Predicting Individual Action Switching in Covert and Continuous Interactive Tasks Using the Fluid Events Model.

Area of Science:

• Cognitive Science
• Human-Computer Interaction
• Psychology

Background:

• Existing models focus on action selection reasons, not behavioral change likelihood.
• The Fluid Events Model predicts action changes based on task and experience factors.
• Prior evaluations were limited to simple, independent interactive events.

Purpose of the Study:

• Extend the Fluid Events Model to covert experiences (narrative reading) and continuous interactive events (gaming).
• Assess the model's predictive accuracy across diverse event types and participant experiences.
• Integrate event structure and experiential factors for enhanced behavioral prediction.

Main Methods:

• Applied the Fluid Events Model to existing datasets of reading times and event segmentation for stories.
• Utilized datasets of performance in strategy board games, aerial combat, and first-person shooter games.
• Incorporated participant state dependency on prior events in continuous interactive scenarios.

Main Results:

• The Fluid Events Model demonstrated strong predictive accuracy for behavior changes.
• Model performance accounted for both the structural properties of events and individual prior experience.
• Successfully extended predictive capabilities to narrative reading and complex gaming environments.

Conclusions:

• The Fluid Events Model effectively predicts behavioral shifts in various contexts.
• Event cognition theories benefit from considering both event structure and subjective experience.
• The model offers a robust framework for understanding dynamic human action selection.