New rule use drives the relation between working memory capacity and Raven's Advanced Progressive Matrices

Area of Science:

• Cognitive Psychology
• Neuroscience
• Psychometrics

Background:

• The relationship between working memory capacity and performance on complex reasoning tasks like Raven's Advanced Progressive Matrices (RAPM) is established but lacks a clear mechanistic explanation.
• Existing research highlights a correlation, but the underlying cognitive processes driving this link remain debated.

Purpose of the Study:

• To propose and test a novel explanation for the working memory-RAPM performance correlation.
• To investigate the role of novel rule combination requirements in RAPM problems as a driver of this relationship.

Main Methods:

• An item-based analysis of performance during standard Raven's Advanced Progressive Matrices (RAPM) administration was conducted.
• A controlled experiment was designed to manipulate the necessity of using new rule combinations across different RAPM item subsets.

Main Results:

• The item-based analysis provided initial support for the hypothesis that rule novelty influences performance.
• The experimental manipulation confirmed that tasks requiring novel rule combinations significantly predict working memory capacity's impact on RAPM scores.

Conclusions:

• The findings support a new theoretical account: the need to integrate novel rule combinations in Raven's Advanced Progressive Matrices (RAPM) is a key factor explaining the link with working memory capacity.
• This research offers a more nuanced understanding of fluid intelligence and working memory, suggesting rule flexibility is crucial.