weiwei zhang, , University of California-Riverside
Computational modeling is one of the three foundations of cognitive neuroscience, in addition to behavior (cognition) and the brain. Although a lot of Psychology departments, including us, have strong emphases on cognition, the brain, and statistics, we often fail to introduce the computational thinking to our students. In Cognitive Neuroscience, "... hallmark of cognitive models is that they are derived from basic principles of cognition. This is what makes cognitive models different from generic statistical models or empirical curve fitting models" (Busemeyer & Diederich, 2006). As such computational modeling in psychology is pivotal for several reasons. First, models are abstractions that explain behavior and neural activities. Second, models provide interpretation of data that may be counter-intuitive. There are major exemplars of this almost in every corner of psychology. For instance, our recent work that attributes a fundamental limit of short-term memory to the limited amount of information that can be briefly maintained in mind (Zhang & Luck, 2008, Nature) instead of memory quality (Bays and Hussain, 2009, Science) is inconsistent with phenomenal experiences or visual inspection of the data. Third, models are the absolute form of theories. Compared to verbal descriptions of theories in Psychology which are often vague and underdefined, models are explicit and testable. "Formal (i.e., mathematical or computational) theories have a number of advantages that psychologists often overlook. They force the theorist to be explicit, so that assumptions are publicly accessible and the reliability of derivations can be confirmed ..." (Hintzman, 1990). It is potentially an effective way to solve the biggest challenge in social science, that is the lack of replicability.
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