| Paper: | PS-1A.14 | ||
| 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: | A neurally-constrained process model of prior-informed decision making | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1275-0 | ||
| Authors: | Elaine Corbett, Redmond O'Connell, Trinity College Dublin, Ireland; Simon Kelly, University College Dublin, Ireland | ||
| Abstract: | How does the brain exploit prior information about stimulus probability when selecting actions in response to noisy sensory stimuli? Most behavioral modelling studies account for the influence of priors through a single parameter, typically starting point bias, but it is unclear whether these parsimonious models truly reflect the underlying neural computations. Here we make use of recently characterized human scalp potentials reflecting decision formation to construct and constrain a model of prior-informed decision making. We explicitly modelled two decision levels—a motor-independent representation of cumulative evidence feeding build-to-threshold motor signals receiving additional dynamic urgency components. The starting points of the motor-level signals were directly constrained by neural signals, which built to a fixed threshold at response. The model provided a better fit to behavior across three task regimes (easy, time-pressured and weak evidence) compared to the standard diffusion model and, when simulated based on the behavioral fit, recapitulated an array of condition- and outcome-related effects in the neural decision signals. We found that prior biases in the rate of evidence accumulation as well as starting point were needed to jointly account for the neural and behavioral data, elucidating multilevel adjustments that would not be discernible from behavioral modelling alone. | ||