The Importance of Constraint Smoothness for Parameter Estimation in Computational Cognitive Modeling

arXiv

View on arXiv

2.0/10

Platform Domination RiskN/A

Market Consolidation RiskN/A

Displacement HorizonN/A

CORE FUNCTION

Provides a methodology and reference implementation for improving the reliability of parameter estimation in computational cognitive models (specifically RL models) by addressing constraint smoothness during model fitting in psychiatric neuroscience research.

TRACTION

citations

0.0 velocity

co_authors

0.0 velocity

REASONING

This is an 8-year-old academic reference implementation for a specific paper. With zero stars and minimal activity, it serves as a niche research artifact rather than a living software project. Its value lies in the mathematical approach to model fitting rather than the codebase itself.

COMPOSABILITY

TECH STACK

PythonNumPySciPyMATLABReinforcement Learning

INTEGRATION

reference_implementation

parameter_estimationcognitive_modelingpsychiatric_neurosciencereinforcement_learning

READINESS

Composabilityalgorithm

Depthreference_implementation

Noveltyincremental