7 research outputs found
Comparison of Novice and Experienced Drivers Using the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving
We compared the eye movements of novice drivers and experienced drivers while they drove a simulated driving scenario that included a number of intersections interspersed with stretches of straight road. The intersections included non-hazard events. Cassavaugh, Bos, McDonald, Gunaratne, & Backs (2013) attempted to model attention allocation of experienced drivers using the SEEV model. Here we compared two SEEV model fits between those experienced drivers and a sample of novice drivers. The first was a simplified model and the second was a more complex intersection model. The observed eye movement data was found to be a good fit to the simplified model for both experienced (R2 = 0.88) and novice drivers (R2 = 0.30). Like the previous results of the intersection model for the experienced drivers, the fit of the observed eye movement data to the intersection model for novice drivers was poor, and was no better than fitting the data to a randomized SEEV model. We concluded based on the simplified SEEV model, fixation count and fixation variance that experienced drivers were found to be more efficient at distributing their visual search compared to novice drivers
Comparison of Novice and Experienced Drivers Using the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving
We compared the eye movements of novice drivers and experienced drivers while they drove a simulated driving scenario that included a number of intersections interspersed with stretches of straight road. The intersections included non-hazard events. Cassavaugh, Bos, McDonald, Gunaratne, & Backs (2013) attempted to model attention allocation of experienced drivers using the SEEV model. Here we compared two SEEV model fits between those experienced drivers and a sample of novice drivers. The first was a simplified model and the second was a more complex intersection model. The observed eye movement data was found to be a good fit to the simplified model for both experienced (R2 = 0.88) and novice drivers (R2 = 0.30). Like the previous results of the intersection model for the experienced drivers, the fit of the observed eye movement data to the intersection model for novice drivers was poor, and was no better than fitting the data to a randomized SEEV model. We concluded based on the simplified SEEV model, fixation count and fixation variance that experienced drivers were found to be more efficient at distributing their visual search compared to novice drivers
Recovering simulated planet and disk signals using SCALES aperture masking
The Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy
(SCALES) instrument is a lenslet-based integral field spectrograph that will
operate at 2 to 5 microns, imaging and characterizing colder (and thus older)
planets than current high-contrast instruments. Its spatial resolution for
distant science targets and/or close-in disks and companions could be improved
via interferometric techniques such as sparse aperture masking. We introduce a
nascent Python package, NRM-artist, that we use to design several SCALES masks
to be non-redundant and to have uniform coverage in Fourier space. We generate
high-fidelity mock SCALES data using the scalessim package for SCALES' low
spectral resolution modes across its 2 to 5 micron bandpass. We include
realistic noise from astrophysical and instrument sources, including Keck
adaptive optics and Poisson noise. We inject planet and disk signals into the
mock datasets and subsequently recover them to test the performance of SCALES
sparse aperture masking and to determine the sensitivity of various mask
designs to different science signals
Simulating medium-spectral-resolution exoplanet characterization with SCALES angular/reference differential imaging
SCALES (Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy) is
a 2 - 5 micron high-contrast lenslet-based integral field spectrograph (IFS)
designed to characterize exoplanets and their atmospheres. The SCALES
medium-spectral-resolution mode uses a lenslet subarray with a 0.34 x 0.36
arcsecond field of view which allows for exoplanet characterization at
increased spectral resolution. We explore the sensitivity limitations of this
mode by simulating planet detections in the presence of realistic noise
sources. We use the SCALES simulator scalessim to generate high-fidelity mock
observations of planets that include speckle noise from their host stars, as
well as other atmospheric and instrumental noise effects. We employ both
angular and reference differential imaging as methods of disentangling speckle
noise from the injected planet signals. These simulations allow us to assess
the feasibility of speckle deconvolution for SCALES medium resolution data, and
to test whether one approach outperforms another based on planet angular
separations and contrasts