Tag clouds generated in social tagging systems can capture the collective knowledge of communities. Using as a basis spreading activation theories, information foraging theory, and the co-evolution model of cognitive and social systems, we present here a model for an "extended information scent," which proposes that both collective and individual knowledge have a significant influence on link selection, incidental learning, and information processing. Two experimental studies tested the applicability of the model to a situation in which individual knowledge and collective knowledge were contradictory to each other. The results of the first experiment showed that a higher individual strength of association between a target in demand and a tag led to a higher probability of selecting corresponding links, combined with less thorough information processing for non-corresponding links. But users also adapted their navigation behavior to the collective knowledge (strength of associations of tags) of the community and showed incidental learning during navigation, which resulted in a change of their individual strength of associations. The second experiment confirmed these results and showed, in addition, that the effects also occurred for indirect associations. Altogether, the results show that the extended information scent is an appropriate and fertile model for describing the interplay of individual knowledge and the collective knowledge of social tags. (Contains 6 tables and 8 figures.)