Animal affect, welfare and the Bayesian brain.

Area of Science:

• Neuroscience
• Cognitive Science
• Animal Behavior

Background:

• The Bayesian brain hypothesis posits the brain as a predictive machine, integrating sensory input based on prior expectations.
• Affective responses are influenced by subjective perception and expectations, potentially overriding situational factors.
• Limited research has explored the Bayesian brain hypothesis in the context of animal affective states.

Purpose of the Study:

• To investigate the applicability of the Bayesian brain hypothesis to understanding animal affective experiences.
• To establish a foundation for novel predictions concerning animal welfare.
• To explore potential welfare consequences arising from deficits in predictive processing in animals.

Main Methods:

• Review of existing literature on predictive processing in human well-being and affective disorders.
• Conceptual exploration of how Bayesian brain theories can inform animal affective responses.
• Analysis of potential welfare implications of viewing animals as predictive entities.

Main Results:

• The Bayesian brain framework offers a valuable lens for understanding how animals' past experiences shape their affective responses.
• Deficits in predictive processing may lead to previously unrecognized welfare challenges in animals.
• Applying Bayesian principles to animal cognition can enhance our understanding of their subjective experiences.

Conclusions:

• Viewing animals as predictive entities significantly improves our comprehension of their affective states.
• This perspective has implications for both fundamental research in animal cognition and practical applications in animal welfare.
• Adopting a Bayesian approach can lead to improved strategies for enhancing the quality of life for animals.