Modeling performance at the trial level within a diffusion framework: a simple yet powerful method for increasing efficiency via error detection and correction.
When faced with a relatively novel task, it is reasonable to assume that increases in performance efficiency depend upon processing adjustments that occur in response to errant or suboptimal performance. For such dynamic corrections to occur, the errors must first be noted, which can be challenging in contexts where no external feedback is provided. In the present article, the authors describe how a "double cross" error monitoring and correction process can be added to diffusion models of binary decision. The authors first outline the logick of our proposed error detection system, and then demonstrate that the addition of this double-cross process in the context of a simulation of lexical decision leads to more efficient responding. That is, with such a mechanism in place, the model was able to gradually respond more quickly and the distribution of errors became more consistent with human response patterns.
Joordens
Steve
S
Center for Computational Cognitive Neuroscience, University of Toronto Scarborough, Ontario. joordens@psych.utoronto.ca
Piercey
C Darren
CD
Azarbehi
Rostam
R
D000465:Algorithms; D001288:Attention; D001364:Awareness; D003657:Decision Making; D004526:Efficiency; D006801:Humans; D008960:Models, Psychological; D016571:Neural Networks, Computer; D011214:Practice, Psychological; D012016:Reference Values; D014706:Verbal Learning