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.
Bibliographical noteFunding: This research was funded by the Economic and Social Research Council grant number ES/S016120/1 to A.D.F.C and A.R.H.
Acknowledgments: The authors would like to thank all the researchers who publicly shared
Data Availability StatementData supporting reported results can be found on Github at https: //github.com/scienceanna/foraging_svg, accessed on 25 April 2022. The datasets analysed in this study can be found at https://osf.io/y6qbv/, accessed on 25 April 2022, and https://journals.plos.
org/plosone/article?id=10.1371/journal.pone.0100752, accessed on 27 May 2022.
- visual search
- Bayesian model