Balancing New Against Old Information: The Role of Surprise in Learning

To guide their behavior, humans and animals rely on previously learned knowledge about the world. Since the world is complex and models of the world are never perfect, the question arises whether we should trust our internal world model that we have built from past data or whether we should readjust it when we receive a new data sample. In noisy environments, a single data sample may not be reliable and in general we need to average over several data samples. However, when a structural change occurs in the environment, the most recent data samples are the most informative ones and we should put more weight on recent data samples than on earlier ones. Indeed, both humans and animals adaptively adjust the relative contribution of old and newly acquired data during learning (Behrens et al., 2007; Nassar et al., 2012; Krugel et al., 2009; Pearce and Hall, 1980) and rapidly adapt to changing environments (Pearce and Hall, 1980; Wilson et al., 1992; Holland, 1997).