Cara L. Buck; Jonathan D. Cohen; Field, Brent; Daniel Kahneman; Samuel M. McClure; Leigh E. Nystrom
Studies of subjective well-being have conventionally relied upon self-report, which directs subjects’ attention to their emotional experiences. This method presumes that attention itself does not influence emotional processes, which could bias sampling. We tested whether attention influences experienced utility (the moment-by-moment experience of pleasure) by using functional magnetic resonance imaging (fMRI) to measure the activity of brain systems thought to represent hedonic value while manipulating attentional load. Subjects received appetitive or aversive solutions orally while alternatively executing a low or high attentional load task. Brain regions associated with hedonic processing, including the ventral striatum, showed a response to both juice and quinine. This response decreased during the high-load task relative to the low-load task. Thus, attentional allocation may influence experienced utility by modulating (either directly or indirectly) the activity of brain mechanisms thought to represent hedonic value.
We implement unsupervised machine learning techniques to identify characteristic evolution patterns and associated parameter regimes in edge localized mode (ELM) events observed on the National Spherical Torus Experiment. Multi-channel, localized measurements spanning the pedestal region capture the complex evolution patterns of ELM events on Alfven timescales. Some ELM events are active for less than 100~microsec, but others persist for up to 1~ms. Also, some ELM events exhibit a single dominant perturbation, but others are oscillatory. Clustering calculations with time-series similarity metrics indicate the ELM database contains at least two and possibly three groups of ELMs with similar evolution patterns. The identified ELM groups trigger similar stored energy loss, but the groups occupy distinct parameter regimes for ELM-relevant quantities like plasma current, triangularity, and pedestal height. Notably, the pedestal electron pressure gradient is not an effective parameter for distinguishing the ELM groups, but the ELM groups segregate in terms of electron density gradient and electron temperature gradient. The ELM evolution patterns and corresponding parameter regimes can shape the formulation or validation of nonlinear ELM models. Finally, the techniques and results demonstrate an application of unsupervised machine learning at a data-rich fusion facility.
Stotler, D.; F. Scotti; R.E. Bell; A. Diallo; B.P. LeBlanc; M. Podesta; A.L. Roquemore; P.W. Ross
Atomic and molecular density data in the outer midplane of NSTX [Ono et al., Nucl. Fusion 40, 557 (2000)] are inferred from tangential camera data via a forward modeling procedure using the DEGAS 2 Monte Carlo neutral transport code. The observed Balmer-b light emission data from 17 shots during the 2010 NSTX campaign display no obvious trends with discharge parameters such as the divertor Balmer-a emission level or edge deuterium ion density. Simulations of 12 time slices in 7 of these discharges produce molecular densities near the vacuum vessel wall of 2–8 10^17 m3 and atomic densities ranging from 1 to 7 10^16 m3; neither has a clear correlation with other parameters. Validation of the technique, begun in an earlier publication, is continued with an assessment of the sensitivity of the simulated camera image and neutral densities to uncertainties in the data input to the model. The simulated camera image is sensitive to the plasma profiles and virtually nothing else. The neutral densities at the vessel wall depend most strongly on the spatial distribution of the source; simulations with a localized neutral source yield densities within a factor of two of the baseline, uniform source, case. The uncertainties in the neutral densities associated with other model inputs and assumptions are 50%.