[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124830-en":3,"doc-seo-124830-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124830,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Designing Optimal Behavioral Experiments Using Machine Learning - Bayesian Optimal Experimental Design (BOED) tutorial","Computational models are powerful tools for explaining human cognition and behavior, but their richness makes experiments designed by intuition and convention poorly aligned with testing competing models. Bayesian optimal experimental design (BOED) formalizes the search for designs expected to yield maximally informative data. This work presents a tutorial combining recent BOED advances and machine learning to find optimal experiments for any simulatable model, enabling efficient evaluation of models and parameters against real experimental data.","arXiv :2305 .07721v2 [ cs .LG] 26 Nov 2023  \nDesigning Optimal Behavioral Experiments Using  \nMachine Learning  \nSimon Valentin 1∗ , Steven Kleinegesse 1∗ , Neil R. Bramley2 , Peggy Seri`es 1 , Michael U. Gutmann 1 , and Christopher G. Lucas 1  \n1 School of Informatics, University of Edinburgh, UK  \n2 Department of Psychology, University of Edinburgh, UK  \nNovember 28, 2023  \nAbstract  \nComputational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely, and offer predictions that can be subtle and often counter-intuitive. However, this same richness and ability to surprise means our scientific intuitions and traditional tools are ill-suited to designing experiments to test and compare these models. To avoid these pitfalls and realize the full potential of computational modeling, we require tools to design experiments that provide clear answers about what models explain human behavior and the auxiliary assumptions those models must make. Bayesian optimal experimental design (BOED) formalizes the search for optimal experimental designs by identifying experiments that are expected to yield informative data. In this work, we provide a tutorial on leveraging recent advances in BOED and machine learning to find optimal experiments for any kind of model that we can simulate data from, and show how byproducts of this procedure allow for quick and straightforward evaluation of models and their parameters against real experimental data. As a case study, we consider theories of how people balance exploration and exploitation in multi-armed bandit decision-making tasks. We validate the presented approach using simulations and a real-world experiment. As compared to experimental designs commonly used in the literature, we show that our optimal designs more efficiently determine which of a set of models best account for individual human behavior, and more efficiently characterize behavior given a preferred model. At the same time, formalizing a scientific question such that it can be adequately addressed with BOED can be challenging, and we discuss several potential caveats and  \n∗ These authors contributed equally to this work.  \nCorrespondence: [s.valentin@ed.ac.uk](s.valentin@ed.ac.uk)  \npitfalls that practitioners should be aware of. We provide code to replicate all analyses as well as tutorial notebooks and pointers to adapt the methodology to different experimental settings.  \nIntroduction  \nComputational modeling of behavioral phenomena is currently experiencing rapid growth, in particular with respect to methodological improvements. For instance, seminal work by Wilson and Collins (2019) has been crucial in raising methodological standards and bringing attention to how computational analyses can add value to the study of human (and animal) behavior. Meanwhile, inmost instances, computational analyses are applied to data collected from experiments that were designed based on intuition and convention. That is, while experimental designs are usually motivated by scientific questions in mind, they are often chosen without explicitly and quantitatively considering how informative these data might be for the computational analyses and, finally, the scientific questions being studied. This can, in the worst case, completely undermine the research effort, especially as particularly valuable experimental designs can be counter-intuitive. Today, advancements in machine learning open up the possibility of applying computational methods when deciding how experiments should be designed in the first place, to yield data that are maximally informative with respect to the scientific question at hand.  \nIn this work, we provide an introduction to modern BOED and a step-bystep tutorial on how these advancements can be combined and leveraged to find optimal experimental designs for any computational model that we can simulate data from, and show how by-products of this procedu","cbCaifcxdq5YdAvQ","https://ap.wps.com/l/cbCaifcxdq5YdAvQ","pdf",1829657,1,58,"English","en",105,"# Abstract\n# Introduction\n## Why optimize experimental designs?","[{\"question\":\"Why can traditional intuition-based experiment design fail for computational model testing?\",\"answer\":\"Because informative value for computational analyses is often not explicitly considered, leading to designs that may be counter-intuitive and can undermine the research goal of discriminating between models.\"},{\"question\":\"What is Bayesian optimal experimental design (BOED) in this context?\",\"answer\":\"BOED formalizes searching over candidate experimental designs to identify experiments expected to produce the most informative data for the models being compared.\"},{\"question\":\"How does the tutorial connect BOED with machine learning to design experiments?\",\"answer\":\"It leverages machine learning techniques to find optimal experimental designs for any computational model that can be simulated, and uses procedure byproducts to evaluate models and parameters against real experimental data.\"}]","Designing Optimal Behavioral Experiments Using Machine Learning - Bayesian Optimal Experimental Design (BOED) tutorial | PDF",1785894870,146,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"designing-optimal-behavioral-experiments-using-machine-learning-bayesian-optimal-experimental-design-boed-tutorial","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/designing-optimal-behavioral-experiments-using-machine-learning-bayesian-optimal-experimental-design-boed-tutorial/124830/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why can traditional intuition-based experiment design fail for computational model testing?","Question",{"text":75,"@type":76},"Because informative value for computational analyses is often not explicitly considered, leading to designs that may be counter-intuitive and can undermine the research goal of discriminating between models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is Bayesian optimal experimental design (BOED) in this context?",{"text":80,"@type":76},"BOED formalizes searching over candidate experimental designs to identify experiments expected to produce the most informative data for the models being compared.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the tutorial connect BOED with machine learning to design experiments?",{"text":84,"@type":76},"It leverages machine learning techniques to find optimal experimental designs for any computational model that can be simulated, and uses procedure byproducts to evaluate models and parameters against real experimental data.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]