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{
    "pk": 29599,
    "title": "Recurrent top-down synaptic connections at different spatial frequencies helpdisambiguate between dynamic emotions",
    "subtitle": null,
    "abstract": "The coarse-to-fine hypothesis posits that, in the Human visualsystem, a coarse representation of visual information is propa-gated quickly through the retina to the cortex, whereas a finer,more detailed representation is propagated more slowly. In aprevious study we showed that recurrent synaptic connectionshelp predict low intensity EFEs. Furthermore, a feedback loopcoming from coarser information processing is postulated toinfluence later processing of finer features. In this paper, weintend to examine the value of coarser information and recur-rence in the processing of dynamic Emotional Facial Expres-sions (EFE). In a step forward in studying the importance ofrecurrent connectivity in the coarse-to-fine model, we testedits advantage for discriminating emotions for different spatialfrequencies and facial expression intensities. Using ArtificialNeural Networks, we modeled recurrent synaptic connectionswith a recurrent feedback loop. Using a Gabor filter bank, wecomputed different levels of spatial frequency features. Our re-sults replicate the advantage of recurrence at first facial expres-sion intensities. Our main finding is that the recurrent model isalso better when predicting high spatial frequencies features.Additionally, mid-to-low spatial frequencies are more usefulto the prediction of EFEs. We conclude that feature process-ing feedback has a significant effect in disambiguating facialexpressions when information is particularly complex, i.e., athigh spatial frequencies and low EFE intensities.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Proactive Brain"
        },
        {
            "word": "Neural Network modeling"
        },
        {
            "word": "Emotional Facial Expressions"
        },
        {
            "word": "Spatial Frequencies."
        }
    ],
    "section": "Poster Session 1",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/8414h2fk",
    "frozenauthors": [
        {
            "first_name": "Erwan",
            "middle_name": "",
            "last_name": "David",
            "name_suffix": "",
            "institution": "Goethe-Universität Frankfurt",
            "department": ""
        },
        {
            "first_name": "Yannick",
            "middle_name": "",
            "last_name": "Bourrier",
            "name_suffix": "",
            "institution": "Université Grenoble Alpes",
            "department": ""
        },
        {
            "first_name": "Roman",
            "middle_name": "",
            "last_name": "Vuillaume",
            "name_suffix": "",
            "institution": "Université de Bourgogne",
            "department": ""
        },
        {
            "first_name": "Martial",
            "middle_name": "",
            "last_name": "Mermillod",
            "name_suffix": "",
            "institution": "Univ. Grenoble Alpes, Univ. Savoie Mont Blanc",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2020-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/29599/galley/19458/download/"
        }
    ]
}