2019 Conference on Cognitive Computational Neuroscience

13-16 September 2019    Berlin, Germany

Technical Program

Paper Detail

Paper: PS-1B.73
Session: Poster Session 1B
Location: H Fläche 1.OG
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: Effects of value on early sensory activity and motor preparation during rapid sensorimotor decisions
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1171-0
Authors: L. Alexandra Martinez-Rodriguez, Elaine A. Corbett, Simon P. Kelly, University College Dublin, Ireland
Abstract: Various computational accounts have been proposed to explain how sensorimotor decisions are biased by value. Although the longstanding dominant account has been the Starting Point Bias model, where the starting point of an evidence-accumulating decision variable is shifted towards the higher value bound, our group recently showed that fast biased decisions are best explained by a Drift Rate Bias model, where the mean tendency of the decision variable is itself biased by value (Afacan-Seref et al., 2018). This account is consistent with an enhancement of representations of higher value alternatives at the sensory level, but there has yet been no empirical neural evidence for such enhancement. Our study examined this by recording EEG data during a value-biased orientation discrimination task under a strict deadline, where each target orientation has a different value. Our neurophysiological analyses revealed that there was no value modulation of the early sensory activity and behavioural data was best fitted by a model in which Drift Rate biases are implemented through a Biased Urgency signal. These findings further demonstrate the inadequacy of standard models in explaining highly time-constrained, value-biased decisions, and highlight novel computational architectures that may explain the more complex decision formation dynamics unfolding in such scenarios, which are prevalent in real life.