Abstract
The probability of an event's occurrence affects event-related potentials (ERPs) on electroencephalograms. The relation between probability and potentials has been discussed by using a quantity called surprise that represents the self-information that humans receive from the event. Previous studies have estimated surprise based on the probability distribution in a stationary state. Our hypothesis is that state transitions also play an important role in the estimation of surprise. In this study, we compare the effects of surprise on the ERPs based on two models that generate an event sequence: a model of a stationary state and a model with state transitions. To compare these effects, we generate the event sequences with Markov chains to avoid a situation that the state transition probability converges with the stationary probability by the accumulation of the event observations. Our trial-by-trial model-based analysis showed that the stationary probability better explains the P3b component and the state transition probability better explains the P3a component. The effect on P3a suggests that the internal model, which is constantly and automatically generated by the human brain to estimate the probability distribution of the events, approximates the model with state transitions because Bayesian surprise, which represents the degree of updating of the internal model, is highly reflected in P3a. The global effect reflected in P3b, however, may not be related to the internal model because P3b depends on the stationary probability distribution. The results suggest that an internal model can represent state transitions and the global effect is generated by a different mechanism than the one for forming the internal model.
Highlights
Humans make predictions by using prior information (Doya et al, 2007; Friston, 2008), and the prior information is derived from what humans have experienced
We investigated the relation between predictive surprise in a generative model that has state transitions and electrophysiological signals via a model-based analysis
The results show different brain activities that seem to be associated with the stationary-state model and the state transition model
Summary
Humans make predictions by using prior information (Doya et al, 2007; Friston, 2008), and the prior information is derived from what humans have experienced. Variation in ERPs by State Transitions the EEG potentials is called P300, and its peak amplitude depends on the probability of the event’s occurrence (Duncan-Johnson and Donchin, 1977) Such ERP components are considered to reflect the process for predicting events and are widely used as a medium for analyzing human cognition (Horovitz et al, 2002; Sanmiguel et al, 2013). Assuming that the observed brain activities reflect the prediction process, the model-based approach can confirm which factors human prediction depends on by finding a model that accurately estimates the amplitude of P300 Their results suggest that surprise estimated by the integration of three factors (long-term history, short-term history, and alternating expectations of the stimulus sequence) adequately predicts the P300 amplitude (Squires et al, 1976; Kolossa et al, 2012). The results show different brain activities that seem to be associated with the stationary-state model and the state transition model
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