Choosing specifiers: an evaluation of the basic tasks model of graphical perception

Area of Science:

• Cognitive psychology
• Information visualization
• Human-computer interaction

Background:

• Graphical displays are crucial for data interpretation.
• Understanding factors influencing graphical efficacy is essential for effective data visualization design.
• Existing models like Tufte's data-ink principle offer guidance but may not fully capture performance nuances.

Purpose of the Study:

• To evaluate the predictive accuracy of the basic tasks model of graphical efficacy.
• To compare the basic tasks model against Tufte's data-ink principle.
• To investigate how different graphical specifiers and task types influence performance.

Main Methods:

• Meta-analysis of effect sizes from 39 experiments on graphical displays.
• Statistical evaluation of model predictions against experimental data.
• Analysis of performance differences based on specifier types (position, length, angle, area, volume) and task characteristics (local vs. global, physical vs. remembered graphs).

Main Results:

• The basic tasks model demonstrated higher predictive accuracy than Tufte's data-ink principle.
• Model predictions were more successful for local (focusing) tasks than global (synthesis) tasks.
• Performance prediction was better for physically present graphs than remembered graphs.
• Graphs using area or volume specifiers led to significantly worse performance compared to position, length, or angle.

Conclusions:

• The basic tasks model provides a more accurate framework for understanding graphical efficacy than the data-ink principle.
• Task type and graph modality (physical vs. remembered) significantly moderate the model's predictive success.
• Designers should be cautious when using area or volume for quantitative data encoding due to performance decrements.