Back to Search Start Over

An Active Inference Agent for Modeling Human Translation Processes

Authors :
Michael Carl
Source :
Entropy, Vol 26, Iss 8, p 616 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

This paper develops an outline for a hierarchically embedded architecture of an artificial agent that models human translation processes based on principles of active inference (AIF) and predictive processing (PP). AIF and PP posit that the mind constructs a model of the environment which guides behavior by continually generating and integrating predictions and sensory input. The proposed model of the translation agent consists of three processing strata: a sensorimotor layer, a cognitive layer, and a phenomenal layer. Each layer consists of a network of states and transitions that interact on different time scales. Following the AIF framework, states are conditioned on observations which may originate from the environment and/or the embedded processing layer, while transitions between states are conditioned on actions that implement plans to optimize goal-oriented behavior. The AIF agent aims at simulating the variation in translational behavior under various conditions and to facilitate investigating the underlying mental mechanisms. It provides a novel framework for generating and testing new hypotheses of the translating mind.

Details

Language :
English
ISSN :
10994300
Volume :
26
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Entropy
Publication Type :
Academic Journal
Accession number :
edsdoj.31871b42f0874c1a98583d235f42c4fa
Document Type :
article
Full Text :
https://doi.org/10.3390/e26080616