FoMo: A Unifying Theory of Visual Foraging

Visual foraging lies at the intersection of visual perception, decision-making and action planning. An attractive feature of this paradigm is that it generates a rich stream of sequential decision data. However, this presents a number of challenges for analysis. To this end, we have developed FoMo, a robust and flexible generative model for spatial-sequential data. Building upon initial work by Clarke et al. (2022b), we present FoMo 2.0, which incorporates spatial structure allowing us to model organised spatial behaviours. We test the model over a range of secondary data sources and show that incorporating these parameters improve model accuracy, predominantly due to improvements for a subset of participants who use grid-following strategies. FoMo therefore allows us to explain results that would previously have been categorised as outliers. A real strength of this generative modelling approach is that it can account for individual differences across the wide range of summary statistics that have previously been used to explore human and non-human animal behaviour, providing a unified framework for analysing these data.

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Publication Details

Published
2025-09-01
DOI
https://doi.org/10.31234/osf.io/ns8uy_v1
Primary Topic
Color perception and design
Type
article
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article

FoMo: A Unifying Theory of Visual Foraging

Alasdair D. F. Clarke, Anna E. Hughes
Color perception and design
article

FoMo: A Unifying Theory of Visual Foraging

Alasdair D. F. Clarke, Anna E. Hughes
article en

Abstract

Visual foraging lies at the intersection of visual perception, decision-making and action planning. An attractive feature of this paradigm is that it generates a rich stream of sequential decision data. However, this presents a number of challenges for analysis. To this end, we have developed FoMo, a robust and flexible generative model for spatial-sequential data. Building upon initial work by Clarke et al. (2022b), we present FoMo 2.0, which incorporates spatial structure allowing us to model organised spatial behaviours. We test the model over a range of secondary data sources and show that incorporating these parameters improve model accuracy, predominantly due to improvements for a subset of participants who use grid-following strategies. FoMo therefore allows us to explain results that would previously have been categorised as outliers. A real strength of this generative modelling approach is that it can account for individual differences across the wide range of summary statistics that have previously been used to explore human and non-human animal behaviour, providing a unified framework for analysing these data.

Openalex Percentile: Top 73%
Color perception and design
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