To better understand the behaviour of animals we need to combine two approaches: understanding why they make decisions and how they make decisions


Great progress has been made in understanding the behavioural of animals by mathematical and computer program modelling. Since the 1970s these have usually assumed that animals can make the optimal decision in any given particular situation and be perfectly flexible. At the same time people watching animals know that animals have mental processes that are not always optimal (sometimes making the wrong decision), partly because they are not perfectly flexible (sometimes not changing their behaviour for different conditions).


This paper argues that these approaches need to be combined if we are going to make great progress in understanding animal behaviour. We can ask questions at three levels: (1) why are mental processes not completely flexible? One possible type of answer is that the world is too complex to have a specific process for each situation and instead animals have a general rule that is reasonable for most situations. (2) for a particular rule, how would natural selection have tuned the details? For instance, how much should animals update their beliefs or behaviour in response to new information? (3) why are mental processes organised as they are? Why do we have emotions that we respond to, rather than responding directly to the outside world directly? Why do hormones have more than one effect on emotions and the body?


Usually models of animal behaviour have studied complex mental processes in simple worlds. Future ones should study simpler mental processes in complex worlds. Only then will they properly match the real world, and give us better insights for how evolution has formed animals’ brains.


Behavioural ecology

Subject Group

Zoology and Ecology


mental mechanism


decision making

optimal foraging

Posted by


on Fri Oct 27 2017

Article ID


Details of original research article:

McNamara JM, Houston AI. Integrating function and mechanism. Trends in Ecology and Evolution. 2009;24: 670-675.

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