No Plan, Yet Human: A Reactive Robotics Model Predicts Human Planning Failures on a Clinical Task

TL;DR

This paper demonstrates that a reactive robotics model, AICON, can predict human planning failures in a clinical task without prior knowledge of human cognition, outperforming traditional planning models especially in reduced capacity scenarios.

Contribution

It applies a robotics-based reactive model to human cognitive testing, showing its ability to replicate human difficulty patterns and behavior shifts under reduced planning capacity.

Findings

AICON reproduces human difficulty ordering across problems better than structural parameters.

The model generalizes well to unseen problems in a clinical planning task.

AICON outperforms planning baselines for groups with reduced capacity, aligning with clinical observations.

Abstract

Understanding why some sequential planning problems are harder than others requires models that go beyond average performance. They should capture the specific pattern of which problems are hard, and ideally fail in the same way people do when planning capacity is reduced. We apply AICON, a reactive gradient-descent framework developed for robotic manipulation, to the Tower of London test, a cognitive test used to assess planning in Parkinson's disease, mild cognitive impairment, and stroke. Without any lookahead planning or knowledge of human cognition, AICON reproduces the fine-grained human difficulty ordering across 24 problems better than structural task parameters and generalizes to held-out problems in a leave-two-out evaluation. Crucially, AICON outperforms a planning baseline for groups with reduced planning capacity while the planning baseline better captures healthy controls. This dissociation was predicted by the original AICON paper, which noted that the model's failure modes resemble those of Parkinson's patients who struggle with goal hierarchies but not move counts. This suggests that as planning capacity is reduced, human behavior shifts toward the reactive mode AICON models. The finding extends a broader pattern: AICON, originally built for robotics, now captures aspects of biological behavior across perception, eye movements, and sequential planning, suggesting its core abstraction reflects something real about how biological systems are organized.