27 individuals with Mild Cognitive Impairment, 31 healthy elderly, and 29 healthy young participants performed recognition tasks on static and dynamic facial expressions. The dataset includes group comparisons based on sex, age, scores on the Goldberg's Anxiety and Depression Scale, and the Montreal Cognitive Assessment. This research dataset was created by Laura Alonso-Recio and was last updated on the dataverse platform in October 2025.
Use Cases
- Compare facial expression recognition accuracy between clinical and control groups based on the described participant cohorts.
- Analyze the relationship between cognitive status (MoCA scores) and emotion recognition performance.
- Investigate the impact of stimulus type (static vs. dynamic expressions) on task performance.
- Model the influence of anxiety and depression scores (GADS) on emotion processing abilities.
Strengths
- Includes data from three distinct participant groups (27 MCI, 31 elderly, 29 young) for comparative analysis.
- Uses standardized clinical assessments (Montreal Cognitive Assessment, Goldberg's Anxiety and Depression Scale).
- Employs stimuli from a validated resource, the Amsterdam Dynamic Facial Expression Set (ADFES).
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment for specific modeling tasks.
- The sample size of 87 total participants may be limited for some machine learning applications.
Provenance
- Source
- Alonso-Recio, Laura; e-cienciaDatos Harvested Dataverse
- Collection Method
- Experimental data collected from participant performance on facial expression recognition tasks.
- Time Range
- null
- Freshness
- Last updated 2025-10-14 21:54:29; freshness should be verified.
- Geography
- null