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Related Experiment Video

Updated: Jul 31, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Value-driven attention and associative learning models: a computational simulation analysis.

Ji Hoon Jeong¹, Jangkyu Ju¹, Sunghyun Kim¹

¹School of Psychology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea.

Psychonomic Bulletin & Review

|May 5, 2023

Summary

Value-driven attentional capture (VDAC) is explained by associative learning. The Schumajuk-Pearce-Hall and Esber-Haselgrove models best predict VDAC phenomena, including uncertainty and extinction resistance.

Keywords:

Associative learning Computational simulation Mathematical implementation Model comparison Value-driven attentional capture

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Area of Science:

Cognitive Psychology
Computational Neuroscience

Background:

Value-driven attentional capture (VDAC) describes how stimuli linked to higher rewards capture attention.
Existing research indicates VDAC follows associative learning principles.
Mathematical models can clarify VDAC's underlying mechanisms.

Purpose of the Study:

To implement and compare four associative learning models for VDAC.
To determine if different models predict distinct outcomes under parameter variations.
To fit model parameters to experimental VDAC data.

Main Methods:

Implemented Rescorla-Wagner, Mackintosh, Schumajuk-Pearce-Hall (SPH), and Esber-Haselgrove (EH) models.
Adjusted critical VDAC parameters within the models.
Used Bayesian information criterion for model fitting and comparison.
Compared simulation results with experimental VDAC data.

Main Results:

SPH model with associative strength (V) and EH model with associability (α) showed superior performance.
Model V parameters adequately simulated VDAC based on expected value.
Model α parameters predicted additional VDAC aspects like uncertainty and extinction resistance.

Conclusions:

Associative learning models align with key VDAC behavioral data.
SPH and EH models offer robust explanations for VDAC dynamics.
Further research is needed to verify novel predictions from these models.