Data from a 2020 study by Guido Marco Cicchini investigates the functional role of serial dependence in perception. It supports a formal model of optimal serial dependence, testing effects based on stimulus reliability and similarity between successive stimuli. The dataset is associated with concepts of optimal behavior, Bayesian inference, and the Kalman filter.
Use Cases
- Analyze the relationship between stimulus reliability and the strength of serial dependence effects in orientation reproduction.
- Model serial dependence as a function of similarity between successive stimuli to test ideal observer predictions.
- Investigate correlations between serial dependence effects and response times to assess perceptual benefits.
Strengths
- Data is derived from a formal model of optimal serial dependence, providing a theoretical foundation.
- Explicitly tests effects based on stimulus reliability and stimulus similarity, offering structured experimental variables.
- Released under a permissive CC0 1.0 public domain license, facilitating unrestricted reuse.
Limitations
- The specific data structure, column names, row count, and file formats are unknown, limiting immediate analytical utility.
- As a single-study dataset from 2020, its scope is narrow and may not generalize to other perceptual domains or tasks.
- Lacks documented sample size, which prevents assessment of statistical power or potential for class imbalance.
Provenance
- Source
- Dryad digital repository.
- Collection Method
- Data collected from psychophysical experiments on orientation reproduction tasks.
- Time Range
- null
- Freshness
- Last updated in June 2020.
- Geography
- null