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Theoretically, the additional variability post\r\nfailure might reflect exploration of possibilities to obtain success. When average\r\nbehavior is changing (as in learning), variability can be estimated from differences\r\nbetween subsequent movements. Can one estimate exploration reliably from such\r\ntrial-to-trial changes when studying reward-based motor learning? To answer this\r\nquestion, we tried to reconstruct the exploration underlying learning as described by\r\nfour existing reward-based motor learning models. We simulated learning for various\r\nlearner and task characteristics. If we simply determined the additional change post\r\nfailure, estimates of exploration were sensitive to learner and task characteristics. We\r\nidentified two pitfalls in quantifying exploration based on trial-to-trial changes. Firstly,\r\nperformance-dependent feedback can cause correlated samples of motor noise and\r\nexploration on successful trials, which biases exploration estimates. Secondly, the trial\r\nrelative to which trial-to-trial change is calculated may also contain exploration, which\r\ncauses underestimation. As a solution, we developed the additional trial-to-trial change\r\n(ATTC) method. By moving the reference trial one trial back and subtracting trial-to-trial\r\nchanges following specific sequences of trial outcomes, exploration can be estimated\r\nreliably for the three models that explore based on the outcome of only the previous\r\ntrial. Since ATTC estimates are based on a selection of trial sequences, this method\r\nrequires many trials. In conclusion, if exploration is a binary function of previous trial\r\noutcome, the ATTC method allows for a model-free quantification of exploration."},"dsDescriptionDate":{"typeName":"dsDescriptionDate","multiple":false,"typeClass":"primitive","value":"2021-06-23"}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Medicine, Health and Life Sciences"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"exploration"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"https://osf.io/w2v7q/\r\nDataset 20201208T141221 Default reward criterion = adaptive (mean).\r\nDataset 20201210T101138 Default reward criterion = random. \r\nOrganized into five folders: Var alfa, var eta, var mn, var rewcrit and var target.\r\nSee readme files."},"publicationIDType":{"typeName":"publicationIDType","multiple":false,"typeClass":"controlledVocabulary","value":"url"}}]},{"typeName":"notesText","multiple":false,"typeClass":"primitive","value":"See the OSF page: https://osf.io/x7hp9/"},{"typeName":"depositor","multiple":false,"typeClass":"primitive","value":"Mastrigt, van, Nina"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2021-06-23"}]},"dansDataVaultMetadata":{"displayName":"Data Vault Metadata","name":"dansDataVaultMetadata","fields":[{"typeName":"dansDataversePid","multiple":false,"typeClass":"primitive","value":"doi:10.34894/ANJOPR"},{"typeName":"dansDataversePidVersion","multiple":false,"typeClass":"primitive","value":"1.0"},{"typeName":"dansBagId","multiple":false,"typeClass":"primitive","value":"urn:uuid:7ecc50bf-5dd0-44e2-a1a7-a4f5d9a30a1b"},{"typeName":"dansNbn","multiple":false,"typeClass":"primitive","value":"urn:nbn:nl:ui:13-b90e6336-fed8-4d62-aadb-bb9569e0a28a"}]}},"files":[{"description":"Read me.","label":"20201208T141221 ADAPTIVE Manuscript v20.txt","restricted":false,"version":1,"datasetVersionId":3762,"dataFile":{"id":101259,"persistentId":"","filename":"20201208T141221 ADAPTIVE Manuscript v20.txt","contentType":"text/plain","friendlyType":"Plain Text","filesize":141,"description":"Read me.","storageIdentifier":"surf://store:17a3ca75f4c-aaa04b655bb8","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"1da197ad8991cd869e36480e3029828076036421"},"tabularData":false,"creationDate":"2021-06-24","publicationDate":"2021-06-24","lastUpdateTime":"2021-06-24T19:18:56Z","fileAccessRequest":false}},{"description":"Read me.","label":"20201210T101138 RANDOM Manuscript v20.txt","restricted":false,"directoryLabel":"DatasetRandomRewcrit","version":1,"datasetVersionId":3762,"dataFile":{"id":101285,"persistentId":"","filename":"20201210T101138 RANDOM Manuscript v20.txt","contentType":"text/plain","friendlyType":"Plain Text","filesize":138,"description":"Read me.","storageIdentifier":"surf://store:17a3e316a16-61059fde1262","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"54e1d81d653206b023c84643647fce10ca6c2e73"},"tabularData":false,"creationDate":"2021-06-24","publicationDate":"2021-06-24","directoryLabel":"DatasetRandomRewcrit","lastUpdateTime":"2021-06-24T19:18:56Z","fileAccessRequest":false}},{"description":"All default; m = 1.","label":"M4_S1k_T500_m1_eta40_a15_t400_adaptm_18_sim_sens2learning_all_v18_20201208T141221.mat","restricted":false,"directoryLabel":"DatasetAdaptiveCriterion/Var mn","version":2,"datasetVersionId":3762,"dataFile":{"id":101272,"persistentId":"","filename":"M4_S1k_T500_m1_eta40_a15_t400_adaptm_18_sim_sens2learning_all_v18_20201208T141221.mat","contentType":"application/matlab-mat","friendlyType":"MATLAB Data","filesize":1121201545,"description":"All default; m = 1.","storageIdentifier":"surf://store:17a3d87d834-51ef07c00c74","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"fa926000e8f8869d3761171a32830284265281c3"},"tabularData":false,"creationDate":"2021-06-24","publicationDate":"2021-06-24","directoryLabel":"DatasetAdaptiveCriterion/Var mn","lastUpdateTime":"2021-06-24T19:18:56Z","fileAccessRequest":false}},{"description":"All default; m = 1. 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