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Paper Detail

Paper: PS-1A.18
Session: Poster Session 1A
Location: H Lichthof
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Identifying the Neurophysiological Correlates of Learning in Human Perceptual Decision-Making
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: David McGovern, Dublin City University, Ireland; Ciara Devine, Christine Gaffney, Trinity College Dublin, Ireland; Simon Kelly, University College Dublin, Ireland; Redmond O'Connell, Trinity College Dublin, Ireland
Abstract: Despite the well-established benefits of training on perceptual decision-making, there is still considerable uncertainty regarding the precise stages of information processing that are altered by learning. Here, we sought to characterize the neural adjustments that take place along the sensorimotor hierarchy following training on a perceptual task. To this end, we isolated distinct electrophysiological signatures of perceptual decision-making at the three key stages of information processing necessary for simple sensorimotor transformations- sensory evidence encoding, decision formation and motor preparation- as participants trained on a contrast discrimination task over five days. Steady-state visual evoked potentials (SSVEPs) reliably traced changes in stimulus contrast, thereby providing a read-out of sensory evidence encoding, while the centroparietal positivity (CPP) and lateralized beta-band activity provided domain-general and effector-selective indices of decision formation, respectively. Over the course of training, subjects learned to make quicker and more accurate perceptual decisions. These improvements were accompanied by a progressive boosting of sensory evidence representation, which in turn led to an increase in the build-up rate and peak amplitude of the CPP. A diffusion model analysis attributed the learning effects to increases in the rate of evidence accumulation, but no changes in the decision bound were observed.