Common mechanisms for between- and within-trial learning dynamics
Two of the most fundamental difficulties we face when learning is deciding which information is relevant, and when to use it. To overcome these difficulties, humans continuously make choices about which dimensions of information to selectively attend to, and monitor how useful those dimensions are in the context of the current goal. Although previous theories have specified how observers learn to attend to relevant dimensions over time, those theories have largely remained silent about how attention should be allocated on a within-trial basis, which dimensions of information should be sampled, and how the temporal ordering of information sampling influences learning. Here, we use the Adaptive Attention and Representation Model (AARM) to demonstrate that a common set of mechanisms can be used to specify: 1) how the distribution of attention is updated between trials over the course of learning; and 2) how attention dynamically shifts among dimensions within-trial. We validate or proposed set of mechanisms by comparing AARM’s predictions to observed behavior in the context of five case studies, which collectively encompass different theoretical aspects of selective attention. Importantly, we use both eye-tracking and choice response data to provide a stringent test of how attention and decision processes dynamically interact. Specifically, how does attention to selected stimulus dimensions gives rise to decision dynamics, and in turn, how do decision dynamics influence our continuous choices about which dimensions to attend to via gaze fixations?