Group C: White Paper Draft
“Multivariate longitudinal approach to the macro scale neuroscience of expertise”
March 12, 2015
Conceptual Framework:
We propose to design a comprehensive macro-scale platform for measuring changes associated with learning in expert populations. In our approach we will embed a longitudinal, multi-layer, multiscale measurement approach that incorporates neurophysiological, behavioral, and social measurements in specific cohorts of trainees. In this manner we will acquire multivariate measurements on select populations of domain-specific trainees who are participating in advanced skill acquisition programs. We will target multiple training cohorts in three areas of domain-specific learning for deliberative, timesensitive, and sensiomotor skills. From these comprehensive assessment programs we seek to understand the underlying/hidden links that either predict the optimal trajectory of learning, or account for the eventual state of the learner. Using models developed from data acquired within groups, we will then perform model comparisons on subsets of elite, virtuosos to understand the generalization, specificity, and communalitites between expert populations.
Using specific neurophysiological assessments, discussed below, we will seek to determine how more accomplished experts differ from their peers on
 State – neurocognitive state (attention); autonomic state; circadian, rest/recovery
 Process – difference in processing stream; information processing heuristics
 Content – information representation in the brain
Methodology:
 Naturalistic – measurements amenable to the context under which performance occurs.
 Static – at intervals we will collect data in the lab
 Targeted collection of data that is informed by analysis
 Multimodal and Multiscale (possibly developing specialized equipment) o Neural –
 EEG – used in the field during applied activities
 fNIRS – used in field during applied activities
 MRI/fMRI – collected at regular intervals
 BOLD fMRI o Standard neuropsych tasks in the scanner – WM, Attention, emotion o Specialized – tailored to the population of interest
 DTI – to track white matter changes
 VBM – to track changes in cortical density
 Neuropharmacology
 cortisol and testosterone o Performance – Objective expertise measurements
 On-field performance stats (competition and training)
 Achievement progression
 Accelerometer/PSI for TBI/trauma detection

 Subjective evaluation of experts (coaches) and self-report (reflective) o Mobile Tracking
 Mobile health techniques to track participant (and coaches) movement and interactions.
 Video – using video capture, in some situations we will be able to monitor
‘performance situations” as well as teaching interactions
 Sleep – using the mobile tracking device we will monitor sleep cycles o Other
 Structure of practice – self-report logs
 Demographics (DOB, parents, SES)
 Possible Populations --- the following is a list of possible populations that we could target. The optimal targets will have tractable inflection points (start and end together) allowing us to track interactions. o Juilliard / Musicians o Professional/Olympic / Collegiate athletes o Modern Dancers o Spelling Bee o Math Olympians o Chess academies o Radiologists / Surgeons o Business / Law Schools o Special Ops o Pilots (Fighter/Helli) o VGP contests o Marksmanship
 Analyses – our aim is to build models that determine if there are any underlying/hidden links that either predict the optimal trajectory of learning, or account for the eventual state of the learner.
Among the approaches we will use are ... o Multiscale multimodal data integration – i.e. multivariate analyses
 Deep learning and Neural Networks o Network-modeling and graph-theoretic modeling
 Community detection analyses o Non-stationary modeling
 Drift Diffusion models:
 Hidden state estimation and parameter estimation o Comparison of distributions o Model selection (BIC, AIC)
Hypotheses:
 There are certain neurocognitive processes that occur in synchrony with transition points in learning.

 Using information acquired in the study we will be able to beat the “eye test” (improve selection above current state-of-the-art)... “computer assisted selection”
 We can identify commonalities and differences in the neuro-cognitive substrates of expertise
With this information we can... in the future:
 We can accelerate the training progression
 We can extend the shelf life of the experts