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Prediction errors modulate brain responses during multisensory feedback learning in adults
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Poster D108 in Poster Session D, Wednesday, October 25, 4:45 - 6:30 pm CEST, Espace Vieux-Port
Nina Raduner1,2,3,4,5, Carmen Providoli1,5, Sarah Di Pietro1,4,5, Saurabh Bedi5,6, Ella Casimiro5,6, Nico Ehrhardt1,2,3,4,5, Sina Gubler1, Iliana I. Karipidis1,4, Maya Schneebeli1, Michael Von Rhein3,5, Nora M. Raschle2,4,5, Christian Ruff4,5,6*, Silvia Brem1,4,5*; 1Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, 2Jacobs Center for Productive Youth Development, University of Zurich, 3University Children's Hospital Zurich and University of Zurich, 4Neuroscience Center Zurich (ZNZ), 5URPP Adaptive Brain Circuits in Development and Learning, University of Zurich, 6Department of Neuroeconomics, University Zurich, 7*equal contribution
Living in a multisensory world requires individuals to learn how to integrate information from different sensory modalities. Multimodal integration optimizes behaviour by improving processing speed and memory performance, making it particularly relevant for language function (Barutchu et al., 2011; Denervaud et al., 2020; Dionne-Dostie et al., 2015). From a brain perspective, learning and processing of multisensory information depend on an extended cortico-striato-thalamic network (Van Den Brink et al., 2014). In this study, we aimed to characterize the dynamics of multisensory learning across sensory modalities in the brain. Our objective was to establish a potential framework to advance our understanding of multisensory learning and processing during development and in clinical populations with developmental language or reading impairments. 25 healthy adults (mean age = 25.81 years, SD = 2.756, 20 female) conducted four runs of a multisensory feedback learning task during fMRI recordings. In two audio-visual (AV) and two visuo-tactile (TV) runs participants learned associations between three pairs of symbols and environmental sounds or vibrations, respectively. 50% of the AV or TV pairs were correct pairings, the others incorrect. We analysed stimulus and feedback processing for AV and TV runs. We than applied a Rescorla-Wagner model (Rescorla & Wagner, 1972) to derive trial-wise prediction error (PE) values, in addition to reaction times (RT) and accuracies (ACC). Linear mixed models were used to analyse the behavioural data. PE was entered as parametric modulator of BOLD activation on feedback onset to explore learning in more detail. The behavioural data showed that participants learned the correct pairings. Mean RT and ACC improved from the first to the last third within runs (ps<.001). Furthermore, learning AV pairings was less difficult than learning TV pairings (ACC AV > TV runs; p=.046). The fMRI analyses of AV or TV runs showed activations during stimulus processing in visual and auditory or tactile sensory processing regions including frontal, occipital, temporal and parietal cortex (pFWEc<.05, pCDT<.05 (FWE)). The PE modulated BOLD activation during feedback processing in the precentral gyrus, the left hippocampus, putamen, and amygdala (pFWEc<.05, pCDT<.001 (unc.)). We show that audio-visual and visuo-tactile stimulation yielded the expected activation during stimulus processing in sensory regions in this associative learning task. Furthermore, the modulation of activation by PE in the network comprising the precentral cortex, temporal cortex, and putamen may reflect the brain's dynamic adjustments and adaptations in motor planning, sensory integration, and reward processing based on feedback. In summary, our task may provide a framework for studying developmental trajectories of children with and without language impairments, advancing our understanding of the behavioural and brain dynamics of multisensory learning.
Topic Areas: Multisensory or Sensorimotor Integration, Computational Approaches