Action similarity judgment based on kinematic primitives

TL;DR

This study compares human and computational model judgments of action similarity based on kinematic primitives, revealing both shared accuracy and differences in bias and sensitivity, advancing understanding of action perception.

Contribution

It demonstrates that a kinematic primitive-based model can reliably predict human action similarity judgments, highlighting the role of kinematic features in action perception.

Findings

Both humans and the model accurately identify similar actions

The model exhibits more false positives and bias than humans

Humans rely primarily on kinematic information for action identification

Abstract

Understanding which features humans rely on -- in visually recognizing action similarity is a crucial step towards a clearer picture of human action perception from a learning and developmental perspective. In the present work, we investigate to which extent a computational model based on kinematics can determine action similarity and how its performance relates to human similarity judgments of the same actions. To this aim, twelve participants perform an action similarity task, and their performances are compared to that of a computational model solving the same task. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The comparative experiment results show that both the model and human participants can reliably identify whether two actions are the same or not. However, the model produces more false hits and has a greater selection bias than human participants. A possible reason for this is the particular sensitivity of the model towards kinematic primitives of the presented actions. In a second experiment, human participants' performance on an action identification task indicated that they relied solely on kinematic information rather than on action semantics. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features, which can provide an essential basis for classifying human actions.