Behavioral evidence for the hierarchical execution of sequential movements

TL;DR

This study shows that humans naturally use hierarchical planning for complex movements without training, based on how they move their hands toward targets.

Contribution

The study provides direct kinematic evidence of hierarchical movement control without requiring prior training.

Findings

Most participants dynamically adjusted their movement strategy based on the sequence of targets.

Hierarchical planning was inferred through model-based analysis of movement trajectories.

Results suggest humans switch between flat and hierarchical planning depending on task structure.

Abstract

Movements in humans and other animals are known to be hierarchically organized, with simple movements forming the building blocks to more complex, sequential movements, a phenomenon often referred to as chunking. Neuroimaging studies have highlighted this layered structure, implicating the primary motor cortex in simple movements, and pre-motor and parietal areas in sequences of movements. Behavioral experiments designed to study this hierarchy have required extensive training of simple movement sequences, such as key presses, using error rates and reaction times as indirect markers of chunking. In this work, we provide kinematic evidence that the hierarchical organization of movements naturally emerges during reaching movements toward large targets, without the need for extensive training. Through model-based analyses of the observed trajectories’ geometry in a sequential pointing task (N = 20 participants), we infer the underlying hierarchy of the mechanism guiding movement generation. Our results show that most participants adapt their strategy dynamically using hierarchical planning, depending on the sequence. These findings offer insights into the process of chunking, as well as the conditions on how and when humans switch between flat and hierarchical planning during movement.

This study analyzes sequential human reaching movements to find kinematic evidence of their hierarchical control. The model-based analyses are based on comparing state-of-the-art optimal control models with or without a hierarchy of controllers.

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