Animal Decision Making

A major research question in the group is how to model the mechanisms of behavioural control in animals. This has led us to studies of how evolution by natural selection can adapt the hormone system into allostatic control of the phenotype, but most of our efforts are towards understanding cognitive control of behaviour. We study how to model the modular and degenerate architecture from sensing via cognition to behaviour. Further, we study ecological effects of the architecture through evolutionary simulation models. The targets of the models are usually animal behaviour and ecology but can also be animal wellbeing and animal welfare in aquaculture. In the future maybe evolution of consciousness and human cultures.

Right: The architecture from sensing to hunger includes both modularity and degeneracy. Degeneracy is the ability of structurally different components in an organism to perform the same function (here: evoke appetite) so that the failure or absence of a component can be counteracted through compensatory adjustments elsewhere. Modularity is the independence and interchangeability of components in the serial structure of an architecture, for instance that one hormonal function can be replaced by another in the appetite regulation after sensing food, and that evolution of hormonal control does not impact evolution of a neuronal response. Modularity and degeneracy increase the robustness of the organism and the evolvability of the system.

Jump to section:

• Cognition and the architecture for decision-making

• Hormonal control of the phenotype

• Learning and sociality

• Sensing and mortality risk

• Animal personalities and behaviour

• Evolution of adaptive behaviour by machine learning

• Evolution of adaptive behaviour by the ING method

 

Cognition and the architecture for decision-making

We study how to model central processes and architectures that enable animals (often fish) to make adaptive desicions, and what impacts these decision-making processes have on the behaviour of individuals and on the ecology and evolution of populations.

Experimenters normally use simple artificial environments and experimental systems focused on a single problem or context that are controllable and lack ambiguity. The natural environment is very different: it is usually complex, heterogeneous, continuously changing, and stochastic. What is the “context” may not be obvious for the organism. The animal is bombarded with numerous, conflicting, and often novel stimuli. A considerable fraction of incoming sensory information is irrelevant, and distracting, the relevant sensory input is often partial, inaccurate, and ambiguous. Thus, the fundamental problem of adaptive behavior and decision-making in a naturally complex environment iswhat are the best stimuli to respond to and what are the best contexts to choose for achieving a specific goal such as allostasis, survival, or reproduction (Budaev & al 2019).

We find the two-step survival circuit concept of Joseph LeDoux very useful. In the first part, survival circuits compete to determine the animal’s Global Organismic State. This is thus a “context” competition to determine what is the current reality: which model of the world shall the animal use now. Thereafter attention will focus on the winning context to determine an appropriate behaviour.

Lately we have focussed on the second half of this process, after an animal has decided its priorities (determined the currently best model of reality) until it decides its behaviour. We investigate the prediction machine concept from neuroscience: that the animal will re-use its neuronal winding from sensing to emotion to simulate the likely feeling it would obtain by executing a behavioural option (Budaev & al 2019, and also Budaev & al 2018).

We first found that modelling a very simple desicion architecture can be sufficient to divide a population into genetically determined personality types (Giske & al 2013). Some may be quite distinct while others show gradual transitions. Further, the degeneracy and modularity of the architecture lead to rich genetic variation in the population that will simplify adaptation to new ecological conditions, if or when this happens (Giske & al 2014). Hence, this architecture facilitates evolvability, and central elements in the architecture are very old (Andersen & al 2016). These results are robust with respect to changes in the architecture (Eliassen & al 2016).

The prediction machine concept and re-use of neuronal wiring (called re-entrance) create a link between animal behaviour and animal welfare, as the normal decision-making process in a animal tries to predict its near-future emotional wellbeing (Budaev & al 2020), which in some situations can lead to stress in the animal.

For more details, links to source codes etc. see The AHA Model web page.

 

Hormonal control of the phenotype

While our work in cognition has focussed on bottom-up mechanisms, we here have asked how a top-down evolutionary optimization perspective can explain the hormonal control of the organism. Thus, while most theories of animal behaviour have top-down evolutionary perspectives, we search for the bottom-up control. And while most studies of the hormone system focus on the bottom-up effects or hormones on physiology, behaviour and life histories, we ask for the top-down evolutionary control. Sometime in the future we hope to be able to join the sensory, cognitive and hormonal influences on the phenotype.

Our model of hormonal control has concentrated on the growth period in a juvenile fish. Since the hormone system shall answer to evolutionary fitness maximization, the hormone system shall both consider growth and mortality risk. The basic description of the model is given in Weidner & al (2020). In Jensen & al (2021) we find that also the hormonal system can be described in terms of predictive ability, in that it tries to achieve an allostatic control that considers likely future changes that the organism can prepare for.

 

Learning and sociality

We have studied what conditions favour evolution of learning, and under which circumstances the learning strategy is better or worse than the non-learning alternative. Our key assumption is that individual learning through exploration incurs a time-cost relative to the innate or genetically fixed strategy. Some situations allow coexistence of learners and non-learners in the same population, while life expectancy may be an important determinant for the adaptive value of learning.

 

Sensing, feeding and mortality risk

We have modelled fish vision as function of light instensity in the depth (Aksnes & Giske 1993, Aksnes & Utne 1997) and used these models to calculate the predation risk for zooplankton or other prey types in the vertical. This model of visual range has been used in very many of the other papers on decision-making and animal behaviour. We have also modelled prey selection based on enounter rates and digestion rates.

 

Animal personalities and behavior

Evolution of genetic variation that can be described as animal personalities emerges in our models with behavioural architecture (e.g. Giske & al 2013, 2014). Sergey Budaev was a poineer in studies of animal personalities long before moving to Bergen.

 

Evolution of adaptive behaviour by machine learning

Most of our simulation models for decision-making utilize machine-learning to arrive at adaptive behaviour. The central component in the ML is a genetic algorithm that sends genes of those individuals who have lived in a way that has made them grow up and become parents off to the next generation. Thus, for each generation, the combinations of alleles in the genomes of individuals that become parents, will advance into the next generation. Due to sexual reproduction, the genomes of the offspring will be a mix of its two parents. In addition, mutations can happen. At first, we used the genetic algorithm to evolve the strengths in an artificial neural network (The ING method, see below). Later, we have represented emotions and cognition more biologically, rather than via a neural network (Giske & al. 2013, Budaev & al 2018). We have also coded for genes that control life history decisions directly (Fiksen 2000).

 

Evolution of adaptive behaviour by the ING method

ING is a method for evolving (by a genetic algorithm) flexible adaptive behaviour (controlled by a neural network) in individuals. The background for wanting to develop this tool was the complexity of decisions often faced by organisms. The classical tools in optimization and game are very good at solving specific aspects of adaptive behavior, but by focussing on this single aspect: Life History Theory is a good tool for studies of long-term strategic decisions (and also has been used for the short-term by implicitly assuming constant motivations), Game Theory is good for studies of conflict and cooperation between organisms, and State-Dependent Optimization is good for modelling short-term fluctuations in motivation driven by changes in the (physiological) state of the organism. Through this new method we wanted an agent to be able to consider all these aspects simultaneously (Giske & al. 1998, Huse & Giske 1998).

ING consists of an Individual-Based Model where the decisions in each individual is controlled by its Artificial Neural Network which again is inherited from the parents and evolved by a Genetic Algorithm. Its ability to find the optimal solution has been studied by comparing with dynamic programming (Huse & al. 1999). The tool has been used to model capelin distribution in the Barents Sea (Huse & Giske 1998) and vertical migration in mesopelagic fish (Strand & al. 2002).

However, while ING is able to evolve adaptive solutions to very complex situations, the method does not consider the ability of the organisms to find these solutions. This is the main reason we continued thinking, and arrived at studying the cognitive architecture for decision-making.