Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing

Abstract

To understand animal wellbeing, we need to consider subjective phenomena and sentience. This is challenging, since these properties are private and cannot be observed directly. Certain motivations, emotions and related internal states can be inferred in animals through experiments that involve choice, learning, generalization and decision-making. Yet, even though there is significant progress in elucidating the neurobiology of human consciousness, animal consciousness is still a mystery. We propose that computational animal welfare science emerges at the intersection of animal behaviour, welfare and computational cognition. By using ideas from cognitive science, we develop a functional and generic definition of subjective phenomena as any process or state of the organism that exists from the first-person perspective and cannot be isolated from the animal subject. We then outline a general cognitive architecture to model simple forms of subjective processes and sentience. This includes evolutionary adaptation which contains top-down attention modulation, predictive processing and subjective simulation by re-entrant (recursive) computations. Thereafter, we show how this approach uses major characteristics of the subjective experience: elementary self-awareness, global workspace and qualia with unity and continuity. This provides a formal framework for process-based modelling of animal needs, subjective states, sentience and wellbeing.

A biological integrity framework for describing animal welfare and wellbeing

“…In this construction, perfectionism describes the fulfilment of an objective list of biological functions, while desire fulfilment and hedonism are two aspects of the subjective mental experience of feelings. A materialist view of biology understands preferences and hedonic experiences to be grounded in (neuro-)physiological and behavioural activities, and to serve a functional role in the fulfilment of the biological potential of the animal (Budaev et al 2020), a view termed hedonic perfectionism (Appleby and Sandöe 2002). Nonetheless, whereas feelings emerge as a system property of (neuro-)physiological and behavioural activities, they have a subjective quality that cannot be reduced to the mere description of the constituent physical activities (Verhoog 2000;Mendl et al 2010;Budaev et al 2020).…”

“…A materialist view of biology understands preferences and hedonic experiences to be grounded in (neuro-)physiological and behavioural activities, and to serve a functional role in the fulfilment of the biological potential of the animal (Budaev et al 2020), a view termed hedonic perfectionism (Appleby and Sandöe 2002). Nonetheless, whereas feelings emerge as a system property of (neuro-)physiological and behavioural activities, they have a subjective quality that cannot be reduced to the mere description of the constituent physical activities (Verhoog 2000;Mendl et al 2010;Budaev et al 2020). As a consequence, from a philosophical perspective, the feelings that attend desire fulfilment and hedonism are attributed a subjective value for the animal as an aspect of its wellbeing that is not adequately captured by current measures of physical functioning.…”

“…Rowland et al (2021) have proposed applying network theory to describe welfare as the state that arises through the interactions between various biological functions in the animal. Budaev et al (2020) have developed a computational model of wellbeing grounded in the account of biological function termed active inference that has prominence in the neurosciences. This account describes biological functions as an ensemble of processes through which the organism reconciles discrepancies between its expectations and current experience by acting on its environment and by updating its expectations (Colditz 2018(Colditz , 2020Kristiansen and Fernö 2020).…”

The Use of Artificial Intelligence in Assessing Affective States in Livestock

“…Artificial intelligence and machine learning can provide an efficient, objective and automatic self-learning mechanism that could classify different affective states based on multiple factors, while using a sensor-based approach to frequently collect data (30,(71)(72)(73)(74)(75). Machine learning consists of computer algorithms learning as more data are added to the system, while being able to recognize patterns and/or features and categorize them.…”

Competitive Coherence Generates Qualia in Bacteria and Other Living Systems

“…In grappling with this hard problem, it is often argued about how far subjective experience extends "down the phylogenetic scale" [5][6][7][8][9][10]. In vertebrates, the capacity for subjective experience is proposed to be due to integrated structures in the midbrain that create a neural simulation of the state of the mobile animal in space whilst similar structures in the insect brain perform similar functions and may provide a similar capacity [11] (however, see [12]).…”