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.
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. Modularity and degeneracy increase the robustness of the organisms and the evolvability of the system.
Jump to section:
The brain as a prediction machine. In the Subjective Internal Model (SIM, Budaev & al 2019) the animal keeps and updates an image (a model) of itself and its surroundings. This image forms the basis for the prediction machine's ability to deliver an expectation to what new sensory information will bring, and also to predict outcomes of its behavioural choices. New sensory information can lead to competition among neurobiological states where the winner determines the next Global Organismic State (GOS, Giske & al 2013) where attention is restricted to the current dominant brain state. As part of the decision-making, the prediction machine will re-use connections between sensing and emotion to simulate the emotional outcome of a behavioural option.
After having for many years used top-down evolutionary methods to model optimal behaviour, we have now turned to 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.
Lately we have focussed on the second half of the decision-making process, after an animal has decided its priorities 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) creates 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.
The graphical model of hormonal control of the phenotype in a growing juvenile fish, from Weidner & al (2020).
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.
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.
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.
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.
The Genetic Algorithm has been a much used research tool in the group for more than a decade. While several papers have used the GA to evolve adaptive values of neural networks or animal decison architectures, the GA can also be used directly to model the major life history decisions in an organism, as done by Fiksen (2000) for the copepod Calanus finmarchicus.
The 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.
Since 1990 we have been involved in modeling decision-making, particularly in fish and plankton. Much of this work is based on a theoretically derived model for visual range of aquatic organisms (Aksnes & Giske 1993, Aksnes & Utne 1997), which again has allowed calculations of feeding rates and predation risks (Giske & al. 1994, Fiksen & al. 2002).
We have been using Life History Theory (Aksnes & Giske 1990, Giske & Aksnes 1992, Salvanes & al. 1994, Giske & Salvanes 1995, Eiane & al. 1998), Game Theory (Giske & al. 1997) and State-Dependent Optimization (Giske & al. 1992, Rosland & Giske 1994, 1997, Fiksen & Giske 1995, Fiksen 1997, Rosland 1997, Fiksen & Carlotti 1998, Kirby & al. 2000) to model both short-term and life-history decisions.
These theoretical models have also been used in applied models (Fiksen & MacKenzie 2002, Fiksen & al. 2007, Kristiansen & al. 2007, Fouzai et al. 2015), and applied models have been used to compare and test goodness of fit of different behavioural models in applied ecological situations (Vikebø & al. 2007, Kristiansen & al. 2009). We also work on making efficient simplified model formulations (Castellani et al. 2013, Fiksen & Opdal 2015, Sainmont & al. 2015). This is a step from individual behaviour to population dynamics, and maybe a step towards ecosystem and fisheries management tools.