CCN 2025: Inferring cognitive load from deviations in simulated human planning behavior

Having attended COSYNE and courses from the Neuromatch Academy (open science and computational neuroscience), I have positive expectations on the Cognitive Computational Neuroscience conference seeing many people within the Neuromatch circles are also there. I am so happy that I get to present a poster there, though I realize I still have a lot more to do to make my work appealing to the neuroscience community.

The poster I presented at the CCN 2025

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Details

Title: Inferring cognitive load from deviations in simulated human planning behavior

Authors: May Kristine Jonson Carlon

Venue: 8th Annual Conference on Cognitive Computational Neuroscience

Date: August 13, 2025

Abstract

Understanding how cognitive load shapes human planning behavior is crucial for building AI systems that collaborate effectively with people. While traditional approaches to measuring cognitive load such as self-report questionnaires or dual-task paradigms are valuable, they often lack real-time responsiveness or introduce artificial task constraints. This work is a proof-of-concept for inferring cognitive load from deviations in planning, thus avoiding intrusive or retrospective measures. We simulate user profiles performing a structured task (summarizing an article), with behavioral noise introduced via repetition, backtracking, pausing, and skipping actions. A Hidden Markov Model (HMM) is used to infer latent cognitive states from the resulting behavioral traces. Results from 100 Monte Carlo trials show that the HMM reliably recovers latent states aligned with intuitive levels of cognitive load. Emission patterns are interpretable, stable across trials, and distinct for each state, capturing predetermined behavioral signatures of low, medium, and high mental effort. State assignments also show alignment with simulated user profiles. Our approach provides a simulation-based foundation for modeling cognitive variability and may inform future work in user modeling, Theory of Mind, and adaptive systems.