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Paper Detail

Paper: PS-2B.32
Session: Poster Session 2B
Location: H Fläche 1.OG
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Attentional orienting relies on Bayesian estimates of expected and unexpected uncertainty
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Anna Marzecová, Ghent University, Belgium; Eva Van den Bussche, KU Leuven, Belgium; Tom Verguts, Ghent University, Belgium
Abstract: Computational modelling work proposes that the attentional system relies on Bayesian estimates of two forms of uncertainty. Expected uncertainty tracks the unreliability of predictive relationships within a familiar context. Unexpected uncertainty signals sudden changes of the environmental context. In the current study, we empirically dissociated expected and unexpected uncertainty in a spatial orienting paradigm. Furthermore, we probed the postulated link between these two forms of uncertainty and neuromodulatory brainstem responses using two measures of phasic pupil dilation: pupil diameter and its temporal derivative. Expected and unexpected uncertainty levels in the task were estimated using an approximate Bayesian learning algorithm. Uncertainty influenced attentional orienting on the behavioural level. Attentional efficiency decreased with increasing levels of unexpected and expected uncertainty. Pupil diameter and its temporal derivative differently fluctuated with expected and uncertainty, thus supporting the links between computational estimates of uncertainty and neuromodulatory systems.