Feature coding dataset for trained and untrained working memory tasks in randomized controlled trials of working...

Area of Science:

• Cognitive Psychology
• Neuroscience
• Human Factors

Background:

• Working memory (WM) training is widely studied for cognitive enhancement.
• Understanding the nature of skills learned during WM training is crucial for interpreting transfer effects.
• Existing research often lacks detailed task feature analysis.

Purpose of the Study:

• To present a comprehensive dataset of coded features for working memory tasks.
• To facilitate the analysis of skill learning within working memory training paradigms.
• To support research on the transferability of cognitive skills.

Main Methods:

• Compiled data from 23 randomized controlled trials of WM training.
• Coded features for 113 untrained WM tasks, each paired with a similar trained task.
• Features include stimulus category, domain, modality, response modality, and recall paradigm.

Main Results:

• The dataset provides detailed feature coding for numerous WM task pairs.
• Includes transfer effect sizes for each pair, enabling quantitative analysis.
• Offers a structured resource for researchers investigating WM training mechanisms.

Conclusions:

• The dataset supports the hypothesis that working memory training involves learning new skills.
• Provides a foundation for future research into the specific skills trained and their transfer.
• Enables more nuanced investigations into the efficacy and mechanisms of WM interventions.