Introduction. Modelling natural action selection

Action selection is the task of resolving conflicts between competing behavioural alternatives. This theme issue is dedicated to advancing our understanding of the behavioural patterns and neural substrates supporting action selection in animals, including humans. The scope of problems investigated includes: (i) whether biological action selection is optimal (and, if so, what is optimized), (ii) the neural substrates for action selection in the vertebrate brain, (iii) the role of perceptual selection in decision-making, and (iv) the interaction of group and individual action selection. A second aim of this issue is to advance methodological practice with respect to modelling natural action section. A wide variety of computational modelling techniques are therefore employed ranging from formal mathematical approaches through to computational neuroscience, connectionism and agent-based modelling. The research described has broad implications for both natural and artificial sciences. One example, highlighted here, is its application to medical science where models of the neural substrates for action selection are contributing to the understanding of brain disorders such as Parkinson's disease, schizophrenia and attention deficit/hyperactivity disorder.

Action selection is the task of resolving conflicts between competing behavioural alternatives, or, more simply put, of deciding ‘what to do next’. As a general problem facing all autonomous beings—animals and artificial agents—it has exercised the minds of scientists from many disciplines: those concerned with understanding the biological bases of behaviour (ethology, neurobiology and psychology) and those concerned with building artefacts, real or simulated, that behave appropriately in complex worlds (artificial intelligence, artificial life and robotics). Work in these different domains has established a wide variety of methodologies that address the same underlying problems from different perspectives. One approach to characterizing this multiplicity of methods is to distinguish between the analytical and the synthetic branches of the behavioural and brain sciences (Braitenberg 1986). From the perspective of analytical science, an important goal is to describe transitions in behaviour; these can occur at many different temporal scales, and can be considered as instances of ‘behavioural switching’ or, more anthropomorphically, as ‘choice points’. Analytical approaches also seek to identify the biological substrates that give rise to such transitions, for instance, by probing in the nervous system to find critical components—candidate action-selection mechanisms—on which effective and appropriate switching may depend. Beyond such descriptions, of course, a central goal of behavioural science is to explain why any observed transition (or sequence of transitions) occurs in a given context, perhaps referencing such explanation to normative concepts such as ‘utility’ or ‘fitness’. These explanations may also make use of mechanistic accounts that explain how underlying neural control systems operate to generate observed behavioural outcomes. It is at the confluence of these mechanistic and normative approaches that the synthetic approach in science is coming to have an increasing influence. The experimentalist seeks the help of the mathematician or engineer and asks ‘what would it take to build a system that acts in this way?’

Modelling—the synthesis of artificial systems that mimic natural ones—has always played an important role in biology; however, the last few decades have seen a dramatic expansion in the range of modelling methodologies that have been employed. Formal, mathematical models with provable properties continue to be of great importance (e.g. Bogacz et al. 2007; Houston et al. 2007). Now, added to these, there is a burgeoning interest in larger-scale simulations that allow the investigation of systems for which formal mathematical solutions are, as a result of their complexity, either intractable or simply unknown. However, synthetic models, once built, may often be elucidated by analytical techniques; thus synthetic and analytical approaches are best pursued jointly. Analysis of a formally intractable simulation often consists of observing the system's behaviour then measuring and describing it using many of the same tools as traditional experimental science (Bryson et al. 2007). Such an analysis can serve to uncover heuristics for the interpretation of empirical data as well as to generate novel hypotheses to be tested experimentally.

The questions to be addressed in considering models of action selection include: is the model sufficiently constrained by biological data that its functioning can capture interesting properties of the natural system of interest? Do manipulations of the model, intended to mirror scientific procedures or observed natural processes, result in similar outcomes to those seen in real life? Does the model make predictions? Is the model more complex than it needs to be in order to describe a phenomenon, or is it too simple to engage with empirical data? A potential pitfall of more detailed computational models is that they may trade the sophistication with which they match biological detail with comprehensibility. The scientist is then left with two systems, one natural and the other synthesized, neither of which is well understood. Hence, the best models hit upon a good trade-off between accurately mimicking key properties of a target biological system at the same time as remaining understandable to the extent that new insights into the natural world are generated.

In this theme issue, we present a selection of some of the most promising contemporary approaches to modelling action selection in natural systems. The range of methodologies is broad—from formal mathematical models, through to models of artificial animals, here called agents, embedded in simulated worlds (often containing other agents). We also consider mechanistic accounts of the neural processes underlying action selection through a variety of computational neuroscience and connectionist approaches. In this article, we summarize the main substantive areas of this theme issue and the contributions of each article and then return briefly to a discussion of the modelling techniques.