System factorial technology applied to artificial neural network information processing

Abstract

System Factorial Technology is a recent methodology for the analysis of information processing architectures. SFT can discriminate between three processing architectures, namely serial, parallel and coactive processing. In addition, it can discriminate between two stopping rules, self-terminating and exhaustive. Although the previously stated architectures fit to many psychological skills as performed by human beings (i.e. recognition task, categorization, visual search, etc.), the analysis of processing architectures that lie outside of the five original choices remain unclear. An example of such architecture is the recall process as performed by iterative systems. Results indicate that an iterative recall neural network is mistakenly detected by SFT as being a serial exhaustive architecture. This research shows a limit of SFT as an analytic tool but could lead to advancements in cognitive modeling by improving the strategies used for the analysis of underlying information processing architectures. © 2014 Springer International Publishing.