baseline patterns and drift indicators.
Currently Active
Detection Model Development
Build and validate models for real-time cognitive state classification. Develop the fusion architecture that integrates multiple data streams into coherent risk assessment.
In Progress
Intervention Design
Research optimal intervention modalities and timing. Test different approaches to cognitive redirection— voice, visual, haptic—and measure effectiveness.
Planned
Closed-Loop Learning
Implement reinforcement learning systems that personalize detection and intervention based on individual response patterns. Continuous improvement through feedback loops.
Planned
Domain Transfer
Generalize the cognitive operating system to other high-stakes domains: aviation, medicine, emergency response. Validate transfer learning approaches.
Future
Methodology
Data Collection
We capture multimodal data during live trading sessions: screen activity, mouse and keyboard dynamics, voice recordings, and optional biometric inputs. All data collection is consent-based with privacy by design.
Ground Truth Labeling
Traders annotate their own sessions post-hoc, marking decision quality and mental state. This self-report data, combined with outcome analysis, provides labeled training data.
Model Architecture
We use a multi-stream architecture that processes different data modalities through specialized encoders before fusion. Temporal modeling captures the evolution of cognitive state over time windows.
Validation
We measure detection accuracy, false positive rates, and intervention effectiveness. The goal is high sensitivity to genuine pre-error states while minimizing disruptive false alarms.
Technical Architecture
For a detailed technical overview of CognitiveOS architecture, including our detection engines, risk scoring, and learning systems.
View Product MapResearch Collaboration
We're interested in collaborating with researchers in cognitive psychology, human factors, and applied machine learning.