A small tool to view real-world ActivityPub objects as JSON! Enter a URL
or username from Mastodon or a similar service below, and we'll send a
request with
the right
Accept
header
to the server to view the underlying object.
{
"@context": [
"https://www.w3.org/ns/activitystreams",
{
"ostatus": "http://ostatus.org#",
"atomUri": "ostatus:atomUri",
"inReplyToAtomUri": "ostatus:inReplyToAtomUri",
"conversation": "ostatus:conversation",
"sensitive": "as:sensitive",
"toot": "http://joinmastodon.org/ns#",
"votersCount": "toot:votersCount",
"blurhash": "toot:blurhash",
"focalPoint": {
"@container": "@list",
"@id": "toot:focalPoint"
}
}
],
"id": "https://mastodon.social/users/MatteoCarandini/statuses/111788464790125885",
"type": "Note",
"summary": null,
"inReplyTo": null,
"published": "2024-01-20T13:16:43Z",
"url": "https://mastodon.social/@MatteoCarandini/111788464790125885",
"attributedTo": "https://mastodon.social/users/MatteoCarandini",
"to": [
"https://www.w3.org/ns/activitystreams#Public"
],
"cc": [
"https://mastodon.social/users/MatteoCarandini/followers"
],
"sensitive": false,
"atomUri": "https://mastodon.social/users/MatteoCarandini/statuses/111788464790125885",
"inReplyToAtomUri": null,
"conversation": "tag:mastodon.social,2024-01-20:objectId=624370329:objectType=Conversation",
"content": "<p>Back in the 90s, when I was in grad school, sensory decisions were analyzed with “signal detection theory”. Since then, many have found it more fruitful to use logistic classification: the observer weighs the factors, uses their sum to bias a coin, and flips the coin. </p><p>However, a student starting today would find the relevant information scattered around. To fix this, I wrote “Sensory choices as logistic classification": <a href=\"https://www.biorxiv.org/content/10.1101/2024.01.17.576029v1\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://www.</span><span class=\"ellipsis\">biorxiv.org/content/10.1101/20</span><span class=\"invisible\">24.01.17.576029v1</span></a>. If you have suggestions, please let me know!</p>",
"contentMap": {
"en": "<p>Back in the 90s, when I was in grad school, sensory decisions were analyzed with “signal detection theory”. Since then, many have found it more fruitful to use logistic classification: the observer weighs the factors, uses their sum to bias a coin, and flips the coin. </p><p>However, a student starting today would find the relevant information scattered around. To fix this, I wrote “Sensory choices as logistic classification": <a href=\"https://www.biorxiv.org/content/10.1101/2024.01.17.576029v1\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://www.</span><span class=\"ellipsis\">biorxiv.org/content/10.1101/20</span><span class=\"invisible\">24.01.17.576029v1</span></a>. If you have suggestions, please let me know!</p>"
},
"updated": "2024-01-20T13:28:36Z",
"attachment": [
{
"type": "Document",
"mediaType": "image/png",
"url": "https://files.mastodon.social/media_attachments/files/111/788/400/351/738/951/original/6a97ed0d83798e0f.png",
"name": "A \"graphical abstract\" of the paper, showing a cartoon of a logistic classifier, and two examples of fits by the model, with traditional psychometric curves on the left and log odds representations on the right.",
"blurhash": "UAS6Plt6xu%L~pRkofRkD%jZofs:V@ayofof",
"focalPoint": [
0.07,
0.86
],
"width": 1512,
"height": 1814
}
],
"tag": [],
"replies": {
"id": "https://mastodon.social/users/MatteoCarandini/statuses/111788464790125885/replies",
"type": "Collection",
"first": {
"type": "CollectionPage",
"next": "https://mastodon.social/users/MatteoCarandini/statuses/111788464790125885/replies?min_id=111788496141120995&page=true",
"partOf": "https://mastodon.social/users/MatteoCarandini/statuses/111788464790125885/replies",
"items": [
"https://mastodon.social/users/MatteoCarandini/statuses/111788496141120995"
]
}
},
"likes": {
"id": "https://mastodon.social/users/MatteoCarandini/statuses/111788464790125885/likes",
"type": "Collection",
"totalItems": 32
},
"shares": {
"id": "https://mastodon.social/users/MatteoCarandini/statuses/111788464790125885/shares",
"type": "Collection",
"totalItems": 32
}
}