Hemifield-specific offline learning of coherent motion detection | Zendy

Matthew S. Cain | Zendy; Satoshi Sato | Zendy; T. Watanabe | Zendy; Yuka Sasaki | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Hemifield-specific offline learning of coherent motion detection

Author(s) -

Matthew S. Cain,

Satoshi Sato,

T. Watanabe,

Yuka Sasaki

Publication year - 2013

Publication title -

journal of vision

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.126

H-Index - 113

ISSN - 1534-7362

DOI - 10.1167/13.9.562

Subject(s) - perceptual learning , motion perception , motion (physics) , motor learning , computer science , perception , artificial intelligence , psychology , neuroscience

• Further work is needed to determine the relative contribuitions of sleep and wake to offline motion detection improvement Task (Based on Shibata et al., 2012): • Two patches of moving dots appear in succession • One patch is random, one has some coherent motion (2-IFC) • Coherence held constant at individualized 72% threshold (Quest procedure) • Two motion directions (blocked): Trained and Untrained (90° CCW of trained) Task: • Two patches of moving dots appear simultaneously • Attended patch has random motion on 50% of trials, coherent motion on 50% • Coherence held constant at individualized 72% threshold (Quest procedure) • Two visual hemifields: Trained and Untrained (side counterbalanced)

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research