API Endpoint for journals.

GET /api/articles/26301/?format=api
HTTP 200 OK
Allow: GET
Content-Type: application/json
Vary: Accept

{
    "pk": 26301,
    "title": "Modeling the Contribution of Central Versus Peripheral Vision in Scene, Object,and Face Recognition",
    "subtitle": null,
    "abstract": "It is commonly believed that the central visual field (fovea andparafovea) is important for recognizing objects and faces, andthe peripheral region is useful for scene recognition. However,the relative importance of central versus peripheral informa-tion for object, scene, and face recognition is unclear. Larsonand Loschky (2009) investigated this question in the context ofscene processing using experimental conditions where a cir-cular region only reveals the central visual field and blocksperipheral information (”Window”), and in a ”Scotoma” con-dition, where only the peripheral region is available. Theymeasured the scene recognition accuracy as a function of vi-sual angle, and demonstrated that peripheral vision was indeedmore useful in recognizing scenes than central vision in termsof achieving maximum recognition accuracy. In this work,we modeled and replicated the result of Larson and Loschky(2009), using deep convolutional neural networks (CNNs).Having fit the data for scenes, we used the model to predictfuture data for large-scale scene recognition as well as for ob-jects and faces. Our results suggest that the relative order ofimportance of using central visual field information is facerecognition>object recognition>scene recognition, and vice-versa for peripheral information. Furthermore, our results pre-dict that central information is more efficient than peripheralinformation on a per-pixel basis across all categories, which isconsistent with Larson and Loschky’s data.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "face recognition; object recognition; scene recog-nition; central and peripheral vision; deep neural networks"
        }
    ],
    "section": "Papers",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/5788x57g",
    "frozenauthors": [
        {
            "first_name": "Panqu",
            "middle_name": "",
            "last_name": "Wang",
            "name_suffix": "",
            "institution": "University of California San Diego",
            "department": ""
        },
        {
            "first_name": "Garrison",
            "middle_name": "W.",
            "last_name": "Cottrell",
            "name_suffix": "",
            "institution": "University of California San Diego",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2016-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/26301/galley/15937/download/"
        }
    ]
}