Explicit information-seeking actions are needed to evaluate alternative actions in problem-solving tasks. Information-seeking costs are often traded off against the utility of information. We present three experiments that show how subjects adapt to the cost and information structures of environments in a map-navigation task. We found that subjects often stabilize at suboptimal levels of performance. A Bayesian satisficing model (BSM) is proposed and implemented in the ACT-R architecture to predict information-seeking behavior. The BSM uses a local decision rule and a global Bayesian learning mechanism to decide when to stop seeking information. The model matched the human data well, suggesting that adaptation to cost and information structures can be achieved by a simple local decision rule. The local decision rule, however, often limits exploration of the environment and leads to suboptimal performance. We propose that suboptimal performance is an emergent property of the dynamic interactions between cognition and the environment.