Many theories propose that top-down attentional signals control processing in sensory cortices by modulating neural activity. But who controls the controller? Here we investigate how a biologically plausible neural reinforcement learning scheme can create higher order representations and top-down attentional signals. The learning scheme trains neural networks using two factors that gate Hebbian plasticity: (1) an attentional feedback signal from the response-selection stage to earlier processing levels and (2) a globally available neuromodulator that encodes the reward prediction error. We demonstrate how the neural network learns to direct attention to one of two coloured stimuli that are arranged in a rank-order (Lennert & Martinez-Trujillo, 2011). Like monkeys trained on this task, the network develops units that are tuned to the rank-order of the colours and it generalizes this newly learned rule to previously unseen colour combinations. These results provide new insight into how individuals can learn to control attention as a function of reward contingency.
neural network model, attention, reward-based learning, feedback
Neural nets and related approaches (msc 62M45)
Life Sciences (theme 5)
Taylor & Francis
Visual Cognition
Teaching and Learning in Multi Agent Systems
This work was funded by the The Netherlands Organisation for Scientific Research (NWO); grant id nwo/612.066.826 - Teaching and Learning in Multi Agent Systems
Life Sciences and Health

Rombouts, J.O, Bohte, S.M, Martinez-Trujillo, J, & Roelfsema, P.R. (2015). A learning rule that explains how rewards teach attention. Visual Cognition. doi:10.1080/13506285.2015.1010462