Visual Search Without Selective Attention: A Cognitive Architecture Account.

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Visual Perception

Background:

• Visual search experiments commonly show a linear relationship between reaction time (RT) and set size.
• The dominant theory attributes this to covert selective attention sequentially binding features, with varying effects based on task and features.

Purpose of the Study:

• To present a cognitive architectural model that explains visual search effects without invoking selective attention.
• To investigate the role of basic visual mechanisms, eye movements, and task strategies in visual search.
• To incorporate visual crowding effects into the model and assess their impact on reaction time and error rates.

Main Methods:

• Development of a cognitive architectural model simulating visual search.
• Testing the model across three different search task conditions.
• Inclusion of a mechanism for visual crowding, often confounded with set size.

Main Results:

• The model successfully replicates the linear RT-set size relationship observed in experiments using basic visual mechanisms and task strategies.
• The model demonstrates that selective attention is not necessary to explain these RT effects.
• Incorporating visual crowding improved the model's ability to account for significant effects on error rates (ER).

Conclusions:

• A comprehensive model of visual search can be achieved by integrating basic visual mechanisms, eye movements, task strategies, and visual crowding.
• This approach offers a more fruitful alternative to the dominant theory of selective attention in visual search.