Overfitting the Literature to One Set of Stimuli and Data
Tijl Grootswagers, Amanda K. Robinson
Abstract
A large number of papers in Computational Cognitive Neuroscience are developing and testing novel analysis methods using one specific neuroimaging dataset and problematic experimental stimuli. Publication bias and confirmatory exploration will result in overfitting to the limited available data. We highlight the problems with this specific dataset and argue for the need to collect more good quality open neuroimaging data using a variety of experimental stimuli, in order to test the generalisability of current published results, and allow for more robust results in future work.
Topics & Concepts
OverfittingNeuroimagingComputer scienceCognitive neuroscienceVariety (cybernetics)Set (abstract data type)Functional neuroimagingMachine learningArtificial intelligenceComputational neuroscienceCognitionData setCognitive psychologyOpen scienceData sciencePsychologyNeuroscienceArtificial neural networkAstronomyPhysicsProgramming languageFace Recognition and PerceptionNeural dynamics and brain functionNeural and Behavioral Psychology Studies