# Expanding the V1-MT model to the estimation of perceived fluid direction

## TL;DR

This study expands a visual model to better understand how humans perceive the direction of liquid flow.

## Contribution

The study introduces a weighted mean approach to improve predictions of non-rigid motion perception.

## Key findings

- The winner-take-all approach failed to predict perceived fluid direction accurately.
- A weighted mean of directional energies strongly predicted human perceptions of liquid flow.
- The visual system integrates directional energies from non-rigid motion components.

## Abstract

Humans can readily perceive the direction of liquid flow, yet computational modeling of this process remains challenging due to the complexity of non-rigid motion. Previous models based on neural activities in the primary visual cortex (V1) and the middle temporal area (MT) have been effective in explaining rigid motion perception. In this study, we extend the V1-MT model to address the perception of liquid flow direction. Participants observed video clips of liquid flow and reported the perceived direction, while the V1-MT model was used to predict these perceptions. The winner-take-all approach failed to accurately capture the observed perceptions. In contrast, a weighted mean of directional energies yielded strong predictions, highlighting that the human visual system spatially integrates directional energies from non-rigid motion components. These findings broaden the applicability of the V1-MT model to non-rigid motion and provide insights into how the visual system bridges the gap between computational models of rigid and non-rigid motion perception.