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{
    "pk": 49823,
    "title": "LLM-Generated Semantic Networks Predict Semantic Priming Effects on Human Reaction Times in a Word-Recognition Task",
    "subtitle": null,
    "abstract": "A well-known empirical result in human linguistic processing finds that humans are quicker to correctly recognize a string of letters as word when they are first shown a word that is semantically related to the word they must recognize. This is known as the \"semantic priming effect.\" Since Collins and Loftus (1975), it has been widely theorized that this effect is due to graphical storage of words in memory and a \"spreading activation\" model of priming. On this theory, words are related to one another in human semantic memory via a graphical structure encoding semantic relationships between words, with participants more likely to quickly recognize a word when they are primed with one that is graphically nearby; the prime word \"activates\" the node of a participant's semantic memory network representing the prime word and this activation \"spreads\" to words at nearby nodes. Today, large language models increasingly excel at generating structured data representations, like graphs, when prompted to do so (Ghanem & Cruz, 2024; Dagdelen et al., 2024). In the current paper we investigate whether a language model can be prompted to represent a set of words as a semantic graph, and whether human reaction times in a word recognition task are predicted by the minimum path length between words in such an LLM-generated semantic graph. Using two versions of the Gemini language model, we use a prompting strategy to generate semantic graphs relating all words used in a large semantic priming experiment conducted by Hutchinson et al. (2013), under a variety of different temperatures and settings for the number of maximum output tokens. While we find that all LLM-generated semantic graphs produced during our experiments are such that the minimum path length between two words predicts the reaction time in which a person primed by one word recognizes the other, this effect is most pronounced for graphs generated via a smaller version of the model. It is under these conditions, we find, that LLMs produce the dense graphs that are more predictive of human semantic priming effects in lexical decision tasks.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Computer Science; Psychology; Natural Language Processing; Semantic memory; Computational Modeling"
        }
    ],
    "section": "Papers with Poster Presentation",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/7978p7t3",
    "frozenauthors": [
        {
            "first_name": "David",
            "middle_name": "B",
            "last_name": "Kinney",
            "name_suffix": "",
            "institution": "Washington University in St. Louis",
            "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/49823/galley/37785/download/"
        }
    ]
}