1,465 research outputs found

    Learning motion: Human vs. optimal Bayesian learner

    Get PDF
    We used the optimal perceptual learning paradigm (Eckstein, Abbey, Pham, & Shimozaki, 2004) to investigate the dynamics of human rapid learning processes in motion discrimination tasks and compare it to an optimal Bayesian learner. This paradigm consists of blocks of few trials defined by a set of target attributes, and it has been shown its ability to detect learning effects appearing as soon as after the first trial. In the present task a sequence consisting of four patches containing random-dot patterns is presented at four separate locations equidistant from a fixation point. On each trial, the random dots in three patches moved with a mean speed and the fourth, target patch, could move either with slower or faster mean speed. Observers' task was to indicate what speed, faster or slower, was present in the display. The mean direction of the target patch was kept invariant along a block of trials. Observers learned the target relevant motion direction through indirect feedback, leading to an improvement in speed identification performance ranging from 15% to 30% which is greater than previously studied contrast defined targets and faces. However, comparison to an ideal learner revealed incomplete or partial learning for the motion task which was lower than previously measured for contrast defined targets and faces. A sub-optimal model that included inefficiencies in the updating of motion direction weights due to memory effects could account for the human learning. Finally, the similarity of the rapid learning effect observed here for motion perception with that found for contrast defined targets for localization and identification tasks could be suggesting a general strategy for learning in the human visual system and some common limitations such as memory.Fil: Trenti, Edgardo Javier. Universidad Nacional de Salta. Facultad de Ciencias Exactas; ArgentinaFil: Barraza, Jose Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Investigación en Luz, Ambiente y Visión. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Instituto de Investigación en Luz, Ambiente y Visión; ArgentinaFil: Eckstein, Miguel P.. University of California; Estados Unido

    Object Detection Through Exploration With A Foveated Visual Field

    Get PDF
    We present a foveated object detector (FOD) as a biologically-inspired alternative to the sliding window (SW) approach which is the dominant method of search in computer vision object detection. Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery. Consequently, more computational resources are allocated at the fovea and relatively fewer at the periphery. The FOD processes the entire scene, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. Our approach combines modern object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We assessed various eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD performs on par with the SW detector while bringing significant computational cost savings.Comment: An extended version of this manuscript was published in PLOS Computational Biology (October 2017) at https://doi.org/10.1371/journal.pcbi.100574

    Evolution and Optimality of Similar Neural Mechanisms for Perception and Action during Search

    Get PDF
    A prevailing theory proposes that the brain's two visual pathways, the ventral and dorsal, lead to differing visual processing and world representations for conscious perception than those for action. Others have claimed that perception and action share much of their visual processing. But which of these two neural architectures is favored by evolution? Successful visual search is life-critical and here we investigate the evolution and optimality of neural mechanisms mediating perception and eye movement actions for visual search in natural images. We implement an approximation to the ideal Bayesian searcher with two separate processing streams, one controlling the eye movements and the other stream determining the perceptual search decisions. We virtually evolved the neural mechanisms of the searchers' two separate pathways built from linear combinations of primary visual cortex receptive fields (V1) by making the simulated individuals' probability of survival depend on the perceptual accuracy finding targets in cluttered backgrounds. We find that for a variety of targets, backgrounds, and dependence of target detectability on retinal eccentricity, the mechanisms of the searchers' two processing streams converge to similar representations showing that mismatches in the mechanisms for perception and eye movements lead to suboptimal search. Three exceptions which resulted in partial or no convergence were a case of an organism for which the targets are equally detectable across the retina, an organism with sufficient time to foveate all possible target locations, and a strict two-pathway model with no interconnections and differential pre-filtering based on parvocellular and magnocellular lateral geniculate cell properties. Thus, similar neural mechanisms for perception and eye movement actions during search are optimal and should be expected from the effects of natural selection on an organism with limited time to search for food that is not equi-detectable across its retina and interconnected perception and action neural pathways

    Studies of η\eta and η\eta' production in pppp and ppPb collisions

    Full text link
    The production of η\eta and η\eta' mesons is studied in proton-proton and proton-lead collisions collected with the LHCb detector. Proton-proton collisions are studied at center-of-mass energies of 5.025.02 and 13 TeV13~{\rm TeV}, and proton-lead collisions are studied at a center-of-mass energy per nucleon of 8.16 TeV8.16~{\rm TeV}. The studies are performed in center-of-mass rapidity regions 2.5<yc.m.<3.52.5<y_{\rm c.m.}<3.5 (forward rapidity) and 4.0<yc.m.<3.0-4.0<y_{\rm c.m.}<-3.0 (backward rapidity) defined relative to the proton beam direction. The η\eta and η\eta' production cross sections are measured differentially as a function of transverse momentum for 1.5<pT<10 GeV1.5<p_{\rm T}<10~{\rm GeV} and 3<pT<10 GeV3<p_{\rm T}<10~{\rm GeV}, respectively. The differential cross sections are used to calculate nuclear modification factors. The nuclear modification factors for η\eta and η\eta' mesons agree at both forward and backward rapidity, showing no significant evidence of mass dependence. The differential cross sections of η\eta mesons are also used to calculate η/π0\eta/\pi^0 cross section ratios, which show evidence of a deviation from the world average. These studies offer new constraints on mass-dependent nuclear effects in heavy-ion collisions, as well as η\eta and η\eta' meson fragmentation.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lhcbproject.web.cern.ch/Publications/p/LHCb-PAPER-2023-030.html (LHCb public pages

    Observation of Cabibbo-suppressed two-body hadronic decays and precision mass measurement of the Ωc0\Omega_{c}^{0} baryon

    Full text link
    The first observation of the singly Cabibbo-suppressed Ωc0ΩK+\Omega_{c}^{0}\to\Omega^{-}K^{+} and Ωc0Ξπ+\Omega_{c}^{0}\to\Xi^{-}\pi^{+} decays is reported, using proton-proton collision data at a centre-of-mass energy of 13TeV13\,{\rm TeV}, corresponding to an integrated luminosity of 5.4fb15.4\,{\rm fb}^{-1}, collected with the LHCb detector between 2016 and 2018. The branching fraction ratios are measured to be B(Ωc0ΩK+)B(Ωc0Ωπ+)=0.0608±0.0051(stat)±0.0040(syst)\frac{\mathcal{B}(\Omega_{c}^{0}\to\Omega^{-}K^{+})}{\mathcal{B}(\Omega_{c}^{0}\to\Omega^{-}\pi^{+})}=0.0608\pm0.0051({\rm stat})\pm 0.0040({\rm syst}), B(Ωc0Ξπ+)B(Ωc0Ωπ+)=0.1581±0.0087(stat)±0.0043(syst)±0.0016(ext)\frac{\mathcal{B}(\Omega_{c}^{0}\to\Xi^{-}\pi^{+})}{\mathcal{B}(\Omega_{c}^{0}\to\Omega^{-}\pi^{+})}=0.1581\pm0.0087({\rm stat})\pm0.0043({\rm syst})\pm0.0016({\rm ext}). In addition, using the Ωc0Ωπ+\Omega_{c}^{0}\to\Omega^{-}\pi^{+} decay channel, the Ωc0\Omega_{c}^{0} baryon mass is measured to be M(Ωc0)=2695.28±0.07(stat)±0.27(syst)±0.30(ext)MeV/c2M(\Omega_{c}^{0})=2695.28\pm0.07({\rm stat})\pm0.27({\rm syst})\pm0.30({\rm ext})\,{\rm MeV}/c^{2}, improving the precision of the previous world average by a factor of four.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2023-011.html (LHCb public pages
    corecore