A grain of truth in the grain size effect: Retrieval practice is more effective when interspersed during learning.

Retrieval practice is a powerful method for consolidating long-term learning. When learning takes place over an extended period, how should tests be scheduled to obtain the maximal benefit? In an end-test schedule, all material is studied prior to a large practice test on all studied material, whereas in an interim test schedule, learning is divided into multiple study/test cycles in which each test is smaller and only assesses material from the preceding study block. Past investigations have generally found a difference between these schedules during practice but not during a final assessment, although they may have been underpowered.

What makes a stimulus worthy of attention: Cue-outcome correlation and choice relevance in the learned predictiveness effect.

The learned predictiveness effect refers to the tendency for predictive cues to attract greater attention and show faster learning in subsequent tasks. However, in typical designs, the predictiveness of each cue (its objective cue-outcome correlation) is confounded with the degree to which it is informative for making the correct response on each trial (a feature we term choice relevance). In four experiments, we tested the unique contributions of cue-outcome correlation and choice relevance to the learned predictiveness effect by manipulating the outcome choices available on each trial.

Aversion, interpretation and determinability: Three factors of uncertainty that may play a role in psychopathology.

This opinion piece considers the construct of tolerance of uncertainty and suggests that it should be viewed in the context of three psychological factors: uncertainty aversion, uncertainty interpretation, and uncertainty determinability. Uncertainty aversion refers to a dislike of situations in which the outcomes are not deterministic and is similar to conventional conceptions of (in)tolerance of uncertainty. Uncertainty interpretation refers to the extent to which variability in an observed outcome is interpreted as random fluctuation around a relatively stable base-rate versus frequent and rapid changes in the base-rate.

Do partial and distributed tests enhance new learning?

Testing facilitates subsequent learning of new information, a phenomenon known as the The effect is often investigated in multilist procedures, where studied lists are followed by a retrieval test, or a control task such as restudying, and learning is compared on the final list. In most studies of the effect, tests include all material from the preceding list. We report four experiments, three of which were preregistered, to determine whether tests that are partial (not including all studied items) and distributed (including retrieval of items from earlier lists) are effective in enhancing new learning.

Neural regions associated with gain-loss frequency and average reward in older and younger adults.

Research on the biological basis of reinforcement-learning has focused on how brain regions track expected value based on average reward. However, recent work suggests that humans are more attuned to reward frequency. Furthermore, older adults are less likely to use expected values to guide choice than younger adults.

Frequency effects in action versus value learning.

Recent work in reinforcement learning has demonstrated a choice preference for an option that has a lower probability of reward (A) when paired with an alternative option that has a higher probability of reward (C), if A has been experienced more frequently than C (the frequency effect). This finding is critical as it is inconsistent with widespread assumptions that expected value is based on average reward, and instead suggests that value is based on cumulative instances of reward. However, option frequency may also affect instrumental reinforcement of choosing A during training, which may then transfer to choice on AC trials.

Hearing hooves, thinking zebras: A review of the inverse base-rate effect.

People often fail to use base-rate information appropriately in decision-making. This is evident in the inverse base-rate effect, a phenomenon in which people tend to predict a rare outcome for a new and ambiguous combination of cues. While the effect was first reported in 1988, it has recently seen a renewed interest from researchers concerned with learning, attention and decision-making.

Does learning history shape the associability of outcomes? Further tests of the outcome predictability effect.

In recent years, several studies of human predictive learning demonstrated better learning about outcomes that have previously been experienced as consistently predictable compared to outcomes previously experienced as less predictable, namely the outcome predictability effect. As this effect may have wide-reaching implications for current theories of associative learning, the present study aimed to examine the generality of the effect with a human goal-tracking paradigm, employing three different designs to manipulate the predictability of outcomes in an initial training phase. In contrast to the previous studies, learning in a subsequent phase, when every outcome was equally predictable by novel cues, was not reliably affected by the outcomes' predictability in the first phase.

Attention biases in the inverse base-rate effect persist into new learning.

The inverse base-rate effect is a tendency to predict the rarer of two outcomes when presented with cues that make conflicting predictions. Attention-based accounts of the effect appeal to prioritised attention to predictors of rare outcomes. Changes in the processing of these cues are predicted to increase the rate at which they are learned about in the future (i.

Relational rule discovery in complex discrimination learning.

Failure to learn and generalize abstract relational rules has critical implications for education. In this study, we aimed to determine which training conditions facilitate relational transfer in a relatively simple (patterning) discrimination versus a relatively complex (biconditional) discrimination. The amount of training participants received had little influence on rates of relational transfer.

Learning reward frequency over reward probability: A tale of two learning rules.

Learning about the expected value of choice alternatives associated with reward is critical for adaptive behavior. Although human choice preferences are affected by the presentation frequency of reward-related alternatives, this may not be captured by some dominant models of value learning, such as the delta rule. In this study, we examined whether reward learning is driven more by learning the probability of reward provided by each option, or how frequently each option has been rewarded, and assess how well models based on average reward (e.

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.

Transfer of associability and relational structure in human associative learning.

A wealth of recent studies have demonstrated that predictive cues involved in a linearly solvable component discrimination gain associability in subsequent learning relative to nonpredictive cues. In contrast, contradictory findings have been reported about the fate of cues involved in learning biconditional discriminations in which the cues are relevant but none are individually predictive of a specific outcome. In 3 experiments we examined the transfer of learning from component and biconditional discriminations in a within-subjects design.

A Case of Divergent Predictions Made by Delta and Decay Rule Learning Models.

The Delta and Decay rules are two learning rules used to update expected values in reinforcement learning (RL) models. The delta rule learns rewards, whereas the decay rule learns rewards for each option. Participants learned to select between pairs of options that had reward probabilities of .

Relational discovery in category learning.

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.

Effects of outcome and trial frequency on the inverse base-rate effect.

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.

Rule abstraction, model-based choice, and cognitive reflection.

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.

Resistance to instructed reversal of the learned predictiveness effect.

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.