The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades

• Published in: PLoS computational biology 2017-08, Vol.13 (8), p.e1005692-e1005692
• Main Authors: Aponte, Eduardo A, Schöbi, Dario, Stephan, Klaas E, Heinzle, Jakob
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Biomedical engineering; Computational Biology; Decision making; Engineering; Eye; Eye movements; Humans; Inhibition; Male; Mathematical models; Medicine and Health Sciences; Models, Biological; Neural networks; Normal distribution; Observations; Physical Sciences; Probability theory; Psychological aspects; Psychomotor Performance - physiology; Reaction time; Reaction time (Psychology); Reaction Time - physiology; Reaction time task; Saccades (Eye movements); Saccades - physiology; Saccadic eye movements; Schizophrenia; Software; Stochastic Processes; Stochasticity; Visual perception
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1005692

The antisaccade task is a classic paradigm used to study the voluntary control of eye movements. It requires participants to suppress a reactive eye movement to a visual target and to concurrently initiate a saccade in the opposite direction. Although several models have been proposed to explain error rates and reaction times in this task, no formal model comparison has yet been performed. Here, we describe a Bayesian modeling approach to the antisaccade task that allows us to formally compare different models on the basis of their evidence. First, we provide a formal likelihood function of actions (pro- and antisaccades) and reaction times based on previously published models. Second, we introduce the Stochastic Early Reaction, Inhibition, and late Action model (SERIA), a novel model postulating two different mechanisms that interact in the antisaccade task: an early GO/NO-GO race decision process and a late GO/GO decision process. Third, we apply these models to a data set from an experiment with three mixed blocks of pro- and antisaccade trials. Bayesian model comparison demonstrates that the SERIA model explains the data better than competing models that do not incorporate a late decision process. Moreover, we show that the early decision process postulated by the SERIA model is, to a large extent, insensitive to the cue presented in a single trial. Finally, we use parameter estimates to demonstrate that changes in reaction time and error rate due to the probability of a trial type (pro- or antisaccade) are best explained by faster or slower inhibition and the probability of generating late voluntary prosaccades.