Modeling the cognitive content of displays

Area of Science:

• Human-Computer Interaction
• Cognitive Psychology
• Information Visualization

Background:

• Evaluating the cognitive complexity of visual displays is crucial for effective human-computer interaction, especially in high-demand environments like aviation.
• Existing methods for assessing display complexity often lack quantitative rigor, hindering objective comparisons between different display designs.

Purpose of the Study:

• To develop and validate a quantitative approach for measuring the cognitive complexity of visual displays.
• To apply this approach to dynamic avionic information displays and assess its predictive power for display effectiveness.

Main Methods:

• A semantic network formalism was employed to model operator knowledge, encompassing both general world knowledge and specific display knowledge.
• Four orthogonal predictor measures of cognitive complexity were derived from these semantic networks.
• An experiment was conducted to test the correlation between these predictors and task performance.

Main Results:

• Three of the four derived orthogonal predictors of cognitive complexity were significantly correlated with operator task performance.
• These three significant predictors collectively accounted for 99% of the variation in display effectiveness when averaged across operators.
• The semantic network model demonstrated a strong ability to predict how effectively a display format would perform.

Conclusions:

• A cognitive complexity model based on semantic network formalism offers a viable quantitative technique for evaluating and comparing visual display formats.
• This method can aid designers in creating more effective and user-friendly avionic information displays.
• The findings suggest broader applicability of this approach to other complex visual display design challenges.