Visual Working Memory Precision Data from Nine Delayed-Estimation Experiments

Nine delayed-estimation experiments provide data on encoding precision in human visual working memory. The dataset supports a normative theory analyzing set size effects and probing probability. It was authored by Ronald Van den Berg and last updated in June 2020.

Use Cases

• Analyze the relationship between set size and encoding precision to model working memory limitations.
• Investigate how probing probability mediates set size effects on behavioral error and neural encoding cost.
• Model the non-monotonic variation of total invested resource with set size across experimental conditions.
• Test predictions on optimal encoding strategies, including encoding only a subset of items, based on cost minimization.

Strengths

• Data originates from nine distinct delayed-estimation experiments, providing a multi-study foundation.
• The dataset is associated with a peer-reviewed, normative theory published in a scientific context.
• It is published under a permissive CC0 1.0 public domain license, allowing unrestricted use.

Limitations

• The specific data structure, including column names, row counts, and file formats, is not detailed in the provided metadata.
• The dataset's raw experimental data samples are unavailable for review in this description.
• The data is from 2020 and may not reflect the most recent experimental findings in the field.

Provenance

Source

Dryad digital repository.

Collection Method

Data collected from nine delayed-estimation experiments on human visual working memory.

Freshness

Last updated on 2020-06-24.