# Predicting the intended action using internal simulation of perception

arXiv: https://arxiv.org/abs/2202.04466 · HTML: https://mlsanity.com/paper/2202.04466/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 9 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [First and Second Order Dynamics in a Hierarchical SOM system for Action Recognition](https://mlsanity.com/paper/2104.06059/) (2021)
- [Online Recognition of Actions Involving Objects](https://mlsanity.com/paper/2104.06070/) (2021)
- [Hierarchical growing grid networks for skeleton based action recognition](https://mlsanity.com/paper/2104.11165/) (2021)
- [Online recognition of unsegmented actions with hierarchical SOM architecture](https://mlsanity.com/paper/2104.11637/) (2021)
- [Action in Mind: A Neural Network Approach to Action Recognition and Segmentation](https://mlsanity.com/paper/2104.14870/) (2021)

## Abstract (arXiv)

This article proposes an architecture, which allows the prediction of intention by internally simulating perceptual states represented by action pattern vectors. To this end, associative self-organising neural networks (A-SOM) is utilised to build a hierarchical cognitive architecture for recognition and simulation of the skeleton based human actions. The abilities of the proposed architecture in recognising and predicting actions is evaluated in experiments using three different datasets of 3D actions. Based on the experiments of this article, applying internally simulated perceptual states represented by action pattern vectors improves the performance of the recognition task in all experiments. Furthermore, internal simulation of perception addresses the problem of having limited access to the sensory input, and also the future prediction of the consecutive perceptual sequences. The performance of the system is compared and discussed with similar architecture using self-organizing neural networks (SOM).