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Daniele Marinazzo

Dados Biográficos

ID1751455
NOMEDaniele Marinazzo
PRENOMESDaniele
SOBRENOMEMarinazzo
ASSINATURAMARINAZZO D
AFILIAÇÕESGhent University
ORCID0000-0002-9803-0122
VERIFICADOSim
TOTAL DE OBRAS3
TOTAL DE CITAÇÕES1
OBRAS COMO AUTOR2
OBRAS COMO EDITOR1
PRIMEIRO ANO DE PUBLICAÇÃO2017
ANO MAIS RECENTE DE PUBLICAÇÃO2024
ÍNDICE H1
  • Cognitive modelling of concepts in the mental lexicon with multilayer networks

    Open Access•Milou Stella, Salvatore Citraro et al.•ARTICLE•Psychonomic Bulletin & Review•2024

    The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study…

  • Large language models surpass human experts in predicting neuroscience results

    Open Access•X Luo, Akilles Rechardt et al.•ARTICLE•Nature Human Behaviour•2024•Citada por: 1•Referências: 14

    Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predi…

  • Estimating psychopathological networks

    Open Access•Sacha Epskamp, Joost Kruis et al.•EDITOR•ARTICLE•PLoS ONE•2017

    Network models, in which psychopathological disorders are conceptualized as a complex interplay of psychological and biological components, have become increasingly popular in the recent psychopathological literature (Borsboom, et. al., 2011). These network models often contain significant numbers of unknown parameters, yet the sample sizes available in psychological research are limited. As such, general assumptions about the true network are in…

  • Large language models surpass human experts in predicting neuroscience results

    Open Access•X Luo, Akilles Rechardt et al.•ARTICLE•Nature Human Behaviour•2024•Citada por: 1•Referências: 14

    Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predi…

  • Estimating psychopathological networks

    Open Access•Sacha Epskamp, Joost Kruis et al.•EDITOR•ARTICLE•PLoS ONE•2017

    Network models, in which psychopathological disorders are conceptualized as a complex interplay of psychological and biological components, have become increasingly popular in the recent psychopathological literature (Borsboom, et. al., 2011). These network models often contain significant numbers of unknown parameters, yet the sample sizes available in psychological research are limited. As such, general assumptions about the true network are in…

  • Cognitive modelling of concepts in the mental lexicon with multilayer networks

    Open Access•Milou Stella, Salvatore Citraro et al.•ARTICLE•Psychonomic Bulletin & Review•2024

    The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study…

  • Large language models surpass human experts in predicting neuroscience results

    Open Access•X Luo, Akilles Rechardt et al.•ARTICLE•Nature Human Behaviour•2024•Citada por: 1•Referências: 14

    Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predi…

Psychology (3 obras) · Cognitive science (2 obras) · Computer Science (2 obras) · Neuroscience (2 obras) · Topic Modeling (2 obras) · Advanced Text Analysis Techniques (1 obra) · Artificial Intelligence (1 obra) · Artificial Intelligence (1 obra) · Clinical Psychology (1 obra) · Cognition (1 obra)

Ethnos_APP • Projeto Open Source • Licença MIT • Frontend v2.0.0 • Privacidade e Cookies • Documentação da API: api.ethnos.app/docs • Código da API: GitHub • DOI: 10.5281/zenodo.17049435 • Código do Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae