Unconscious emotions of human learning

Area of Science:

• Cognitive Neuroscience
• Computational Psychiatry
• Affective Science

Background:

• Extensive research indicates that a significant portion of emotional processing happens outside conscious awareness.
• Emotions are increasingly understood as homeostatic signals essential for regulating biological and psychological needs.

Purpose of the Study:

• To present computational models elucidating the interplay between emotion and cognition.
• To propose that unconscious emotions are integral to innate drives for information regulation and meaning generation.
• To identify and categorize unconscious emotions that regulate learning.

Main Methods:

• Development of computational models detailing the emotion-cognition relationship.
• Review of empirical evidence concerning unconscious emotions in human deep learning.
• Proposal of novel methods for measuring unconscious emotional processes.

Main Results:

• Computational models highlight emotions as critical homeostatic regulators.
• Evidence suggests an evolutionary drive for information regulation and meaning, largely driven by unconscious emotions.
• Three distinct families of unconscious emotions regulating learning have been identified.

Conclusions:

• Unconscious emotions play a fundamental role in the innate mechanisms of knowledge acquisition.
• These emotions are crucial for the optimal functioning of cognition, language comprehension, and decision-making.
• Further research and validated measurement methods are needed to fully understand and leverage unconscious emotional processes.