Computational Cognitive Neuroscience

Computational cognitive neuroscience is an emerging discipline that employs mathematical analysis and computational models to understand the neural basis of cognitive functions. The papers in this special issue are based on a selection of the best presentations at the 2007 meeting of the Computational Cognitive Neuroscience (CCN) conference. The CCN conference focuses on research at the intersection of neuroscience, cognitive psychology and computational modeling, where neuroscience-based computational models are used to simulate and understand cognitive functions such as learning, memory, attention, language, perception, decision making and cognitive control. Because CCN research complements traditional empirical approaches such as neuroimaging, cellular electrophysiology and behavioral measurements, a major goal of this conference is to encourage cross-disciplinary interactions between theoreticians and empiricists, across multiple levels of investigation within cognitive neuroscience. It is for this reason of encouraging cross-disciplinary interactions that the CCN conference partners with different host conferences each year: In 2007, the CCN meeting took place prior to the Society for Neuroscience conference in San Diego and in 2009, the meeting will be held in conjunction with the Psychonomics Society meeting in Boston. The CCN program committee reviewed all oral and poster presentations at the 2007 conference and invited seven authors to submit full-length papers for this special issue based on their presentations. Selection criteria included a significant contribution to the field, a substantial computational modeling component, and a clear linkage between the neural and cognitive levels of explanation. All papers in this issue underwent a rigorous reviewing process, with 2–3 reviewers providing at least two rounds of reviews per article. While acceptance was not guaranteed from the outset, all seven of the invited submissions were finally accepted for inclusion. The papers in this issue are grouped according to three themes: (1) Vision and visual working memory; (2) High-level memory systems; and (3) Reward and decision making. In the vision theme, two articles (by Rokem and Silver and by Johnson, Spencer, and Schner) span visual phenomena ranging from early sensory coding to high-level visual decision-making. In Rokem and Silver's article “A model of encoding and decoding in V1 and MT...” the authors first