{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lopes Rego AT"],"funding":["Dutch Research Council (NWO)"],"pagination":["e1012117"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11458034"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(9)"],"pubmed_abstract":["Although word predictability is commonly considered an important factor in reading, sophisticated accounts of predictability in theories of reading are lacking. Computational models of reading traditionally use cloze norming as a proxy of word predictability, but what cloze norms precisely capture remains unclear. This study investigates whether large language models (LLMs) can fill this gap. Contextual predictions are implemented via a novel parallel-graded mechanism, where all predicted words at a given position are pre-activated as a function of contextual certainty, which varies dynamically as text processing unfolds. Through reading simulations with OB1-reader, a cognitive model of word recognition and eye-movement control in reading, we compare the model's fit to eye-movement data when using predictability values derived from a cloze task against those derived from LLMs (GPT-2 and LLaMA). Root Mean Square Error between simulated and human eye movements indicates that LLM predictability provides a better fit than cloze. This is the first study to use LLMs to augment a cognitive model of reading with higher-order language processing while proposing a mechanism on the interplay between word predictability and eye movements."],"journal":["PLoS computational biology"],"pubmed_title":["Language models outperform cloze predictability in a cognitive model of reading."],"pmcid":["PMC11458034"],"funding_grant_id":["406.21.GO.019"],"pubmed_authors":["Snell J","Meeter M","Lopes Rego AT"],"additional_accession":[]},"is_claimable":false,"name":"Language models outperform cloze predictability in a cognitive model of reading.","description":"Although word predictability is commonly considered an important factor in reading, sophisticated accounts of predictability in theories of reading are lacking. Computational models of reading traditionally use cloze norming as a proxy of word predictability, but what cloze norms precisely capture remains unclear. This study investigates whether large language models (LLMs) can fill this gap. Contextual predictions are implemented via a novel parallel-graded mechanism, where all predicted words at a given position are pre-activated as a function of contextual certainty, which varies dynamically as text processing unfolds. Through reading simulations with OB1-reader, a cognitive model of word recognition and eye-movement control in reading, we compare the model's fit to eye-movement data when using predictability values derived from a cloze task against those derived from LLMs (GPT-2 and LLaMA). Root Mean Square Error between simulated and human eye movements indicates that LLM predictability provides a better fit than cloze. This is the first study to use LLMs to augment a cognitive model of reading with higher-order language processing while proposing a mechanism on the interplay between word predictability and eye movements.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Sep","modification":"2025-04-18T13:03:34.344Z","creation":"2025-04-06T22:33:12.027Z"},"accession":"S-EPMC11458034","cross_references":{"pubmed":["39321153"],"doi":["10.1371/journal.pcbi.1012117"]}}