Department of
Biological Sciences

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.

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Cognition and the architecture for decision-making

The brain as a prediction machine. By its Subjective Internal Model (SIM, Budaev & al 2019) the animal keeps and updates an image 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.

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) 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.

Budaev S, Kristiansen TS, Giske J, Eliassen S. 2020.
Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing
Royal Society Open Science. 7. [ doi:10.1098/rsos.201886 ] [ open access ] [ pdf ]
Budaev S, Jørgensen C, Mangel M, Eliassen S, Giske J. 2019.
Decision-making from the animal perspective: Bridging ecology and subjective cognition
Frontiers in Ecology and Evolution. 7: 164. [ doi:10.3389/fevo.2019.00164 ] [ open access ] [ pdf ]
Budaev S, Giske J, Eliassen S. 2018.
AHA: A general cognitive architecture for Darwinian agents
Biologically Inspired Cognitive Architectures. 25: 51-57. [ doi:10.1016/j.bica.2018.07.009 ] [ open access ] [ pdf ]
Eliassen S, Andersen BS, Jørgensen C, Giske J. 2016.
From sensing to emergent adaptations: Modelling the proximate architecture for decision-making
Ecological Modelling. 326: 90-100. [ doi:10.1016/j.ecolmodel.2015.09.001 ] [ open access ] [ pdf ]
Andersen BS, Jørgensen C, Eliassen S, Giske J. 2016.
The proximate architecture for decision-making in fish
Fish and Fisheries. 17: 680-695. [ doi:10.1111/faf.12139 ] [ open access ] [ pdf ]
Giske J, Eliassen S, Fiksen Ø, Jakobsen PJ, Aksnes DL, Mangel M, Jørgensen C. 2014.
The emotion system promotes diversity and evolvability
Proceedings of the Royal Society B. 281: 20141096. [ doi:10.1098/rspb.2014.1096 ] [ open access ] [ pdf ] [ fortran code for model at ]
Giske J, Eliassen S, Fiksen Ø, Jakobsen PJ, Aksnes DL, Jørgensen C, Mangel M. 2013.
Effects of the emotion system on adaptive behavior
American Naturalist. 182: 689-703. [ doi:10.1086/673533 ] [ open access ] [ pdf ] [ popularized version ]
Giske J, Mangel M, Jakobsen P, Huse G, Wilcox C, Strand E. 2003.
Explicit trade-off rules in proximate adaptive agents
Evolutionary Ecology Research. 5: 835-865. [ pdf ]


Hormonal control of the phenotype

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.

Jensen CH, Weidner J, Giske J, Budaev S, Jørgensen C, Eliassen S. 2021.
Hormonal adjustments to future expectations impact growth and survival in juvenile fish
Oikos. 130: 41-51. [ doi:10.1111/oik.07483 ] [ pdf ] [ Code on Github ]
Weidner J, Jensen CH, Giske J, Eliassen S, Jørgensen C. 2020.
Hormones as adaptive control systems in juvenile fish
Biology Open. 9: bio046144. [ doi:10.1242/bio.046144 ] [ open access ] [ pdf ] [ Interview with Jacqueline ]


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.

Eliassen S, Jørgensen C, Mangel M, Giske J. 2009.
Quantifying the adaptive value of learning in foraging behaviour
American Naturalist. 174: 478-489. [ doi:10.1086/605370 ] [ pdf ]
Eliassen S, Jørgensen C, Mangel M, Giske J. 2007.
Exploration or exploitation: life expectancy changes the value of learning in foraging strategies
Oikos. 116: 513-523. [ doi:10.1111/j.2006.0030-1299.15462.x ] [ pdf ]
Eliassen S, Jørgensen C, Giske J. 2006.
Co-existence of learners and stayers maintains the advantage of social foraging
Evolutionary Ecology Research. 8: 1311-1324. [ pdf ]
Budaev SV, Zhuikov AY. 1998.
Avoidance learning and “personality” in the guppy (Poecilia reticulata)
Journal of Comparative Psychology. 112: 92-94. [ pdf ]


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.

Fiksen Ø, Aksnes DL, Flyum MH, Giske J. 2002.
The influence of turbidity on growth and survival of fish larvae: a numerical analysis
Hydrobiologia. 484: 49-59. [ pdf ]
Giske J, Huse G, Fiksen Ø. 1998.
Modelling spatial dynamics of fish
Reviews in Fish Biology and Fisheries. 8: 57-91. [ pdf ]
Aksnes DL, Utne ACW. 1997.
A revised model of visual range in fish
Sarsia. 82: 137-147. [ pdf ]
Giske J, Salvanes AGV. 1995.
Why pelagic planktivores should be unselective feeders
Journal of Theoretical Biology. 173: 41-50. [ pdf ]
Giske J, Aksnes DL, Fiksen Ø. 1994.
Visual predators, environmental variables and zooplankton mortality risk
Vie et Milieu. 44: 1-9. [ pdf ]
Aksnes DL, Giske J. 1993.
A theoretical model of aquatic visual feeding
Ecological Modelling. 67: 233-250. [ pdf ]


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.

Budaev SV, Mikheev VN, Pavlov DS. 2015.
Individual differences in behavior and mechanisms of ecological differentiation on the example of fish
Biology Bulletin Reviews. 5: 462-479. [ doi:10.1134/S2079086415050023 ] [ pdf ]
Giske J, Eliassen S, Fiksen Ø, Jakobsen PJ, Aksnes DL, Mangel M, Jørgensen C. 2014.
The emotion system promotes diversity and evolvability
Proceedings of the Royal Society B. 281: 20141096. [ doi:10.1098/rspb.2014.1096 ] [ open access ] [ pdf ] [ fortran code for model at ]
Giske J, Eliassen S, Fiksen Ø, Jakobsen PJ, Aksnes DL, Jørgensen C, Mangel M. 2013.
Effects of the emotion system on adaptive behavior
American Naturalist. 182: 689-703. [ doi:10.1086/673533 ] [ open access ] [ pdf ] [ popularized version ]
Budaev S, Brown C. 2011.
Personality traits and behaviour
Pages 135-165 in Fish cognition and behavior, edited by Brown C, Laland K, Krause J. Oxford, UK: Blackwell Publishing. [ pdf ]
Budaev SV, Andrew RJ. 2009.
Shyness and behavioural asymmetries in larval zebrafish (Brachydanio rerio) developed in light and dark
Behaviour. 146: 1037-1052. [ doi:10.1163/156853909X404448 ] [ pdf ]
Budaev SV, Zworykin DD. 2003.
Habituation of predator inspection and boldness in the guppy (Poecilia reticulata)
Journal of Ichthyology. 43: 243-246. [ pdf ]
Budaev S, Zworykin D. 2002.
Individuality in fish behavior: Ecology and comparative psychology
Journal of Ichthyology. 42: S189-S195. [ pdf ]
Budaev SV, Zworykin DD, Mochek AD. 1999.
Individual differences in parental care and behaviour profile in the convict cichlid: A correlation study
Animal Behaviour. 58: 195-202. [ doi:10.1006/anbe.1999.1124 ] [ pdf ]
Budaev SV, Zworykin DD, Mochek AD. 1999.
Consistency of individual differences in behaviour of the lion-headed cichlid, Steatocranus casuarius
Behavioural Processes. 48: 49-56. [ pdf ]
Budaev SV. 1999.
Sex differences in the Big Five personality factors: Testing an evolutionary hypothesis
Personality and Individual Differences. 26: 801-814. [ pdf ]
Budaev SV, Zhuikov AY. 1998.
Avoidance learning and “personality” in the guppy (Poecilia reticulata)
Journal of Comparative Psychology. 112: 92-94. [ pdf ]
Budaev S. 1998.
How many dimensions are needed to describe temperament in animals: A factor reanalysis of two data sets
International Journal of Comparative Psychology. 11: 17-29. [ pdf ]
Budaev SV, Zworykin DD. 1998.
Difference in shoaling behaviour between ocellated (Symphodus ocellatus) and long-striped (S. tinca) wrasses and its relation to other behavioural patterns
Marine and Freshwater Behaviour and Physiology. 31: 115-121. [ pdf ]
Budaev SV. 1997.
The statistical analysis of behavioural latency measures
ISCP Newsletter. 14: 1-4. [ pdf ]
Budaev SV. 1997.
Alternative styles in the European wrasse, Symphodus ocellatus: Boldness-related schooling tendency
Environmental Biology of Fishes. 49: 71-78. [ pdf ]
Budaev SV. 1997.
“Personality” in the guppy (Poecilia reticulata): A correlational study of exploratory behavior and social tendency
Journal of Comparative Psychology. 111: 399-411. [ pdf ]


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.

Strand E, Huse G, Giske J. 2002.
Artificial evolution of life history and behavior
American Naturalist. 159: 624-644. [ pdf ]
Huse G, Strand E, Giske J. 1999.
Implementing behaviour in individual-based models using neural networks and genetic algorithms
Evolutionary Ecology. 13: 469-483. [ pdf ]
Huse G, Giske J. 1998.
Ecology in Mare Pentium: An individual-based spatio-temporal model for fish with adapted behaviour
Fisheries Research. 37: 163-178. [ pdf ]
Giske J, Huse G, Fiksen Ø. 1998.
Modelling spatial dynamics of fish
Reviews in Fish Biology and Fisheries. 8: 57-91. [ pdf ]
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