Processing graded feedback: Electrophysiological correlates of learning from small and large errors

Feedback processing is important for learning and therefore may affect the consolidation of skills. Considerable research demonstrates electrophysiological differences between correct and incorrect feedback, but how we learn from small versus large errors is usually overlooked. This study investigated electrophysiological differences when processing small or large error feedback during a time estimation task. Data from high-learners and low-learners were analyzed separately. In both high- and low-learners, large error feedback was associated with higher feedback-related negativity (FRN) and small error feedback was associated with a larger P300 and increased amplitude over the motor related areas of the left hemisphere. In addition, small error feedback induced larger desynchronization in the alpha and beta bands with distinctly different topographies between the two learning groups: The high-learners showed a more localized decrease in beta power over the left frontocentral areas, and the low-learners showed a widespread reduction in the alpha power following small error feedback. Furthermore, only the high-learners showed an increase in phase synchronization between the midfrontal and left central areas. Importantly, this synchronization was correlated to how well the participants consolidated the estimation of the time interval. Thus, although large errors were associated with higher FRN, small errors were associated with larger oscillatory responses, which was more evident in the high-learners. Altogether, our results suggest an important role of the motor areas in the processing of error feedback for skill consolidation

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment

Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalised Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a ``Learning with privileged information'' approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants.MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls based on the learning performance and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on the learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal for structured stimuli is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI

Auditory but Not Audiovisual Cues Lead to Higher Neural Sensitivity to the Statistical Regularities of an Unfamiliar Musical Style

It is still a matter of debate whether visual aids improve learning of music. In a multisession study, we investigated the neural signatures of novel music sequence learning with or without aids (auditory-only: AO, audiovisual: AV). During three training sessions on 3 separate days, participants (nonmusicians) reproduced (note by note on a keyboard) melodic sequences generated by an artificial musical grammar. The AV group (n = 20) had each note color-coded on screen, whereas the AO group (n = 20) had no color indication. We evaluated learning of the statistical regularities of the novel music grammar before and after training by presenting melodies ending on correct or incorrect notes and by asking participants to judge the correctness and surprisal of the final note, while EEG was recorded. We found that participants successfully learned the new grammar. Although the AV group, as compared to the AO group, reproduced longer sequences during training, there was no significant difference in learning between groups. At the neural level, after training, the AO group showed a larger N100 response to lowprobability compared to high-probability notes, suggesting an increased neural sensitivity to statistical properties of the grammar; this effect was not observed in the AV group. Our findings indicate that visual aids might improve sequence reproduction while not necessarily promoting better learning, indicating a potential dissociation between sequence reproduction and learning. We suggest that the difficulty induced by auditory-only input during music training might enhance cognitive engagement, thereby improving neural sensitivity to the underlying statistical properties of the learned material

Development and initial validation of the Brazil Mood Scale

The present study developed and evaluated the Brazil Mood Scale, a 24-item measure based on the Brunel Mood Scale. Mood descriptors were converted into Portuguese using the translation-back translation method. The revised scale was administered on two occasions to 298 participants in Brazil (173 males and 125 females; age: M = 18.3, SD = 5.1 yr.). Exploratory factor analysis recovered the hypothesized, 6-factor solution (anger, confusion, depression, fatigue, tension, vigour) at Time 1 and Time 2, explaining 68% and 72% of variance, respectively. Subscales for depression, fatigue, and vigour were recovered cleanly in both solutions; whereas the anger, confusion, and tension scales showed some complexity. Cronbach alpha coefficients were acceptable for all subscales at T1 (range = .76 - .85) and T2 (range = .79 - .90). Confirmatory factor analysis showed support for the measurement model, although some values were marginal (χ2 /df: T1 = 2.42, T2 = 2.28; CFI: T1 = .91, T2 = .93; TLI: T1 = .88, T2 = .91; RMSEA: T1 = .06, T2 = .06). Overall, the measure showed encouraging psychometric characteristics