An important form of cultural evolution involves individual learning of behaviour by the members of a population of agents and cultural transmission of learned behaviour to the following generations. The selection of behaviours generated in the process of individual learning requires some "guiding criteria". As with behaviour, guiding criteria can be innate or originate from individual or social learning. Guiding criteria play a fundamental role in cultural evolution because they strongly contribute to determine the behaviours that will enter the pool of cultural traits of the population. This work presents a computational model that investigates the nature and function of some forms of "guiding criteria" in the cultural evolution of a population of agents that learn and adapt to the environment using neural networks. The model focuses on the interplay of individual learning and cultural transmission of behaviour and those forms of guiding criteria. The model contributes to clarify the nature and role in culture evolution of the guiding criteria studied. Also, within the assumptions of the model, it shows that the cultural transmission of behaviour is more effective than the transmission of the guiding criteria
