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Paper Detail

Paper: PS-1B.20
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: Neural Network Mechanisms Underlying Confirmation Bias in Stimulus Estimation
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Jose M. Esnaola-Acebes, Centre de Recerca Matemàtica, Spain; Bharath C. Talluri, Tobias Donner, University Medical Center Hamburg- Eppendorf, Germany; Alex Roxin, Klaus Wimmer, Centre de Recerca Matemàtica, Spain
Abstract: Perception is influenced by past choices. For example, an intermittent categorical choice biases the estimation of average motion direction across two stimuli (confirmation bias). To shed light on the underlying neural mechanisms, we develop a ring attractor model that integrates stimulus direction and represents a continuous estimate of the average stimulus in the phase of an activity bump. Depending on the relative strength of sensory input compared to the intrinsic network dynamics, the model can account for qualitatively distinct decision behaviors (uniform temporal weighting, and "recency" regime). We studied two potential mechanisms underlying confirmation bias and found that they predict different modulations of the estimation curve: (i) applying an urgency signal after the first stimulus leads to a shift modulation, (ii) a feature-based attention signal that boosts stimuli that are consistent with the intermittent choice leads to a gain modulation, the main feature observed in human behavior. Our work suggests bump attractor dynamics together with feature-based attention as a potential underlying mechanism of confirmation bias in stimulus estimation tasks.