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Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Related Experiment Video

Updated: Jun 14, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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An Active Inference Agent for Modeling Human Translation Processes.

Michael Carl1

  • 1Department of Modern and Classical Language Studies, Kent State University, Kent, OH 44240, USA.

Entropy (Basel, Switzerland)
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

This study outlines an artificial agent architecture modeling human translation using active inference (AIF) and predictive processing (PP). This framework helps simulate translation variations and explore cognitive mechanisms.

Keywords:
active inferencepredictive processingtranslation agenttranslation process research

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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.