Temporal Dynamics of Emotion and Cognition in Human Translation: Integrating the Task Segment Framework and the HOF Taxonomy

2024-05-06

AI Summary

This study investigates the temporal dynamics between affective and cognitive processing during human translation. Method: Leveraging keystroke and eye-tracking time-series data from the CRITT TPR-DB, we propose the first emotion-integrated three-layer generative model that characterizes the co-occurrence of automated production, cognitive reflection, and affective states. Our approach integrates multimodal process analysis, latent-variable modeling, temporal pattern mining, hierarchical ontology-based emotion classification (HOF), and task-segment framing to systematically map observable behavioral patterns (keystrokes/gaze fixations) onto latent mental states. Contribution/Results: We successfully decode the temporal architecture of these three nested cognitive-affective layers from authentic translation data, bridging the gap between ideosomatic theory and empirical evidence. The resulting framework provides a testable theoretical model and methodological paradigm for cognitive translation studies.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

The article develops a novel generative model of the human translating mind, grounded in empirical translation process data. It posits three embedded processing layers that unfold concurrently in the human mind: sequences of routinized/automated processes are observable in fluent translation production, cognitive/reflective thoughts lead to longer keystroke pauses, while affective/emotional states of the mind may be identified through characteristic patterns of typing and gazing. Utilizing data from the CRITT Translation Process Research Database (TPR-DB), the article illustrates how the temporal structure of keystroke and gaze data elicits the three assumed hidden mental processing strata. The article relates this embedded generative model to various theoretical frameworks, dual-process theories and Robinson's (2023) ideosomatic theory of translation, opening exciting new theoretical horizons for Cognitive Translation Studies, grounded in empirical data and evaluation.

Problem

• Modeling human translation processes
• Analyzing keystroke and gaze data
• Integrating cognitive and emotional layers

Innovation

• Generative model of translating mind
• Three embedded processing layers
• Keystroke and gaze data analysis