Beyond novelty: Learnability in the interplay between creativity, curiosity and artistic endeavours.

Using art and aesthetics as context, we explore the notion that curiosity and creativity emanate from a single novelty-seeking mechanism and outline support for the idea. However, we also highlight the importance of learning progress tracking in exploratory action and advocate for a nuanced understanding that aligns novelty-seeking with learnability. This, we argue, offers a more comprehensive framework of how curiosity and creativity are related.

Multivariate patterns and long-range temporal correlations of alpha oscillations are associated with flexible manipulation of visual working memory representations.

The ability to flexibly manipulate memory representations is embedded in visual working memory (VWM) and can be tested using paradigms with retrospective cues. Although valid retrospective cues often facilitate memory recall, invalid ones may or may not result in performance costs. We investigated individual differences in utilising retrospective cues and evaluated how these individual differences are associated with brain oscillatory activity at rest. At the behavioural level, we operationalised flexibility as the ability to make effective use of retrospective cues or disregard them if required. At the neural level, we tested whether individual differences in such flexibility were associated with properties of resting-state alpha oscillatory activity (8-12 Hz). To capture distinct aspects of these brain oscillations, we evaluated their power spectral density and temporal dynamics using long-range temporal correlations (LRTCs). In addition, we performed multivariate patterns analysis (MVPA) to classify individuals' level of behavioural flexibility based on these neural measures. We observed that alpha power alone (magnitude) at rest was not associated with flexibility. However, we found that the participants' ability to manipulate VWM representations was correlated with alpha LRTC and could be decoded using MVPA on patterns of alpha power. Our findings suggest that alpha LRTC and multivariate patterns of alpha power at rest may underlie some of the individual differences in using retrospective cues in working memory tasks.

Right temporal alpha oscillations as a neural mechanism for inhibiting obvious associations.

Creative cognition requires mental exploration of remotely connected concepts while suppressing dominant ones. Across four experiments using different samples of participants, we provide evidence that right temporal alpha oscillations play a crucial role in inhibiting habitual thinking modes, thereby paving the way for accessing more remote ideas. In the first experiment, participants completed the compound remote associate task (RAT) in three separate sessions: during right temporal alpha (10 Hz) transcranial alternating current brain stimulation (tACS), left temporal alpha tACS, and sham tACS. Participants performed better under right tACS only on RAT items in which two of the three words shared misleading semantic associations. In the second experiment, we measured EEG while the participants solved RAT items with or without shared misleading associations. We observed an increase in right temporal alpha power when participants correctly solved RAT items with misleading semantic associations. The third experiment demonstrated that while solving divergent thinking tasks participants came up with more remote ideas when stimulated by right temporal alpha tACS. In the fourth experiment, we found that participants showed higher right temporal alpha power when generating more remote uses for common objects. These studies altogether indicate that right temporal alpha oscillations may support creativity by acting as a neural mechanism for an active inhibition of obvious semantic associations.

A rational-choice model of Covid-19 transmission with endogenous quarantining and two-sided prevention.

This paper offers a parsimonious, rational-choice model to study the effect of pre-existing inequalities on the transmission of COVID-19. Agents decide whether to "go out" (or self-quarantine) and, if so, whether to wear protection such as masks. Three elements distinguish the model from existing work. First, non-symptomatic agents do not know if they are infected. Second, some of these agents unknowingly transmit infections. Third, we permit two-sided prevention via the use of non-pharmaceutical interventions: the probability of a person catching the virus from another depends on protection choices made by each. We find that a mean-preserving increase in pre-existing income inequality unambiguously increases the equilibrium proportion of unprotected, socializing agents and may increase or decrease the proportion who self-quarantine. Strikingly, while higher pre-COVID inequality may or may not raise the overall risk of infection, it increases the risk of disease in social interactions.

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 three 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 low-probability compared with 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.

From learning to creativity: Identifying the behavioural and neural correlates of learning to predict human judgements of musical creativity.

Human creativity is intricately linked to acquired knowledge. However, to date learning a new musical style and subsequent musical creativity have largely been studied in isolation. We introduced a novel experimental paradigm combining behavioural, electrophysiological, and computational methods, to examine the neural correlates of unfamiliar music learning, and to investigate how neural and computational measures can predict human creativity. We investigated music learning by training non-musicians (N = 40) on an artificial music grammar. Participants' knowledge of the grammar was tested before and after three training sessions on separate days by assessing explicit recognition of the notes of the grammar, while additionally recording their EEG. After each training session, participants created their own musical compositions, which were later evaluated by human experts. A computational model of auditory expectation was used to quantify the statistical properties of both the grammar and the compositions. Results showed that participants successfully learned the new grammar. This was also reflected in the N100, P200, and P3a components, which were higher in response to incorrect than correct notes. The delta band (2.5-4.5 Hz) power in response to grammatical notes during first exposure to the grammar positively correlated with learning, suggesting a potential neural mechanism of encoding. On the other hand, better learning was associated with lower alpha and higher beta band power after training, potentially reflecting neural mechanisms of retrieval. Importantly, learning was a significant predictor of creativity, as judged by experts. There was also an inverted U-shaped relationship between percentage of correct intervals and creativity, as compositions with an intermediate proportion of correct intervals were associated with the highest creativity. Finally, the P200 in response to incorrect notes was predictive of creativity, suggesting a link between the neural correlates of learning, and creativity. Overall, our findings shed light on the neural mechanisms of learning an unfamiliar music grammar, and offer novel contributions to the associations between learning measures and creative compositions based on learned materials.

Cardiac afferent activity modulates early neural signature of error detection during skilled performance.

Behavioral adaptations during performance rely on predicting and evaluating the consequences of our actions through action monitoring. Previous studies revealed that proprioceptive and exteroceptive signals contribute to error-monitoring processes, which are implemented in the posterior medial frontal cortex. Interestingly, errors also trigger changes in autonomic nervous system activity such as pupil dilation or heartbeat deceleration. Yet, the contribution of implicit interoceptive signals of bodily states to error-monitoring during ongoing performance has been overlooked. This study investigated whether cardiovascular interoceptive signals influence the neural correlates of error processing during performance, with an emphasis on the early stages of error processing. We recorded musicians' electroencephalography and electrocardiogram signals during the performance of highly-trained music pieces. Previous event-related potential (ERP) studies revealed that pitch errors during skilled musical performance are preceded by an error detection signal, the pre-error-negativity (preERN), and followed by a later error positivity (PE). In this study, by combining ERP, source localization and multivariate pattern classification analysis, we found that the error-minus-correct ERP waveform had an enhanced amplitude within 40-100 ms following errors in the systolic period of the cardiac cycle. This component could be decoded from single-trials, was dissociated from the preERN and PE, and stemmed from the inferior parietal cortex, which is a region implicated in cardiac autonomic regulation. In addition, the phase of the cardiac cycle influenced behavioral alterations resulting from errors, with a smaller post-error slowing and less perturbed velocity in keystrokes following pitch errors in the systole relative to the diastole phase of the cardiac cycle. Lastly, changes in the heart rate anticipated the upcoming occurrence of errors. This study provides the first evidence of preconscious visceral information modulating neural and behavioral responses related to early error monitoring during skilled performance.

Modulations in resting state networks of subcortical structures linked to creativity.

Creativity is a sine qua non ability for almost all aspects of everyday life. Although very profound behavioural models were provided by 21st century psychologists, the neural correlates of these personality features associated with creativity are largely unknown. Recent models suggest strong relationships between dopamine release and various creative skills. Herein, we employed functional connectivity analyses of resting-state functional magnetic imaging data in order to shed light on these neural underpinnings of creative aspects. For improved sensitivity, we performed the study at ultra-high magnetic field (7 T). Seed regions were defined based on subcortical (ventral tegmental area/substantia nigra, nucleus caudatus) activation foci of a remote associates task (RAT). In addition, bilateral PCC was used as seed region to examine the default-mode network. Network strength across subjects was regressed against a battery of psychological variables related to creativity. Dopaminergic network variations turned out to be indicative for individual differences in creative traits. In this regard, the caudate network showed stronger connectivity in individuals with higher extraversion measures, while connectivity with the midbrain network was found increased with higher ideational behaviour and emotional stability.

Neural entrainment is associated with subjective groove and complexity for performed but not mechanical musical rhythms.

Both movement and neural activity in humans can be entrained by the regularities of an external stimulus, such as the beat of musical rhythms. Neural entrainment to auditory rhythms supports temporal perception, and is enhanced by selective attention and by hierarchical temporal structure imposed on rhythms. However, it is not known how neural entrainment to rhythms is related to the subjective experience of groove (the desire to move along with music or rhythm), the perception of a regular beat, the perception of complexity, and the experience of pleasure. In two experiments, we used musical rhythms (from Steve Reich's Clapping Music) to investigate whether rhythms that are performed by humans (with naturally variable timing) and rhythms that are mechanical (with precise timing), elicit differences in (1) neural entrainment, as measured by inter-trial phase coherence, and (2) subjective ratings of the complexity, preference, groove, and beat strength of rhythms. We also combined results from the two experiments to investigate relationships between neural entrainment and subjective perception of musical rhythms. We found that mechanical rhythms elicited a greater degree of neural entrainment than performed rhythms, likely due to the greater temporal precision in the stimulus, and the two types only elicited different ratings for some individual rhythms. Neural entrainment to performed rhythms, but not to mechanical ones, correlated with subjective desire to move and subjective complexity. These data, therefore, suggest multiple interacting influences on neural entrainment to rhythms, from low-level stimulus properties to high-level cognition and perception.

Ultra-high-field fMRI insights on insight: Neural correlates of the Aha!-moment.

Finding creative solutions to difficult problems is a fundamental aspect of human culture and a skill highly needed. However, the exact neural processes underlying creative problem solving remain unclear. Insightful problem solving tasks were shown to be a valid method for investigating one subcomponent of creativity: the Aha!-moment. Finding insightful solutions during a remote associates task (RAT) was found to elicit specific cortical activity changes. Considering the strong affective components of Aha!-moments, as manifested in the subjectively experienced feeling of relief following the sudden emergence of the solution of the problem without any conscious forewarning, we hypothesized the subcortical dopaminergic reward network to be critically engaged during Aha. To investigate those subcortical contributions to insight, we employed ultra-high-field 7 T fMRI during a German Version of the RAT. During this task, subjects were exposed to word triplets and instructed to find a solution word being associated with all the three given words. They were supposed to press a button as soon as they felt confident about their solution without further revision, allowing us to capture the exact event of Aha!-moment. Besides the finding on cortical involvement of the left anterior middle temporal gyrus (aMTG), here we showed for the first time robust subcortical activity changes related to insightful problem solving in the bilateral thalamus, hippocampus, and the dopaminergic midbrain comprising ventral tegmental area (VTA), nucleus accumbens (NAcc), and caudate nucleus. These results shed new light on the affective neural mechanisms underlying insightful problem solving.

That note sounds wrong! Age-related effects in processing of musical expectation.

Part of musical understanding and enjoyment stems from the ability to accurately predict what note (or one of a small set of notes) is likely to follow after hearing the first part of a melody. Selective violation of expectations can add to aesthetic response but radical or frequent violations are likely to be disliked or not comprehended. In this study we investigated whether a lifetime of exposure to music among untrained older adults would enhance their reaction to unexpected endings of unfamiliar melodies. Older and younger adults listened to melodies that had expected or unexpected ending notes, according to Western music theory. Ratings of goodness-of-fit were similar in the groups, as was ERP response to the note onset (N1). However, in later time windows (P200 and Late Positive Component), the amplitude of a response to unexpected and expected endings was both larger in older adults, corresponding to greater sensitivity, and more widespread in locus, consistent with a dedifferentiation pattern. Lateralization patterns also differed. We conclude that older adults refine their understanding of this important aspect of music throughout life, with the ability supported by changing patterns of neural activity.

High-learners present larger mid-frontal theta power and connectivity in response to incorrect performance feedback.

A crucial aspect of cognitive control and learning is the ability to integrate feedback, that is, to evaluate action outcomes and their deviations from the intended goals and to adjust behavior accordingly. However, how high-learners differ from low-learners in relation to feedback processing has not been characterized. Further, little is known about the underlying brain connectivity patterns during feedback processing. This study aimed to fill these gaps by analyzing electrical brain responses from healthy adult human participants while they performed a time estimation task with correct and incorrect feedback. As compared with low-learners, high-learners presented larger mid-frontal theta (4-8 Hz) oscillations and lower sensorimotor beta (17-24 Hz) oscillations in response to incorrect feedback. Further, high-learners showed larger theta connectivity from left central, associated with motor activity, to mid-frontal, associated with performance monitoring, immediately after feedback (0-0.3 s), followed by (from 0.3 to 0.6 s after feedback) a flux from mid-frontal to prefrontal, associated with executive functioning. We suggest that these results reflect two cognitive processes related to successful feedback processing: first, the obtained feedback is compared with the expected one, and second, the feedback history is updated based on this information. Our results also indicate that high- and low-learners differ not only on how they react to incorrect feedback, but also in relation to how their distant brain areas interact while processing both correct and incorrect feedback. This study demonstrates the neural underpinnings of individual differences in goal-directed adaptive behavior.

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.

Is income inequality linked to infectious disease prevalence? A hypothesis-generating study using tuberculosis.

We study the association between infectious disease incidence and income inequality. We hypothesize that random social mixing in an income-unequal society brings into contact a) susceptible and infected poor and b) the infected-poor and the susceptible-rich, raising infectious disease incidence. We analyzed publicly available, country-level panel data for a large cross-section of countries between 1995 and 2013 to examine whether countries with elevated levels of income inequality have higher rates of pulmonary Tuberculosis (TB) incidence per capita. A "negative control" using anemia and diabetes (both non-communicable diseases and hence impervious to the hypothesized mechanism) is also applied. We find that high levels of income inequality are positively associated with tuberculosis incidence. All else equal, countries with income-Gini coefficients 10% apart show a statistically significant 4% difference in tuberculosis incidence. Income inequality had a null effect on the negative controls. Our cross-country regression results suggest that income inequality may create conditions where TB spreads more easily, and policy action to reduce income inequities could directly contribute to a reduced TB burden.

Functional connectivity based machine learning approach for autism detection in young children using MEG signals.

Objective.Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD.Approach.We recorded resting-state magnetoencephalogram signals from thirty children with ASD (4-7 years) and thirty age and gender-matched typically developing (TD) children. We used a complex coherency-based functional connectivity analysis to understand the interactions between different brain regions of the neural system. The work characterizes the large-scale neural activity at different brain oscillations using functional connectivity analysis and assesses the classification performance of coherence-based (COH) measures for autism detection in young children. A comparative study has also been carried out on COH-based connectivity networks both region-wise and sensor-wise to understand frequency-band-specific connectivity patterns and their connections with autism symptomatology. We used artificial neural network (ANN) and support vector machine (SVM) classifiers in the machine learning framework with a five-fold CV technique.Main results.To classify ASD from TD children, the COH connectivity feature yields the highest classification accuracy of 91.66% in the high gamma (50-100 Hz) frequency band. In region-wise connectivity analysis, the second highest performance is in the delta band (1-4 Hz) after the gamma band. Combining the delta and gamma band features, we achieved a classification accuracy of 95.03% and 93.33% in the ANN and SVM classifiers, respectively. Using classification performance metrics and further statistical analysis, we show that ASD children demonstrate significant hyperconnectivity.Significance.Our findings support the weak central coherency theory in autism detection. Further, despite its lower complexity, we show that region-wise COH analysis outperforms the sensor-wise connectivity analysis. Altogether, these results demonstrate the functional brain connectivity patterns as an appropriate biomarker of autism in young children.

A Fusion-Based Machine Learning Approach for Autism Detection in Young Children Using Magnetoencephalography Signals.

In this study, we aimed to find biomarkers of autism in young children. We recorded magnetoencephalography (MEG) in thirty children (4-7 years) with autism and thirty age, gender-matched controls while they were watching cartoons. We focused on characterizing neural oscillations by amplitude (power spectral density, PSD) and phase (preferred phase angle, PPA). Machine learning based classifier showed a higher classification accuracy (88%) for PPA features than PSD features (82%). Further, by a novel fusion method combining PSD and PPA features, we achieved an average classification accuracy of 94% and 98% for feature-level and score-level fusion, respectively. These findings reveal discriminatory patterns of neural oscillations of autism in young children and provide novel insight into autism pathophysiology.

Cortical signatures of visual body representation develop in human infancy.

Human infants cannot report their experiences, limiting what we can learn about their bodily awareness. However, visual cortical responses to the body, linked to visual awareness and selective attention in adults, can be easily measured in infants and provide a promising marker of bodily awareness in early life. We presented 4- and 8-month-old infants with a flickering (7.5 Hz) video of a hand being stroked and recorded steady-state visual evoked potentials (SSVEPs). In half of the trials, the infants also received tactile stroking synchronously with visual stroking. The 8-month-old, but not the 4-month-old infants, showed a significant enhancement of SSVEP responses when they received tactile stimulation concurrent with the visually observed stroking. Follow-up experiments showed that this enhancement did not occur when the visual hand was presented in an incompatible posture with the infant's own body or when the visual stimulus was a body-irrelevant video. Our findings provide a novel insight into the development of bodily self-awareness in the first year of life.

Autism Detection in Children using Common Spatial Patterns of MEG Signals.

Autism exhibits a wide range of developmental disabilities and is associated with aberrant anatomical and functional neural patterns. To detect autism in young children (4-7 years) in an automatic and non-invasive fashion, we have recorded magnetoencephalogram (MEG) signals from 30 autistic and 30 age-matched typically developing (TD) children. We have used a machine learning classification framework with common spatial pattern (CSP)-based logarithmic band power (LBP) features. When comparing the LBP feature to the conventional logarithmic variance (LV) spatial pattern, CSP + LBP (92.77%) has performed better than CSP + LV (90.66%) in the 1-100 Hz frequency range for distinguishing autistic children from TD children. In frequency band-wise analysis using our proposed method, the high gamma frequency band (50-100 Hz) has shown the highest classification accuracy (97.14%). Our findings reveal that the occipital lobe exhibits the most distinct spatial pattern in autistic children over the whole frequency range. This study shows that spatial brain activation patterns can be utilized as potential biomarkers of autism in young children. The improved performance signifies the clinical relevance of the work for autism detection using MEG signals.

Flow in contemporary musicians: Individual differences in flow proneness, anxiety, and emotional intelligence.

Flow is a highly focussed state of consciousness that is rewarding, fulfilling, and sought after by many, especially musicians. It is characterised by exceptional levels of concentration, loss of self-consciousness, and competent control over one's actions. Several personality and non-cognitive traits have been positively linked with flow proneness, such as emotional intelligence; however, anxiety is thought to be the antithesis of flow, yet the relationship between trait anxiety and flow proneness in musicians is not adequately characterised. This study investigated the individual differences in flow proneness in contemporary musicians (N = 664), focusing on the interaction of trait anxiety and emotional intelligence. We identified a significant negative correlation between trait anxiety and flow. Emotional intelligence was positively correlated with flow proneness and negatively with trait anxiety. Moderation analysis revealed a difference in the relationship between trait anxiety and flow depending on the level of emotional intelligence; there was no correlation in those with low emotional intelligence, whereas a strong negative relationship was found in those with high emotional intelligence. Finally, hierarchical regression indicated that musical training was the most substantial predictor of all the tested variables and that trait anxiety did not add any predictive power on top of the known predictors. Altogether, this study provided new insights into the possible disruption of flow proneness linked to high anxiety and low emotional intelligence in contemporary musicians.

ASMR amplifies low frequency and reduces high frequency oscillations.

Autonomous sensory meridian response (ASMR) describes an atypical multisensory experience of calming, tingling sensations in response to a specific subset of social audiovisual triggers. To date, the electrophysiological (EEG) correlates of ASMR remain largely unexplored. Here we sought to provide source-level signatures of oscillatory changes induced by this phenomenon and investigate potential decay effects-oscillatory changes in the absence of self-reported ASMR. We recorded brain activity using EEG as participants watched ASMR-inducing videos and self-reported changes in their state: no change (Baseline); enhanced relaxation (Relaxed); and ASMR sensations (ASMR). Statistical tests in the sensor-space were used to inform contrasts in the source-space, executed with beamformer reconstruction. ASMR modulated oscillatory power by decreasing high gamma (52-80 Hz) relative to Relaxed and by increasing alpha (8-13 Hz) and decreasing delta (1-4 Hz) relative to Baseline. At the source level, ASMR increased power in the low-mid frequency ranges (8-18 Hz) and decreased power in high frequency (21-80 Hz). ASMR decay effects reduced gamma (30-80 Hz) and in the source-space reduced high-beta/gamma power (21-80 Hz). The temporal profile of ASMR modulations in high-frequency power later shifts to lower frequencies (1-8 Hz), except for an enhanced alpha, which persists for up to 45 min post self-reported ASMR. Crucially, these results provide the first evidence that the cortical sources of ASMR tingling sensations may arise from decreases in higher frequency oscillations and that ASMR may induce a sustained relaxation state.