Research

Our research aims to understand the computational principles underlying human cognition through mathematical modeling, empirical studies, and theoretical analysis. We investigate fundamental questions about how the mind works and how these insights can inform artificial intelligence and applications.

Computational Modeling

We develop mathematical and computational models to formalize theories of cognition and generate testable predictions. Our modeling approaches include Bayesian inference, neural networks, reinforcement learning, and dynamical systems.

Current Projects

Neural Mechanisms of Working Memory

PI: Dr. [REDACTED] | Funded by [REDACTED AGENCY]

This project investigates how neural oscillations and synaptic plasticity mechanisms interact to support working memory function. We combine biologically-realistic neural network models with behavioral and neuroimaging data to understand capacity limitations and interference effects.

Key Questions:

  • How do neural oscillations coordinate information storage and manipulation?
  • What mechanisms limit working memory capacity?
  • How do individual differences arise from network parameters?
Related Publications Code

Hierarchical Reinforcement Learning Models of Cognitive Control

PI: Dr. [REDACTED] | Funded by [REDACTED ORGANIZATION]

We develop hierarchical reinforcement learning frameworks to understand cognitive control processes such as task switching, working memory updating, and interference resolution. These models capture how humans learn to organize behavior at multiple temporal scales.

Applications:

  • Understanding cognitive flexibility disorders
  • Designing adaptive interfaces
  • Improving AI agent architectures
Related Publications Datasets

Bayesian Models of Causal Learning

Co-PI: [REDACTED] | Funded by [REDACTED AGENCY]

This research examines how people learn about causal relationships from observation and intervention. We develop Bayesian models that capture both the computational principles of causal inference and the psychological constraints that shape human causal reasoning.

Methods:

  • Behavioral experiments with causal learning tasks
  • Bayesian network models of causal representation
  • Developmental studies across the lifespan

Research Areas

Funding

[REDACTED FUNDING AGENCY]

Grant [REDACTED]

[REDACTED INSTITUTE]

[REDACTED PROGRAM]

Collaborations

  • [REDACTED INSTITUTE] for AI
  • [REDACTED RESEARCH INSTITUTE]
  • [REDACTED INSTITUTE] Computer Science and Artificial Intelligence Laboratory
  • [REDACTED INSTITUTE] for Human Development

Empirical Studies

Our empirical research provides critical data to test computational theories and discover new phenomena. We use a variety of experimental methods to study cognition across different populations and contexts.

Behavioral Experiments

We conduct carefully controlled behavioral experiments to measure cognitive performance, reaction times, and error patterns. Our studies often involve novel task paradigms designed to test specific theoretical predictions.

Recent Studies:

  • Attention and memory interaction in visual scenes
  • Decision making under uncertainty
  • Metacognitive monitoring in problem solving

Neuroimaging

We use EEG and fMRI to investigate the neural mechanisms underlying cognitive processes. Our neuroimaging studies are tightly integrated with computational models to test specific hypotheses about brain function.

Techniques:

  • Event-related potentials (ERPs)
  • Time-frequency analysis
  • Multivariate pattern analysis (MVPA)
  • Connectivity analysis

Individual Differences

We study how cognitive abilities vary across individuals and populations, examining the sources of this variation through both behavioral and computational approaches. This work informs personalized interventions and educational applications.

Populations:

  • Healthy young and older adults
  • Children and adolescents
  • Clinical populations
  • Cross-cultural studies

Applications

We translate our basic research findings into practical applications that can benefit society. Our applied work spans education, artificial intelligence, and cognitive rehabilitation.

Educational Technology

We develop cognitive science-informed educational tools that adapt to individual learning styles and optimize knowledge acquisition. Our work focuses on leveraging computational models of learning and memory to improve educational outcomes.

Projects:

  • Adaptive tutoring systems
  • Spaced repetition algorithms
  • Metacognitive training programs
  • Assessment of learning transfer
Educational Tools →

Artificial Intelligence

Our research contributes to the development of more human-like AI systems by providing insights into the computational principles of human intelligence. We work on cognitive architectures, interpretable AI, and human-AI collaboration.

Contributions:

  • Cognitive architectures for AGI
  • Resource-rational algorithms
  • Explainable AI systems
  • Human-in-the-loop learning
Open Source Code →

Cognitive Rehabilitation

We apply our understanding of cognitive mechanisms to develop interventions for individuals with cognitive impairments. Our computational models help identify optimal training regimens and predict treatment outcomes.

Focus Areas:

  • Working memory training
  • Attention rehabilitation
  • Executive function therapy
  • Cognitive assessment tools
Related Research →

Human Factors

We study how cognitive limitations and capabilities should inform the design of technology, interfaces, and work environments. Our research helps create systems that work well with human psychology.

Applications:

  • Interface design principles
  • Attention management systems
  • Decision support tools
  • Workload assessment
Collaboration Opportunities →

Research Philosophy

Our research is guided by the belief that understanding cognition requires both formal mathematical theories and careful empirical investigation. We strive to develop models that are both psychologically plausible and computationally precise, enabling us to make specific predictions that can be tested experimentally.

We emphasize open science practices, including preregistration of studies, sharing of data and code, and collaborative research. We believe that science progresses most rapidly when knowledge is freely shared and when diverse perspectives are brought to bear on fundamental questions.

Our work is inherently interdisciplinary, drawing insights from psychology, neuroscience, computer science, mathematics, and philosophy. We value both depth of expertise and breadth of perspective, encouraging our team members to engage with multiple research communities and methodological approaches.

Publications

Our research output spans computational modeling, empirical studies, and theoretical contributions to cognitive science. Search and filter our publications by research area, year, or type.