A Bayesian Statistical Model Is Able to Predict Target-by-Target Selection Behaviour in a Human Foraging Task

Abstract

Foraging refers to search involving multiple targets or multiple types of targets, and as a model task has a long history in animal behaviour and human cognition research. Foraging behaviour is usually operationalized using summary statistics, such as average distance covered during target collection (the path length) and the frequency of switching between target types. We recently introduced an alternative approach, which is to model each instance of target selection as random selection without replacement. Our model produces estimates of a set of foraging biases, such as a bias to select closer targets or targets of a particular category. Here we apply this model to predict individual target selection events. We add a new start position bias to the model, and generate foraging paths using the parameters estimated from individual participants’ pre-existing data. The model predicts which target the participant will select next with a range of accuracy from 43% to 69% across participants (chance is 11%). The model therefore explains a substantial proportion of foraging behaviour in this paradigm. The situations where the model makes errors reveal useful information to guide future research on those aspects of foraging that we have not yet explained.

The time course of visual foraging in the lifespan: Spatial scanning, organization search, and target processing

“…In any case, more research is needed to understand these new Bayesian spatial parameters in foraging. Several models focused on target processing, such as guided visual search (Wolfe, 2021b ) or the theory of visual attention (Bundesen, 1990 ), may provide theoretical frameworks to further study the proximity bias parameters recently described by Clarke et al ( 2022a , b ) and others (Le et al, 2023 ; Tünnermann et al, 2022 ). For the present work, they seem to show interesting results and significant correlations with some organization parameters worth to continue studying and understanding.…”

“…Another parameter, ρ Θ , captures the “perseverance” or the trend to keep searching in the same direction, not varying the saccade angles. See Clarke et al ( 2022a , b ) for a deeper description of these parameters and their mathematical foundation.…”