97 research outputs found
Relativistic three-body bound states and the reduction from four to three dimensions
Beginning with an effective field theory based upon meson exchange, the
Bethe-Salpeter equation for the three-particle propagator (six-point function)
is obtained. Using the one-boson-exchange form of the kernel, this equation is
then analyzed using time-ordered perturbation theory, and a three-dimensional
equation for the propagator is developed. The propagator consists of a
pre-factor in which the relative energies are fixed by the initial state of the
particles, an intermediate part in which only global propagation of the
particles occurs, and a post-factor in which relative energies are fixed by the
final state of the particles. The pre- and post-factors are necessary in order
to account for the transition from states where particles are off their mass
shell to states described by the global propagator with all of the particle
energies on shell. The pole structure of the intermediate part of the
propagator is used to determine the equation for the three-body bound state: a
Schr{\"o}dinger-like relativistic equation with a single, global Green's
function. The role of the pre- and post-factors in the relativistic dynamics is
to incorporate the poles of the breakup channels in the initial and final
states. The derivation of this equation by integrating over the relative times
rather than via a constraint on relative momenta allows the inclusion of
retardation and dynamical boost corrections without introducing unphysical
singularities.Comment: REVTeX, 21 pages, 4 figures, epsf.st
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AGL StimSelect: Software for automated selection of stimuli for artificial grammar learning
Artificial Grammar Learning (AGL) is an experimental paradigm that has been used extensively in cognitive research for many years to study implicit learning, associative learning, and generalization based either on similarity or rules. Without computer assistance it is virtually impossible to generate appropriate grammatical training stimuli along with grammatical or non-grammatical test stimuli that control relevant psychological variables. We present the first flexible, fully automated software for selecting AGL stimuli. The software allows users to specify a grammar of interest, and to manipulate characteristics of training and test sequences, and their relationship to each other. The user thus has direct control over stimulus features that may influence learning and generalization in AGL tasks. The software enables researchers to develop AGL designs that would not be feasible without automatic stimulus selection. It is implemented in Matlab
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