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{
    "pk": 49405,
    "title": "Boosting Cognitive Modelling for Human Reasoning",
    "subtitle": null,
    "abstract": "AI models are often developed to solve reasoning problems optimally.\nIn contrast, cognitive models focus on explaining and\npredicting replicative cognitive patterns of human information\nprocessing. And while many of the theories aim to explain\nan assumed ‘general' human reasoner, only few are aimed at\nthe individual. This paper addresses the challenge of the latter\nby investigating the automatic generation of individualised\npredictive algorithms using transformer-based models. These\nmodels which have been trained on huge amounts of human\ndata, potentially exhibit built-in cognitive patterns. Leveraging\nsuch characteristics and architecture of transformer-based\nmodels, we outline a generalized methodology for establishing\na human-AI collaborative framework, to generate explainable\nand reproducible algorithms with cross-domain applicability.\nWhile predictive accuracy and generalizability pose less of a\nproblem, the bigger challenges in using machine learning approaches\nor transformer-based models may be explainability\nand replicability. Hence, instead of ‘just' using such a model\nfor directly fitting the data, we use it to extract features and\nto propose cognitive algorithms that are executable in systems\noutside of the model. Using two datasets pertaining to syllogistic\nand spatial reasoning, the predictive algorithms thus\ngenerated applying the presented framework, achieve mean accuracies\nof 68% and 81%, respectively. Both algorithms outperform\nother established, state-of-the-art cognitive models by\nfar, surpassing the (previously) best state-of-the art models in\nsyllogistic and spatial human reasoning by 19% and 13%, respectively.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Artificial Intelligence; Reasoning; Symbolic computational modeling"
        }
    ],
    "section": "Papers with Poster Presentation",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/32g676zt",
    "frozenauthors": [
        {
            "first_name": "Meghna",
            "middle_name": "",
            "last_name": "Bhadra",
            "name_suffix": "",
            "institution": "Technische UniversitŠt Dresden",
            "department": ""
        },
        {
            "first_name": "Marco",
            "middle_name": "",
            "last_name": "Ragni",
            "name_suffix": "",
            "institution": "TU Chemnitz",
            "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/49405/galley/37367/download/"
        }
    ]
}