More than a filter: Feature-based attention regulates the distribution of visual working memory resources.

Area of Science:

• Cognitive Psychology
• Neuroscience
• Visual Perception

Background:

• The filter account of attention suggests it prevents irrelevant information from entering visual working memory (VWM).
• This account traditionally aligns with discrete-capacity (
• slot
• ) models of VWM.
• Recent challenges to discrete-capacity models necessitate re-evaluating the role of feature-based attention in memory regulation.

Purpose of the Study:

• To investigate the broader role of feature-based attention in regulating memory beyond simple filtering.
• To test whether attention dynamically regulates resource distribution within VWM.

Main Methods:

• Two experiments employed partial report tasks using colored circle and square stimuli.
• A feature-based goal was manipulated by varying the probe likelihood for each shape.
• Mixture and variable-precision models were used to analyze response errors, estimating guesses, nontarget responses, and memory imprecision.

Main Results:

• Participants were less likely to guess when the probed item matched the feature-based goal, supporting the filter account.
• The influence of attention on filtering varied with goal strength, indicating strategic control.
• Memory precision and nontarget errors changed continuously with feature-based attention, suggesting dynamic resource allocation.

Conclusions:

• Feature-based attention extends beyond filtering to dynamically regulate resource distribution within VWM.
• Attention enhances the precision of encoding for the most relevant items.
• These findings support an extended filter account of attention's role in VWM.