Bayesian accounts of covert selective attention: A tutorial review.

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Decision Science

Background:

• Optimal observer models provide a theoretical framework for understanding decision-making and attention.
• Probabilistic formulations allow quantitative comparisons between models and human performance.
• Bayesian approaches offer a powerful lens for studying covert selective attention.

Purpose of the Study:

• To present novel Bayesian models for four key covert attention paradigms.
• To illustrate optimal observer predictions across various experimental manipulations.
• To demonstrate the utility of graphical model notation and supplementary code for accessibility.

Main Methods:

• Formulation of novel Bayesian models for covert attention paradigms.
• Application of graphical model notation for clear model representation.
• Review of empirical and modeling evidence on attention phenomena.

Main Results:

• Optimal observer models predict experimental outcomes in covert attention tasks.
• Many observed phenomena in covert selective attention are by-products of Bayesian inference.
• Model predictions align with empirical findings across diverse experimental manipulations.

Conclusions:

• Bayesian inference with noisy sensory data and prior knowledge explains covert attention phenomena.
• Optimal observer models offer a unified framework for understanding covert selective attention.
• The study bridges theoretical modeling with practical implementation for cognitive science research.