Article Instance

HTTP 200 OK
Allow: GET
Content-Type: application/json
Vary: Accept

{
    "pk": 49854,
    "title": "Primitive Linguistic Compositionality in a Hebbian Neural Network",
    "subtitle": null,
    "abstract": "Humans have a powerful ability to generate novel compositional representations. For example, imagining a *pink banana* requires compositional mappings between signifiers *pink* and *banana* and the perceptual referents of these signifiers. This essential cognitive faculty remains challenging to model in a biologically plausible way. Here, we present a model that implements signifier-referent compositional associations using Hebbian associative learning. The model satisfies the following constraints: (1) once associated, both signific and referential inputs can activate the shared representation, and (2) when  signific and referential inputs are compositional, the model should generalize to novel compositional combinations. When trained on MNIST, the model successfully learns to associate number labels with corresponding images. On colored MNIST, the model learns signific-referential associations for both digits and colors, with somewhat successful generalization to new digit-color combinations. This work serves as a proof of concept for biologically plausible models of signifier-referent association.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Artificial Intelligence; Cognitive Neuroscience; Language and thought; Language Production; Language understanding"
        }
    ],
    "section": "Papers with Poster Presentation",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/9dt1927p",
    "frozenauthors": [
        {
            "first_name": "George",
            "middle_name": "Rocco",
            "last_name": "Flint",
            "name_suffix": "",
            "institution": "University of California, Berkeley",
            "department": ""
        },
        {
            "first_name": "Anna",
            "middle_name": "",
            "last_name": "Ivanova",
            "name_suffix": "",
            "institution": "Georgia Institute of Technology",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2025-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/49854/galley/37816/download/"
        }
    ]
}