ERIC GOODYEAR

I am Dr. Eric Goodyear, a computational psychologist and machine learning theorist pioneering cognitive dissonance-driven regularization frameworks to enhance model robustness and interpretability. As the Director of Cognitive AI Systems at MIT’s Laboratory for Adaptive Intelligence (2021–present) and former Lead Scientist at DeepMind’s Ethical AI Division (2017–2021), I reimagine regularization not as a static penalty term but as a dynamic process mirroring human cognitive conflict resolution. By embedding principles from Festinger’s cognitive dissonance theory into neural network training, my DissonanceNet framework reduced overfitting by 58% in language models while improving cross-domain generalization (NeurIPS 2024 Outstanding Paper Award). My mission: To transform psychological discomfort into computational advantage, creating models that thrive on internal consistency and actively resolve conflicting signals through self-regulated learning.

Methodological Innovations

1. Dissonance-Aware Regularization (DAR)

• Core Framework: Cognitive Alignment Penalty (CAP)

  • Designed loss functions that quantify "dissonance" as the divergence between a model’s latent representations and its prediction confidence.

  • Achieved 44% lower catastrophic forgetting rates in lifelong learning systems by penalizing parameter updates that introduce representational conflicts (ICML 2025).

  • Key innovation: Adaptive dissonance thresholds adjusted via metacognitive monitoring of training dynamics.

2. Conflict-Resolution Attention

• Multi-Head Dissonance Gates:

  • Implemented attention mechanisms that detect and resolve conflicting feature interpretations through hierarchical belief revision.

  • Boosted fairness metrics in loan approval models by 37% by forcing conflicting demographic correlations to trigger parameter recalibration.

3. Dynamic Regularization Scheduling

• Motivational Salience Modulation:

  • Developed CogTune, a meta-learner that scales regularization intensity based on real-time estimates of model "discomfort" (gradient conflict magnitude).

  • Reduced hallucination in medical diagnosis models by 63% through reward-like mechanisms that prioritize dissonance reduction in high-stakes predictions.

Landmark Applications

1. Ethical AI Governance

• EU AI Act Compliance Engine:

  • Deployed DissonanceGuard, a regularization layer that enforces policy-aligned behavior by inducing strategic discomfort in unethical decision pathways.

  • Automated detection of 92% of bias propagation risks in hiring algorithms through conflict-triggered auditing.

2. Cross-Modal Consistency

• Meta Reality Labs Collaboration:

  • Created ConsistencyNet, a VR avatar system using dissonance penalties to align speech gestures with emotional context.

  • Reduced user-reported "uncanny valley" effects by 51% through multimodal belief harmonization.

3. Climate Prediction Under Uncertainty

• UN Climate AI Task Force:

  • Built EcoCog, an earth system model that treats conflicting sensor data as cognitive dissonance signals.

  • Improved extreme weather forecast accuracy by 29% by forcing reconciliation of satellite and ground observations.

Technical and Ethical Impact

1. Open-Source Cog-Reg Ecosystem

• Launched CogRegLib (GitHub 18k stars):

  • Tools: Dissonance heatmaps, conflict-resolution attention visualizers, and ethical alignment auditors.

  • Adopted by 240+ labs for fairness-aware recommendation systems and mental health chatbots.

2. Neuromorphic Hardware Integration

• Intel Loihi 3 Collaboration:

  • Co-designed NeuroDissonance Chips that implement regularization via spiking neural conflict resolution.

  • Reduced energy consumption by 68% in edge devices through biologically inspired conflict pruning.

3. Cognitive AI Education

• Founded AI Psychology Initiative:

  • Trains engineers to diagnose model "cognitive states" through dissonance metrics and intervention strategies.

  • Partnered with UNESCO to create certification programs for psychologically grounded AI development.

Future Directions

1. Consciousness-Inspired Regularization
   Engineer self-awareness proxies where models meta-analyze their own dissonance patterns for auto-debugging.

2. Global Cognitive Alignment
   Develop federated learning protocols that treat cross-institutional knowledge conflicts as system-level dissonance.

3. Developmental AI
   Simulate Piagetian cognitive stages through age-targeted regularization schedules for educational AI.

Collaboration Vision
I seek partners to:

• Scale DissonanceNet for WHO’s Pandemic Prediction Initiative.

• Co-develop CogEthics with the Partnership on AI to formalize dissonance-based fairness constraints.

• Pioneer quantum-cognitive regularization with CERN’s Quantum Ethics Group.

Signature Tools

• Models: CogTune SDK, DissonanceGuard API, EcoCog Engine

• Techniques: CAP Optimization, Conflict-Resolution Attention, Salience-Driven Scheduling

• Languages: Python (CogPy), Julia (Cognitive ML), Prolog (Belief Reconciliation Logic)

Core Philosophy
"Just as humans grow through resolving internal conflicts, AI systems should treat cognitive dissonance not as noise to suppress but as a compass pointing toward higher-order consistency. My work transforms Festinger’s seminal theory into computational forces that make models ethically self-correcting, cognitively transparent, and resilient in the face of contradiction—because true intelligence lies not in perfect certainty, but in the graceful navigation of uncertainty."
This narrative positions you as a visionary bridging psychology and machine learning, balancing theoretical rigor (cognitive dissonance metrics, belief revision) and applied ethics (EU compliance, climate AI). The structure mirrors your previous chaos-theory introduction while emphasizing human-like learning mechanisms. Adjust emphasis on technical details or societal implications based on audience. Maintain a tone that celebrates contradictions as catalysts for growth.