Attentional Tracking of Multiple Vehicles in a Highway Driving Scenario

In this paper we introduce a \u27vehicle tracking\u27 task, which tests the ability of a driver to track the location of multiple vehicles on the roadway. Based on the \u27multiple object tracking\u27 task (Pylyshyn & Storm, 1988), the vehicle tracking task presents the driver with an array of identical vehicles immediately in front of the subject vehicle. The task consists of three distinct stages: encoding, during which the target vehicles are indicated to the driver; tracking, during which all vehicles change lanes in a random order; and report, during which the participant indicates the final location of the target vehicles. Using this methodology, we test the accuracy with which university-aged drivers can track multiple vehicles in a 3-lane highway driving scenario. Our particular interest in this paper is how the ability to attend to multiple vehicles changes as task load increases

The muscarinic antagonists scopolamine and atropine are competitive antagonists at 5-HT3 receptors.

Scopolamine is a high affinity muscarinic antagonist that is used for the prevention of post-operative nausea and vomiting. 5-HT3 receptor antagonists are used for the same purpose and are structurally related to scopolamine. To examine whether 5-HT3 receptors are affected by scopolamine we examined the effects of this drug on the electrophysiological and ligand binding properties of 5-HT3A receptors expressed in Xenopus oocytes and HEK293 cells, respectively. 5-HT3 receptor-responses were reversibly inhibited by scopolamine with an IC50 of 2.09 μM. Competitive antagonism was shown by Schild plot (pA2 = 5.02) and by competition with the 5-HT3 receptor antagonists [(3)H]granisetron (Ki = 6.76 μM) and G-FL (Ki = 4.90 μM). The related molecule, atropine, similarly inhibited 5-HT evoked responses in oocytes with an IC50 of 1.74 μM, and competed with G-FL with a Ki of 7.94 μM. The reverse experiment revealed that granisetron also competitively bound to muscarinic receptors (Ki = 6.5 μM). In behavioural studies scopolamine is used to block muscarinic receptors and induce a cognitive deficit, and centrally administered concentrations can exceed the IC50 values found here. It is therefore possible that 5-HT3 receptors are also inhibited. Studies that utilise higher concentrations of scopolamine should be mindful of these potential off-target effects.Our thanks are given to John Peters (University of Dundee) for the 5-HT3A subunit. ML thanks the Swiss National Science Foundation for financial support (SNSF- professorship PP00P2_123536 and PP00P2_146321). AJT thanks the British Heart Foundation for financial support (PG/13/39/30293).This is final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.neuropharm.2016.04.02

2021-4 Innis Lecture: Return on Student Loans in Canada

This paper uses new administrative data with detailed borrower information and lengthy repayment histories from the Canada Student Loans Program (CSLP) to measure rates of return on undergraduate student loans. We document substantial heterogeneity in returns based on information available at the time loans were disbursed, including province of residence, field of study, and institution of attendance. Field of study is a particularly important determinant of rates of return, explaining 22% of the variation in predicted returns across borrowers. We explore the implications of this variation for CSLP cross-subsidization across borrowers and potential risk-based loan limits. Given the variation in ex ante predicted returns across borrowers, using all available information at the time of loan disbursement, we study the implications of potential cream-skimming of high-return borrowers by private lenders

The Effects of Task Load and Vehicle Heterogeneity on Performance in the Multiple-Vehicle Tracking Task

When crossing traffic at busy intersections, drivers must keep track of the changing positions of cyclists, pedestrians and other vehicles to avoid collision. Multiple-object tracking is the ability to monitor the positions of a number of selected moving objects (targets) among others (distractors) in a complex scene. Most young adults can track 3-5 items at once but older adults cannot track as many, a finding that may partially explain older drivers’ increased risk at intersections. Because tracking represents an important component of driving, a variant of the multiple-object tracking task called multiple-vehicle was created to measure tracking performance in a driving simulator. However, it is unclear whether tracking while driving works the same as tracking carried out on its own. Laboratory studies suggest that tracking improves when the moving items are heterogeneous, and on the road, it is far more typical that vehicles differ from one another rather than being all the same. Drivers were given the task of tracking the positions of 4 vehicles in a field of 8 on a highway, and the effects of task load (tracking alone, tracking while driving) on tracking performance were measured as a function of whether the target and distractor vehicles were homogeneous. Steering and headway maintenance variability were also assessed. The results indicated that heterogeneity only enabled better tracking when drivers were tracking in isolation. Heterogeneity had no significant effect on tracking when participants were tracking while driving though it did significantly reduce their steering variability

Extending BEAMS to incorporate correlated systematic uncertainties

New supernova surveys such as the Dark Energy Survey, Pan-STARRS and the LSST will produce an unprecedented number of photometric supernova candidates, most with no spectroscopic data. Avoiding biases in cosmological parameters due to the resulting inevitable contamination from non-Ia supernovae can be achieved with the BEAMS formalism, allowing for fully photometric supernova cosmology studies. Here we extend BEAMS to deal with the case in which the supernovae are correlated by systematic uncertainties. The analytical form of the full BEAMS posterior requires evaluating 2^N terms, where N is the number of supernova candidates. This `exponential catastrophe' is computationally unfeasible even for N of order 100. We circumvent the exponential catastrophe by marginalising numerically instead of analytically over the possible supernova types: we augment the cosmological parameters with nuisance parameters describing the covariance matrix and the types of all the supernovae, \tau_i, that we include in our MCMC analysis. We show that this method deals well even with large, unknown systematic uncertainties without a major increase in computational time, whereas ignoring the correlations can lead to significant biases and incorrect credible contours. We then compare the numerical marginalisation technique with a perturbative expansion of the posterior based on the insight that future surveys will have exquisite light curves and hence the probability that a given candidate is a Type Ia will be close to unity or zero, for most objects. Although this perturbative approach changes computation of the posterior from a 2^N problem into an N^2 or N^3 one, we show that it leads to biases in general through a small number of misclassifications, implying that numerical marginalisation is superior.Comment: Resubmitted under married name Lochner (formally Knights). Version 3: major changes, including a large scale analysis with thousands of MCMC chains. Matches version published in JCAP. 23 pages, 8 figure

Towards the Future of Supernova Cosmology

For future surveys, spectroscopic follow-up for all supernovae will be extremely difficult. However, one can use light curve fitters, to obtain the probability that an object is a Type Ia. One may consider applying a probability cut to the data, but we show that the resulting non-Ia contamination can lead to biases in the estimation of cosmological parameters. A different method, which allows the use of the full dataset and results in unbiased cosmological parameter estimation, is Bayesian Estimation Applied to Multiple Species (BEAMS). BEAMS is a Bayesian approach to the problem which includes the uncertainty in the types in the evaluation of the posterior. Here we outline the theory of BEAMS and demonstrate its effectiveness using both simulated datasets and SDSS-II data. We also show that it is possible to use BEAMS if the data are correlated, by introducing a numerical marginalisation over the types of the objects. This is largely a pedagogical introduction to BEAMS with references to the main BEAMS papers.Comment: Replaced under married name Lochner (formally Knights). 3 pages, 2 figures. To appear in the Proceedings of 13th Marcel Grossmann Meeting (MG13), Stockholm, Sweden, 1-7 July 201

Auditory target identification in a visual search task

Previous research has shown that simultaneous auditory identification of the target in a visual search task can lead to more efficient (i. e. ?flatter?) search functions (Spivey et al. , 2001). Experiment 1 replicates the paradigm of Spivey et al. , providing subjects with auditory identification of the search target either before (Consecutive condition) or simultaneously with (Concurrent condition) the onset of the search task. RT x Set Size slopes in the Concurrent condition are approximately 1/2 as steep as those in the Consecutive condition. Experiment 2 employs a distractor ratio manipulation to test the notion that subjects are using the simultaneous auditory target identification to ?parse? the search set by colour, thus reducing the search set by 1/2. The results of Experiment 2 do not support the notion that subjects are parsing the search set by colour. Experiment 3 addresses the same question as Experiment 2, but obtains the desired distractor ratios by holding the amount of relevantly-coloured items constant while letting overall set size vary. Unlike Experiment 2, Experiment 3 supports the interpretation that subjects are using the auditory target identification to parse the search set

Manipulating Drive Characteristics to Study the Effects of Mental Load on Older and Younger Drivers

A driving simulator was used to assess performance in younger and older drivers (M ages 18 and 71 years). The impacts of three challenges were assessed: visibility (clear day, fog), traffic density (low, high) and wayfinding (no challenge, drivers challenged to use signs and landmarks to find their destination). Performance was measured in terms of hazard RT, collisions, wayfinding errors (missed or extra turns), and driving speed. The challenge manipulations produced interactive effects and age was a factor in some of these interactions. Older drivers missed more turns in wayfinding but overall they performed as well or better than younger drivers and reduced their speed more to driving challenges