TY  - JOUR
A1  - Albert, Stefan
A1  - Schmack, Katharina
A1  - Sterzer, Philipp
A1  - Schneider, Gaby
T1  - A hierarchical stochastic model for bistable perception
T2  - PLoS Computational Biology
N2  - Viewing of ambiguous stimuli can lead to bistable perception alternating between the possible percepts. During continuous presentation of ambiguous stimuli, percept changes occur as single events, whereas during intermittent presentation of ambiguous stimuli, percept changes occur at more or less regular intervals either as single events or bursts. Response patterns can be highly variable and have been reported to show systematic differences between patients with schizophrenia and healthy controls. Existing models of bistable perception often use detailed assumptions and large parameter sets which make parameter estimation challenging. Here we propose a parsimonious stochastic model that provides a link between empirical data analysis of the observed response patterns and detailed models of underlying neuronal processes. Firstly, we use a Hidden Markov Model (HMM) for the times between percept changes, which assumes one single state in continuous presentation and a stable and an unstable state in intermittent presentation. The HMM captures the observed differences between patients with schizophrenia and healthy controls, but remains descriptive. Therefore, we secondly propose a hierarchical Brownian model (HBM), which produces similar response patterns but also provides a relation to potential underlying mechanisms. The main idea is that neuronal activity is described as an activity difference between two competing neuronal populations reflected in Brownian motions with drift. This differential activity generates switching between the two conflicting percepts and between stable and unstable states with similar mechanisms on different neuronal levels. With only a small number of parameters, the HBM can be fitted closely to a high variety of response patterns and captures group differences between healthy controls and patients with schizophrenia. At the same time, it provides a link to mechanistic models of bistable perception, linking the group differences to potential underlying mechanisms.
KW  - Hidden Markov models
KW  - Perception
KW  - Schizophrenia
KW  - Brownian motion
KW  - Probability distribution
KW  - Neurophysiology
KW  - Random variables
KW  - Vision
Y1  - 2017
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/43810
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-438103
SN  - 1553-7358
SN  - 1553-734X
N1  - Copyright: © 2017 Albert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
VL  - 13
IS  - (11): e1005856
SP  - 1
EP  - 38
PB  - Public Library of Science
CY  - San Francisco, Calif.
ER  -