In most models of perceptual learning, the amount of improvement of performance does not depend on the regime of stimulus presentations, but only on the sheer number of trials. Here, we kept the number of stimulus presentations constant while varying the number of trials per session. We show that a minimal number of stimulus presentations per session is necessary, transfer depends strongly on the presentation regime, but sleep has only weak, if at all, effects.

In (perceptual) learning, performance improves with practice either by changes in sensitivity or decision criterion. Often, changes in sensitivity are regarded as the appropriate measure of learning while changes in criterion are considered unavoidable nuisances. Very little is known about the distinguishing characteristics of both learning types. Here, we show first that block feedback, which affects sensitivity, does not affect criterion. Second, contrary to changes in sensitivity, changes in decision criterion are limited to the training session and do not transfer overnight. Finally, training with biased trial-wise feedback induces a sensitivity change such that a left offset Vernier may be perceived as a right offset Vernier.

Some individuals are better at learning about rewarding situations, whereas others are inclined to avoid punishments (i.e., enhanced approach or avoidance learning, respectively). In reinforcement learning, action values are increased when outcomes are better than predicted (positive prediction errors [PEs]) and decreased for worse than predicted outcomes (negative PEs). Because actions with high and low values are approached and avoided, respectively, individual differences in the neural encoding of PEs may influence the balance between approach-avoidance learning. Recent correlational approaches also indicate that biases in approach-avoidance learning involve hemispheric asymmetries in dopamine function. However, the computational and neural mechanisms underpinning such learning biases remain unknown. Here we assessed hemispheric reward asymmetry in striatal activity in 34 human participants who performed a task involving rewards and punishments. We show that the relative difference in reward response between hemispheres relates to individual biases in approach-avoidance learning. Moreover, using a computational modeling approach, we demonstrate that better encoding of positive (vs negative) PEs in dopaminergic midbrain regions is associated with better approach (vs avoidance) learning, specifically in participants with larger reward responses in the left (vs right) ventral striatum. Thus, individual dispositions or traits may be determined by neural processes acting to constrain learning about specific aspects of the world.

In perceptual learning, performance usually improves when observers train with one type of stimulus, for example, a bisection stimulus. Roving denotes the situation when, instead of one, two or more types of stimuli are presented randomly interleaved, for example, a bisection stimulus and a vernier. For some combinations of stimulus types, performance improves in roving situations whereas for others it does not. To investigate when roving impedes perceptual learning, we conducted four experiments. Performance improved, for example, when we roved a bisection stimulus and a vernier but not when we roved certain types of bisection stimuli. We propose that roving hinders perceptual learning when the stimulus types are clearly distinct from each other but still excite overlapping but not identical neural populations.