Learned Predictiveness Models Predict Opposite Attention Biases in the Inverse Base-Rate Effect

Several attention-based models of associative learning are built upon the learned predictiveness principle, whereby learning is optimized by attending to the most predictive features and ignoring the least predictive features. Despite their functional similarity, these models differ in their formal mechanisms and thus may produce very different predictions in some circumstances. As we demonstrate, this is particularly evident in the inverse base-rate effect. Using simulations with a modified Mackintosh model and the EXIT model, we found that models based on the learned predictiveness principle can account for rare-outcome choice biases associated with the inverse base-rate effect, despite making opposite predictions for relative attention to rare versus common predictors. The models also make different predictions regarding changes in attention across training, and effects of context associations on attention to cues. Using a human causal learning task, we replicated the inverse base-rate effect and a recently reported reduction in this effect when the context is not predictive of the common outcome and used eye-tracking to test model predictions about changes in attention both prior to making a decision, and during feedback. The results support the predictions made by EXIT, where the rare predictor commands greater attention than the common predictor throughout training. In addition, patterns of attention prior to making a decision differed to those during feedback, where effects of using a partially predictive context were evident only prior to making a prediction.