Our project mainly investigates how the human visual system perceives multiple ensembles of information in attention. That is…
- Which features drive selection for ensembles?
- How is one ensemble ignored or selected over another?
- How are ensembles defined in attention?
Additional questions/topics we are investigating include:
- How color feature selection works for cases of multiple ensembles in attention
- How combinations of features (like shape, color, and orientation) either boost or aid attentional selection for multiple ensembles in attention
- How to design and evaluate visualizations (i.e., multi-class scatterplots)
- Color spaces and models for visual design
We are currently gearing up to run several new experiments:
- A multi-class correlation task examining the limits of color feature selection for correlation ensembles, based on findings from Elliott & Rensink (VSS 2018)
- A replication of Elliott & Rensink (VSS 2018) with extension for an Estimation Task
- An estimation comparison task between numerosity displays and correlation displays.
- A centroid detection task using the color space from Rensink & Elliott (VSS 2018)
We recently ran these two experiments: