An Active Inference Agent for Modeling Human Translation Processes.

Area of Science:

• Cognitive Science
• Artificial Intelligence
• Computational Linguistics

Background:

• Human translation involves complex cognitive processes.
• Active inference (AIF) and predictive processing (PP) offer frameworks for understanding perception and action.
• Existing models may not fully capture the nuances of human translation.

Purpose of the Study:

• To propose a novel, hierarchically embedded architecture for an artificial translation agent.
• To model human translation processes using principles of AIF and PP.
• To provide a computational framework for investigating the mental mechanisms underlying translation.

Main Methods:

• Development of a three-layered agent architecture: sensorimotor, cognitive, and phenomenal.
• Application of AIF principles where states are conditioned on observations and transitions on actions.
• Modeling interactions between layers operating on different timescales.

Main Results:

• The proposed architecture integrates predictive processing and active inference for translation modeling.
• The model allows for simulating variations in translational behavior.
• The framework facilitates the generation and testing of hypotheses about the translating mind.

Conclusions:

• The hierarchically embedded AIF agent offers a new computational approach to studying human translation.
• This model provides a testbed for exploring the cognitive and phenomenal aspects of translation.
• The framework advances our understanding of the interplay between prediction, action, and translation.