Several attention-based models of associative learning are built upon the learned predictiveness principle, whereby learning is optimised 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 baserate 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.

The inverse base-rate effect is a bias in contingency learning in which participants tend to predict a rare outcome for a conflicting set of perfectly predictive cues. Although the effect is often explained by attention biases during learning, inferential strategies at test may also contribute substantially to the effect. In three experiments, we manipulated the frequencies of outcomes and trial types to determine the critical conditions for the effect, thereby providing novel tests of the reasoning processes that could contribute to it. The rare bias was substantially reduced when the outcomes were experienced at equal rates in the presence of predictive-cue frequency differences (Exp. 2), and when the predictive cues were experienced at equal rates in the presence of outcome frequency differences (Exp. 3). We also found a consistent common-outcome bias for novel cue compounds. The results indicate the importance of both cue and outcome frequencies to the inverse base-rate effect, and reveal a combination of necessary conditions that are not well captured by appealing to inferential strategies at test. Although both attention-based and inferential theories explain some aspects of these data, no existing theory fully accounts for these effects of relative novelty.

Learning categories defined by the relations among objects supports the transfer of knowledge from initial learning contexts to novel contexts that share few surface similarities. Often relational categories have correlated (but nonessential) surface features, which can be a distraction from discovering the category-defining relations, preventing knowledge transfer. This is one explanation for "the inert knowledge problem" in education wherein many students fail to spontaneously apply their learning outside the classroom. Here we present a series of experiments using artificial categories that correlate surface features and relational patterns during learning. Our goal was to determine what task parameters and individual differences in learners shift focus to the relational aspect of the category and foster transfer to novel disparate exemplars. We consistently showed that the effectiveness of task structure manipulations (e.g., the sequence of learning exemplars) depended on the learners' strategies (e.g., whether learners are oriented toward discovering rules or focusing on exemplars). Further, we found support that "inference-learning," wherein learners are presented with incomplete exemplars and learn how to infer the missing pieces, is an effective way to promote relational discovery and transfer, even for learners who are not predisposed to make such discoveries. (PsycINFO Database Record

The learned predictiveness effect is a widely observed bias towards previously predictive cues in novel situations. Although the effect is generally attributed to an automatic attentional shift, it has recently been explained as the product of controlled inferences about the predictive value of cues. This view is supported by the susceptibility of learned predictiveness to instruction manipulation. However, recent research has shown conflicting results. Three experiments investigated the parameters of the instructed reversal effect in a human causal learning task, to determine the relative contribution of automatic and controlled attention processes. Experiment 1 showed that reversal instructions abolished, but did not reverse, the learned predictiveness effect, although length of initial training had no effect on the extent to which predictive cues subsequently captured attention. Experiment 2 explored whether particular causal scenarios lend themselves more readily to instructed reversal, but still failed to establish a significant reversal effect. Experiment 3 demonstrated a significant reversal effect when nonpredictive cues were explicitly and individually identified as the causes of outcomes. However, this effect was considerably weaker than the learned predictiveness effect when predictive cues were identified in the same way. Taken together, the results are inconsistent with a purely controlled account of learned predictiveness and provide support for dual-process theories of learning and attention.

Numerous tasks in learning and cognition have demonstrated differences in response patterns that may reflect the operation of two distinct systems. For example, causal and reinforcement learning tasks each show responding that considers abstract structure as well as responding based on simple associations. Nevertheless, there has been little attempt to verify whether these tasks are measuring related processes. The current study therefore investigated the relationship between rule- and feature-based generalization in a causal learning task, and model-based and model-free responding in a reinforcement learning task, including cognitive reflection as a predictor of individual tendencies to use controlled, deliberative processes in these tasks. We found that the use of rule-based generalization in a patterning task was a significant predictor of model-based, but not model-free, choice. Individual differences in cognitive reflection were significantly correlated with performance in both tasks, although this did not predict variation in model-based choice independently of rule-based generalization. Thus, although there is evidence of stable individual differences in the use of higher order processes across tasks, there may also be differences in mechanisms that these tasks reveal.