Kristoffer C. Aberg

Perceptual learning with Chevrons requires a minimal number of trials, transfers to untrained directions, but does not require sleep

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.

Different types of feedback change decision criterion and sensitivity differently in perceptual learning

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.

Perceptual learning and roving: Stimulus types and overlapping neural populations.

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.

About similar characteristics of visual perceptual learning and LTP

Perceptual learning is an implicit form of learning which induces long-lasting perceptual enhancements. Perceptual learning shows intriguing characteristics. For example, a minimal number of trials per session is needed for learning and the interleaved presentation of more than one stimulus type can hinder learning. Here, we show that these and other characteristics of perceptual learning are very similar to characteristics of long-term potentiation (LTP), the basic mechanism of memory formation. We outline these characteristics and discuss results of electrophysiological experiments which indirectly link LTP and perceptual learning.

Interleaving bisection stimuli - randomly or in sequence - does not disrupt perceptual learning, it just makes it more difficult.

Presenting stimuli of two or more stimulus types randomly interleaved, so called roving, disrupts perceptual learning in many paradigms. Recently, it was shown that no disruption occurs when Gabor stimuli were presented interleaved in sequence, instead of randomly. Here, using bisection stimuli, we found the opposite pattern of results. Presenting bisection stimuli in a sequence disrupted perceptual learning, whereas we found improvement under roving conditions. A meta-analysis showed that parts of this deviation from previous studies is possibly caused by the initial performance level of participants. These results do not prove previous results wrong, they just show that multiple factors play a crucial role in perceptual learning which cannot always be easily controlled for.

The “Creative Right Brain” Revisited: Individual Creativity and Associative Priming in the Right Hemisphere Relate to Hemispheric Asymmetries in Reward Brain Function

The idea that creativity resides in the right cerebral hemisphere is persistent in popular science, but has been widely frowned upon by the scientific community due to little empirical support. Yet, creativity is believed to rely on the ability to combine remote concepts into novel and useful ideas, an ability which would depend on associative processing in the right hemisphere. Moreover, associative processing is modulated by dopamine, and asymmetries in dopamine functionality between hemispheres may imbalance the expression of their implemented cognitive functions. Here, by uniting these largely disconnected concepts, we hypothesize that relatively less dopamine function in the right hemisphere boosts creativity by releasing constraining effects of dopamine on remote associations. Indeed, participants with reduced neural responses in the dopaminergic system of the right hemisphere (estimated by functional MRI in a reward task with positive and negative feedback), displayed higher creativity (estimated by convergent and divergent tasks), and increased associative processing in the right hemisphere (estimated by a lateralized lexical decision task). Our findings offer unprecedented empirical support for a crucial and specific contribution of the right hemisphere to creativity. More importantly our study provides a comprehensive view on potential determinants of human creativity, namely dopamine-related activity and associative processing.

Does Perceptual Learning Suffer from Retrograde Interference

In motor learning, training a task B can disrupt improvements of performance of a previously learned task A, indicating that learning needs consolidation. An influential study suggested that this is the case also for visual perceptual learning [1]. Using the same paradigm, we failed to reproduce these results. Further experiments with bisection stimuli also showed no retrograde disruption from task B on task A. Hence, for the tasks tested here, perceptual learning does not suffer from retrograde interference.

Hemispheric Asymmetries in Striatal Reward Responses Relate to Approach-Avoidance Learning and Encoding of Positive-Negative Prediction Errors in Dopaminergic Midbrain Regions

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. SIGNIFICANCE STATEMENT Individuals differ in how they behave toward rewards or punishments. Here, we demonstrate that functional hemispheric asymmetries measured in dopaminergic reward regions dictate whether someone will learn to choose rewarding options or instead avoid punishing outcomes. We also show that hemispheric reward asymmetries involve a differential neural encoding of signals controlling approach and avoidance learning. We thus provide experimental evidence for a mechanism that accounts for individual differences in approach and avoidance learning. Disabling mental illnesses have previously been associated with hemispheric asymmetries in dopamine function and extreme biases in approach–avoidance behavior. By showing that these observations implicate biased learning processes, the present study may offer important insights into the development and maintenance of some psychiatric disorders.

Increased Reward-Related Behaviors during Sleep and Wakefulness in Sleepwalking and Idiopathic Nightmares

Background We previously suggested that abnormal sleep behaviors, i.e., as found in parasomnias, may often be the expression of increased activity of the reward system during sleep. Because nightmares and sleepwalking predominate during REM and NREM sleep respectively, we tested here whether exploratory excitability, a waking personality trait reflecting high activity within the mesolimbic dopaminergic (ML-DA) system, may be associated with specific changes in REM and NREM sleep patterns in these two sleep disorders. Methods Twenty-four unmedicated patients with parasomnia (12 with chronic sleepwalking and 12 with idiopathic nightmares) and no psychiatric comorbidities were studied. Each patient spent one night of sleep monitored by polysomnography. The Temperament and Character Inventory (TCI) was administered to all patients and healthy controls from the Geneva population (n = 293). Results Sleepwalkers were more anxious than patients with idiopathic nightmares (Spielberger Trait anxiety/STAI-T), but the patient groups did not differ on any personality dimension as estimated by the TCI. Compared to controls, parasomnia patients (sleepwalkers together with patients with idiopathic nightmares) scored higher on the Novelty Seeking (NS) TCI scale and in particular on the exploratory excitability/curiosity (NS1) subscale, and lower on the Self-directedness (SD) TCI scale, suggesting a general increase in reward sensitivity and impulsivity. Furthermore, parasomnia patients tended to worry about social separation persistently, as indicated by greater anticipatory worry (HA1) and dependence on social attachment (RD3). Moreover, exploratory excitability (NS1) correlated positively with the severity of parasomnia (i.e., the frequency of self-reported occurrences of nightmares and sleepwalking), and with time spent in REM sleep in patients with nightmares. Conclusions These results suggest that patients with parasomnia might share common waking personality traits associated to reward-related brain functions. They also provide further support to the notion that reward-seeking networks are active during human sleep.

Linking Individual Learning Styles to Approach-Avoidance Motivational Traits and Computational Aspects of Reinforcement Learning

Learning how to gain rewards (approach learning) and avoid punishments (avoidance learning) is fundamental for everyday life. While individual differences in approach and avoidance learning styles have been related to genetics and aging, the contribution of personality factors, such as traits, remains undetermined. Moreover, little is known about the computational mechanisms mediating differences in learning styles. Here, we used a probabilistic selection task with positive and negative feedbacks, in combination with computational modelling, to show that individuals displaying better approach (vs. avoidance) learning scored higher on measures of approach (vs. avoidance) trait motivation, but, paradoxically, also displayed reduced learning speed following positive (vs. negative) outcomes. These data suggest that learning different types of information depend on associated reward values and internal motivational drives, possibly determined by personality traits.