Topic: Q-values

Predicting the future outcomes of actions forms the basis for reinforcement learning to shape goal-directed behaviours. Recent work showed that learning based on predicting future sensory experience using the current state and action of an agent leads to representations that resemble those in the brain, for instance place and grid cells of the medial temporal lobe. Here we ask if combining ongoing predictive learning of sensory events and of notional value of actions leading to rewards forms representations that enable more efficient goal-directed learning than reinforcement learning alone. Si

Analysis of behavioral data usually averages responses across subjects and/or across trials. However, this approach obscures the actual dynamics of learning, which can occur within few trials and differ between subjects. For instance, reexamination of previous data (Uengoer and Lachnit, 2006) reveals that during learning subjects do not change their responses gradually, but abruptly (Fig. A). These “jumps” occur at different trials for each subject and averaging therefore paints a misleading picture of gradual learning curves. These data are not easily accounted for with simple associative lea