Mahi Luthra

I am pursuing a Dual PhD in Cognitive Science and Psychology from Indiana University under the guidance of Dr. Peter Todd. I have two broad research interests: (1) How do humans process information when making decisions? (2) How do humans search for resources that they need? These resources can be abstract (e.g., information in memory or on the Web) or they can be concrete (e.g., food).

Research methods/modelling techniques: Bayesian modelling, agent-based modelling, evolutionary algorithms, reinforcement learning, dynamical systems theory, artifical neural networks

Find out more about my research below and feel free to contact me for any discussions on science!

Research Interests

Human Decision Making and Information Sampling

Humans make surprisingly good decisions in everyday life; in fact, several researchers compare human decision making to rational inductive approaches like Bayesian analysis. However, unlike these rational approaches, humans are able to achieve optimality with limited information availability and manipulation capacity. This broad research area focuses on how humans make effective everyday decisions despite informational constraints and how they search for information to be included in decision samples. Our recent paper explored influence of recency biases and proposed that informational sampling limits stem from limitations in working memory capacity.

Mutual Interaction Between Search Behavior and Distribution of Resources

I study how collective search behaviors influence distribution of resources and how emergent resource distributions in turn influences search behavior. In our recent paper, we found that cognitive search parameters and environmental resource distributions regulated each other, producing self-organizing systems. Currently, we are using multi-agent neural network models to study such systems further. So far, this work has focused on ecological resources (food); we hope to also apply our model to domains of Web search.