Corpus Callosum Dysgenesis is concomitant with reduced metacognitive efficiency and sensitivity across multiple cohorts and modalities

The corpus callosum is the largest commissure in the mammalian brain and has been implicated in supporting cognitive processes required for adapting to complex environments. Individuals born with Corpus Callosum Dysgenesis, characterized by malformations of the corpus callosum, commonly exhibit deficits in social navigation, abstract problem-solving, decision-making, and self-awareness. Metacognition is a key cognitive process that supports these functions; however, it has yet to be tested comprehensively in individuals with Corpus Callosum Dysgenesis.

Over three experiments, and three Corpus Callosum Dysgenesis cohorts, we examine perceptual accuracy, confidence judgments, and metacognitive efficiency in individuals with Corpus Callosum Dysgenesis using two variants of a Random Dot Kinematogram task within lab, online, and virtual reality conditions. We find that individuals with Corpus Callosum Dysgenesis typically display normal perceptual accuracy but fail to adjust their confidence judgments in line with task difficulty. Computational modeling reveal that this difference is characterized by lower metacognitive efficiency concomitant with lower metacognitive sensitivity.

Together, these results provide evidence that the corpus callosum is consistently associated with reduced metacognitive efficiency. The authors would like to thank the participants for their time in completing these studies, and the family support groups, Australian Disorders of the Corpus Callosum (AusDoCC), and the National Organization for Disorders of the Corpus Callosum (NODCC), for their help in recruitment and support of our research. The authors also thank Lisa MacKenzie and Tiffany Earle for their efforts in participant enrollment and liaison, and Drs.

Paul and Warren Brown for discussions and sharing CCD diagnoses on some participants. This research was supported by the Australian Research Council (DP210101712 to LJ.R.), as well as laboratory startup funds from Washington University in St Louis (L.J.R.), by the Max Planck Society (P.D.) and the Humboldt Foundation (P.D.), the FENS-Kavli Network of Excellence (000-001 to J.M.B.), the Wellcome Trust (WT228268/Z/23/Z to J.M.B.) and the Centre for AI and Machine Learning (J.M.B.). These authors contributed equally: Joseph M.

These authors jointly supervised this work: Peter Dayan, Linda J. Richards Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK Centre for AI and Machine Learning, Edith Cowan University, Perth, WA, Australia Perron Institute, University of Western Australia, Perth, WA, Australia Department of Neuroscience, Washington University in St. Louis Medical School, St Louis, MO, USA Ryan J.

Dean, Henry Burgess & Linda J. Richards The University of Queensland, Queensland Brain Institute, Brisbane, Australia Max Planck Institute for Biological Cybernetics, Tübingen, Germany University of Tübingen, Tübingen, Germany Correspondence to Joseph M. The authors declare no competing interests.