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

Alasdair D F Clarke¹, Amelia R Hunt², Anna E Hughes¹

¹Department of Psychology, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK.

Vision (Basel, Switzerland)

|November 22, 2022

Summary

This summary is machine-generated.

This study models individual foraging behavior, predicting target selection with 43-69% accuracy. The approach reveals biases and guides future research into unexplained foraging patterns.

Keywords:

bayesian model decision foraging strategy visual search

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Area of Science:

Cognitive psychology
Animal behavior research
Computational modeling

Background:

Foraging behavior, a search for multiple targets, is studied in animal and human cognition.
Traditional methods use summary statistics like path length and target-switching frequency.
A new model treats target selection as random selection without replacement.

Purpose of the Study:

To apply a novel model for predicting individual target selection events in foraging.
To incorporate a start position bias into the existing foraging model.
To assess the model's accuracy in predicting subsequent target choices.

Main Methods:

Utilized a model based on random selection without replacement for target choices.
Estimated foraging biases (e.g., proximity, category preference) from participant data.
Generated foraging paths using estimated parameters and a new start position bias.

Main Results:

The model predicted individual target selection events with accuracy ranging from 43% to 69%.
This accuracy significantly exceeds the 11% chance level.
The model substantially explains foraging behavior within the studied paradigm.

Conclusions:

The developed model effectively predicts individual foraging choices.
Model errors highlight areas for future research in foraging behavior.
This approach offers a powerful tool for understanding cognitive foraging strategies.