Creative flow as optimized processing: Evidence from brain oscillations during jazz improvisations by expert and non-expert musicians.

Using a creative production task, jazz improvisation, we tested alternative hypotheses about the flow experience: (A) that it is a state of domain-specific processing optimized by experience and characterized by minimal interference from task-negative default-mode network (DMN) activity versus (B) that it recruits domain-general task-positive DMN activity supervised by the fronto-parietal control network (FPCN) to support ideation. We recorded jazz guitarists' electroencephalograms (EEGs) while they improvised to provided chord sequences. Their flow-states were measured with the Core Flow State Scale. Flow-related neural sources were reconstructed using SPM12. Over all musicians, high-flow (relative to low-flow) improvisations were associated with transient hypofrontality. High-experience musicians' high-flow improvisations showed reduced activity in posterior DMN nodes. Low-experience musicians showed no flow-related DMN or FPCN modulation. High-experience musicians also showed modality-specific left-hemisphere flow-related activity while low-experience musicians showed modality-specific right-hemisphere flow-related deactivations. These results are consistent with the idea that creative flow represents optimized domain-specific processing enabled by extensive practice paired with reduced cognitive control.

Classification model with weighted regularization to improve the reproducibility of neuroimaging signature selection.

Machine learning (ML) has been extensively applied in brain imaging studies to aid the diagnosis of psychiatric disorders and the selection of potential biomarkers. Due to the high dimensionality of imaging data and heterogeneous subtypes of psychiatric disorders, the reproducibility of ML results in brain imaging studies has drawn increasing attention. The reproducibility in brain imaging has been primarily examined in terms of prediction accuracy. However, achieving high prediction accuracy and discovering relevant features are two separate but related goals. An important yet under-investigated problem is the reproducibility of feature selection in brain imaging studies. We propose a new metric to quantify the reproducibility of neuroimaging feature selection via bootstrapping. We estimate the reproducibility index (R-index) for each feature as the reciprocal coefficient of variation of absolute mean difference across a larger number of bootstrap samples. We then integrate the R-index in regularized classification models as penalty weight. Reproducible features with a larger R-index are assigned smaller penalty weights and thus are more likely to be selected by our proposed models. Both simulated and multimodal neuroimaging data are used to examine the performance of our proposed models. Results show that our proposed R-index models are effective in separating informative features from noise features. Additionally, the proposed models yield similar or higher prediction accuracy than the standard regularized classification models while further reducing coefficient estimation error. Improvements achieved by the proposed models are essential to advance our understanding of the selected brain imaging features as well as their associations with psychiatric disorders.

Comparing ecological momentary assessment to sensor-based approaches in predicting dietary lapse.

Ecological momentary assessment (EMA; brief self-report surveys) of dietary lapse risk factors (e.g., cravings) has shown promise in predicting and preventing dietary lapse (nonadherence to a dietary prescription), which can improve weight loss interventions. Passive sensors also can measure lapse risk factors and may offer advantages over EMA (e.g., objective, automatic, semicontinuous data collection), but currently can measure only a few lapse predictors, a notable limitation. This study preliminarily compared the burden and accuracy of commercially available sensors versus established EMA in lapse prediction. N = 23 adults with overweight/obesity completed a 6-week commercial app-based weight loss program. Participants wore a Fitbit, enabled GPS tracking, completed EMA, and reported on EMA and sensor burden poststudy via a 5-point Likert scale. Sensed risk factors were physical activity and sleep (accelerometer), geolocation (GPS), and time, from which 233 features (measurable characteristics of sensor signals) were extracted. EMA measured 19 risk factors, lapse, and categorized GPS into meaningful geolocations. Two supervised binary classification models (LASSO) were created: the sensor model predicted lapse with 63% sensitivity (true prediction rate of lapse) and 60% specificity (true prediction rate of non-lapse) and EMA model with 59% sensitivity and 72% specificity. EMA model accuracy was higher, but self-reported EMA burden (M = 2.96, SD = 1.02) also was higher (M = 1.50, SD = 0.94). EMA model accuracy was superior, but EMA burden was higher than sensor burden. Findings highlight the promise of sensors in contributing to lapse prediction, and future research may use EMA, sensors, or both depending on prioritization of accuracy versus participant burden.

Dual-process contributions to creativity in jazz improvisations: An SPM-EEG study.

Conflicting theories identify creativity either with frontal-lobe mediated (Type-2) executive control processes or (Type-1) associative processes that are disinhibited when executive control is relaxed. Musical (jazz) improvisation is an ecologically valid test-case to distinguish between these views because relatively slow, deliberate, executive-control processes should not dominate during high-quality, real-time improvisation. In the present study, jazz guitarists (n â€‹= â€‹32) improvised to novel chord sequences while 64-channel EEGs were recorded. Jazz experts rated each improvisation for creativity, technical proficiency and aesthetic appeal. Surface-Laplacian-transformed EEGs recorded during the performances were analyzed in the scalp-frequency domain using SPM12. Significant clusters of high-frequency (beta-band and gamma-band) activity were observed when higher-quality versus lower-quality improvisations were compared. Higher-quality improvisations were associated with predominantly posterior left-hemisphere activity; lower-quality improvisations were associated with right temporo-parietal and fronto-polar activity. However, after statistically controlling for experience (defined as the number of public performances previously given), performance quality was a function of right-hemisphere, largely right-frontal, activity. These results support the notion that superior creative production is associated with hypofrontality and right-hemisphere activity thereby supporting a dual-process model of creativity in which experience influences the balance between executive and associative processes. This study also highlights the idea that the functional neuroanatomy of creative production depends on whether creativity is defined in terms of the quality of products or the type of cognitive processes involved.

Resting-state brain oscillations predict trait-like cognitive styles.

Anecdotal reports suggest the existence of individual differences in peoples' cognitive styles for solving problems, in particular, the tendency to rely on insight (the "aha" phenomenon) versus deliberate analytical thought. We hypothesized that such stable individual differences exist and are associated with trait-like individual differences in resting-state brain activity. We tested this idea by recording participants' resting-state electroencephalograms (RS-EEGs) on 4 occasions over approximately 7 weeks and then tasking them with solving anagrams and compound remote associates problems that are solvable by either strategy. We found that peoples' tendency to solve problems consistently by insight or by analysis spans both tasks and time. Moreover, we discovered trait-like individual differences in the balance between frontal and posterior resting-state brain activity and in temporal-lobe hemispheric asymmetries that predict, at least weeks in advance, the tendency to solve by insight versus analysis. The discovery of an insight-analytic dimension of cognitive style and its neural basis in resting state brain activity suggests new avenues for the development of neuroscience-based methods for intellectual, educational, and vocational assessment.

Redefining neuromarketing as an integrated science of influence.

Multiple transformative forces target marketing, many of which derive from new technologies that allow us to sample thinking in real time (i.e., brain imaging), or to look at large aggregations of decisions (i.e., big data). There has been an inclination to refer to the intersection of these technologies with the general topic of marketing as "neuromarketing". There has not been a serious effort to frame neuromarketing, which is the goal of this paper. Neuromarketing can be compared to neuroeconomics, wherein neuroeconomics is generally focused on how individuals make "choices", and represent distributions of choices. Neuromarketing, in contrast, focuses on how a distribution of choices can be shifted or "influenced", which can occur at multiple "scales" of behavior (e.g., individual, group, or market/society). Given influence can affect choice through many cognitive modalities, and not just that of valuation of choice options, a science of influence also implies a need to develop a model of cognitive function integrating attention, memory, and reward/aversion function. The paper concludes with a brief description of three domains of neuromarketing application for studying influence, and their caveats.

Elastic net-based framework for imaging mass spectrometry data biomarker selection and classification.

Imaging mass spectrometry (IMS) shows great potential for the rapid mapping of protein localization and for detecting of sizeable differences in protein expression. However, data processing remains challenging due to the difficulty of analyzing high dimensionality, the fact that the number of predictors is significantly larger than the number of observations, and the need to consider both spectral and spatial information in order to represent the advantage of IMS technology. Ideally one would like to efficiently analyze all acquired data to find trace features based on both spectral and spatial patterns. Therefore, biomarker selection from IMS data is a problem of global optimization. A recently developed regularization and variable selection method,elastic net (EN), produces a sparse model with admirable prediction accuracy and can be an effective tool for IMS data processing. In this paper, we incorporate a spatial penalty term into the EN model and develop anew tool for IMS data biomarker selection and classification. A comprehensive IMS data processing software package, called EN4IMS, is also presented. The results of applying our method to both simulated and real data show that the EN4IMS algorithm works efficiently and effectively for IMS data processing: producing a more precise listing of selected peaks, helping confirmation of new potential biomarkers discovery, and providing more accurate classification results.