Extracting the dynamics of perceptual switching from 'noisy' behaviour: an application of hidden Markov modelling to...

When studying animal perception, one normally has the chance of localizing perceptual events in time, that is via behavioural responses time-locked to the stimuli. With multistable stimuli, however, perceptual changes occur despite stationary stimulation. Here, the challenge is to infer these not directly observable perceptual states indirectly from the behavioural data. This estimation is complicated by the fact that an animal's performance is contaminated by errors. We propose a two-step approach to overcome this difficulty: First, one sets up a generative, stochastic model of the behavioural time series based on the relevant parameters, including the probability of errors. Second, one performs a model-based maximum-likelihood estimation on the data in order to extract the non-observable perceptual state transitions. We illustrate this methodology for data from experiments on perception of bistable apparent motion in pigeons. The observed behavioural time series is analysed and explained by a combination of a Markovian perceptual dynamics with a renewal process that governs the motor response. We propose a hidden Markov model in which non-observable states represent both the perceptual states and the states of the renewal process of the motor dynamics, while the observable states account for overt pecking performance. Showing that this constitutes an appropriate phenomenological model of the time series of observable pecking events, we use it subsequently to obtain an estimate of the internal (and thus covert) perceptual reversals. These may directly correspond to changes in the activity of mutually inhibitory populations of motion selective neurones tuned to orthogonal directions.