Dataset Information

Tracking the contribution of inductive bias to individualised internal models.

ABSTRACT: Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead they rely on an approximate model that is continually adapted throughout learning. However, experimenters assume an ideal observer model, which captures stimulus structure but ignores the diverging hypotheses that humans form during learning. We combine non-parametric Bayesian methods and probabilistic programming to infer rich and dynamic individualised internal models from response times. We demonstrate that the approach is capable of characterizing the discrepancy between the internal model maintained by individuals and the ideal observer model and to track the evolution of the contribution of the ideal observer model to the internal model throughout training. In particular, in an implicit visuomotor sequence learning task the identified discrepancy revealed an inductive bias that was consistent across individuals but varied in strength and persistence.

SUBMITTER: Torok B

PROVIDER: S-EPMC9255757 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Tracking the contribution of inductive bias to individualised internal models.

Török Balázs B Nagy David G DG Kiss Mariann M Janacsek Karolina K Németh Dezső D Orbán Gergő G

PLoS computational biology 20220622 6

Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead they rely on an approximate model that is continually adapted throughout learning. However, experimenters assume an ideal observer model, which captures stimulus structure but ignores the diverging hypotheses that humans form during learning. We combine non-parametric Bayesian methods and ...[more]

PMID: 35731822

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Project description:ImportanceThe analysis of electronic medical records at scale to learn from clinical experience is currently very challenging. The integration of artificial intelligence (AI), specifically foundational large language models (LLMs), into an analysis pipeline may overcome some of the current limitations of modest input sizes, inaccuracies, biases, and incomplete knowledge bases.ObjectiveTo explore the effectiveness of using an LLM for generating realistic clinical data and other LLMs for summarizing and synthesizing information in a model system, simulating a randomized clinical trial (RCT) in epilepsy to demonstrate the potential of inductive reasoning via medical chart review.DesignAn LLM-generated simulated RCT based on a RCT for treatment with an antiseizure medication, cenobamate, including a placebo arm and a full-strength drug arm, evaluated by an LLM-based pipeline versus a human reader.SettingSimulation based on realistic seizure diaries, treatment effects, reported symptoms and clinical notes generated by LLMs with multiple different neurologist writing styles.ParticipantsSimulated cohort of 240 patients, divided 1:1 into placebo and drug arms.InterventionUtilization of LLMs for the generation of clinical notes and for the synthesis of data from these notes, aiming to evaluate the efficacy and safety of cenobamate in seizure control either with a human evaluator or AI-pipeline.MeasuresThe AI and human analysis focused on identifying the number of seizures, symptom reports, and treatment efficacy, with statistical analysis comparing the 50%-responder rate and median percentage change between the placebo and drug arms, as well as side effect rates in each arm.ResultsAI closely mirrored human analysis, demonstrating the drug's efficacy with marginal differences (<3%) in identifying both drug efficacy and reported symptoms.Conclusions and relevanceThis study showcases the potential of LLMs accurately simulate and analyze clinical trials. Significantly, it highlights the ability of LLMs to reconstruct essential trial elements, identify treatment effects, and recognize reported symptoms, within a realistic clinical framework. The findings underscore the relevance of LLMs in future clinical research, offering a scalable, efficient alternative to traditional data mining methods without the need for specialized medical language training.

Dataset Information

Tracking the contribution of inductive bias to individualised internal models.

Publications

Tracking the contribution of inductive bias to individualised internal models.

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