| Paper: | PS-2A.12 |
| Session: | Poster Session 2A |
| Location: | Symphony/Overture |
| Session Time: | Friday, September 7, 17:15 - 19:15 |
| Presentation Time: | Friday, September 7, 17:15 - 19:15 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
Inference of dynamic probabilistic internal representations from reaction time data |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1051-0 |
| Authors: |
Balázs Török, Dávid G. Nagy, MTA Wigner Research Centre for Physics, Hungary; Karolina Janacsek, Dezső Németh, Eötvös Loránd University, Hungary; Gergő Orbán, MTA Wigner Research Centre for Physics, Hungary |
| Abstract: |
Sequential predictions are ubiquitous in a learning agent's existence. In order to devise efficient responses in a dynamic environment, one needs to build an internal representation of the latent dynamics of the environment. Humans have been shown to create dynamical models such as intuitive physics that approximate the laws of Newtonian physics and are able to reason about their model in terms of formulating new predictions or imagining hypothetical situations. However, subject-by-subject differences in temporal predictions resulting from variations in subjective internal models and individual learning paths have remained unexplored due to the immense difficulty related to inferring dynamical subjective representations. Cognitive Tomography has been proposed to discover static internal representations from discrete choices. We extend this method in two critical ways: 1, We aim to infer internal representations from a richer set of behavioural measures, specifically we use reaction times; 2, Our goal is to infer a dynamical representation. We demonstrate its utility by predicting reaction times and choices of human participants in a probabilistic learning task on a trial-by-trial basis. Inferred behaviour-based trial-specific subjective predictions can be directly used to test theories of neural underpinnings of computations in physiological and imaging data. |
