To plan movements toward objects our brain must recognize whether retinal displacement is due to self-motion and/or to object-motion. Here, we aimed to test whether motion areas are able to segregate these types of motion. We combined an event-related functional magnetic resonance imaging experiment, brain mapping techniques, and wide-field stimulation to study the responsivity of motion-sensitive areas to pure and combined self- and object-motion conditions during virtual movies of a train running within a realistic landscape. We observed a selective response in MT to the pure object-motion condition, and in medial (PEc, pCi, CSv, and CMA) and lateral (PIC and LOR) areas to the pure self-motion condition. Some other regions (like V6) responded more to complex visual stimulation where both object- and self-motion were present. Among all, we found that some motion regions (V3A, LOR, MT, V6, and IPSmot) could extract object-motion information from the overall motion, recognizing the real movement of the train even when the images remain still (on the screen), or moved, because of self-movements. We propose that these motion areas might be good candidates for the “flow parsing mechanism,” that is the capability to extract object-motion information from retinal motion signals by subtracting out the optic flow components.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Neural Bases of Self- And Object-Motion in a Naturalistic Vision


    Beteiligte:
    Pitzalis, S (Autor:in) / Serra, C (Autor:in) / Sulpizio, V (Autor:in) / Committeri, G (Autor:in) / de Pasquale, F (Autor:in) / Fattori, P (Autor:in) / Galletti, C (Autor:in) / Sepe, R (Autor:in) / Galati, G (Autor:in) / Pitzalis, S

    Erscheinungsdatum :

    2020-01-01



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Neural bases of self- and object-motion in a naturalistic vision

    Pitzalis S. / Serra C. / Sulpizio V. et al. | BASE | 2020

    Freier Zugriff

    Neural bases of self- and object-motion in a naturalistic vision

    Pitzalis S. / Serra C. / Sulpizio V. et al. | BASE | 2020

    Freier Zugriff

    Modelling naturalistic decision making using a neural network

    Harris, D. / Duggan, S. J. | British Library Conference Proceedings | 2001


    Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks

    Zyner, Alex / Worrall, Stewart / Nebot, Eduardo | IEEE | 2020


    Pedelec - Naturalistic Cycling Study

    Gesamtverband der Deutschen Versicherungswirtschaft e. V. | FID move | 2014

    Freier Zugriff