Active object exploration is one of the hallmarks of human and animal intelligence. Research in psychology has shown that the use of multiple exploratory behaviors is crucial for learning about objects. Inspired by such research, recent work in robotics has demonstrated that by performing multiple exploratory behaviors a robot can dramatically improve its object recognition rate. But what is the cause of this improvement? To answer this question, this paper examines the conditions under which combining information from multiple behaviors and sensory modalities leads to better object recognition results. Two different problems are considered: interactive object recognition using auditory and proprioceptive feedback, and surface texture recognition using tactile and proprioceptive feedback. Analysis of the results shows that metrics designed to estimate classifier model diversity can explain the improvement in recognition accuracy. This finding establishes, for the first time, an important link between empirical studies of exploratory behaviors in robotics and theoretical results on boosting in machine learning.