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

Decoding the language of fear: Unveiling objective and subjective indicators in rodent models through a systematic review and meta-analysis

While rodent models are vital for studying mental disorders, the underestimation of construct validity of fear indicators has led to limitations in translating to effective clinical treatments. Addressing this gap, we systematically reviewed 5054 articles from the 1960 s, understanding underlying theoretical advancement, and selected 68 articles with at least two fear indicators for a three-level meta-analysis. We hypothesized correlations between different indicators would elucidate similar functions, while magnitude differences could reveal distinct neural or behavioral mechanisms. Our findings reveal a shift towards using freezing behavior as the primary fear indicator in rodent models, and strong, moderate, and weak correlations between freezing and conditioned suppression ratios, 22-kHz ultrasonic vocalizations, and autonomic nervous system responses, respectively. Using freezing as a reference, moderator analysis shows treatment types and fear stages significantly influenced differences in magnitudes between two indicators. Our analysis supports a two-system model of fear in rodents, where objective and subjective fears could operate on a threshold-based mechanism.

A general framework for modelling trade-offs in adaptive behaviour

An animal's behaviour can influence many variables, such as its energy reserves, its risk of injury or mortality, and its rate of reproduction. To identify the optimal action in a given situation, these various effects can be compared in the common currency of reproductive value. While this idea has been widely used to study trade-offs between pairs of variables, e.g. between energy gain versus survival, here we present a unified framework that makes explicit how these various trade-offs fit together. This unification covers a wide range of biological phenomena, highlighting similarities in their logical structure and helping to identify knowledge gaps. To fill one such gap, we present a new model of foraging under the risk of predation and damage accumulation. We conclude by discussing the use and limitations of state-dependent optimisation theory in predicting biological observations.

Recognition of emotion and pain by owners benefits the welfare of donkeys in a challenging working environment

Working donkeys (Equus asinus) support human living standards globally. However, there is little information on the effect of human perceptions of emotion and pain on the welfare of working donkeys. We interviewed donkey owners (n = 332) in Pakistan to determine the relationship between human perspectives on donkey sentience: emotions and the ability to feel pain, and the routine working practices that could impact donkey welfare. The majority of donkey owners used padding under the saddle (n = 211; 63.6%; 95% CI (58.3%-68.9%)) and provided access to food (n = 213; 64.2%; 95% CI (58.9%-69.3%)) and water (n = 195; 58.7%; 95% CI (53.4%-64.1%)) during the working day. Owners reported that at some point in their donkey's life, 65.3% (95% CI (60.2%-70.5%)) had load-associated injuries, of which 27.7% (n = 92; 95% CI (22.8%-32.5%)) were wounds, 20.5% (n = 68; 95% CI (16.1%-24.8%)) were lameness and 7.2% (n = 24; 95% CI 4.4%-10.0%) were back pain. In total, 81.3% (95% CI 77.1%-85.5%; n = 270) of owners believed that their donkeys felt pain, and 70.2% (95% CI (65.2%-75.1%; n = 233) of owners believed that their donkeys had emotions. Multiple correspondence analysis (MCA) was used to understand the relationship between owners' recognition of emotions and pain in donkeys and their working practices. The MCA factor map revealed two clusters, named positive and negative clusters. The positive cluster included owner's recognition of donkey pain and emotions, the availability of food and water, use of padding under the saddle, absence of injuries along with the willingness to follow loading guidelines. The negative cluster represented practices that did not benefit donkey welfare, such as using saddles without padding and a lack of food and water during work. The presence of injuries, owners not recognizing that donkeys feel pain and emotion along with an unwillingness to follow loading guidelines were also found in the negative cluster. We show that the owners who recognized sentience in their donkeys were more likely to use practices that are good for donkey welfare. The ability of owners to identify sentience in donkeys, along with their willingness to follow welfare guidelines, are important factors in improving the lives of working donkeys.

Why it hurts: with freedom comes the biological need for pain

We argue that pain is not needed to protect the body from damage unless the organism is able to make free choices in action selection. Then pain (including its affective and evaluative aspects) provides a necessary prioritising motivation to select actions expected to avoid it, whilst leaving the possibility of alternative actions to serve potentially higher priorities. Thus, on adaptive grounds, only organisms having free choice over action selection should experience pain. Free choice implies actions must be selected following appraisal of their effects, requiring a predictive model generating estimates of action outcomes. These features give organisms anticipatory behavioural autonomy (ABA), for which we propose a plausible system using an internal predictive model, integrated into a system able to produce the qualitative and affective aspects of pain. Our hypothesis can be tested using behavioural experiments designed to elicit trade-off responses to novel experiences for which algorithmic (automaton) responses might be inappropriate. We discuss the empirical evidence for our hypothesis among taxonomic groups, showing how testing for ABA guides thinking on which groups might experience pain. It is likely that all vertebrates do and plausible that some invertebrates do (decapods, cephalopods and at least some insects).

A general motivational architecture for human and animal personality

To achieve integration in the study of personality, researchers need to model the motivational processes that give rise to stable individual differences in behavior, cognition, and emotion. The missing link in current approaches is a motivational architecture-a description of the core set of mechanisms that underlie motivation, plus a functional account of their operating logic and inter-relations. This paper presents the initial version of such an architecture, the General Architecture of Motivation (GAM). The GAM offers a common language for individual differences in humans and other animals, and a conceptual toolkit for building species-specific models of personality. The paper describes the main components of the GAM and their interplay, and examines the contribution of these components to the emergence of individual differences. The final section discusses how the GAM can be used to construct explicit functional models of personality, and presents a roadmap for future research.

An evolutionary view of self-awareness

The capacity to be self-aware is regarded as a fundamental difference between humans and other species. However, growing evidence challenges this notion, indicating that many animals show complex signs and behaviors that are consonant with self-awareness. In this review, we suggest that many animals are indeed self-aware, but that the complexity of this process differs among species. We discuss this topic by addressing several different questions regarding self-awareness: what is self-awareness, how has self-awareness been studied experimentally, which species may be self-aware, what are its potential adaptive advantages. We conclude by proposing alternative models for the emergence of self-awareness in relation to species evolutionary paths, indicating future research questions to advance this field further.

Competitive Coherence Generates Qualia in Bacteria and Other Living Systems

The relevance of bacteria to subjective experiences or qualia is underappreciated. Here, I make four proposals. Firstly, living systems traverse sequences of active states that determine their behaviour; these states result from competitive coherence, which depends on connectivity-based competition between a Next process and a Now process, whereby elements in the active state at time n+1 are chosen between the elements in the active state at time n and those elements in the developing n+1 state. Secondly, bacteria should help us link the mental to the physical world given that bacteria were here first, are highly complex, influence animal behaviour and dominate the Earth. Thirdly, the operation of competitive coherence to generate active states in bacteria, brains and other living systems is inseparable from qualia. Fourthly, these qualia become particularly important to the generation of active states in the highest levels of living systems, namely, the ecosystem and planetary levels.

The Use of Artificial Intelligence in Assessing Affective States in Livestock

In order to promote the welfare of farm animals, there is a need to be able to recognize, register and monitor their affective states. Numerous studies show that just like humans, non-human animals are able to feel pain, fear and joy amongst other emotions, too. While behaviorally testing individual animals to identify positive or negative states is a time and labor consuming task to complete, artificial intelligence and machine learning open up a whole new field of science to automatize emotion recognition in production animals. By using sensors and monitoring indirect measures of changes in affective states, self-learning computational mechanisms will allow an effective categorization of emotions and consequently can help farmers to respond accordingly. Not only will this possibility be an efficient method to improve animal welfare, but early detection of stress and fear can also improve productivity and reduce the need for veterinary assistance on the farm. Whereas affective computing in human research has received increasing attention, the knowledge gained on human emotions is yet to be applied to non-human animals. Therefore, a multidisciplinary approach should be taken to combine fields such as affective computing, bioengineering and applied ethology in order to address the current theoretical and practical obstacles that are yet to be overcome.