A Neural Network Model of the Eriksen Task: Reduction, Analysis, and Data Fitting

We analyze a neural network model of the Eriksen task, a twoalternative forced choice task in which subjects must correctly identify a central stimulus and disregard flankers that may or may not be compatible with it. We linearize and decouple the model, deriving a reduced drift-diffusion process with variable drift rate that describes the accumulation of net evidence in favor of either alternative, and we use this to analytically describe how accuracy and response time data depend on model parameters. Such analyses both assist parameter tuning in network models, and suggest explanations of changing drift rates in terms of attention. We compare our results with numerical simulations of the full nonlinear model and with empirical data, and show that it provides good fits to both with fewer parameters. Key words: connectionist, decoupling, drift-diffusion model, Eriksen task, free-response protocol, linearization, reduction. Running title: Models of the Eriksen task.

• Type: Journal
• Year: 2008
• Where: NECO
• Authors: Yuan Sophie Liu, Philip Holmes, Jonathan D. Cohen
• Keywords: Eriksen Task, Model, NECO 2008, Network Models